Data Science

Seriously, have you tried this AI stuff? I've never written code like this in my life.

Unearned confidence #datascience #datasciencetok #python #swe #datavisualization #dataanalytics #codinglife #vscode #ide #rstudio #positron #pycharm #jupyter #cursor #windsurf

Positron rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

AI-Powered Data Engineering Workflows: Positron for Databricks Users

Modern data engineering demands more. Move beyond the limitations of hosted notebooks and embrace the power of an IDE for modern data engineering. We’ll show you how Positron combines the best of local development with the scale of Databricks. In this session led by James Blair at Posit, we’ll cover: 1. AI-Driven Development: Moving from concept to code faster with built-in AI. 2. Seamless Exploration: Navigating data effortlessly with the Catalog Explorer. 3. Production-Grade Pipelines: Building, testing, and deploying robust workflows. Helpful Links: Download Positron: https://positron.posit.co/ Databricks Asset Bundles: http://docs.databricks.com/dev-tools/bundles GitHub Repo for Demo: https://github.com/blairj09/databricks-data-engineering Lakeflow Declarative Pipelines: http://docs.databricks.com/aws/en/ldp Talk to us about integrating Posit & Databricks: https://posit.co/schedule-a-call/

Positron Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

querychat in R: Query Your Data with Natural Language | Shiny + LLMs | Veerle van Leemput

Learn how to use querychat, an R package that lets users query data using natural language instead of predefined filters. Perfect for data scientists who want to build flexible, interactive Shiny dashboards without anticipating every possible user question. In this tutorial, you'll discover: - How querychat translates natural language to SQL using LLMs - Setting up querychat in seconds with your data - Building custom Shiny interfaces with reactive data frames - Why it's reliable, transparent, and safe for production use - Enterprise deployment with Azure, AWS Bedrock, or Google Vertex AI Key Features: Drop-in Shiny component Works with any DBI-compatible database Read-only by design for data safety Full SQL transparency for reproducibility Supports Claude, GPT, Gemini via ellmer Resources: - querychat Documentation: [https://posit-dev.github.io/querychat/py/index.html] - Blog Post: "Where Questions Become Queries" [https://shiny.posit.co/blog/posts/querychat-python-r/ - Github repo: [https://github.com/posit-dev/querychat/tree/main/pkg-r] Perfect for R users working with Shiny, data exploration, and interactive analytics. Timestamps 00:00 - Introduction: The Dashboard Problem 00:50 - Why Dashboards Can't Answer Every Question 01:47 - What is querychat? 02:46 - Getting Started (Installation & Setup) 03:08 - API Key Setup (Claude, GPT, Gemini) 03:20 - First querychat App in 5 Lines of Code 04:04 - Live Demo: Querying Data with Natural Language 05:01 - How querychat Works Under the Hood 06:44 - Four Key Benefits (Reliability, Transparency, Reproducibility, Safety) 07:37 - Tool Calling & DuckDB Integration 08:46 - Building Custom Shiny Interfaces 09:01 - Customizing querychat Behavior (Greetings, Data Descriptions, Instructions) 11:00 - Creating Custom Dashboards with Reactive Data Frames 12:00 - Complete App Structure Walkthrough 13:10 - Safety & Security Deep Dive 14:00 - Enterprise Deployment (Azure, AWS, Google Vertex AI) 15:12 - Resources & Documentation

DBI ellmer querychat Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

querychat in Python: Query Your Data with Natural Language | Shiny + LLMs | Veerle van Leemput

Learn how to use QueryChat to add natural language querying to your Python data applications. This tutorial shows you how to let users ask questions about data without building complex filters or dashboards. In this video, data scientist Veerle Eeftink demonstrates how to integrate QueryChat with Shiny for Python to create interactive data exploration tools powered by LLMs (Claude, GPT-4, Gemini). What You'll Learn: - Set up QueryChat in minutes with any data source - Understand how QueryChat translates natural language to SQL - Build custom dashboards with reactive data frames - Configure LLM behavior with greetings, data descriptions, and instructions - Ensure safety and read-only access for production environments Key Features: Works with Claude, GPT-4, Gemini, and other LLMs Transparent SQL generation (no black box) Integrates with Shiny, Streamlit, and other frameworks Uses DuckDB or bring your own SQL database Read-only operations for data safety Enterprise-ready with private LLM support Resources: - QueryChat Documentation: [https://posit-dev.github.io/querychat/py/index.html] - Blog Post: "Where Questions Become Queries" [https://shiny.posit.co/blog/posts/querychat-python-r/] - SheScores Demo App [https://github.com/hypebright/shescores-dashboard/tree/main/Python] #Python #DataScience #LLM #AI #Shiny #QueryChat #NaturalLanguage #SQL #DataVisualization Timestamps 00:00 - Introduction: The dashboard problem 01:28 - What is QueryChat? 02:36 - Demo: Three things you'll learn today 03:00 - Setup: LLM API keys and installation 04:04 - Quick start: First QueryChat app in 5 lines 04:27 - Live demo: Natural language queries 06:10 - How QueryChat works under the hood 07:19 - Four benefits: Reliability, transparency, reproducibility, safety 08:17 - Tool calling explained 08:54 - Schema information and DuckDB integration 09:29 - Building custom interfaces 09:45 - Customizing behavior: Greetings and data descriptions 11:16 - Adding custom instructions to the LLM 11:35 - Building a Shiny dashboard with QueryChat 13:09 - Code walkthrough: Sidebar chat and reactive data frames 14:14 - Complete dashboard demo 14:52 - Safety and security deep dive 16:22 - Resources and links

querychat Shiny for Python Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

How Open Source, Python and AI Are Shaping the Data Future with Wes McKinney

The future of analytics isn’t just about bigger models — it’s about building smarter, more interoperable data systems. Wes McKinney, Principal Architect of Posit PBC, Chief Scientist of Voltron Data and a General Partner at Composed Ventures, joins us to explore how the modern data stack is evolving and what it means for the future of analytics. Wes reflects on his journey building pandas and Apache Arrow, sharing how open-source ecosystems grow, transform and shape the way organizations work with data today. Wes also highlights the rising importance of semantic layers, agentic workflows and defensive coding practices as teams embrace AI-driven development. Key Takeaways: 00:00 Introduction. 02:32 Wes didn’t expect pandas to drive AI but he recognized Python’s unrealized potential. 05:09 A lucky convergence helped Python’s tools snowball into the AI standard. 10:40 Early big data focused on essentials, not the interoperable stacks we rely on today. 15:44 The composable data stack grew through bottom-up, grassroots open-source momentum. 21:56 Many “data science” roles ultimately became business intelligence and dashboard work. 25:24 Complex statistical work still depends on human judgment, not fully autonomous agents. 30:27 Frontier models retrieve table data reliably, while smaller models fail dramatically. 35:16 Better models and coding agents shifted Wes from an AI skeptic to an adopter. 40:07 AI-driven code demands stronger testing and review to avoid costly failures. 45:14 An AI-built finance project ballooned, revealing how agents inflate codebases. Resources Mentioned: Wes McKinney https://www.linkedin.com/in/wesmckinn/ Posit PBC | LinkedIn https://www.linkedin.com/company/posit-software/ Posit PBC | Website https://posit.co/ Voltron Data | LinkedIn https://www.linkedin.com/company/voltrondata/ Voltron Data | Website https://voltrondata.com/ Composed Ventures | LinkedIn https://www.linkedin.com/company/composedvc/ Composed Ventures | Website https://composed.vc/ pandas https://pandas.pydata.org/ Apache Arrow https://arrow.apache.org/ DuckDB https://duckdb.org/ DataFusion https://datafusion.apache.org/ Jupyter Notebook https://jupyter.org/ Parquet https://parquet.apache.org/ Iceberg https://iceberg.apache.org/ Delta Lake https://delta.io/ Thanks for listening to the “Data Masters Podcast.” If you enjoyed this episode, be sure to subscribe so you never miss our latest discussions and insights into the ever-changing world of data. #DataStrategy #DataManagement #DataMastersPodcast

Take Positron to Work with Positron Pro

While you've downloaded Positron on your desktop and are loving it, you may still have a few questions about using it at work: 1. What happens when my analysis requires more memory than my laptop has? 2. How can I bring Positron into my company's secure environment? 3. How can I access data in Databricks or Snowflake from Positron? Nick Rohrbaugh, Senior Product Marketing Manager at Posit shares how Positron Pro, available exclusively within Posit Workbench, transforms from a powerful desktop editor into a fully governed, enterprise-ready IDE. In this webinar, you will learn how: 1. Data teams get immediate access to scalable, server-side compute and secure, one-click data authorization. 2. IT leaders can centralize, secure, and manage Positron alongside RStudio, Jupyter, and VS Code, all from a single platform. Helpful links: Positron: https://posit.co/products/ide/positron/ Posit Workbench: https://posit.co/products/enterprise/workbench/

Positron rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

The Psychology of Technologists (Cat Hicks, Catharsis Consulting) | posit::conf(2025)

The Psychology of Technologists Speaker(s): Cat Hicks Abstract: Technology teams are struggling to be heard. Technologists are grappling with high turnover, high burnout, and low resilience in the face of rapidly-accelerating technological change. Even though effective technical teams are an essential part of our world continuing to work, many organizations struggle to understand their technical teams, stuck in old stereotypes that treat technical people like isolated machines. To build a thriving future, we need a new way of working together and understanding our own minds as we build technology. In my empirical research across thousands of developers, technical managers and their teams, we've explored what a psychologically-rich environment looks like for modern technologists and found important signals that can guide teams toward greater resilience, innovation, and thriving. In this keynote we'll unlock access to the psychological sciences and evidence that you can use to make your every day more human. Links: Dr. Cat Hicks - https://www.drcathicks.com/ Catharsis Consulting - https://www.catharsisinsight.com/ Center for Open Science (COS) - https://www.cos.io/ Fight for the Human Newsletter - https://www.fightforthehuman.com/ posit::conf(2025) Subscribe to posit::conf updates: https://posit.co/about/subscription-management/

Observability at scale (Barret Schloerke, Posit) | posit::conf(2025)

Observability at scale: Monitoring Shiny Applications with OpenTelemetry Speaker(s): Barret Schloerke Abstract: Understanding what happens behind the scenes in production Shiny applications has always been challenging. When users experience slow response times or unexpected behavior, developers are left guessing where bottlenecks occur. This talk introduces OpenTelemetry integration for Shiny for R, a new approach to profiling code and understanding application behavior. Through a chat-enabled weather application, we'll explore how complex user interactions trigger cascading events across multiple processes. You'll learn how OpenTelemetry's "high-quality, ubiquitous, and portable telemetry" can provide complete visibility into your Shiny application's performance with minimal setup... just a few environment variables! After walking through the chat app, you'll have a taste of how to implement comprehensive monitoring for your Shiny applications in production, enabling you to proactively identify and resolve unexpected performance issues. Materials - https://github.com/schloerke/presentation-2025-09-17-posit-conf-otel posit::conf(2025) Subscribe to posit::conf updates: https://posit.co/about/subscription-management/

Barret Schloerke

Making the most of artificial and human intelligence for data science (Hadley Wickham, Joe Cheng)

Making the most of artificial and human intelligence for data science Speaker(s): Hadley Wickham; Joe Cheng Abstract: This presentation explores the complex and often contradictory nature of large language models (LLMs) in data science, acknowledging the simultaneous excitement and apprehension that we feel toward these technologies. We’ll provide a practical framework to help you understand the LLM ecosystem (from foundation models and hosting to SDKs and applications) that supports our current philosophy: augmenting, not replacing human intelligence. The talk demonstrates how Posit is addressing this space through two complementary approaches: building SDKs and tools that help you create your own LLM-powered solutions, and developing integrated LLM capabilities directly into data science workflows through tools like Positron assistant and databot. We’ll showcase practical, immediately useful applications while addressing current limitations, providing you with both the emotional preparation and technical foundation needed to effectively leverage LLMs in their data science practice today. posit::conf(2025) Subscribe to posit::conf updates: https://posit.co/about/subscription-management/

Hadley Wickham, Joe Cheng

Engineer Insightful Shiny Apps with Behavioral Science (Jeremy Winget, CrossStack) | posit::conf

Death by Dropdown? Engineer Insightful Shiny Apps with Behavioral Science Speaker(s): Jeremy Winget, PhD Abstract: Opening a Shiny app to endless dropdowns and scattered filters leads to immediate disengagement and feeling like "death by dropdown". This talk introduces the Behavioral Insights Design (BID) Framework, a behavioral science roadmap that transforms overwhelming dashboards into guided user journeys. Using the "Interfaces Need A Smart Vision" mnemonic, we'll explore five stages: Interpret user needs through data storytelling, Notice cognitive friction points, Anticipate user behavior, Structure the application, and Validate & empower users. Drawing from established theories and real-world production examples, attendees will see how applying behavioral science can dramatically reduce the time between "Where do I even start?" and "Aha, now I get it!". You'll leave with practical techniques using the new {bidux} package to help transform user confusion into confident exploration and build dashboards that guide users toward better decisions (and fewer ghosted users). BID Framework - https://github.com/jrwinget/bid-framework bidux docs - https://github.com/jrwinget/bidux posit::conf(2025) Subscribe to posit::conf updates: https://posit.co/about/subscription-management/

AI-Powered Data Science in Positron

We're so excited to introduce Positron, a free, next-generation data science IDE that makes it easy to work in both R and Python. Positron builds on our years of experience developing RStudio and is a fork of VS Code, designed specifically for data work. This means you get a modern coding interface with features tailored for data science, like a built-in data explorer, AI assistance, interpreter management, and more. This is our 2nd event in our Positron series and focused on AI-Powered Data Science. Here at Posit, we strive to create products where AI works with you, not against you. In efforts to continue this mission, we are excited to introduce agentic AI capabilities in Positron, our new, free code editor for R and Python, that are designed from the ground up to follow these principles. Positron’s AI capabilities automate the tedious parts of the data science workflow, but always keep you, the expert, in control. 00:00 Introduction 00:32 What is Positron? (The Next-Gen Data Science IDE) 03:43 Introducing Positron Assistant 05:10 Bring your own LLM 05:47 Your environment as context 07:19 Inline code suggestions 07:58 Introducing Databot 08:22 The WEAR loop 10:15 Demo time 10:56 Opening Positron Pro in Posit Team 14:29 Opening a new folder in Positron 15:22 Cloning a repo from GitHub 17:24 Positron icons 19:15 Positron search bar and command palette 20:18 Changing interpreters and opening a Quarto document 21:30 Running code and populating the Variables Pane 22:41 Using the Data Explorer 25:28 Creating a plot and debugging with Positron Assistant 30:46 Editing code using inline code suggestions 34:42 Sharing a Quarto document 36:12 Opening Databot 37:02 Exploring data with Databot 43:37 Creating a report using Databot findings 45:12 Wrap up Additional resources: GitHub Repo for this Example: https://github.com/posit-dev/positron-ai-workshop Getting Started with Positron: Quick Tour: https://posit.co/resources/videos/get-started-with-positron-a-quick-tour-and-community-qa/ Introducing Databot (Blog Post): https://posit.co/blog/introducing-databot/ Posit AI Newsletter: https://posit.co/blog/2025-09-26-ai-newsletter/ Positron Assistant: https://positron.posit.co/assistant.html

Positron Quarto rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Accelerating Study Insights with Shiny and Posit | Regeneron x Atorus

In this session, Atorus and Regeneron, and Posit showcase their innovative partnership. Topics covered: -Building a Unified Analytical Environment: Learn how to reduce friction by integrating fragmented SAS and R workflows. -Case Study: A Config-Driven App: See a live demo of how a simple configuration file can build a fully functional application for data review and safety monitoring. -Operational Shifts: Understand how internal teams are empowered to independently manage and extend their own applications in a regulated environment. This isn’t just about technology—it’s about the strategic shift required to simplify development and accelerate insights within a regulated environment. Learn more about Posit's work in the pharma space: https://posit.co/use-cases/pharma/?utm_source=youtube&utm_medium=social&utm_term=pharma&utm_content=atorus-regeneron

config Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Programming Education RStats Open Source Oss Regeneron Atorus R Shiny Positron R Data Science Pharma

Top AI Powered Coding Assistants

which have you tried? #datascience #datasciencetok #python #swe #datavisualization #dataanalytics #codinglife #vscode #ide #rstudio #positron #pycharm #jupyter #cursor #windsurf

Positron rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

What can you do with Positron Assistant?

Positron Assistant is an AI client integrated within Positron, designed to enhance data science workflows. Positron Assistant brings LLM integration for both chat and inline code completions.This tool is built to assist with a range of tasks, including generating and refactoring code, answering questions, providing debugging support, and offering insightful suggestions for next steps in your projects. It can interact with the Variables pane, Plots pane, and the Console in your IDE. Capabilities include: • Editor inline chat (chat with Positron Assistant in your script to augment your code) • Contextual code completion (add instructional comments to your scripts to complete code) • Agent-assisted workflows (allow Positron Assistant to execute tasks autonomously) Learn more about Positron Assistant: https://positron.posit.co/assistant.html Sign up for our Workflow Demo on October 29, where we will demonstrate Positron Assistant: https://posit.co/workflow-demo/ai-powered-data-science-in-positron/

Positron Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Deploy a Streamlit web app with me

deploy all the things! #datascience #datasciencetok #python #swe #datavisualization #dataanalytics #codinglife #vscode #ide #rstudio #positron #pycharm #jupyternotebook

Positron rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

10 tips for data science beginners

Do you have any to add? #datascience #datasciencetok #python #swe #datavisualization #dataanalytics #codinglife #vscode #ide #rstudio #positron #pycharm #jupyternotebook

Positron rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Let's try Positron Assistant for the first time together

lil ai buddy #datascience #datasciencetok #python #swe #datavisualization #dataanalytics #codinglife #vscode #ide #rstudio #positron #pycharm #jupyter

Positron rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Data Exploration 101

Don't skimp #datascience #datasciencetok #python #swe #datavisualization #dataanalytics #codinglife #vscode #ide #rstudio #positron #pycharm #jupyternotebook

Positron rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Positron 101: Make your DS life easier

Personally, I think the Plots pane is #datascience #datasciencetok #python #swe #datavisualization #dataanalytics #codinglife #vscode #ide #rstudio #positron #pycharm #jupyter

Positron rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Set up your first coding project in less than 10 minutes

What are you waiting for #datascience #datasciencetok #python #swe #datavisualization #dataanalytics #codinglife #vscode #ide #rstudio #positron #pycharm #jupyter

Positron rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Get starting with data - load data into your IDE

Finding the data is the hardest part, prove me wrong #datascience #datasciencetok #python #swe #datavisualization #dataanalytics #codinglife #vscode #ide #rstudio #positron #pycharm #jupyternotebook

Positron rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Deploying Scikit-learn models for in-database scoring with Snowflake and Posit Team

Modern data science workflows often face a critical challenge: how to efficiently deploy machine learning models where the data lives. Traditional approaches require moving large datasets out of the database for scoring, creating bottlenecks, security concerns, and unnecessary data movement costs. In this demo, Nick Pelikan at Posit highlights how orbital bridges the gap between Python’s rich ML ecosystem and database-native execution by converting scikit-learn pipelines to optimized SQL. This enables data scientists to: 1. Use familiar Python tools for model development 2. Automatically translate complex feature engineering to SQL 3. Deploy models without rewriting code in different languages 4. Maintain model accuracy through faithful SQL translations While this example focuses on Python, Nick also gave this demo using Orbital for R users: https://youtu.be/pnEjYNgOG9c?feature=shared Helpful resources: 1. Follow-along blog post: https://posit.co/blog/snowflake-orbital-scikit-learn-models/ 2. Supported models for Orbital: https://orbital.tidymodels.org/articles/supported-models.html 3. Orbital (Python) GitHub Repo: https://github.com/posit-dev/orbital 4. Orbital (R) GitHub Repo: https://github.com/tidymodels/orbital 5. Databricks and Orbital for R and Python model deployment: https://posit.co/blog/databricks-orbital-r-python-model-deployment/ 6. Emily Riederer's XGBoost example with Orbital: https://www.emilyriederer.com/post/orbital-xgb/ 7. Sara Altman's blog post on Shiny + Databases: https://posit.co/blog/shiny-with-databases/ 8. Emil's introduction of orbital: https://emilhvitfeldt.com/talk/2024-08-13-orbital-positconf/ 9. Nick's Blog on Running Tidymodel Prediction Workflows with Orbital: https://posit.co/blog/running-tidymodel-prediction-workflows-inside-databases/ If you'd like to learn more about using Posit Team, you can always schedule time to chat with Posit here: https://posit.co/schedule-a-call/

Shiny tidymodels Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

What is an IDE & which one should you use as a beginner

Did you have a first? #datascience #datasciencetok #python #swe #datavisualization #dataanalytics #codinglife #vscode #ide #rstudio #positron #pycharm #jupyter

Positron rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Next-gen data science: a conversation with the Ravit Show and Eric Pité

Eric Pité, SVP of Product & Strategy, sat down with the Ravit Show at Databricks Data + AI Summit to have a conversation about the evolution of open source data science, the Posit and Databricks integration, and the place of AI in modern data science. Some highlights from the conversation are below: -- The evolution from RStudio to Posit It’s more than just a name change — it’s a signal of how seriously they’re embracing Python while staying true to their R roots and open-source mission. -- Open source meets enterprise Eric shared how Posit is navigating the balance between community contribution and commercial sustainability — and why that mission still matters in an AI-first world. -- Positron + Databricks Their strategic partnership is one to watch. Positron is making it easier to do high-quality data science (in R and Python) inside the Databricks ecosystem, with an emphasis on collaboration, reproducibility, and performance. -- AI’s place in modern data science Eric and Ravit chatted about how Posit sees the role of AI in helping data teams be more productive, without losing rigor or transparency. -- The power of storytelling with code One of our favorite parts is the emphasis on communication. Building models is not enough—data scientists need to share insights clearly, and Posit is building for that. Learn more about the Databricks and Posit partnership by exploring the partnership page and resources: https://posit.co/use-cases/databricks/

Positron rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Monthly Workflow Demos now on Zoom Sessions | Recordings still shared below

Please note while we previously hosted the Posit Workflow Demos here on YouTube, we have moved over to Zoom Sessions for a seamless event experience and more interactive Q&A. You can view upcoming workflow demos and register to join us here: https://events.zoom.us/ev/Ajss5j9VeRMe0zw-AtFKf7AUAsthzYhaYjYPeEIu1uYAQe1K0ud1~Agb_hSt1UGxd9fUyIDC32e6jAdEtbl7G_AaLZUYhRvyZR5cGpY5Kp_A0-w

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Welcome to PydyTuesday! | How to Level Up your Python Skills

In this video, you'll learn where to find the PydyTuesday github landing page, where data lives on the tidytuesday repo, and how to get started to try stuff out and build new things! We hope you join us in participating in PydyTuesday! Don't forget to use the hashtags #TidyTuesday and #PydyTuesday wherever you like to hangout online - Bluesky, Mastodon, LinkedIn, etc. - have fun out there! We can't wait to see the predictive models, visualizations, dashboards, and data apps that you create Resources and Repos to star: TidyTuesday GitHub Repo: https://github.com/rfordatascience/ti... Posit PydyTuesday GitHub Repo: https://github.com/posit-dev/python-t... TidyTuesday hashtag search on Bluesky: https://bsky.app/search?q=tidytuesday Other videos in this PydyTuesday playlist: • PydyTuesday | Python How-to Videos #pythoncontent

Rstudio Data Science Machine Learning Python Stats Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Github Data Wrangling Tidy Data Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Open Source Oss Reticulate Pydytuesday Python Projects Python Projects for Beginners Python Tutorial Positron Positron Ide

Let's set up your Python environment with only 2 steps | PydyTuesday Python Projects

In this video, let's get your project set up. Learn how to install Positron, create a new Python project, and set up a virtual environment. We hope you join us in participating in PydyTuesday! Don't forget to use the hashtags #TidyTuesday and #PydyTuesday wherever you like to hangout online - Bluesky, Mastodon, LinkedIn, etc. - have fun out there! We can't wait to see the predictive models, visualizations, dashboards, and data apps that you create Resources and Repos to star: TidyTuesday GitHub Repo: https://github.com/rfordatascience/ti... Posit PydyTuesday GitHub Repo: https://github.com/posit-dev/python-t... TidyTuesday hashtag search on Bluesky: https://bsky.app/search?q=tidytuesday Other videos in this PydyTuesday playlist: • PydyTuesday | Python How-to Videos #pythoncontent

Positron Rstudio Data Science Machine Learning Python Stats Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Blogdown Gt Tidymodels Statistics Debugging Programming Education Open Source Reticulate Python Tutorial Positron Ide Python for Beginners Pydytuesday

Let's install your first Python library in 2 minutes

Here is how to get set up to install your Python library in Positron. Learn how to use the terminal to install your first Python library, in this case pydytuesday. Also, learn the basics for managing packages using "pip." We hope you join us in participating in PydyTuesday! Don't forget to use the hashtags #TidyTuesday and #PydyTuesday wherever you like to hangout online - Bluesky, Mastodon, LinkedIn, etc. - have fun out there! We can't wait to see the predictive models, visualizations, dashboards, and data apps that you create Resources and Repos to star: TidyTuesday GitHub Repo: https://github.com/rfordatascience/ti... Posit PydyTuesday GitHub Repo: https://github.com/posit-dev/python-t... TidyTuesday hashtag search on Bluesky: https://bsky.app/search?q=tidytuesday Other videos in this PydyTuesday playlist: • PydyTuesday | Python How-to Videos #pythoncontent

Positron Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Forcats Ggplot2 Purrr Github Data Wrangling Tidy Data Odbc Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Reticulate Python for Beginners Install Python Libraries Python Tutorial Positron Ide Positron Data Science

Let's Import Free, High-Quality Datasets into your Python IDE (using Positron and PydyTuesday)

Let's download data. Learn how to install a dataset using the pydytuesday package and do some basic visualization of the data using Positron's data viewers. We hope you join us in participating in PydyTuesday! Don't forget to use the hashtags #TidyTuesday and #PydyTuesday wherever you like to hangout online - Bluesky, Mastodon, LinkedIn, etc. - have fun out there! We can't wait to see the predictive models, visualizations, dashboards, and data apps that you create Resources and Repos to star: TidyTuesday GitHub Repo: https://github.com/rfordatascience/ti... Posit PydyTuesday GitHub Repo: https://github.com/posit-dev/python-t... TidyTuesday hashtag search on Bluesky: https://bsky.app/search?q=tidytuesday Other videos in this PydyTuesday playlist: • PydyTuesday | Python How-to Videos #pythoncontent

Positron Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Package Manager CRAN Interoperability Serious Data Science Ggplot2 Github Data Wrangling Tidy Data Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Python Datasets Python for Beginners Positron Ide Pydytuesday Python Tutorials Python Tutorial

885: Python Polars: The Definitive Guide — with Jeroen Janssens and Thijs Nieuwdorp

Python #Polars #Pandas Jeroen Janssens and Thijs Nieuwdorp are data frame library Polars’ greatest advocates in this episode with @JonKrohnLearns where they discuss their book, Python Polars: The Definitive Guide, best practice for using Polars, why Pandas users are switching to Polars for data frame operations in Python, and how the library reduces memory usage and compute time up to 10x more than Pandas. Listen to the episode to be a part of an O’Reilly giveaway! This episode is brought to you by: • Trainium2, the latest AI chip from AWS: https://aws.amazon.com/ai/machine-learning/trainium/ • Adverity, the conversational analytics platform: https://eu1.hubs.ly/H0jxK210 • Dell AI Factory with NVIDIA: https://www.dell.com/superdatascience Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information. In this episode you will learn: • (00:00:00) Introduction • (00:04:46) Why Jeroen and Thijs wrote Python Polars: The Definitive Guide • (00:18:18) Best practices in Polars • (00:25:08) Why Polars has so many users • (00:32:37) The benefits of the Great Tables package • (00:50:05) Jeroen and Thijs’ partnership with NVIDIA and Dell for Python Polars: The Definitive Guide Additional materials: https://www.superdatascience.com/885

Jeroen Janssens

Great Tables Python Polars: The Definitive Guide Super Data Science Podcast SuperDataScience Jon Krohn Jeroen Janssens Python Polars Data Science Expertise Development Michael Chow Data Visualization Data Presentation Styled Tables DF Style Accessor Make Data Presentable Data Science Tools Deep Learning Illustrated Publishing a Book Pandas AI Tools Polars Richie Fink Rust Programming Language Alliander Great Tables NVIDIA Dell Uv Package Manager Kurzgesagt ODSC

Great Tables 3: Data Color and Polishing

This workshop is all about using Great Tables to make beautiful tables for publication and display purposes. We believe that effective tables have these things in common: structuring that aids in the reading of the table well-formatted values, fitting expectations for the field of study styling that reduces time to insight and improves aesthetics These materials are for you if: • you have some experience with data analysis in Python • you often create reporting that involves summarizations of data • you were often frustrated with making tables for display purposes outside of Python • you found beautiful-looking tables in the wild and wondered: 'How could I do that?' Other videos in this series: Great Tables 1: Structure, Format, and Style: https://youtu.be/QM7DbsY-nc4 Great Tables 2: Introducing Units Notation: https://youtu.be/SN0_vIL1Rhk About us: Michael Chow, Senior Software Engineer, Posit Michael is a data scientist and software engineer. He has programmed in Python for well over a decade, and he obtained a PhD in cognitive psychology from Princeton University. His interests include statistical methods, skill acquisition, and human memory. Richard Iannone, Senior Software Engineer, Posit Richard is a software engineer and table enthusiast. He's been vigorously working on making display tables easier to create/display in Python. And generally Rich enjoys creating open source packages so that people can great things in their own work. Workshop repo: https://github.com/rich-iannone/great-tables-mini-workshop?tab=readme-ov-file Learn more at https://posit-dev.github.io/great-tables/articles/intro.html

Michael Chow

Great Tables Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Great Tables 2: Introducing Units Notation

This workshop is all about using Great Tables to make beautiful tables for publication and display purposes. We believe that effective tables have these things in common: structuring that aids in the reading of the table well-formatted values, fitting expectations for the field of study styling that reduces time to insight and improves aesthetics These materials are for you if: • you have some experience with data analysis in Python • you often create reporting that involves summarizations of data • you were often frustrated with making tables for display purposes outside of Python • you found beautiful-looking tables in the wild and wondered: 'How could I do that?' Other videos in this series: Great Tables 1: Structure, Format, and Style: https://youtu.be/QM7DbsY-nc4 Great Tables 3: Data Color and Polishing https://youtu.be/Huteb5OmcrA About us: Michael Chow, Senior Software Engineer, Posit Michael is a data scientist and software engineer. He has programmed in Python for well over a decade, and he obtained a PhD in cognitive psychology from Princeton University. His interests include statistical methods, skill acquisition, and human memory. Richard Iannone, Senior Software Engineer, Posit Richard is a software engineer and table enthusiast. He's been vigorously working on making display tables easier to create/display in Python. And generally Rich enjoys creating open source packages so that people can great things in their own work. Workshop repo: https://github.com/rich-iannone/great-tables-mini-workshop?tab=readme-ov-file Learn more at https://posit-dev.github.io/great-tables/articles/intro.html

Michael Chow

Great Tables Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Great Tables 1: Structure, Format, and Style

This workshop is all about using Great Tables to make beautiful tables for publication and display purposes. We believe that effective tables have these things in common: structuring that aids in the reading of the table well-formatted values, fitting expectations for the field of study styling that reduces time to insight and improves aesthetics These materials are for you if: • you have some experience with data analysis in Python • you often create reporting that involves summarizations of data • you were often frustrated with making tables for display purposes outside of Python • you found beautiful-looking tables in the wild and wondered: 'How could I do that?' Other videos in this series: Great Tables 2: Introducing Units Notation: https://youtu.be/SN0_vIL1Rhk Great Tables 3: Data Color and Polishing https://youtu.be/Huteb5OmcrA About us: Michael Chow, Senior Software Engineer, Posit Michael is a data scientist and software engineer. He has programmed in Python for well over a decade, and he obtained a PhD in cognitive psychology from Princeton University. His interests include statistical methods, skill acquisition, and human memory. Richard Iannone, Senior Software Engineer, Posit Richard is a software engineer and table enthusiast. He's been vigorously working on making display tables easier to create/display in Python. And generally Rich enjoys creating open source packages so that people can great things in their own work. Workshop repo: https://github.com/rich-iannone/great-tables-mini-workshop?tab=readme-ov-file Learn more at https://posit-dev.github.io/great-tables/articles/intro.html

Michael Chow

Great Tables Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Johnson & Johnson's Open Source Journey with R in Clinical Trials

Learn how Johnson & Johnson embraces R and open-source tooling in clinical trials. Abstract: Embracing open source has been a transformative journey for J&J, fueled by a commitment to collaboration, transparency, and innovation. Our R journey began with a mindset shift—transitioning from decades-old solutions to actively contributing to and harnessing the power of open-source communities. We navigated the challenges of cultural adaptation while cultivating a programmer-friendly environment that promotes sharing and continuous learning. We enhanced our technical capabilities by open-sourcing key projects focused on R and strengthened our ecosystem and industry partnerships. This webinar reflects on our experiences, lessons learned, and the strategic impact of embracing R. Speakers: Sumesh Kalappurakal, Tadeusz Lewandowski, Nicholas Masel, and Mark Bynens. Learn more about Posit's work in Pharma: https://posit.co/use-cases/pharma/

Rstudio Data Science Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Readr Tidyr Github Data Wrangling Tidy Data Odbc Gt Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Pharma Clinical Trials Clinical Trials Statistical Programming Pharmaverse Johnson & Johnson Posit Posit Data Science Pharma Data Science

Quarto Live: WebAssembly powered data science learning | pyOpenSci

How Quarto Live Brings Code to Life in the Browser! Quarto Live takes interactive coding & publishing to the next level with WebAssembly-powered execution—run Python & R right in the browser, no setup needed! Real-time code execution without servers ️ Dynamic exercises & instant feedback for learners Full integration with Quarto for publishing & sharing Works on any device—even mobile! Watch as George Stagg (Posit) who develops and works on Quarto Live breaks down how it works & why it matters! ----------------------------- CHAPTERS 00:00:00 Intro 00:01:00 What is Quarto? 00:03:15 Add functionality to Quarto with extensions 00:03:30 Quarto and other interactive technologies like pyodide and web assembly 00:06:03 Web assembly powers interactive data science in the browser 00:07:53 How to use Quarto Live to create your own online interactive data science environment with Quarto 00:13:24 Create interactive data science enviornments for mobile 00:14:27 Demo interactive data science activities using quarto and quarto live 00:16:38 Wrap up and summary! -------------------------------- pyOpenSci makes learning open source and open science concepts easier. LEARN PYTHON PACKAGING Build a pure Python package tutorial: https://www.pyopensci.org/python-package-guide/tutorials/intro.html Explore packaging tools & best practices: https://www.pyopensci.org/python-package-guide/ 🤝 SCIENTIFIC PYTHON SOFTWARE REVIEW Learn how peer review works: https://www.pyopensci.org/software-peer-review/ Check out our peer-reviewed packages: https://www.pyopensci.org/python-packages.html We partner with the Journal of OpenSource Software and other communities - learn more: https://www.pyopensci.org/software-peer-review/partners/scientific-communities.html Get Involved Submit your packagei for review: https://github.com/pyopensci/software-submission/ ‍️ pyOpenSci governance: https://www.pyopensci.org/governance/ Connect with pyOpenSci pyopensci.org 🦣 fosstodon.org/@pyOpenSci 🦋 bsky.app/profile/pyopensci.bsky.social www.linkedin.com/company/pyopensci ABOUT pyOpenSci pyOpenSci is an inclusive and vibrant community of practice that breaks down technical and social open source barriers. promotes open and reproducible research through peer-review of scientific Python packages. Through peer review, education, and community support, we help make scientific Python software more accessible, citable, and impactful. Join our community & level up your open source game and support open science! #pyopensci #opensource #openscience

George Stagg

Build a Python table in under 1 minute using Great Tables

Create wonderful-looking tables directly from Pandas or Polars DataFrames with the Great Tables package. Typically, Great Tables is used in a notebook environment or within a Quarto document and rendered in HTML or as an image file. Ready to build some awesome tables? Install from PyPI with: $ pip install great_tables. GitHub Repo: https://github.com/posit-dev/great-tables Design Philosophy of Great Tables: https://posit-dev.github.io/great-tables/blog/design-philosophy/

Great Tables Quarto Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Tidyr Purrr Github Data Wrangling Tidy Data Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Great Tables Polars Dataframe Polars Dataframes

TidyTuesday + Posit | PydyTuesday | Weekly Community Python Data Project

Posit software engineer Isabel Zimmerman discusses the TidyTuesday project and the Posit PydyTuesday Initiative. Learn how to participate in weekly TidyTuesday projects, watch Isabel explore Central Park squirrel data, and discover how to deploy your work to Posit Connect Cloud. Find her code here: https://github.com/isabelizimm/pydy-tuesday Check out these repositories to join the TidyTuesday and the Posit PydyTuesday Initiative: TidyTuesday repo with datasets: https://github.com/rfordatascience/tidytuesday Posit PydyTuesday repo: https://github.com/posit-dev/python-tidytuesday Learn more about Quarto and Connect Cloud: Quarto website: https://quarto.org/ Posit Connect Cloud: https://connect.posit.cloud/ Other videos in this Posit PydyTuesday playlist: https://www.youtube.com/playlist?list=PL9HYL-VRX0oSDQjicFMLIIdcLv5NuvDp9 #pythoncontent

Isabel Zimmerman

python-tidytuesday Quarto Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Tidy Tuesday Pythoncontent

Create Quarto dashboards with Python

Learn how to create Quarto dashboards with Python! Senior Product Marketing Manager Isabella Velásquez walks through building a Quarto dashboard from scratch, using water insecurity data from the TidyTuesday project. You'll learn how to structure a Quarto document, preview dashboards, and customize layouts and themes. See Isabella's code here: https://github.com/ivelasq/water-insecurity-dashboard And the dashboard here: https://ivelasq-water-insecurity-dashboard.share.connect.posit.cloud/ Resources: Quarto dashboards guide: https://quarto.org/docs/dashboards/ Posit Connect Cloud: connect.posit.cloud Hello, Dashboards!: https://www.youtube.com/watch?v=HW7QbqI4fH0&t=590s TidyTuesday project: https://github.com/rfordatascience/tidytuesday Posit PydyTuesday GitHub repo: https://github.com/posit-dev/python-tidytuesday Other videos in this PydyTuesday playlist: https://www.youtube.com/playlist?list=PL9HYL-VRX0oSDQjicFMLIIdcLv5NuvDp9 #pythoncontent

python-tidytuesday Quarto Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Open Source Oss Reticulate Tidy Tuesday Pythoncontent

How to Build a Data Science Portfolio Website with Python & Quarto!

Learn how to build and deploy a professional data portfolio website using Quarto and Python! This comprehensive tutorial covers everything from creating a basic website structure to adding navigation, custom themes, and interactive elements like dashboards and slideshows. By the end, you'll know how to showcase your data science projects on your own custom domain using either Posit Connect Cloud or Netlify for deployment. Quarto Crash Course Video: https://youtu.be/_VKxTPWDhA4?si=1xoYNeZMy2SDvwFO Resources: - Read the Quarto Docs: https://quarto.org/docs/websites/ - Check out the website! https://quartoisfun.com - Link to Code (Starter): https://github.com/KeithGalli/quarto-portfolio/tree/starter - Link to Code (Finished): https://github.com/KeithGalli/quarto-portfolio Video by @KeithGalli Video Timeline! - 0:00 - Video Overview & Topics Covered - 2:22 - Getting Started Building Websites with Quarto & Python - 12:46 - Adding a Sidebar with Navigation to Your Website - 16:15 - Building a Data Portfolio of Our Past Work - 18:44 - Choosing a Custom Website Theme - 21:29 - Adding Many Pages & Sections to Our Website 28:38 - Adding Social Media Links (with Icons) to Your Site (GitHub, LinkedIn, YouTube, etc.) - 29:50 - Adding More Sections to Our Site & Linking to Specific Pages - 31:32 - Adding a Navbar Menu with Clickable Links to Our Website - 33:30 - Adding Custom Branding and Logo Favicons & Images (_brand.yml) - 40:25 - Deploying Our Website with Posit Connect Cloud - 48:21 - Deploying to Your Own Custom Domain (Netlify) #pythoncontent

Free AI packages for R and Python #pythoncontent

Some free R and Python packages that can help when working with LLMs #datascience #artificialintelligence #machinelearning #openai #pythonprogramming #pythonprojects #dataanalysis #dataanalytics

Rstudio Data Science Machine Learning Python Stats Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Lazy Evaluation Statistics Debugging Programming Education RStats Open Source Oss Reticulate Artificial Intelligence Python Packages Free Python Tools

Transform Boring Forms into Eye-Catching UI: Shiny Party Tricks That Will Make You Look Like a Pro 🎉

Learn how to elevate your Shiny forms from basic to professional with dynamic accordions. This tutorial demonstrates how to implement intelligent form organization and visual feedback using Shiny's accordion components. We cover both basic accordion implementation and advanced dynamic features including status indicators, automatic title updates, and section validation. Perfect for developers looking to enhance their Shiny applications with modern, user-friendly form interfaces. Key features covered: Basic form to accordion conversion Dynamic panel updates Status indicator implementation Visual feedback systems Section validation techniques Reference Documentation: For accordion implementation, see: https://shiny.posit.co/r/reference/shiny/latest/accordion For dynamic updates, see: https://shiny.posit.co/r/reference/shiny/latest/updateAccordion For input validation, see: https://shiny.posit.co/r/reference/shiny/latest/validate For modular development, see: https://shiny.posit.co/r/articles/build/modules/ Timestamps: 0:00 - Introduction 0:23 - Basic form structure overview 0:50 - Introduction to accordion components 1:11 - Basic accordion implementation 1:35 - Dynamic accordion enhancement 2:00 - Status indicators and visual feedback 2:29 - Updating accordion panels 2:55 - Documentation resources 3:18 - Conclusion

Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Stop Your Shiny Apps from Breaking on Mobile! Master Responsive Design in Minutes 📱💻

A comprehensive guide to implementing responsive design in Shiny applications using UI layout columns. This tutorial covers how to create adaptive layouts that automatically adjust based on screen size, from desktop to mobile devices. Learn how to leverage Shiny's bootstrap-based grid system to create professional, responsive applications that look great on any device. Key concepts covered: • Default Shiny responsive behavior • UI layout column implementation • Screen size breakpoints • Bootstrap grid system • Responsive card layouts • Mobile-first design principles Reference Documentation: UI Layout Columns: https://shiny.posit.co/r/reference/shiny/latest/layout_column Layout Guide: https://shiny.posit.co/r/articles/build/layout-guide/ Bootstrap Integration: https://shiny.posit.co/r/articles/build/bootstrap/ Responsive Design: https://shiny.posit.co/r/articles/build/responsive/ Timestamps: 0:00 - Introduction 0:23 - Default Shiny layout behavior 0:38 - Understanding screen responsiveness 1:05 - Introduction to UI layout columns 1:38 - Screen size breakpoints explained 2:02 - Desktop layout demonstration 2:30 - Mobile layout adaptation 2:44 - Bootstrap grid system overview 3:21 - Documentation resources 3:45 - Conclusion

Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Shiny Forms with Accordions 🎉

Learn how to elevate your Shiny forms from basic to professional with dynamic accordions. This tutorial demonstrates how to implement intelligent form organization and visual feedback using Shiny's accordion components

Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Who are the data heroes of...data heroes? 💫

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Create slideshows with Markdown & Python Code! (Quarto Tutorial)

Learn how to transform Markdown and Python code into interactive slide presentations using Quarto and reveal.js. This tutorial covers essential features like code execution, data visualization, LaTeX equations, custom styling, and slide transitions. Starting with basic slides, we'll explore advanced functionality including background customization, animations, chalkboard annotations, and brand theming. Perfect for data scientists, educators, and developers looking to create engaging technical presentations. Github repo: https://github.com/KeithGalli/quarto-projects Quarto slideshow demo page: https://quarto.org/docs/presentations/revealjs/ Quarto crash course video: https://youtu.be/_VKxTPWDhA4?si=VZxkW3kgtx1W_AIW Blog article on _brand.yml: https://posit.co/blog/unified-branding-across-posit-tools-with-brand-yml/ Video by @KeithGalli Video timeline! 0:00 - Video Overview 0:39 - Getting Started with the Code (link in description) 2:22 - Quarto Slideshow Basics 6:15 - Generating Powerpoint (pptx) Presentation Outputs 7:14 - RevealJS HTML Output Options & Features (Code Animation, Line Highlighting) 11:00 - Data Visualization in Quarto (Matplotlib, Plotly, Seaborn) 14:40 - Displaying Dataframes on Slides 18:15 - Presentation Features (Incrementally Revealing Items, Image Positioning, Slide Transitions, etc.) 25:02 - Keyboard Shortcuts Functionality (Zoom in, Chalkboard, Save Presentation as PDF, etc.) 29:14 - Styling Presentations with a _brand.yml File (New in Quarto 1.6) 34:39 - Jupyter Notebook to RevealJS Slideshow #Python #Quarto #DataVisualization #pythoncontent

Pharma Open-Source Packages and the R Validation Hub | A conversation with Aaron Clark

Posit's Director of Life Sciences, Phil Bowsher, sat down with Aaron Clark, Senior Principal Clinical Scientist at Arcus Biosciences, to discuss various topics within the open-source clinical reporting space including Aaron's career in pharma, the R Validation Hub, the open-source package riskmetric, and more. More about Posit's work in the pharma space: https://posit.co/use-cases/pharma/

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Readr Stringr Tidyr Github Data Wrangling Tidy Data Odbc Blogdown Gt Tidymodels Statistics Programming Education RStats Open Source Oss R Validation Hub Clinical Reporting Drug Development Open Source Pharma Pharma SAS Pharma Data Science

Open Source in Clinical Reporting | A Conversation with Ben Arancibia at GSK

Posit's Director of Life Sciences, Phil Bowsher, sat down with GSK's Director of Data Science, Ben Arancibia, to discuss various topics within the open-source clinical reporting space. More about Posit's work in the pharma space: https://posit.co/use-cases/pharma/ Watch GSK's latest web event with Posit: https://www.youtube.com/watch?v=xDrt6txplek

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss GSK Pharma SAS Pharma Clinical Reporting Statistial Programming

Real-World Python Dashboard Project w/ Quarto! (Airbnb Review Analysis)

In this video we use real-world Airbnb data to put together a dashboard with Quarto & Python. We start with the basics of layout and simple components and then progress to more advanced topics such as integrating Shiny within your Quarto doc. - Github Repo: https://github.com/KeithGalli/quarto-projects - Check out the recent Quarto Crash Course Video! https://youtu.be/_VKxTPWDhA4?si=sf7cbvSJGj6gnlCl - Check out Mine’s Quarto Dashboards with R Code Videos! https://youtu.be/HW7QbqI4fH0?si=oyJhLaJ0n3Vg4v7Y - Learn more about Shiny for Dashboards! https://youtu.be/I2W7i7QyJPI?si=Osqk5odwSOZ0ub01 - Bootstrap Icons: https://icons.getbootstrap.com/ Video Timeline! 0:00 - Video Overview 0:41 - About our Data & Get Started with the Code 1:38 - Basic Quarto Dashboard Layout Information 5:08 - Adding Value Boxes to our Dashboard 10:56 - Prepping our Airbnb Data for our Dashboard 17:00 - Adding Matplotlib Data Visualizations to our Dashboard (line graph, bar chart) 26:26 - Add a Map of Airbnb Listings using Latitude & Longitude (Folium Library) 33:18 - Adding Additional Dashboard Pages with Interactive Tables (itables library) 42:23 - When to use Quarto versus Shiny for Dashboards? 44:00 - Using Shiny within a Quarto Project 49:14 - Hosting your Quarto Documents with Posit Connect Cloud #python #quarto #dashboards #pythoncontent

Fastest way to Convert Jupyter Notebooks into Analytics Reports! (using Quarto)

Transform messy Jupyter notebooks into polished HTML reports with Quarto. Learn how to create professional, client-ready reports that effectively communicate data science insights to stakeholders. Using a telecom customer churn analysis example, we'll cover: - Converting notebooks to formatted HTML reports - Customizing layouts and interactive elements - Adding business context to technical analysis - Publishing and sharing reports effectively Perfect for data scientists and analysts who need to present technical work to business audiences. Link to Code: https://github.com/KeithGalli/telecom-churn-analysis Quarto Crash Course Video: https://youtu.be/_VKxTPWDhA4?si=kcbQ8M9p6HH5QE2w Share your work with Posit Connect Cloud: https://pos.it/keith_qc Video by @KeithGalli Video timeline! 0:00 - Video Overview & Accessing Code/Data 1:46 - Rendering Jupyter Notebook as HTML Output 3:30 - Making quick improvements to our HTML Report (adjusting YAML parameters, hiding code/output cells) 8:15 - Understanding the Business Context & Insights from our Analysis and adding written details to report. 14:30 - Improving page formatting (margins, body size, etc) 17:30 - Adding business recommendations for our telecom client 21:00 - Further aesthetic improvements (larger font-size, organizing info into columns, using a qmd file, etc.) 30:17 - Publishing our HTML report using Posit Connect Cloud #python #jupyter #quarto

Quarto Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Rmarkdown CRAN Interoperability Serious Data Science Dplyr Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Statistics Debugging Programming Education RStats Open Source Oss Quarto

Generate 100s of custom reports in minutes with Python & Quarto! (Parameterized report automation)

A practical guide to generating hundreds of customized reports using Quarto and Python. Learn how to leverage Quarto's parameter system to create PDFs and HTML reports at scale. Using a movie dataset example, we'll cover: - How to automate report generation with Python and Quarto - Create dynamic templates with data visualization and formatting - Adapt parameters to work with both Python and R code - Build scalable reporting workflows for any dataset This tutorial demonstrates how to transform what could be hours of manual reporting work into an efficient automated process. Quarto Crash Course Video: https://youtu.be/_VKxTPWDhA4?si=Q09V5xXlyo1YVQqs Generating Analytics Reports (typst) Video: https://youtu.be/Q3phTByW138?si=FIMiAWAHP6HhUnhG Link to Code (starting): https://github.com/KeithGalli/quarto-crash-course Link to Code (finished): https://github.com/KeithGalli/quarto-crash-course/tree/parameterized-reports Information on the dataset can be found here: https://github.com/KeithGalli/quarto-crash-course/tree/master?tab=readme-ov-file#resources Video timeline! 0:00 - Video Overview 0:58 - Walking through some starter code (linked in description) 3:20 - Passing parameters with Python into Quarto Markdown Files 4:27 - Passing parameters using R 5:18 - Using a Python script to render many reports with variable parameters. 6:36 - Dynamically generating markdown sections with Python (output: asis - execution option) 11:56 - Adding images, table of contents, & pagebreaks to our reports 15:00 - Adding graphs to our reports 17:54 - Tying everything together 21:47 - Adding fenced divs & styling/formatting dynamically with code

Quarto Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Quarto Websites 4: Add lists of content with listings | Charlotte Wickham | Posit

Adding a listing page to your website is a great way to showcase your projects, talks, publications or blog posts. In this video you’ll learn how to create a listing page in Quarto and see two ways to populate it with content: Quarto documents, or a yaml file. In this video: 0:50 Use a listing to add a blog 3:36 Listing options 5:47 Why use a listing? 7:22 Use a YAML file to populate a project portfolio 9:50 Customize the display of a listing 12:10 Advanced customization of listings 13:42 Remove pages Links: Listings: https://quarto.org/docs/websites/website-listings.html Andrew Heiss’ teaching listing: https://www.andrewheiss.com/teaching/ Code: Starter source code: https://github.com/cwickham/quarto-website-video/tree/v0.3 Final source code: https://github.com/cwickham/quarto-website-video/tree/v0.4 For more in-depth coverage and slides check out: https://posit-conf-2024.github.io/quarto-websites/ Do you need a professional website to showcase your work? If you’ve used Quarto to produce a document, you’ve already got the technical skills to create a Quarto website. In this video series, you’ll learn everything else you need to build a website and customize its appearance. This video series is for you if you: - Have used Quarto to generate documents (e.g. HTML, PDF, MS Word etc.) - Are comfortable editing plain text documents (e.g .qmd) in your IDE (e.g. RStudio, Visual Studio Code etc.) - Want to walk away with your own personal website Taught by: Charlotte Wickham (https://www.cwick.co.nz/) Emil Hvitfeldt (https://emilhvitfeldt.com/) Videos in this series: 1. Build your homepage [https://youtu.be/l7r24gTEkEY] 2. Add pages and navigation [https://youtu.be/k65E-8PXZmA] 3: Customize appearance with CSS/SCSS [https://youtu.be/pAN2Hiq0XGs] 4: Add lists of content with listings [https://youtu.be/bv_Cw-3HI1Y]

Charlotte Wickham, Emil Hvitfeldt

Quarto rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Quarto Websites 3: Customize appearance with CSS/SCSS | Emil Hvitfeldt | Posit

You now have a set of content you are happy with on your website, but how do you customize the look and feel of your site beyond options set in YAML? In this video, you’ll start by learning the basics of CSS and SCSS and how to make good design choices. Then, you’ll see how to apply these choices to your Quarto website. In this video: 0:14 What is HTML? 3:23 CSS Selectors 8:05 CSS Attributes 8:25 Layout attributes 10:24 Reducing repetition with SASS/SCSS? 15:26 Consistent design 16:36 Choosing colors 17:50 Choosing fonts 19:28 Maintaining accessibility 22:13 Apply SCSS to your website 24:16 Change the appearance of headings 25:28 Change the appearance of navigation bar 30:30 Use google fonts Links: Color contrast checker: https://colourcontrast.cc/ Google fonts: https://fonts.google.com/ Code: Starter source code: https://github.com/cwickham/quarto-website-video/tree/v0.2 Final source code: https://github.com/cwickham/quarto-website-video/tree/v0.3 For more in-depth coverage and slides check out: https://posit-conf-2024.github.io/quarto-websites/ Do you need a professional website to showcase your work? If you’ve used Quarto to produce a document, you’ve already got the technical skills to create a Quarto website. In this video series, you’ll learn everything else you need to build a website and customize its appearance. This video series is for you if you: - Have used Quarto to generate documents (e.g. HTML, PDF, MS Word etc.) - Are comfortable editing plain text documents (e.g .qmd) in your IDE (e.g. RStudio, Visual Studio Code etc.) - Want to walk away with your own personal website Taught by: Charlotte Wickham (https://www.cwick.co.nz/) Emil Hvitfeldt (https://emilhvitfeldt.com/) Videos in this series: 1. Build your homepage [https://youtu.be/l7r24gTEkEY] 2. Add pages and navigation [https://youtu.be/k65E-8PXZmA] 3: Customize appearance with CSS/SCSS [https://youtu.be/pAN2Hiq0XGs] 4: Add lists of content with listings [https://youtu.be/bv_Cw-3HI1Y]

Charlotte Wickham, Emil Hvitfeldt

Quarto rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Quarto Websites 2: Add pages and navigation | Charlotte Wickham | Posit

Now you’ve got a homepage, you’ll likely want to add some other pages. In this video, learn how to add pages to your website, and help people find them, by adding them to your website navigation. In this video: 1:00 Add a page to your website 2:54 Your file structure determines your URL structure 5:49 Add a link to your page in navigation 7:50 Customize navigation item text and icon 9:12 Control where items appear in the navigation bar 10:16 Navigation bar options 11:11 Switch to side navigation 12:22 Other types of navigation 16:30 Wrap Up Links: List of icons you can use in navigation items: https://icons.getbootstrap.com/ Top navigation bar options: https://quarto.org/docs/websites/website-navigation.html#top-navigation Quarto website navigation: https://quarto.org/docs/websites/website-navigation.html Code: Starter source code: https://github.com/cwickham/quarto-website-video/tree/v0.1 Final source code: https://github.com/cwickham/quarto-website-video/tree/v0.2 For more in-depth coverage and slides check out: https://posit-conf-2024.github.io/quarto-websites/ Do you need a professional website to showcase your work? If you’ve used Quarto to produce a document, you’ve already got the technical skills to create a Quarto website. In this video series, you’ll learn everything else you need to build a website and customize its appearance. This video series is for you if you: - Have used Quarto to generate documents (e.g. HTML, PDF, MS Word etc.) - Are comfortable editing plain text documents (e.g .qmd) in your IDE (e.g. RStudio, Visual Studio Code etc.) - Want to walk away with your own personal website Taught by: Charlotte Wickham (https://www.cwick.co.nz/) Emil Hvitfeldt (https://emilhvitfeldt.com/) Videos in this series: 1. Build your homepage [https://youtu.be/l7r24gTEkEY] 2. Add pages and navigation [https://youtu.be/k65E-8PXZmA] 3: Customize appearance with CSS/SCSS [https://youtu.be/pAN2Hiq0XGs] 4: Add lists of content with listings [https://youtu.be/bv_Cw-3HI1Y]

Charlotte Wickham, Emil Hvitfeldt

Quarto rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Quarto Websites 1: Build your homepage | Charlotte Wickham & Emil Hvitfeldt | Posit

In this video, you’ll get a running start by using a template we've designed to be functional and attractive, and that will serve as a foundation for the rest of the video series. You’ll customize the content of your homepage, and how it looks, and along the way learn about the two key files in a Quarto website index.qmd and _quarto.yml. Finally, you’ll learn one way to publish your website so other people can see it. In this video: 0:21 Use a template to get started 2:33 Preview the template homepage, index.qmd 4:12 Customize the content of your homepage 5:45 “About” pages 7:22 Customize the image on your homepage 9:24 Website configuration, _quarto.yml 10:40 Customize colors with YAML 13:45 Customize fonts with YAML 17:00 Publish your site Links: About pages: https://quarto.org/docs/websites/website-about.html Appearance options you can set in YAML: https://quarto.org/docs/output-formats/html-themes.html#basic-options Code: Starter source code: https://github.com/EmilHvitfeldt/website-template Final source code: https://github.com/cwickham/quarto-website-video/tree/v0.1 For more in-depth coverage and slides check out: https://posit-conf-2024.github.io/quarto-websites/ Do you need a professional website to showcase your work? If you’ve used Quarto to produce a document, you’ve already got the technical skills to create a Quarto website. In this video series, you’ll learn everything else you need to build a website and customize its appearance. This video series is for you if you: - Have used Quarto to generate documents (e.g. HTML, PDF, MS Word etc.) - Are comfortable editing plain text documents (e.g .qmd) in your IDE (e.g. RStudio, Visual Studio Code etc.) - Want to walk away with your own personal website Taught by: Charlotte Wickham (https://www.cwick.co.nz/) Emil Hvitfeldt (https://emilhvitfeldt.com/) Videos in this series: 1. Build your homepage [https://youtu.be/l7r24gTEkEY] 2. Add pages and navigation [https://youtu.be/k65E-8PXZmA] 3: Customize appearance with CSS/SCSS [https://youtu.be/pAN2Hiq0XGs] 4: Add lists of content with listings [https://youtu.be/bv_Cw-3HI1Y]

Charlotte Wickham, Emil Hvitfeldt

Quarto rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

pandas vs Polars DataFrames? 👨🏻‍🍳 #python

Here's Marco Gorelli's take on why lazy execution matters So with pandas, everything happens eagerly. You give the library some instructions, and it evaluates your instructions the moment you give them. With Polars, there's an option to give it some instructions, it can wait a bit, and then do them all together in the most optimal way that it can detect. If I was to tell you to cook me a recipe ‍ ...and I gave you the steps one at a time ...and for each step, you have to go out and buy the ingredients that you need for that step ^ This is going to be a pretty inefficient way of making a cake But if I can give you the recipe beforehand: ...you can buy all the ingredients together ...you can maybe do 2 steps at the same time ^ This is going to be a much nicer cooking experience

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Quarto Crash Course | Create Professional Reports, Dashboards & Websites w/ Markdown & Python Code!

Welcome to this comprehensive Quarto crash course using Python! Whether you're a complete beginner or an experienced user, this tutorial covers the topics you need to know about the Quarto publishing system. We'll explore: - Basic setup and installation - Creating HTML reports, PDFs, and interactive dashboards from your Python script or Jupyter notebook. - Building presentations with Revealjs - Customizing outputs with CSS and layouts (fenced divs, classes, and more) - Working with parameters for dynamic reports - Publishing to Posit Connect Cloud - Creating complete websites - Automated report generation Perfect for data scientists, analysts, and developers looking to create beautiful, reproducible reports from their code. We'll use Python throughout the tutorial with real-world examples using movie analytics data. Let's dive in! Video by @KeithGalli Github repo: https://github.com/KeithGalli/quarto-crash-course Deploy with Posit Connect Cloud! https://pos.it/keith_qc #python #quarto #posit — Resources Mentioned — Slideshow example: https://quarto.org/docs/presentations/revealjs/demo/#/title-slide Slideshow example (source code): https://github.com/quarto-dev/quarto-web/blob/main/docs/presentations/revealjs/demo/index.qmd Example HTML report: https://019302a7-e9e3-3454-3575-23148999a7f7.share.connect.posit.cloud/ Quarto Gallery: https://quarto.org/docs/gallery/ Bootstrap Icons: https://icons.getbootstrap.com/ Video timeline! 0:00 - About the Crash Course 0:50 - Quarto Overview 2:12 - Installation & Setup 6:22 - Markdown Basics 8:46 - Quarto Markdown Features 19:37 - Quarto Styling & Formatting (fenced divs, CSS classes, etc.) 34:53 - Parameters & CLI Options 40:46 - HTML & Publishing 49:46 - Static Docs (PDFs, Docx) 54:53 - Dashboards 1:06:50 - Slideshows (Revealjs) 1:16:11 - Websites 1:19:52 - Automated Report Generation (Parameterized Reports)

cli Quarto revealjs Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Quarto Dashboards 3: Theming and Styling | Mine Çetinkaya-Rundel | Posit

Theming and styling Quarto dashboards built with R and/or Python. Before watching this video, you might want to watch Parts 1 & 2. This video takes you through 0:00 - Theming (including Bootswatch themes, light/dark mode, customizing themes with SCSS) 3:55 - Styling 4:55 - Live coding demo Slides can be found at https://mine.quarto.pub/quarto-dashboards/3-theming-styling and the starter documents for the accompanying exercises at https://github.com/mine-cetinkaya-rundel/olympicdash. Materials for all parts of the videos can be accessed at https://mine.quarto.pub/quarto-dashboards. You already analyze and summarize your data in computational notebooks with R and/or Python. What’s next? You can share your insights or allow others to make their own conclusions in eye-catching dashboards and straight-forward to author, design, and deploy Quarto Dashboards, regardless of the language of your data processing, visualization, analysis, etc. With Quarto Dashboards, you can create elegant and production-ready dashboards using a variety of components, including static graphics (ggplot2, Matplotlib, Seaborn, etc.), interactive widgets (Plotly, Leaflet, Jupyter Widgets, htmlwidgets, etc.), tabular data, value boxes, text annotations, and more. Additionally, with intelligent resizing of components, your Quarto Dashboards look great on devices of all sizes. And importantly, you can author Quarto Dashboards without leaving the comfort of your “home” – in plain text markdown with any text editor (VS Code, RStudio, Neovim, etc.) or any notebook editor (JupyterLab, etc.). This workshop will walk you through building an increasingly complex dashboard using various layout options and deploy them as static web pages (with no special server required) as well as with a Shiny Server on the backend for enhanced interactivity. This course is for you if you: * do data analysis in computational notebooks * share your results with your audience in static or interactive dashboards * want to improve the design, user interface, and experience of your dashboards

Mine Çetinkaya-Rundel

ggplot2 leaflet Quarto rstudio Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Quarto Dashboards 2: Components | Mine Çetinkaya-Rundel | Posit

Building dashboards in R and/or Python with Quarto, one component at a time. Before watching this video, you might want to watch Part 1. This video takes you through 0:00 - An overview of dashboard components 0:11 - Navigation bar and pages 4:55 - Sidebars, rows, columns, and tabsets 11:07 - Cards 22:40 - Live coding demo Slides can be found at https://mine.quarto.pub/quarto-dashboards/2-dashboard-components and the starter documents for the accompanying exercises at https://github.com/mine-cetinkaya-rundel/olympicdash. Materials for all parts of the videos can be accessed at https://mine.quarto.pub/quarto-dashboards

Mine Çetinkaya-Rundel

Quarto Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Quarto Dashboards 1: Hello, Dashboards! | Mine Çetinkaya-Rundel | Posit

You already analyze and summarize your data in computational notebooks with R and/or Python. What’s next? You can share your insights or allow others to make their own conclusions in eye-catching dashboards and straight-forward to author, design, and deploy Quarto Dashboards, regardless of the language of your data processing, visualization, analysis, etc. With Quarto Dashboards, you can create elegant and production-ready dashboards using a variety of components, including static graphics (ggplot2, Matplotlib, Seaborn, etc.), interactive widgets (Plotly, Leaflet, Jupyter Widgets, htmlwidgets, etc.), tabular data, value boxes, text annotations, and more. Additionally, with intelligent resizing of components, your Quarto Dashboards look great on devices of all sizes. And importantly, you can author Quarto Dashboards without leaving the comfort of your “home” – in plain text markdown with any text editor (VS Code, RStudio, Neovim, etc.) or any notebook editor (JupyterLab, etc.). This video takes you through 0:00 - Overview of building dashboards with Quarto 0:15 - Dashboard basics 7:40 - First dashboard in R 10:30 - First dashboard in Python 11:43 - Live coding demo Slides can be found at https://mine.quarto.pub/quarto-dashboards/1-hello-dashboards/#/title-slide and the starter documents for the accompanying exercises at https://github.com/mine-cetinkaya-rundel/olympicdash. Materials for all parts of the videos can be accessed at https://mine.quarto.pub/quarto-dashboards

Mine Çetinkaya-Rundel

ggplot2 leaflet Quarto rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Updates from Posit, with Hadley Wickham, Charlotte Wickham, George Stagg, and James Blair

6:45 Hadley introduces the conference. 10:37 Hadley on Posit, PBC. Who are we, and what we do. 20:41 Charlotte Wickham on Quarto, an open-source scientific and technical publishing system. 31:05 George Stagg on webR, R for WebAssembly. Execute R code in your web browser. 43:34 James Blair with the latest on Posit's partnerships with Databricks and Snowflake. Please join us for our first Posit Conf 2024 keynote, where we’ll tell you about our mission, our products, and some of the exciting things we’ve been working on over the last year. Hadley Wickham, Chief Scientist, will talk briefly about Posit’s mission and products, before introducing the three speakers who will update you on some of the coolest projects we’ve worked on over the last year. James Blair, Senior Product Manager, will give you the latest on our partnerships with Databricks and Snowflake, and how we’re building seamless integrations that let you focus on data science instead of dealing with technical details. Charlotte Wickham, Developer Educator, will show you what’s new in Quarto, focusing on new ways to build beautiful PDFs with Typst. Finally, George Stagg, Senior Software Engineer, will tell you about the latest innovations in teaching using webR, a tool that lets you compile your R code into standalone HTML files. Talk by Hadley Wickham, James Blair, Charlotte Wickham, and George Stagg

Charlotte Wickham, George Stagg, Hadley Wickham

Quarto Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Shiny 1.2 Release: Wrap your data frame with Narwhals! 🎉 | Barret Schloerke | Posit

In this video, Barret Schloerke from the Shiny team walks us through the exciting features of Shiny 1.2. One of the major updates includes the integration of Narwhals, allowing effortless computation on data frames without needing detailed knowledge of the data structure. Barret also demonstrates a simple Shiny app displaying S&P 500 data using PyArrow, Polars, and Narwhals. Learn how these updates simplify your data workflows, with minimal code needed to run complex data frame operations. Useful Links: • Shiny Component Gallery: [https://shiny.posit.co/py/components/] • Learn more about Narwhals: [https://narwhals-dev.github.io/narwhals/] Timestamps: 0:00 - Introduction and Shiny 1.2 Release Overview 0:12 - Narwhals Integration and Data Computation 0:31 - PyArrow Application Code Breakdown 1:20 - Using S&P 500 Data with Pandas, Polars, PyArrow, and Narwhals #Shiny #DataScience #narwhals #ShinyApp #Polars #Python

Barret Schloerke, Shiny Team

Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

How Can Data Teams Get Out of Their Own Way (Alex Gold) - KNN Ep. 196

Today, I had the pleasure of interviewing Alex Gold. Alex is the Director of Solutions Engineering + Support at posit and author of DevOps for Data Science. In this episode, we talk about Alex's experience working in data for presidential campaigns, his experience in solutions engineering, and the role that DevOps plays in the data domain. Book: https://www.routledge.com/DevOps-for-Data-Science/Gold/p/book/9781003213345 (You can use Code AFLY04 for a 20% discount) Alex's Links: LinkedIn - https://www.linkedin.com/in/alexkgold/ Twitter - https://x.com/alexkgold Podcast Sponsors, Affiliates, and Partners: - Pathrise - http://pathrise.com/KenJee | Career mentorship for job applicants (Free till you land a job) - Taro - http://jointaro.com/r/kenj308 (20% discount) | Career mentorship if you already have a job - 365 Data Science (57% discount) - https://365datascience.pxf.io/P0jbBY | Learn data science today - Interview Query (10% discount) - https://www.interviewquery.com/?ref=kenjee | Interview prep questions Listen to Ken's Nearest Neighbors on all the main podcast platforms! On Apple Podcasts: https://podcasts.apple.com/us/podcast/kens-nearest-neighbors/id1538368692 (Please rate if you enjoy it!) On Spotify: https://open.spotify.com/show/7fJsuxiZl4TS1hqPUmDFbl On Google: https://podcasts.google.com/feed/aHR0cHM6Ly9mZWVkcy5idXp6c3Byb3V0LmNvbS8xNDMwMDQxLnJzcw?sa=X&ved=0CAMQ4aUDahcKEwjQ2bGBhfbsAhUAAAAAHQAAAAAQAQ MORE DATA SCIENCE CONTENT HERE: My Twitter - https://twitter.com/KenJee_DS LinkedIn - https://www.linkedin.com/in/kenjee/ Kaggle - https://www.kaggle.com/kenjee Medium Articles - https://medium.com/@kenneth.b.jee Github - https://github.com/PlayingNumbers My Sports Blog - https://www.playingnumbers.com ️ 66DaysOfData Discord Server - https://discord.com/invite/4p37sy5muZ

Build Shiny apps with AI ✨

View the full video here: https://www.youtube.com/watch?v=fJNKdwdVQ8Q Try it out here, free: https://gallery.shinyapps.io/assistant/ Get started with Shiny for R and Python: https://shiny.posit.co #pythoncontent

Shiny shinyapps Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Shiny Assistant: Prototype and build Shiny applications with the help of AI | Winston Chang | Posit

Have you ever had an idea for a great web application with Shiny but felt something holding you back from getting started? Maybe it’s that you don’t know where to start, or that you don’t know which packages use to build the app, or maybe it’s just that you can’t muster the energy to get started. Sometimes you just need a little help to get unstuck. We’re excited to announce a new addition to the Shiny ecosystem that can help: Shiny Assistant. Try it out here, free: https://gallery.shinyapps.io/assistant/ Get started with Shiny for R and Python: https://shiny.posit.co

Winston Chang

Shiny shinyapps Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

What AI skill should we learn? 🌱

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Deploy an LLM-powered Shiny for Python app to Connect Cloud in minutes!

Connect Cloud lets you quickly deploy data applications and documents from public GitHub repositories for Python and R projects. This short demo showcases secret variable management on Connect Cloud to help deploy an LLM-powered Shiny for Python application. → Signup for a free Connect Cloud account → https://connect.posit.cloud/ → View the how-to guide → https://docs.posit.co/connect-cloud/how-to/python/llm-shiny-python.html

Shiny for Python Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

So You Think You Can ANALYZE? (Data Content Creator Hackathon)

Watch your favorite data science content creators compete for a piece of history. They will either prove their skills in front of the world or fail in the pursuit of eternal glory. This is so you think you can analyze. Special thanks to Posit for sponsoring this competition and make this all possible! Competitors: Team Jack @averysmith Jack Blandin @KeithGalli Team MMA @AlexTheAnalyst @nerdnourishment @Miki_ML Team Null Nick Singh Mark Freeman @SeattleDataGuy Team Shashank @ShashankData Team Posit-ively Skewed Greg Coquillo @datascienceharp Richad Nieves-Becker Ian Greengross Interviewer: Elijah Butler - https://www.tiktok.com/@imelijahbutler Posit Team - Michael Chow - Joe Cheng - Carolos Scheidegger - Julia Silge Sponsors, Affiliates, and Partners: - Pathrise - http://pathrise.com/KenJee | Career mentorship for job applicants (Free till you land a job) - Taro - http://jointaro.com/r/kenj308 (20% discount) | Career mentorship if you already have a job - 365 Data Science (57% discount) - https://365datascience.pxf.io/P0jbBY | Learn data science today - Interview Query (10% discount) - https://www.interviewquery.com/?ref=kenjee | Interview prep questions MORE DATA SCIENCE CONTENT HERE: My Twitter - https://twitter.com/KenJee_DS LinkedIn - https://www.linkedin.com/in/kenjee/ Kaggle - https://www.kaggle.com/kenjee Medium Articles - https://medium.com/@kenneth.b.jee Github - https://github.com/PlayingNumbers My Sports Blog -https://www.playingnumbers.com Check These Videos Out Next! My Leaderboard Project: https://www.youtube.com/watch?v=myhoWUrSP7o&ab_channel=KenJee 66 Days of Data: https://www.youtube.com/watch?v=qV_AlRwhI3I&ab_channel=KenJee How I Would Learn Data Science in 2021: https://www.youtube.com/watch?v=41Clrh6nv1s&ab_channel=KenJee My Playlists Data Science Beginners: https://www.youtube.com/playlist?list=PL2zq7klxX5ATMsmyRazei7ZXkP1GHt-vs Project From Scratch: https://www.youtube.com/watch?v=MpF9HENQjDo&list=PL2zq7klxX5ASFejJj80ob9ZAnBHdz5O1t&ab_channel=KenJee Kaggle Projects: https://www.youtube.com/playlist?list=PL2zq7klxX5AQXzNSLtc_LEKFPh2mAvHIO

Joe Cheng, Julia Silge, Michael Chow

Using controllers to write robust Shiny for Python app tests | Karan Gathani | Posit

With the Shiny for Python v1.0 release, controllers were exposed to provide a structured and consistent way for users to interact with and test UI components in Shiny applications. They offer an abstraction layer that encapsulates the complexity of interacting with specific UI elements, making it easier for developers to write robust tests and automate interactions with their Shiny apps. Reference documentation - https://shiny.posit.co/py/api/testing/ https://shiny.posit.co/py/docs/playwright-testing.html

Karan Gathani

Shiny for Python Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Creating tests for Shiny for Python apps | Karan Gathani | Posit

With the Shiny for Python v1.0 release, Shiny provides a simple way to create a test file for your Shiny app. The shiny add test command is a helpful CLI tool for Shiny app developers. It simplifies the process of creating test files for their applications. When you run this command, it prompts you to input two pieces of information: the path to your Shiny app file and the desired location for the new test file. Once you provide these details, the command automatically generates a test file at the specified location. This new file includes a pre-made test template, giving you a solid starting point for writing your app's tests. Reference documentation - https://shiny.posit.co/py/docs/unit-testing.html https://shiny.posit.co/py/docs/playwright-testing.html

Karan Gathani

cli Shiny for Python Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

GenAI and Pharma: Learning as we go | Episode 1: Copilot

In this episode, Phil and Cole discuss using Copilot in clinical trial submissions. Due to an industry-wide shift in pharma, statistical programmers are transitioning to open-source tooling in drug development. This skills gap that occurs when moving to open source is one area that can be aided with generative AI tools. In this episode, Cole and Phil show some of the features and functionality of a popular tool in the data science space, Copilot. More about open source in Pharma: https://posit.co/solutions/pharma/

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Parallelize R code using user-defined functions in sparklyr

If you’re an Apache Spark user, you benefit from its speed and scalability for big data processing. However, you might still want to leverage R’s extensive ecosystem of packages and intuitive syntax. One effective way to do this is by writing user-defined functions (UDFs) with sparklyr. UDFs enable you to execute R functions within Spark, harnessing Spark’s processing power and combining the strengths of both tools. In this tutorial, you'll learn how to: - Open Posit Workbench as a Databricks user - Start a Databricks cluster within Posit Workbench - Connect to a cluster within Posit Workbench - View Databricks data in RStudio - Create a prediction function - Create a user-defined function with sparklyr Read our most recent blog that covers parallelizing R code using user-defined functions (UDFs) in sparklyr: https://posit.co/blog/databricks-udfs/ Learn more about our Databricks partnership: https://posit.co/solutions/databricks/ Watch other tutorials on using Databricks and RStudio: https://youtube.com/playlist?list=PL9HYL-VRX0oR-3AgWbXtlfdr29626EjRJ&feature=shared

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Reproducible data science with webR and Shinylive | George Stagg | Posit

A fundamental principle of the scientific method is peer review and independent verification of results. Good science depends on transparency and reproducibility. However, in a recent study a substantial 74% of research code failed to run without errors, often caused by diverse computing environments. This talk will discuss the principles of numerical reproducibility in research and show how software can be pinned to specific versions and self-contained as a universal binary package using WebAssembly. This ensures seamless reproducibility on any machine equipped with a modern web browser and, using tools such as Shinylive, could provide a new way for researchers to share results with the community. webR demo website: https://webr.r-wasm.org/v0.3.2/ Shinylive examples: https://shinylive.io/r/ https://shinylive.io/py/ Documentation: https://docs.r-wasm.org/webr/v0.3.2/ https://github.com/posit-dev/shinylive https://github.com/quarto-ext/shinylive

George Stagg

Quarto shinylive Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Quarto: Elevating R Markdown for Advanced Publishing | Christophe Dervieux

In the dynamic landscape of data analysis and scientific publishing, R Markdown has been pivotal for the R community, allowing users to seamlessly blend code, narrative and results in a cohesive narrative. Now, Quarto emerges as a powerful tool that builds on years of experience but also goes beyond R Markdown, providing more flexibility and power in scientific communication. This talk aims to present Quarto as the new alternative for scientific publishing. We will delve into how Quarto enhances the user experience for R enthusiasts, maintaining the syntax familiarity of R Markdown while introducing innovative and improved functionalities across multiple formats, similar to R Markdown ones. Why switch to Quarto from R Markdown? In which cases? How does Quarto integrate with existing workflows? Hopefully everyone will feel inspired to try out Quarto! https://quarto.org/docs/get-started/ Timestamps: 0:00 Introduction 0:41 Quarto is an open-source, scientific and technical publishing system 1:22 Computational documents and scientific markdown made easy for single source publishing 3:08 How to use Quarto 4:24 Quarto works with VS Code, Positron, Jupyter, & RStudio 5:22 Quarto's multi-language workflow 7:21 Quarto syntax 8:40 Quarto formats (html, pdf, docx, typst, beamer, pptx, revealjs, etc.) 12:19 HTML Theming 14:10 Typst CSS for nice table output in PDF 16:24 Publishing (Quarto Pub, GitHub Pages, Posit Connect, Posit Cloud, Netlify, Confluence, Hugging Face, etc.) 17:36 Shortcodes 19:10 Quarto Extensions 19:49 Quarto Projects 22:53 Project configuration examples for a website and a book 23:42 Resources to get started!

Christophe Dervieux

Positron Quarto revealjs rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Great Tables: Make beautiful, publication quality tables in Python | Rich Iannone & Michael Chow

Tables are undeniably useful for data work. We have many great DataFrame libraries available in Python, and they give us flexibility in terms of manipulating data at will, but what happens when presenting tables to others? It's nice to display tables. Tables can efficiently carry information, just like plots do, and at times it is the better way of presenting data. Indeed, it is time to bridge the divide between raw DataFrame output and wondrously structured tables suitable for publication. Now, let us turn our attention to the state of 'display tables' in 2024. Let us go over what comprises key components for building effective information displays in tables. It may surprise one how new a well-crafted table can be hewn. We'll take a look at the combinations of Python packages that fit together to make this important task possible, and marvel together at the tabular results they can provide! Learn more at https://posit-dev.github.io/great-tables/ Timestamps: 0:00 Intro: Meet Rich and Michael 0:41 What we mean by "publication ready tables" 1:29 Overview of what we'll talk about in this video 1:44 Table Goals: Ways to make a table beautiful 4:41 Tables made from reproducible code! 5:11 The history of table generation to influence our API 6:15 Our modern take on a table display framework 6:35 The problem with Excel 7:38 Introducing Great Tables! 8:00 Key Ingredients of making a Great Table 8:24 Structure: Title, column spanners and nice column labels 8:52 Format: Compact dollar values and percentages 9:23 Styling: Fill color and bold text 10:09 Imports and Polars Selectors 11:08 Coding the structure 12:27 Coding the format 13:07 Coding the styling 15:03 Putting images and plots in your table cells 15:49 Advanced Design 16:07 .fmt_nanoplot(): Small plots within table cells 19:08 .data_color(): Heat maps in tales 20:51 Powerful and plentiful methods to format cell values 22:48 To sum up: TABLES RULE

Michael Chow, Rich Iannone

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Your mission matters: A sustainable future with citizen data science at Dow

How do we keep food fresh? How do we use less water in our everyday lives? How do we make trash...valuable? How do we innovate to make the world a better place? The team at Dow Chemical is pushing the boundaries of material innovation for a more sustainable planet. To reach net zero carbon emissions by 2050, requires creativity and ingenuity from all different functions across the company. With Posit Academy and their citizen data science program, they have grown from a handful of researchers to over 300 people using Posit Workbench and deploying to Posit Connect

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How to Perfect Your Python Dashboard with Advanced Styling! (HTML/CSS - Shiny for Python)

This is part 5 of our multi-part series on creating professional dashboards with Shiny for Python. In this video, we dive into advanced styling techniques to enhance the visual appeal and professionalism of your Shiny dashboards. We'll cover: - Adding a logo and custom title - Making use of custom HTML elements - Using CSS to style Shiny components - Customizing Altair charts for a polished look - Advanced Plotly chart modifications - Applying a consistent color theme and layout By the end of this video, you'll have a styled dashboard, ready for a professional presentation. Access the GitHub repo with all parts of this project: https://github.com/KeithGalli/shiny-python-projects Shiny for Python Homepage: https://shiny.posit.co/py/ Check out the complete documentation here: https://shiny.posit.co/py/api/express/ Video by @KeithGalli **Video Timeline:** 0:00 - Video Overview & Setup 1:50 - Modifying HTML and CSS in Shiny 7:44 - Adding a Logo Image 10:26 - Styling Labels and Containers (Aligning our Image w/ the Title — Custom Divs) 20:20 - Customizing Altair Charts (Gridlines, Font, Axis Labels, Etc.) 27:49 - Customizing Plotly Visualizations 37:16 - Customizing Seaborn & Folium Heatmaps 44:15 - Final Touches, Clean Up, Recap and Next Steps If you enjoyed this series, give it a thumbs up and subscribe to the channel to stay updated! All videos in the series: Part 1 - How to Build, Deploy, & Share a Python Application in 20 minutes! (Using Shiny): https://www.youtube.com/watch?v=I2W7i7QyJPI&t=0s Part 2 - How to make Interactive Python Dashboards! (Reactivity in Shiny): https://www.youtube.com/watch?v=SLkA-Z8HTAE&t=0s Part 3 - How to make your Python Dashboard look Professional! (Layouts in Shiny): https://www.youtube.com/watch?v=jemk7DoN4qk&t=0s Part 4 - How to combine Matplotlib, Plotly, Seaborn, & more in a single Python Dashboard! (Shiny for Python): https://youtu.be/xDgO5hB4-VU?si=kk20yhdpsBqkMYcC Part 5 - How to Perfect Your Python Dashboard with Advanced Styling! (HTML/CSS - Shiny for Python): https://youtu.be/uYZUS-eFbqw

Shiny for Python Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

How to combine Matplotlib, Plotly, Seaborn, & more in a single Python Dashboard! (Shiny for Python)

This is part 4 of our multi-part series on creating professional dashboards with Shiny for Python. In this video, we'll explore how to integrate popular Python visualization libraries like Matplotlib, Plotly, Seaborn, and Altair into your Shiny apps. This allows you to leverage your existing visualization skills and seamlessly include them in interactive dashboards. We'll cover: - Integrating Matplotlib and Seaborn for detailed visualizations - Utilizing Altair and Plotly for dynamic charts - Implementing Folium for interactive maps - Customizing data tables with additional filters and selection modes By the end of this video, you'll have a rich and diverse set of visualizations in your Shiny dashboard, setting the stage for the final styling touches in the next video. Shiny for Python Homepage: https://shiny.posit.co/py/ Access the GitHub repo with all parts of this project: https://github.com/KeithGalli/shiny-python-projects Check out the complete documentation here: https://shiny.posit.co/py/api/express/ Video by @KeithGalli Video Timeline! 0:00 - Video Overview & Recap of Previous Video Dashboards 1:38 - Getting Setup with the Code (cloning branch from GitHub) 3:15 - Adding Matplotlib-based visualizations (render.plot Shiny for Python decorator) 10:15 - Create a Seaborn Heatmap Chart (Sales Volume by Hour of the Day) 14:59 - Creating Interactive Charts with Jupyter Widgets (Plotly, Altair, Bokeh, Pydeck, & More…) | render_widget decorator 20:14 - Implementing Folium for Location-Based Heatmaps (render.ui decorator) 25:32 - Enhancing DataFrames with Filters and Selection Modes (render.data_frame, render.DataGrid, render.DataTable, etc.) 28:49 - Additional Rendering Options, Final Touches and Next Steps Stay tuned for part 5, where we'll focus on styling and finalizing our dashboard. If you enjoyed this video, give it a thumbs up and subscribe to the channel to stay updated! All videos in the series: Part 1 - How to Build, Deploy, & Share a Python Application in 20 minutes! (Using Shiny): https://www.youtube.com/watch?v=I2W7i7QyJPI&t=0s Part 2 - How to make Interactive Python Dashboards! (Reactivity in Shiny): https://www.youtube.com/watch?v=SLkA-Z8HTAE&t=0s Part 3 - How to make your Python Dashboard look Professional! (Layouts in Shiny): https://www.youtube.com/watch?v=jemk7DoN4qk&t=0s Part 4 - How to combine Matplotlib, Plotly, Seaborn, & more in a single Python Dashboard! (Shiny for Python): https://youtu.be/xDgO5hB4-VU?si=kk20yhdpsBqkMYcC Part 5 - How to Perfect Your Python Dashboard with Advanced Styling! (HTML/CSS - Shiny for Python): https://youtu.be/uYZUS-eFbqw

Shiny for Python Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

How to make your Python Dashboard look Professional! (Layouts in Shiny)

This is part 3 of our multi-part series on creating professional dashboards with Shiny for Python. In this video, we focus on enhancing the visual appeal and structure of your Shiny dashboards using various layout components. We'll cover: - Implementing layout templates for quick setup - Using cards and sidebars for a clean, organized look - Customizing columns and grids for better data presentation - Adding tab options for multiple views within a dashboard By the end of this video, you'll have a more polished and professional-looking dashboard, setting the stage for advanced visualizations in upcoming videos. Shiny for Python Homepage: https://shiny.posit.co/py/ Access the GitHub repo with all parts of this project: https://github.com/KeithGalli/shiny-python-projects Check out the complete documentation here: https://shiny.posit.co/py/api/express/ Video by @KeithGalli Video Timeline! 0:00 - Video Overview & Progress Thus Far 1:28 - Using Shiny Templates to Get Started Fast 3:21 - Using Layout Components to Customize our Apps (Cards, Sidebars, Tabs, etc.) 7:55 - Adding a Sidebar within a Card 12:38 - Adding a Card with Tabs to Display Various Visualizations 17:35 - Structuring Data in Columns / Grids (layout_columns() & layout_column_wrap()) 26:30 - Final Touches & Tips (Filling in Visualizations into our Tab Views) Stay tuned for part 4, where we'll add more advanced visualizations, and part 5, where we'll focus on customizing styles and adding final touches. If you enjoyed this video, give it a thumbs up and subscribe to the channel to stay updated! All videos in the series: Part 1 - How to Build, Deploy, & Share a Python Application in 20 minutes! (Using Shiny): https://www.youtube.com/watch?v=I2W7i7QyJPI&t=0s Part 2 - How to make Interactive Python Dashboards! (Reactivity in Shiny): https://www.youtube.com/watch?v=SLkA-Z8HTAE&t=0s Part 3 - How to make your Python Dashboard look Professional! (Layouts in Shiny): https://www.youtube.com/watch?v=jemk7DoN4qk&t=0s Part 4 - How to combine Matplotlib, Plotly, Seaborn, & more in a single Python Dashboard! (Shiny for Python): https://youtu.be/xDgO5hB4-VU?si=kk20yhdpsBqkMYcC Part 5 - How to Perfect Your Python Dashboard with Advanced Styling! (HTML/CSS - Shiny for Python): https://youtu.be/uYZUS-eFbqw

Shiny for Python Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

When creating tutorials for beginners, keep this in mind

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

How to make Interactive Python Dashboards! (Reactivity in Shiny)

This is a quick-start guide to Shiny for Python, part 2 of a multi-part series. Data scientists need to quickly build web applications to create and share interactive visualizations, giving others a way to interact with data and analytics. Shiny helps you do this. In this video, we'll build off of the last tutorial where we learned the basics of building, sharing, and deploying a Shiny app in Python. This video specifically focuses on reactivity in Shiny. You can watch this video as a standalone, but it may be helpful to watch the previous video (https://youtu.be/I2W7i7QyJPI). We'll cover: ⬡ Creating toggle options for dynamic visualizations ⬡ Understanding Shiny's reactivity model ⬡ Implementing various input selectors ⬡ Building reactive components and visualizations ⬡ Using reactive calculations and effects ⬡ Adding and formatting plots with Plotly ⬡ Documentation walkthrough to learn more about reactivity (reactivity.effect, reactivity.event, reactivity.isolate, reactivity.invalidate_later, etc…) Video Resources: Video #1: https://youtu.be/I2W7i7QyJPI?si=nx1dk5ovPc91pvlB Starter Code (from end of video #1): https://github.com/KeithGalli/shiny-python-projects/tree/video1 Final App: https://keithgalli.shinyapps.io/final-app/ Shiny Resources: Shiny for Python Homepage: https://shiny.posit.co/py/ Component Gallery: https://shiny.posit.co/py/components/ Express Documentation: https://shiny.posit.co/py/api/express/ Gordon Shotwell’s “How does Shiny Render Things?”: https://youtu.be/jvV4y2xogf8?si=8uGP8ZfboUj1QM4p Joe Cheng’s “Shiny Programming Practices”: https://youtu.be/B2JzHv4FOTU?si=t4Atii-RSc5ojgom Stay tuned for part 3, where we'll explore how to make your dashboard look more professional (layouts in Shiny). Video by @KeithGalli --- Video Timeline! 0:00 - Intro & Overview 1:01 - Getting Started with Code 2:08 - Adding Shiny Components (Inputs, Outputs, & Display Messages) 3:21 - Creating an Additional Visualization (Sales Over Time by City) 7:55 - What are Reactive.Calcs and How Do We Use Them Properly? (DataFrame Best Practices) 10:27 - Creating an Additional Visualization (Sales Over Time by City) — Continued 14:30 - Filtering City Data with Select Inputs (UI.Input_Selectize) 21:15 - Rendering Shiny Inputs Within Text 22:15 - Quick Formatting Adjustments 22:54 - Understanding the Shiny Reactivity Model (How Does Shiny Render Things?) 24:23 - Adding a Checkbox Input to Change Out Bar Chart Marker Colors 28:00 - Deploying Our Updated App! 29:19 - Advanced Concepts in Shiny Reactivity (Reactive.Effect, Reactive.Event, Reactive.Isolate, Reactive.Invalidate_Later) & Other Resources All videos in the series: Part 1 - How to Build, Deploy, & Share a Python Application in 20 minutes! (Using Shiny): https://www.youtube.com/watch?v=I2W7i7QyJPI&t=0s Part 2 - How to make Interactive Python Dashboards! (Reactivity in Shiny): https://www.youtube.com/watch?v=SLkA-Z8HTAE&t=0s Part 3 - How to make your Python Dashboard look Professional! (Layouts in Shiny): https://www.youtube.com/watch?v=jemk7DoN4qk&t=0s Part 4 - How to combine Matplotlib, Plotly, Seaborn, & more in a single Python Dashboard! (Shiny for Python): https://youtu.be/xDgO5hB4-VU?si=kk20yhdpsBqkMYcC Part 5 - How to Perfect Your Python Dashboard with Advanced Styling! (HTML/CSS - Shiny for Python): https://youtu.be/uYZUS-eFbqw

Joe Cheng

Shiny for Python Shiny shinyapps Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Being SO good they can't ignore you

Tom recommended the book So Good They Can't Ignore You in a recent Data Science Hangout. Libby also keeps this awesome list of Hangout book recommendations: https://datahumans.notion.site/2f4552fd18be4d439b6b6977077e6ca5?v=738d0654fd81490890b8f741a2ef0a3c

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Communicating Data Science with Shiny! 🚀 Garrick Aden-Buie #datascience #datavisualization #shiny

Ever seen this diagram? It's from R for Data Science, outlining the journey of tackling new data problems. 1️⃣ Start: Import and tidy your data. 2️⃣ Cycle: Transform, visualize, and model your data. 3️⃣ Communicate: Share your findings. But communication isn't just the end—it's an ongoing process! Shiny apps are your secret weapon for this. Scenario 1: Quick Shiny app for colleague feedback ️ refine your work. Scenario 2: Small app grows into a full-featured web application. From small, quick-use apps to robust final products, Shiny evolves with your project needs. Discover why Shiny is essential for every data scientist! Get Started: https://shiny.posit.co

Garrick Aden-Buie

Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

{shinylive}: Serverless Shiny Apps | Barret Schloerke | Posit

In the rapidly evolving landscape of web technologies, the integration of R (and Python) with modern web frameworks has become increasingly important for data scientists and developers. This presentation introduces {shinylive}, a new R package that exports Shiny applications to be run within statically hosted websites. We will explore the capabilities of {shinylive} through its use of the innovative R package {webR}, which allows for the execution of R code in the browser (via WebAssembly and service workers) without the need for a centralized server. The presentation will cover the technical foundation of {shinylive}, including its architecture and the integration process with Quarto documents. We will also discuss the practical aspects and drawbacks of exporting Shiny apps with {shinylive}, highlighting the ease of exporting apps to a folder for local use or hosting them on GitHub pages. {shinylive} bridges the gap between Shiny and static websites, making it a valuable resource for interactive data analysis and presentation. Link to app: https://schloerke.com/presentation-2024-04-18-appsilon-shinylive/ Link to script: https://github.com/posit-dev/r-shinylive/blob/main/examples/deploy-app.yaml Link to use_github_action(): https://github.com/posit-dev/r-shinylive#github-pages Shinylive website: https://posit-dev.github.io/r-shinylive/ {webr} docs: https://docs.r-wasm.org/webr/latest/

Barret Schloerke

Quarto r-shinylive Shiny shinylive Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

How to inspire people to make a change ✨

Key takeaways from our conversation on the path to effective data stewardship: 1. Change management is hard. To inspire people to change, make it as easy as possible for them to see the path to get there by creating tools to make new tasks easy to complete. A problem not only has to exist, but that problem needs to be recognized to exist. In the data governance space, legal regulations can help push us forward because they create urgency. 2. Data stewardship and data governance enable innovation. You see more of what’s possible when you know what data exists. Core systems form the foundation that enables cutting-edge work like AI development. 3. People have good intentions and want to get their work done and impact the organization. Don’t create processes where the only way to get your job done is to break the process. In the act of governing, remember why you started pulling data together in the first place. 4. Community engagement is incredibly powerful and hearing different perspectives is how we grow. We all face similar problems regardless of our role or industry. An internal community for cross-organizational collaboration is vital for knowing what other people are working on and collaborating. This not only helps you with your data, but also with your career. 5. Implementing effective data stewardship is a journey with companies at all different stages. Those further along the journey may have teams and infrastructure dedicated to this, along with executive buy-in. Others are working to make it happen at the individual level – where it often starts with writing down the definitions of your data (who created it, when it was sourced, intended use, etc.) and recognizing that someone else will use your data output, potentially not in the way you initially intended

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Analyze and explore data stored in Snowflake using R

James Blair, Senior Product Manager, Cloud Integrations at Posit, will demonstrate using the R language to analyze and explore data stored in Snowflake. He will also show you how easy it is to set up an R environment inside Posit Workbench that runs as a native app on Snowpark Container Services. You also find out how using the dbplyr interface can be used to push computation data into Snowflake, giving you access to greater memory and compute power than in a standard R session. It's easy to get started. In just a few minutes, you can work in your R session securely inside Snowflake using the RStudio Pro IDE in Posit Workbench. Posit also supports VS Code and Jupyter for data scientists who prefer to work in other languages like Python, so you can continue to use the tools you know and love. Learn more about the Snowflake and Posit integration: https://posit.co/solutions/snowflake/

dbplyr rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

How to Build, Deploy, & Share a Python Application in 20 minutes! (Using Shiny)

This is a quick-start guide to Shiny for Python. It's part 1 of a multi-part series. Data scientists need to quickly build web applications to create and share interactive visualizations, giving others a way to interact with data and analytics. Shiny helps you do this. In this video, we'll walk you through the basics of setting up Shiny for Python, creating your first app, and deploying it so others can use it. We'll cover: - Installing Shiny and necessary dependencies - Writing and running your first Shiny app - Basic UI and server structure - Deploying your app online - Helpful Links Shiny for Python Homepage: https://shiny.posit.co/py/ The link to the final app can be found here: https://keithgalli.shinyapps.io/final-app/ Follow along with the code examples provided in this repository: https://github.com/KeithGalli/shiny-python-projects Check out the complete documentation here: https://shiny.posit.co/py/api/express/ Stay tuned for part 2, where we'll dive deeper into advanced features and customization options. --- Video Timeline! 0:00 - Intro to Shiny & Video Overview 1:43 - Getting Started with the Shinylive Playground 2:44 - Building a custom visualization with Shinylive 5:18 - Easily sharing the code/application for a Shinylive app 6:12 - Building a Shiny Express App locally (VSCode) 9:40 - How to run app if you're not using VSCode 10:17 - Further customization of our app (adding title, using CSV data, dynamic input) 17:15 - Deploying our Shiny app to the web 21:20 - Conclusion & what's coming next in the series Video by @KeithGalli All videos in the series: Part 1 - How to Build, Deploy, & Share a Python Application in 20 minutes! (Using Shiny): https://www.youtube.com/watch?v=I2W7i7QyJPI&t=0s Part 2 - How to make Interactive Python Dashboards! (Reactivity in Shiny): https://www.youtube.com/watch?v=SLkA-Z8HTAE&t=0s Part 3 - How to make your Python Dashboard look Professional! (Layouts in Shiny): https://www.youtube.com/watch?v=jemk7DoN4qk&t=0s Part 4 - How to combine Matplotlib, Plotly, Seaborn, & more in a single Python Dashboard! (Shiny for Python): https://youtu.be/xDgO5hB4-VU?si=kk20yhdpsBqkMYcC Part 5 - How to Perfect Your Python Dashboard with Advanced Styling! (HTML/CSS - Shiny for Python): https://youtu.be/uYZUS-eFbqw

Shiny for Python Shiny shinyapps shinylive Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Lessons in career growth from gardening 🌱

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Joe Cheng | Managing long-running operations in Shiny | Posit

It’s been years since Shiny evolved to allow asynchronous operations within applications, improving scalability. The introduction of the {promises} package enabled concurrent processing between multiple Shiny sessions, a significant step forward in handling background tasks. However, this did not address the need for intra-session concurrency, where users expect to interact with the application while long-running calculations are executed in the background. Recently, we added a new ExtendedTask feature to Shiny to allow for such intra-session concurrency. This new feature provides a different approach for developers to incorporate asynchronous tasks, enabling smoother user interactions during intensive computations. Alongside ExtendedTask, this talk will also discuss newer methods for launching asynchronous tasks, besides the usual {future} package. The focus will be on the practical application and integration of these features into Shiny applications. Links mentioned in the video: ⬡ Shiny in Production: Principles, practices, and tools, https://youtu.be/Wy3TY0gOmJw?feature=shared Timestamps: 0:20 Make your slow code fast 1:43 Long-running operations are a problem 3:28 Inter-session concurrency and intra-session concurrency 4:24 Introducing ExtendedTask 5:17 Demo of a slow API using ExtendedTask 6:13 Slow code example (R) 7:16 Fix slow code with ExtendedTask (R) 8:55 Slow code example (Python) 7:16 Fix slow code with ExtendedTask (Python) 10:46 Links to get started 11:06 ExtendedTask backstory intro 11:28 ExtendedTask vs. Shiny Async 15:50 How reactive programming works in Shiny 21:31 How ExtendedTask works in the reactive process 25:38 What we’re still working on 26:35 {future} alternatives 31:47 Wrapping up

Joe Cheng

Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Future of Shiny to the FDA with WebR ✨

For more on How Pharma is Pioneering WebAssembly with webR & Shiny for FDA Clinical Trial Submissions: https://posit.co/blog/webr-fda-pilot/

Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Using your dataset in Shiny Templates | Carson Sievert | Posit

Watch the Shiny team’s Carson Sievert change the dataset in a Shiny Template. Find the right template for you at https://shiny.posit.co/py/templates/ 0:00 Intro with Carson Sievert 0:19 How to load the template code 1:14 Running your Shiny app in VS Code with a live reloading preview 1:29 How this template works 2:23 See the contents of your data in a data_frame 2:53 How this template imports data 3:33 A more optimized way to import a large amount of data 5:10 Changing the dataset 5:44 Troubleshooting the inevitable errors when changing the dataset

Carson Sievert, Shiny Team

Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Most important thing a data scientist can do 💰

Data Science Hangout with Greg Shick, Director of Analytics at Charles Schwab. ‍️ Join us live at the Data Science Hangouts on Thursday from 12-1 ET: pos.it/dsh

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Observing the creation of your data 🍕

Data Science Hangout with Zack Fragoso, Head of MLOps at Domino's Pizza. ‍️ Join us live at the Data Science Hangouts on Thursday from 12-1 ET: pos.it/dsh

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Data Science Hangout

Starting a People Analytics team

Data Science Hangout with Elizabeth Esarove, Lead Advanced Analytics, People Analytics at AT&T to chat about using data science to provide supporting evidence for decisions regarding employee outcomes and business objectives. ‍️ Join us live at the Data Science Hangouts on Thursday from 12-1 ET: pos.it/dsh

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Follow these tips when asking for a raise 👀

Data Science Hangout with Elizabeth Esarove, Lead Advanced Analytics, People Analytics at AT&T to chat about using data science to provide supporting evidence for decisions regarding employee outcomes and business objectives. ‍️ Join us live at the Data Science Hangouts on Thursday from 12-1 ET: pos.it/dsh

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Introducing Shiny for Python Templates | Carson Sievert | Posit

Last month we introduced the Shiny for Python Components and Layouts galleries, which are a simple, visual overview of what Shiny can do, mixed with cheatsheet-like information. They are for new and seasoned users alike. We’re excited to announce a third section which brings those two things together into opinionated boilerplate code: Shiny Templates. This is just a start… We expect this gallery to grow as time goes on. These will allow you to hit the ground running, whether you need a quick simple app or a quick complicated one . Get started at https://shiny.posit.co/py/templates/ 0:00 Introduction to Shiny for Python Templates 0:38 How to work with a template locally in VS Code 2:24 Preview your app with the Shiny for Python VS Code Extension 2:45 A data_frame is a quick and easy way to see the contents of your data 4:39 See everything in your window with the “fillable=True” page_opts 5:16 Modifying inputs 7:40 Fix the logic in your @render.plot to show your modified inputs 9:00 Add a component to the sidebar 9:05 How to add input_dark_mode to your app 9:54 How to make your plot change to dark mode as well

Carson Sievert

Shiny for Python Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

70/20/10 model for upskilling at US FDA

Data Science Hangout with Kevin Snyder & Raju Rayavarapu @ U.S. FDA to chat about democratizing data science and utilizing principles of systems biology, pharmacology, toxicology, bioinformatics, and statistics to collaboratively develop open-source software solutions that support decision making and policy development. ‍️ Join us live at the Data Science Hangouts on Thursday from 12-1 ET: pos.it/dsh

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Evolve your perspective by leaning into these 2 things

Data Science Hangout with Brad Zielke, Sr. Director Data Sciences at Target on leaning into empathy and humility to surround yourself with diverse perspectives and evolve your perspective as a leader. ‍️ Join us live at the Data Science Hangouts on Thursday from 12-1 ET: pos.it/dsh

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

This ONE THING is what hiring managers are looking for🤯

Data Science Hangout with Jamie Warner, Managing Director at Plymouth Rock Assurance. ‍️ Join us live at the Data Science Hangouts on Thursday from 12-1 ET: pos.it/dsh

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

How to get your boss to pay attention to your work 👀

How do you capture your boss' imagination and show the value of your data science project? Data Science Hangout with Michael Derstine, Vice President at Wells Fargo & Company. ‍️ Join us live at the Data Science Hangouts on Thursday from 12-1 ET: pos.it/dsh

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Deploying Databricks-backed content on Posit Connect

After you connect to Databricks using sparklyr and create your content, you'll want to deploy and share your work. With the new pysparklyr::deploy_databricks() function, you can deploy your content to Posit Connect. Run the function and confirm to start deployment. The deploy_databricks() function will gather your document location, credentials, and URLs. It uses this information to deploy to Posit Connect. After it's done, click the URL to see the deployed content on Posit Connect. Open the content from the publisher view. Click around and check out your work. In Posit Connect, you can edit sharing permissions, create a vanity URL, and more. Open the content URL to see how your work would look to stakeholders. Learn more: - Databricks x Posit: https://posit.co/solutions/databricks/ - Empowering R and Python Developers: Databricks and Posit Announce New Integrations: https://posit.co/blog/databricks-and-posit-announce-new-integrations/ - Posit Connect: https://posit.co/products/enterprise/connect/ - sparklyr and Databricks Connect v2: https://spark.posit.co/deployment/databricks-connect.html - Deploying to Posit Connect: https://spark.posit.co/deployment/databricks-posit-connect.html Contact our sales team to schedule a demo: https://posit.co/schedule-a-call/?booking_calendar__c=Databricks

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Databricks Pro Driver in Posit Workbench

We've added a Databricks driver to our Professional Drivers. The RStudio Connections Pane allows users to connect to their Databricks clusters from the IDE. Select the Databricks driver from the list of available drivers. Select the Driver to establish the connection. The driver can be used with the new databricks() function from the odbc package to connect to Databricks clusters and SQL warehouses. Learn more: - Databricks x Posit: https://posit.co/solutions/databricks/ - Empowering R and Python Developers: Databricks and Posit Announce New Integrations: https://posit.co/blog/databricks-and-posit-announce-new-integrations/ - RStudio IDE and Posit Workbench 2023.12.0: What’s New: https://posit.co/blog/rstudio-2023-12-0-whats-new/ - Posit Professional Drivers 2024.03.0: Support for Apple Silicon: https://posit.co/blog/pro-drivers-2024-03-0/ Contact our sales team to schedule a demo: https://posit.co/schedule-a-call/?booking_calendar__c=Databricks

odbc rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Databricks Pane in Posit Workbench

We've introduced a Databricks Pane in RStudio Pro for discovering and managing Databricks clusters. Users can stay in RStudio Pro without navigating to the Databricks web interface to see their clusters. Click on the 'Connect to' icon to open up a new Databricks Connection pop-up. This forms the initial connection to Databricks. Once you click 'ok', you will be connected to Databricks. Learn more: - Databricks x Posit: https://posit.co/solutions/databricks/ - Empowering R and Python Developers: Databricks and Posit Announce New Integrations: https://posit.co/blog/databricks-and-posit-announce-new-integrations/ - RStudio IDE and Posit Workbench 2023.12.0: What’s New: https://posit.co/blog/rstudio-2023-12-0-whats-new/ - Posit Professional Drivers 2024.03.0: Support for Apple Silicon: https://posit.co/blog/pro-drivers-2024-03-0/ Contact our sales team to schedule a demo: https://posit.co/schedule-a-call/?booking_calendar__c=Databricks

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Databricks Authentication in Posit Workbench

Posit Workbench now has delegated Databricks credentials. Users can log into a Databricks Workspace when starting an RStudio or VS Code session. Authentication relies on OAuth-backed refresh tokens rather than Personal Access Tokens (PATs). This allows for more granular control over permissions, built-in expiration, and defined scopes. Once logged in, you can interact directly with your Databricks clusters in that Workspace in your preferred environment. Learn more: - Databricks x Posit: https://posit.co/solutions/databricks/ - Empowering R and Python Developers: Databricks and Posit Announce New Integrations: https://posit.co/blog/databricks-and-posit-announce-new-integrations/ - RStudio IDE and Posit Workbench 2023.12.0: What’s New: https://posit.co/blog/rstudio-2023-12-0-whats-new/ - Posit Professional Drivers 2024.03.0: Support for Apple Silicon: https://posit.co/blog/pro-drivers-2024-03-0/ Contact our sales team to schedule a demo: https://posit.co/schedule-a-call/?booking_calendar__c=Databricks

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Connecting RStudio and Databricks with sparklyr

You can connect RStudio and Databricks with the sparklyr package. First, load the necessary packages. Next, set up the connection with sparklyr::spark_connect(). See your data in the Connections pane. Check which databases you're connected to. Navigate the data structure by expanding the levels. Navigate to the table you want to explore...and analyze your data in RStudio. Learn more: - Databricks x Posit: https://posit.co/solutions/databricks/ - Empowering R and Python Developers: Databricks and Posit Announce New Integrations: https://posit.co/blog/databricks-and-posit-announce-new-integrations/ - Posit Connect: https://posit.co/products/enterprise/connect/ - sparklyr and Databricks Connect v2: https://spark.posit.co/deployment/databricks-connect.html - Deploying to Posit Connect: https://spark.posit.co/deployment/databricks-posit-connect.html Contact our sales team to schedule a demo: https://posit.co/schedule-a-call/?booking_calendar__c=Databricks

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Connecting RStudio and Databricks with ODBC

The odbc package, in conjunction with a driver, provides DBI support and an ODBC connection. With the new odbc::databricks_connect function, you can create an ODBC connection to determine and configure the necessary settings to access your Databricks account. Your Databricks HTTP path is the only argument you need to run databricks_connect(). Provide your HTTP path and you will be able to see your Databricks data in the RStudio Connections Pane. Then, you can analyze your data in RStudio. Learn more: - Databricks x Posit: https://posit.co/solutions/databricks/ - Empowering R and Python Developers: Databricks and Posit Announce New Integrations: https://posit.co/blog/databricks-and-posit-announce-new-integrations/ - RStudio IDE and Posit Workbench 2023.12.0: What’s New: https://posit.co/blog/rstudio-2023-12-0-whats-new/ - Posit Professional Drivers 2024.03.0: Support for Apple Silicon: https://posit.co/blog/pro-drivers-2024-03-0/ Contact our sales team to schedule a demo: https://posit.co/schedule-a-call/?booking_calendar__c=Databricks

DBI odbc rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

GitHub Copilot on Posit Cloud

Speed up your coding projects in the RStudio IDE on Posit Cloud with GitHub Copilot, an AI coding assistant. Learn more in our blog post: https://posit.co/blog/github-copilot-on-posit-cloud/ Posit Cloud: https://posit.cloud/ GitHub Copilot: https://github.com/features/copilot RStudio User Guide: https://docs.posit.co/ide/user/ide/guide/tools/copilot.html

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Make data science slides with code using Quarto

Quarto Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

You Can Lead a Horse to Water...Changing the Data Science Culture for Veterinary Scientists

Presented by Jill MacKay A retrospective look at supporting data science skills in a research-focussed veterinary school This is a talk about environment management, but not in the way you're thinking. In many industries, domain-specific experts need enough understanding of data science to support their work and communicate with data scientists, but often have insufficient training in these skills, and limited time with which to obtain data science skills and practice them. This is particularly challenging for those who are interdisciplinary and have limited control over their workload, such as medics and field scientists. In this talk, an educational scientist describes the previous 10 years of supporting veterinary scientists to adopt open science practices surrounding data science. What worked, what failed miserably, and reflections on why it can be so hard to get a horse to drink. Materials: - https://github.com/jillymackay/positconf2023_vetdata - https://jillymackay.com/post/positconf2023/ Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Teaching data science. Session Code: TALK-1095

Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

Why You Should Stop Networking and Start Making Friends - posit::conf(2023)

Presented by Libby Heeren When we think about making connections, we think about networking. I'd like you to forget about networking and start thinking about making friends. I'll share my perspective as a community builder and host of the Data Humans podcast on how I cultivated a community of practice for myself and how I became a force multiplier who increases engagement. You'll learn how I made genuine human connections, the practical steps to making data friends, the power of vulnerability, and why we all benefit when we show up as our whole selves. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Lightning talks. Session Code: TALK-1167

Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

Why You Should Add Logging To Your Code (and make it more helpful) - posit::conf(2023)

Presented by Daren Eiri Learn how the log4r package can help you better understand the errors your code may produce, and how to also get promptly alerted for severe errors by leveraging cloud monitoring solutions like Azure Monitor or AWS CloudWatch When an error happens in your API, Shiny App, or quarto document, it is not always clear what line of code you need to look at, and the error messages aren’t always helpful. By walking through a simple API example, I show how you can use logging packages like log4r to provide error messages that make sense to you. I also show how you can use cloud-based data collection platforms like Azure Monitor or AWS CloudWatch to set up alerts, so you can get notified by email or text message for those severe errors that you need to be immediately aware of. Gain more visibility into the health of your code by incorporating logging and pushing your logs to the cloud. Materials: https://dareneiri.github.io/positconf2023/ Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Lightning talks. Session Code: TALK-1164

log4r Quarto Shiny Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

What's New in the Torch Ecosystem - posit::conf(2023)

Presented by Daniel Falbel torch is an R port of PyTorch, a scientific computing library that enables fast and easy creation and training of deep learning models. In this talk, you will learn about the latest features and developments in torch, such as luz, a higher-level interface that simplifies your model training code, and vetiver, a new integration that allows you to deploy your torch models with just a few lines of code. You will also see how torch works well with other R packages and tools to enhance your data science workflow. Whether you are new to torch or already an experienced user, this talk will show you how torch can help you tackle your data science challenges and inspire you to build your own models. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Lightning talks. Session Code: TALK-1163

Daniel Falbel

Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

What's New in Quarto?* - posit::conf(2023)

Presented by Charlotte Wickham It's been over a year since Quarto 1.0, an open-source scientific and technical publishing system, was announced at rstudio::conf(2022). In this talk, I'll highlight some of the improvements to Quarto since then. You'll learn about new formats, options, tools, and ways to supercharge your content. And, if you haven't used Quarto yet, come to see some reasons to try it out. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Quarto (1). Session Code: TALK-1072

Charlotte Wickham

Quarto rstudio Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

What I Wish I Knew Before Becoming a Data Scientist - posit::conf(2023)

Presented by Kaitlin Bustos In this talk, I'm sharing my personal journey as a data scientist and the key lessons learned along the way. I'll emphasize the importance of finding a positive community of like minded-allies, persevering through setbacks as success is not linear, and exploring by embracing the broad nature of the data science field. By sharing my experiences and acknowledging the challenges I've faced attendees will gain a fresh perspective on what it takes to succeed in a data science career and inspire them to pursue their passions in the field. Overall, this talk aims to provide a glimpse into the reality of a data science career. Attendees will take away a sense of motivation and empowerment to find their own unique path to success. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Lightning talks. Session Code: TALK-1169

Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

What an Early 2000s Reality Show Taught Me about File Management - posit::conf(2023)

Presented by Reiko Okamoto Clutter, whether it's physical or digital, destroys our ability to focus; home organization ideas can be extended to create an workspace where analysts feel inspired to work with data. Ideas from home organization shows are surprisingly applicable to file management. Using a room divider to establish dedicated zones for different activities in a studio apartment is analogous to creating self-contained projects in RStudio. Likewise, swapping mismatched hangers with matching ones to tidy a closet resembles the adoption of a file naming convention to make a directory easier to navigate. In this talk, I will share good practices in file management through the lens of home organization. We all know that clutter, whether it is in our physical space or on our machine, destroys our ability to focus. These practices will help R users of all levels create a serene, relaxing environment where they feel inspired to work with data. https://reikookamoto.github.io/; https://github.com/reikookamoto/posit-conf-2023-neat Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Getting %$!@ done: productive workflows for data science. Session Code: TALK-1090

rstudio Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

We Converted our Documentation to Quarto - posit::conf(2023)

Presented by Melissa Van Bussel Elevate your Quarto projects to new heights with these practical tips and tricks! "Wiki", "User Guide", "Handbook" -- whatever you call yours, we converted ours to Quarto! A year ago, my team's documentation, which had been created using Microsoft Word, was large and lacked version control. Scrolling through the document was slow, and, due to confidentiality reasons, only one person could edit it at a time, which was a significant challenge for our team of multiple developers. After realizing we needed a more flexible solution, we successfully converted our documentation to Quarto. In this talk, I'll discuss our journey converting to Quarto, the challenges we faced along the way, and tips and tricks for anyone else who might be looking to adopt Quarto too. Slides: https://melissavanbussel.quarto.pub/posit-conf-2023; Code for slides: https://github.com/melissavanbussel/posit-conf-2023; My YouTube: https://www.youtube.com/c/ggnot2; My website: https://www.melissavanbussel.com/; My Twitter: https://twitter.com/melvanbussel; My LinkedIn: https://www.linkedin.com/in/melissavanbussel/ Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Quarto (2). Session Code: TALK-1140

Quarto Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

Visualizing Data Analysis Pipelines with Pandas Tutor and Tidy Data Tutor - posit::conf(2023)

Presented by Sean Kross The data frame is a fundamental data structure for data scientists using Python and R. Pandas and the tidyverse are designed to center building pipelines for the transformation of data frames. However, within these pipelines it is not always clear how each operation is changing the underlying data frame. To explain each step in a pipeline data science instructors resort to hand-drawing diagrams to illustrate the semantics of operations such as filtering, sorting, and grouping. In this talk, I will introduce Pandas Tutor and Tidy Data Tutor, step-by-step visual representation engines of data frame transformations. Both tools illustrate the row, column, and cell-wise relationships between an operation's input and output data frames. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Teaching data science. Session Code: TALK-1096

tidyverse Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

Validating and Testing R Dataframes with Pandera via reticulate - R-Python Interoperability

Presented by Niels Bantilan Original Full Title: Validating and Testing R Dataframes with Pandera via reticulate: A Case Study in R-Python Interoperability Data science and machine learning practitioners work with data every day to analyze and model them for insights and predictions. A major component of any project is data quality, which is a process of cleaning, and protecting against flaws in data that may invalidate the analysis or model. Pandera is an open source data testing toolkit for dataframes in the Python ecosystem: but can it validate R dataframes? This talk is composed of three parts: first I'll describe what data testing is and motivate why you need it. Then, I'll introduce the iterative process of creating and refining dataframe schemas in Pandera. Finally, I'll demonstrate how to use it in R with the reticulate package using a simple modeling exercise as an example. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: R or Python? Why not both!. Session Code: TALK-1123

reticulate Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

Using R, Python, and Cloud Infrastructure to Battle Aquatic Invasive Species - posit::conf(2023)

Presented by Uli Muellner and Nicholas Snellgrove Invasive species are a huge threat to lake ecosystems in Minnesota. With over 10,000 water bodies across the state, having up-to-date data and decision support is critical. Researchers at the University of Minnesota have created four complex R and Python models to support lake managers, all pulled together and presented with the most recent infestation data available. Come along with us to see how we connected these models in the AIS Explorer, a decision support application built in Shiny to help prioritize risks and placing watercraft inspectors, using tools like OCPU and cloud toolings like Lambda, EventBridge and AWS S3. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: R or Python? Why not both!. Session Code: TALK-1118

Shiny Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

Using R with Databricks Connect - posit::conf(2023)

Presented by Edgar Ruiz Spark Connect, and Databricks Connect, enable the ability to interact with Spark stand-alone clusters remotely. This improves our ability to perform Data Science at-scale. We will share the work in `sparklyr`, and other products, that will make it easier for R users to take advantage this new framework. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Tidy up your models. Session Code: TALK-1084

Edgar Ruiz

Databricks Data Science Rstudio Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

Using R to develop production modeling workflows at Mayo Clinic - posit::conf(2023)

Presented by Brendan Broderick Developing workflows that help train models and also help deploy them can be a difficult task. In this talk I will share some tools and workflow tips that I use to build production model pipelines using R. I will use a project of predicting patients who need specialized respiratory care after leaving the ICU as an example. I will show how to use the targets package to create a reproducible and easy to manage modeling and prediction pipeline, how to use the renv package to ensure a consistent environment for development and deployment, and how to use plumber, vetiver, and shiny applications to make the model accessible to care providers. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Leave it to the robots: automating your work. Session Code: TALK-1149

plumber renv Shiny Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

Using Data to Protect Traditional Lifeways - posit::conf(2023)

Presented by Angie Reed The spirit of Penobscot Nation's work to protect the health of their relative, the Penobscot River, is embodied in the Penobscot water song which says ""Water, we love you, thank you so much water, we respect you."" Because the Penobscot River is not a natural resource - she is a relative, family - this song describes the foundation of our efforts to protect her health and well-being. The identity of Penobscot people cannot be disconnected from the river, and protecting this traditional lifeway is at the heart of our work. For over a decade we have used R to manage, transform, analyze, and visualize data, and the free, open-source Posit products help us leave a legacy of good data management and the ability to share results with Penobscot Nation citizens. You will learn more about how our use of R has helped us achieve more stringent protections for the Penobscot River and how we engage young people in every step of this work. We are also part of a larger network of tribal environmental professionals, working together to learn R and share data and insights. We will give you information about how you can volunteer to help expand the network of folks providing technical assistance on any R and RStudio related topics. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: End-to-end data science with real-world impact. Session Code: TALK-1144

rstudio Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

USGS R Package Development: 10-year Reflections - posit::conf(2023)

Presented by Laura DeCicco Ten years ago, the first set of git commits was submitted to a new R software package repository "dataRetrieval" with the goal to provide an easy way for R users to retrieve U.S Geological Survey (USGS) water data. At that time, the perception within the USGS was the use of R was exclusive to an elite group of "very serious scientists." Fast forward, we now find many newer USGS hires having a solid grasp of the language from the start along with the use of R in a wide variety of applications. In this talk, I'll discuss my experiences maintaining the dataRetrieval package, how it's shaped my career, impacted USGS R usage, and why data providers should consider sponsoring their own R packages wrapping their data API services. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Lightning talks. Session Code: TALK-1171

Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

Unlock the Power of DataViz Animation and Interactivity in Quarto - posit::conf(2023)

Presented by Deepsha Menghani Plot animated and interactive visualizations with Plotly and Crosstalk in Quarto using R. In thi sintro to Plotly & Crosstalk in R: Using code examples, learn to integrate dashboard elements into Quarto with animated plots, interactive widgets (checkboxes), and linked plots via brushing. This talk showcases how to use packages, such as Plotly and Crosstalk, to create interactive data visualizations and add dashboard-like elements to Quarto. Using a fun dataset available through the “Richmondway” package, we examine the number of times Roy Kent uses salty language throughout all seasons of ""Ted Lasso."" We illustrate this using animated plots, interactive selection widgets such as checkboxes, and by linking two plots with brushing capabilities. Materials: - Slides: https://deepshamenghani.github.io/posit_plotly_crosstalk/#/title-slide - Code repo: https://github.com/deepshamenghani/posit_plotly_crosstalk - Richmondway data package: https://github.com/deepshamenghani/richmondway - In-Depth Guide to Creating and Publishing an R Data Package (Richmondway) Using Devtools: https://medium.com/p/245b0fd4c359 Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Quarto (2). Session Code: TALK-1143

crosstalk devtools Quarto Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

Towards the Next Generation of Shiny UI

Presented by Carson Sievert Create awesome looking and feature rich Shiny dashboards using the bslib R package. Shiny recently celebrated its 10th birthday, and since its birth, has grown tremendously in many areas; however, a hello world Shiny app still looks roughly like it did 10 years ago. The bslib R package helps solve this problem making very easy to apply modern and customizable styling your Shiny apps, R Markdown / Quarto documents, and more. In addition, bslib also provides dashboard-focused UI components like expandable cards, value boxes, sidebar layouts, and more to help you create delightful Shiny dashboards. Materials: - https://rstudio.github.io/bslib/ - https://bslib.shinyapps.io/flights/ Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Shiny user interfaces. Session Code: TALK-1124

Carson Sievert

bslib Quarto rstudio Shiny shinyapps Python Shiny Databricks Data Science Rstudio Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

tidymodels: Adventures in Rewriting a Modeling Pipeline - posit::conf(2023)

Presented by Ryan Timpe An overview of the benefits unlocked on our data science team by adopting tidymodels. Data science sure has changed over the past few years! Everyone's talking about production. RStudio is now Posit. Models are now tidy. This talk is about embracing that change and updating existing models using the tidymodels framework. I recently completed this change, letting go of our in-production code and revisioning it with tidymodels. My team ended up with a faster, more scalable pipeline that enabled us to better automate our workflow and increase our scale while improving our stakeholders' experiences. I'll share tips and tricks for adopting the tidymodels framework in existing products, best practices for learning and upskilling teams, and advice for using tidymodel packages to build more accessible data science tools. Materials: https://www.ryantimpe.com/files/tidymodels_adventures_positconf2023.pdf Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Tidy up your models. Session Code: TALK-1082

rstudio tidymodels AIMLmodelling Automation Humans Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

The Road to Easier Shiny App Deployments - posit::conf(2023)

Presented by Liam Kalita We're often helping developers to assess, fix and improve their Shiny apps, and often the first thing we do is see if we can deploy the app. If you can't deploy your Shiny app, it's a waste of time. If you can deploy it successfully, then at the very least it runs, so we've got something to work with. There are a bunch of reasons why apps fail to deploy. They can be easy to fix, like Hardcoded secrets, fonts, or missing libraries. Or they can be intractable and super frustrating to deal with, like manifest mismatches, resource starvation, and missing libraries. At the end of this talk, I want you to know how to identify, investigate and proactively prevent Shiny app deployment failures from happening. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: The future is Shiny. Session Code: TALK-1089

Shiny Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

The Power of Prototyping in R Shiny: Saving Millions by Building the Right Tool - posit::conf(2023)

Presented by Maria Grycuk The development of software can be costly and time-consuming. If the end users are not involved in the process from the start the tool we built may not meet their needs. In this presentation, I will discuss how prototyping in Shiny can help you build the right tool and save you from spending millions of dollars on a tool no one will use. I will explore the advantages of using Shiny for prototyping, particularly its ability to rapidly build interactive applications. I will also discuss how to design effective prototypes, including techniques for gathering user feedback and using that feedback to refine your tool. I will emphasize the importance of presenting real-life data, particularly when building data-driven tools. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Shiny user interfaces. Session Code: TALK-1125

Shiny Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

The People of Posit: Bringing Personality to R Packages - posit::conf(2023)

Presented by JP Flores and Sarah Parker The R programming language offers the versatility to perform statistical analyses, create publication-ready plots, and render high-quality reports and presentations. Despite having this environment of indispensable tools, it can be daunting for a beginner-level programmer to get started. Luckily, the Posit community is one of a kind and values inclusivity, collaboration, and empathy. By putting a face to the R packages we use on a daily basis, we hope to make every programmer feel included and capable. We want to inspire attendees to create their own projects or packages, connect with others inside and outside of their field of expertise, and challenge themselves to learn something new, knowing the community is right there to support them. Materials: http://www.sarmapar.com/people_of_posit/ Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Lightning talks. Session Code: TALK-1165

Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

The Need for Speed - AccelerateR-ing R Adoption in GSK - posit::conf(2023)

Presented by Ben Arancibia How does a risk-averse Pharma Biostatistics organization with 900+ people switch from using proprietary software to using R and other open-source tools for delivering clinical trial submissions? First slowly, then all at once. GSK started the transition of using R for its clinical trial data analysis in 2020 and now uses R for our regulatory-reviewed outputs. The AccelerateR Team, an agile pod of R experts and data scientists, rotates through GSK Biostatistics study teams sitting side by side to answer questions and mentor during this transition. We will share our experience from AccelerateR and how other organizations can use our learnings to scale R from pilots to full enterprise adoption and contribute to open source industry R packages. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Pharma. Session Code: TALK-1068

Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

The 'I' in Team: Peer-to-Peer Best Practices for Growing Data Science Teams - posit::conf(2023)

Presented by Liz Roten R users don't always come in sets. Often, you may be the only user on in the cubicle-block. But, one miraculous day, your manager finally fills the void and you welcome more folks on your team. Suddenly, the little R system you created to suit your needs, like a custom package, code styling, and file organization, isn't just for you. Want to suddenly overhaul that one package you wrote two years ago? It probably won't work when your colleagues try to update it. Your new teammates are data.table fans, but you prefer the tidyverse. Do you need to refactor? Are style choices, like indentation important when collaborating, or are you just being persnickety? In this talk, you will learn how to bring new teammates on board and blend your respective styles without pulling your hair out. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Building effective data science teams. Session Code: TALK-1063

tidyverse Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

Thanks, I Made It with Quartodoc - posit::conf(2023)

Presented by Isabel Zimmerman When Python package developers create documentation, they typically must choose between mostly auto-generated docs or writing all the docs by hand. This is problematic since effective documentation has a mix of function references, high-level context, examples, and other content. Quartodoc is a new documentation system that automatically generates Python function references within Quarto websites. This talk will discuss pkgdown's success in the R ecosystem and how those wins can be replicated in Python with quartodoc examples. Listeners will walk away knowing more about what makes documentation delightful (or painful), when to use quartodoc, and how to use this tool to make docs for a Python package. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Data science with Python. Session Code: TALK-1139

Isabel Zimmerman

pkgdown Quarto Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

Teaching Data Science in Adverse Circumstances: Posit Cloud and Quarto to the Rescue - posit::conf

Presented by Aleksander Dietrichson The focus of this presentation is on the challenges faced by teachers of data science whose students are not quantitatively inclined and may face some adversity in terms of technology resources available to them and potential language barriers. I identify three main areas of challenges and show how at Universidad Nacional de San Martín (Argentina) we addressed each of the areas through a combination of original curriculum redesign, production of course materials appropriate for the students in question; and the use of OS, and some Posit products, i.e.:posit.cloud and Quarto. I show how these technologies can be used as a pedagogical tool to overcome the challenges mentioned, even on a shoestring budget. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Teaching data science. Session Code: TALK-1094

Quarto Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

Take it in Bits: Using R to Make Eviction Data Accessible to the Legal Aid Community - posit::conf

Presented by Logan Pratico One in five low-income renter households in the US experienced falling behind on rent or being threatened with eviction in 2021. Yet most are unrepresented when facing eviction in court. The complex and fast-paced legal system obscures access to timely information, leaving tenants without assistance. In this talk, I discuss the Civil Court Data Initiative's use of R alongside AWS Cloud and SQL to analyze disaggregate eviction records. I focus on the integration of RMarkdown with Amazon Athena and EC2 to create weekly eviction reports across 20 states for legal aid groups working to assist tenants. The upshot: accessible eviction data to help legal aid providers better address local legal needs. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: End-to-end data science with real-world impact. Session Code: TALK-1146

rmarkdown Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

Sustainable Growth of Global Communities: R-Ladies' Next Ten Years - posit::conf(2023)

Presented by Riva Quiroga In this talk we share how good programming practices inspire the way we manage the R-Ladies community in order to make it sustainable. R-Ladies' first ten years were about growing the community: from being just one chapter in 2012 to becoming a global organization in 2016, and now fostering more than 230 chapters worldwide. But how can we face the challenges of growing an organization based solely on volunteer work? In this talk, we discuss how good programming practices –such as modularity, refactoring, and testing– inspire the way we see the sustainable management of an ever-growing community. To that end, we will present our most recent efforts at creating and documenting workflows, distributing the workload, and automating tasks that allow volunteers to focus their time where it is most needed. After watching this talk, you will get some ideas on how to support volunteers in your own community or project, and on how to use GitHub Actions to automate workflows and tasks. Learn more and join at: https://rladies.org/ Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: It takes a village: building and sustaining communities. Session Code: TALK-1128

Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

Succeed in the Life Sciences with R/Python and the Cloud - posit::conf(2023)

Presented by Colby Ford This talk covers best practices and lessons learned surrounding the use of R and Python by technical teams in the cloud, focusing on Posit Workbench, Azure ML, and Databricks. In the life sciences, whether it's pharma, biotech, research, or another type of organization, we are unique in that we blend scientific knowledge with technical skills to extract insights from large, complex datasets. In the cloud, we can architect solutions to help us scale, automate, and collaborate. Interestingly, the use of R and Python by bioinformatics, genomics, biostatistics, and data science teams can be challenging in a cloud-first world where all the data is somewhere other than your laptop (like a data lake). In this talk, I will share best practices and lessons learned surrounding the use of R and Python by technical teams in the cloud. We'll focus on the use of Posit Workbench and RStudio on various cloud services such as Azure ML and Databricks. Tuple, The Cloud Genomics Company: https://tuple.xyz Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Pharma. Session Code: TALK-1069

rstudio Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

Styling and Templating Quarto Documents - posit::conf(2023)

Presented by Emil Hvitfeldt Quarto is a powerful engine to generate documents, slides, books, websites, and more. The default aesthetics looks good, but there are times when you want and need to change how they look. This is that talk. Whether you want your slides to stand out from the crowd, or you need your documents to fit within your corporate style guide, being able to style Quarto documents is a valuable skill. Once you have persevered and created the perfect document, you don't want the effort to go to waste. This is where templating comes in. Quarto makes it super easy to turn a styled document into a template to be used over and over again. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Compelling design for apps and reports. Session Code: TALK-1106

Emil Hvitfeldt

Quarto Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

Speeding Up Plots in R/Shiny - posit::conf(2023)

Presented by Ryszard Szymański A slow plots can ruin the user experience of our dashboard. This talk covers techniques for speeding up the rendering process of our visualisations. Slow dashboards lead to a poor user experience and cause users to lose interest, or even become frustrated. A common culprit of this situation is a slowly rendering plot. During the talk, we will dive deeper into how plots are rendered in Shiny, identify common bottlenecks that can occur during the rendering process, and learn various techniques for improving the speed of plots in R/Shiny dashboards. These techniques will range from more efficient data processing to library-specific optimisations at the browser level. Materials: I'd like to include a link to my linkedin profile: https://www.linkedin.com/in/ryszard-szyma%C5%84ski-310a7017a/ Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Lightning talks. Session Code: TALK-1172

Shiny Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

Solving a Secure Geocoding Problem (That Hardly Anybody Has) - posit::conf(2023)

Presented by Tesla DuBois Due to data security concerns, the strictest health researchers won't send patient addresses to remote servers for geocoding. The only existing methods for offline geocoding are expensive, cumbersome, or require working with code - all limiting factors for many researchers. So, a couple of classmates and I made a standalone desktop application using shell, Docker, PostGIS, and Python to geocode addresses through a simple GUI without ever sending them off the local machine. Come for the technical ins and outs and stay for the anecdotes about how my R background played into the daunting, frustrating, but ultimately successful task of creating a data science tool using unfamiliar technologies. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Developing your skillset; building your career. Session Code: TALK-1111

Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

Small Package, Broad Impact: How I Discovered the Ultimate New Hire Hack - posit::conf(2023)

Presented by Trang Le Onboarding new hires can be a challenging process, but taking a problem-focused approach can make it more meaningful and rewarding. In this talk, I will share how I discovered the ultimate new hire hack by creating two small packages that gave me the confidence I needed when I started at BMS. Through building these packages, I not only learned R things like using bslib and making font files available for published dashboards, but also gained a deep understanding of my company's internal systems and workflows, and connected with my team via lots of questions. The resulting packages are still heavily used today. Join me to discover how small packages can have a broad impact and what hiring managers can do to help. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Developing your skillset; building your career. Session Code: TALK-1112

bslib Humans Programming Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

Siuba and duckdb: Analyzing Everything Everywhere All at Once - posit::conf(2023)

Presented by Michael Chow Every data analysis in Python starts with a big fork in the road: which DataFrame library should I use? The DataFrame Decision locks you into different methods, with subtly different behavior:: - different table methods (e.g. polars `.with_columns()` vs pandas `.assign()`) - different column methods (e.g. polars `.map_dict()` vs pandas `.map()`) In this talk, I'll discuss how siuba (a dplyr port to python) combines with duckdb (a crazy powerful sql engine) to provide a unified, dplyr-like interface for analyzing a wide range of data sources‚ whether pandas and polars DataFrames, parquet files in a cloud bucket, or pins on Posit Connect. Finally, I'll discuss recent experiments to more tightly integrate siuba and duckdb. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Databases for data science with duckdb and dbt. Session Code: TALK-1101

Michael Chow

dplyr Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

Side Effects of a Year of Blogging - posit::conf(2023)

Presented by Millie Symns A big part of being in the R community is sharing your knowledge in different forums, no matter how big or small. So what better way to do that than a blog? And what better way than using R and Posit products to build and maintain that blog and website? This was the route I took to challenge myself in putting myself out there more in the community to find my voice, share my knowledge and learn new things. In this talk, I will reflect on lessons learned and gains I have spent the past year blogging and sharing my website for all to see. The side effects include professional and personal benefits - from a clear profile of my skills to the progression of the development of my art. You may leave inspired to try the challenge for yourself. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: It takes a village: building and sustaining communities. Session Code: TALK-1130

Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

ShinyUiEditor: From Alpha to Powerful Shiny App Development Tool - posit::conf(2023)

Presented by Nick Strayer Since its alpha debut at last year's conference, the ShinyUiEditor has experienced continuous development, evolving into a powerful tool for crafting Shiny app UIs. Some key enhancements include the integration of new bslib components and the editor's ability to create or navigate to existing server bindings for inputs and outputs. In addition to new features, the editor is now available as a VSCode extension enabling it to integrate smoothly into more developers' workflows. This talk will showcase how these new capabilities empower users to efficiently create visually appealing and production-ready applications with ease. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Shiny user interfaces. Session Code: TALK-1126

Nick Strayer

bslib Shiny shinyuieditor Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

Shiny New Tools for Scaling your Shiny Apps - posit::conf(2023)

Presented by Joe Kirincic So you have a Shiny app your org loves, but as adoption grows, performance starts getting sluggish. Profiling reveals your cool interactive plots are the culprit. What can you do to make things snappy again? We can increase the number of app instances, sure, but suppose that isn't an option for us. Another approach is to shift the plotting work from the server onto the client. In this talk, we'll learn how to leverage two Javascript projects, DuckDB-WASM and Observable's Plot.js, in our Shiny app to create fast, flexible interactive visualizations in the browser without burdening our app's server function. The end result is an app that can scale to more users without needing to increase compute resources. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: The future is Shiny. Session Code: TALK-1088

Shiny Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

Shiny for Python Machine Learning Apps with pandas, scikit-learn and TensorFlow - posit::conf(2023)

Presented by Chelsea Parlett-Pelleriti With the introduction of Shiny for Python in 2022, users can now use the power of reactivity with their favorite Python packages. Shiny can be used to build interactive reports, dashboards, and web apps, that make sharing insights and results both simple and dynamic. This includes apps to display and explore popular Machine Learning models built with staple Python packages like pandas, scikit-learn, and TensorFlow. This talk will demonstrate how to build simple Shiny for Python apps that interface with these packages, and discuss some of the benefits of using Shiny for Python to build your web apps. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: The future is Shiny. Session Code: TALK-1087

Shiny for Python Shiny tensorflow Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

Shiny Developer Secrets: Insights From Over 1200 Applicants and What You MUST Know to Shine

Presented by Vedha Viyash Over 1,200 candidates applied for the R/Shiny developer role at Appsilon in the last year, and I will be sharing some insights that we have gained from going through the qualitative and quantitative feedback collected from every round of the interview process. I will be sharing some key takeaways that would help you focus on things that will make you a better Shiny developer. From reactivity to software testing, there are multiple skills that make up a good Shiny developer and you will get to know the major gaps and how to focus on them. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Lightning talks. Session Code: TALK-1173

Shiny Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

Serenity Now, Productivity Later: Focus Your Project Stack with The Gonzalez Matrix - posit::conf

Presented by Patrick Tennant How should you respond when your boss has too many good ideas for data science projects? In this talk, I'll review our use of an adapted version of the Eisenhower Matrix that lays out our projects according to the effort required and the value they will produce. Given the functionally unlimited number of data science projects a team could do, learn how we keep our team focused on valuable work while reducing the stress of a never-ending list of projects. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Building effective data science teams. Session Code: TALK-1065

Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

Scale Your Data Validation Workflow With {pointblank} and Posit Connect - posit::conf(2023)

Presented by Michael Garcia For the Data Services team at Medable, our number one priority is to ensure the data we collect and deliver to our clients is of the highest quality. The {pointblank} package, along with Posit Connect, modernizes how we tackle data validation within Data Services. In this talk, I will briefly summarize how we develop test code with {pointblank}, share with {pins}, execute with {rmarkdown}, and report findings with {blastula}. Finally, I will show how we aggregate data from test results across projects into a holistic view using {shiny}. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Leave it to the robots: automating your work. Session Code: TALK-1058

blastula pointblank rmarkdown Shiny Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

Running R-Shiny without a Server - posit::conf(2023)

Presented by Joe Cheng A year ago, Posit announced ShinyLive, a deployment mode of Shiny that lets you run interactive applications written in Python, without actually running a Python server at runtime. Instead, ShinyLive turns Shiny for Python apps into pure client-side apps, running on a pure client-side Python installation. Now, that same capability has come to Shiny for R, thanks to the webR project. In this talk, I'll show you how you can get started with ShinyLive for R, and why this is more interesting than just cheaper app hosting. I'll talk about some of the different use cases we had in mind for ShinyLive, and help you decide if ShinyLive makes sense for your app. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: I can't believe it's not magic: new tools for data science. Session Code: TALK-1151

Joe Cheng

Shiny for Python Shiny shinylive Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

Reproducible Manuscripts with Quarto - posit::conf(2023)

Presented by Mine Çetinkaya-Rundel In this talk, we present a new capability in Quarto that provides a straightforward and user-friendly approach to creating truly reproducible manuscripts that are publication-ready for submission to popular journals. This new feature, Quarto manuscripts, includes the ability to produce a bundled output containing a standardized journal format, source documents, source computations, referenced resources, and execution information into a single bundle that is ingested into journal review and production processes. We'll demo how Quarto manuscripts work and how you can incorporate them into your current manuscript development process as well as touch on pain points in your current workflow that Quarto manuscripts help alleviate. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Quarto (1). Session Code: TALK-1070

Mine Çetinkaya-Rundel

Quarto Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

Reliable Maintenance of Machine Learning Models - posit::conf(2023)

Presented by Julia Silge Maintaining machine learning models in production can be quite different from maintaining general software projects, because of the unique statistical characteristics of ML models. In this talk, learn about model drift, the different ways the word "performance" is used with models, what you can monitor about a model, how feedback loops impact models, and how you can use vetiver to set yourself up for success with model maintenance. This talk will help practitioners who are already deploying models, but this is also useful knowledge for practitioners earlier in their MLOps journey; decisions made along the way can make the difference between resilient models that are easier to maintain and disappointing or misleading models. Materials: https://github.com/juliasilge/ml-maintenance-2023 Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Tidy up your models. Session Code: TALK-1083

Julia Silge

Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

R! You Going?! - posit::conf(2023)

Presented by SherAaron Hurt 3 things to remember when starting your journey to become a data scientist Everyone will have a different journey when becoming a data scientist. However, there are a few tips to consider to make the journey less daunting and more enjoyable. Listen, as I tell my story as a data scientist and offer resources and tips to build confidence for those who are new to their journey. The tools are available however, it is not always easy to find them. keywords: openscience, The Carpentries, R programming language, GPS, data science journey, data science resources Materials: - https://www.linkedin.com/in/sheraaronhurt/ - carpentries.org/ Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Teaching data science. Session Code: TALK-1097

Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

R Not Only In Production - posit::conf(2023)

Presented by Kara Woo I will share what our team has learned from successfully integrating R in all areas of our company's operations. InsightRX is a precision medicine company whose goal is to ensure that each patient receives the right drug at the optimal dose. At InsightRX, R is a first-class language that's used for purposes ranging from customer-facing products to internal data infrastructure, new product prototypes, and regulatory reporting. Using R in this way has given us the opportunity to forge fruitful collaborations with other teams in which we can both learn and teach. Join me as I share how the skills of data science and engineering can complement each other to create better products and greater impact. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: R Not Only In Production. Session Code: KEY-1108

Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

Quickly get your Quarto HTML theme in order - posit::conf(2023)

Presented by Greg Swinehart A 5-minute talk to discuss how I've used Quarto and Bootstrap variables to quickly make Shiny's new website look as it should. The Quarto user I have in mind works at an organization with specific brand guidelines to follow. I‚ will discuss how to set up your theme, show some key Quarto settings, and how Bootstrap‚ Sass variables are your best friend. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Lightning talks. Session Code: TALK-1170

Greg Swinehart

Quarto Shiny Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

Parameterized Quarto Reports Improve Understanding of Soil Health - posit::conf(2023)

Presented by Jadey Ryan Learn how to use R and Quarto parameterized reporting in this four-step workflow to automate custom HTML and Word reports that are thoughtfully designed for audience interpretation and accessibility. Soil health data are notoriously challenging to tidy and effectively communicate to farmers. We used functional programming with the tidyverse to reproducibly streamline data cleaning and summarization. To improve project outreach, we developed a Quarto project to dynamically create interactive HTML reports and printable PDFs. Custom to every farmer, reports include project goals, measured parameter descriptions, summary statistics, maps, tables, and graphs. Our case study presents a workflow for data preparation and parameterized reporting, with best practices for effective data visualization, interpretation, and accessibility. Talk materials: https://jadeyryan.com/talks/2023-09-25_posit_parameterized-quarto/ Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Elevating your reports. Session Code: TALK-1160

Quarto tidyverse Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

Package Management for Data Scientists - posit::conf(2023)

Presented by Tyler Finethy In my talk, "Package Management for Data Scientists," we will discuss software dependencies for R and Python and the common issues faced during package installations. I will begin with an overview of package management, highlighting its crucial role in data science. We'll then focus on practical strategies to prevent dependency errors and address effective troubleshooting when these problems occur. Lastly, we will look towards the future, discussing potential package management improvements, focusing on reproducibility and accessibility for those new to the field. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Managing packages. Session Code: TALK-1081

Programming Python Databricks Data Science Rstudio Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

Open Source Solutions to Next-Generation Submissions, After 30 Years of Industry Experience

Presented by Mike K Smith The pharmaceutical industry is undergoing rapid change, driven by a desire from both industry and regulatory agencies to move to more interactive visualizations and web applications to review data and make decisions. These changes would have been unthinkable 30 years ago when I started working at Pfizer. In this talk, I'll consider the drivers for these changes, how open-source tools can help achieve this, and why collaboration across the industry is vital to achieving this goal. I'll contrast this with my experience of 30 years working in the pharma industry - when the R language had only just been released, when the internet was new, and when submissions to agencies were printed out, loaded onto trucks, and shipped to their doors. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Pharma. Session Code: TALK-1067

Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

Open Source Property Assessment: Tidymodels to Allocate $16B in Property Taxes - posit::conf(2023)

Presented by Nicole Jardine and Dan Snow How the Cook County Assessor's Office uses R and tidymodels for its residential property valuation models. The Cook County Assessor's Office (CCAO) determines the current market value of properties for the purpose of property taxation. Since 2020, the CCAO has used R, tidymodels, and LightGBM to build predictive models that value Cook County's 1.5 million residential properties, which are collectively worth over $400B. These predictive models are open-source, easily replicable, and have significantly improved valuation accuracy and equity over time. Join CCAO Chief Data Officer Nicole Jardine and Director of Data Science Dan Snow as they walk through the CCAO's modeling process, shares lessons learned, and offer a sneak peek at changes planned for the 2024 reassessment of Chicago. Materials: https://github.com/ccao-data Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: End-to-end data science with real-world impact. Session Code: TALK-1147

tidymodels Ai .Odelling Modelling Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

Oops I'm a Manager - On More Effective 1-on-1 Meetings - posit::conf(2023)

Presented by Andrew Holz As a team leader (accidental or not), it's easy to get caught up in the daily grind and overlook the importance of 1-on-1s. Bad idea. 1-on-1s are critical for building trust, providing feedback, and ensuring that everyone is on the same page. Keys to good 1-on-1s start with a small amount of prep and a running shared document of notes and takeaways. Another key is to rotate types of 1-on-1s. Possibilities include "heads down" on recent work, "heads up" looking further out, and career-focused sessions. After some tips on the right sort of questions and uncovering sneaky issues, I will also touch on effective feedback. I will share resources and hope to include Seussian visuals and a few poetic lines to help the key points stick. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Building effective data science teams. Session Code: TALK-1064

Andrew Holz

Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

Never again in outer par mode: making next-generation PDFs with Quarto & typst - posit::conf(2023)

Presented by Carlos Scheidegger Quarto 1.4 will introduce support for Typst. Typst is a brand-new open-source typesetting system built from scratch to support the lessons we have learned over almost half a century of high-quality computer typesetting that TeX and LaTeX have enabled. If you've ever had to produce a PDF with Quarto and got stuck handling an inscrutable error message from LaTeX, or wanted to create a new template but were too intimated by LaTeX's arcane syntax, this talk is for you. I'll show you why we need an alternative for TeX and LaTeX , and why it will make Quarto even better. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Quarto (2). Session Code: TALK-1142

Carlos Scheidegger

Quarto Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

Motley Crews: Collaborating with Quarto - posit::conf(2023)

Presented by Susan McMillan, Wyl Schuth, and Michael Zenz Adoption of Quarto for document creation has transformed the collaborative workflow for our small higher-education analytics team. Historically, content experts wrote in Word documents and data analysts used R for statistics and graphics. Specialization in different software tools created challenges for producing collaborative analytic reports, but Quarto has solved this problem. We will describe how we use Quarto for writing and editing text, embedding statistical analysis and graphics, and producing reports with a standard style in multiple formats, including web pages. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Elevating your reports. Session Code: TALK-1157

Quarto Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

Matching Tools to Titans: Tailoring Posit Workbench for Every Cloud - posit::conf(2023)

Presented by James Blair In an era of diverse cloud platforms, leveraging tools effectively is paramount. This talk highlights the adaptability of Posit Workbench within leading cloud platforms. Delve into strategic integrations, understand key challenges, and uncover practical solutions. By the end, attendees will be equipped with insights to harness Posit Workbench's capabilities seamlessly across varied cloud environments. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Data science infrastructure for your org. Session Code: TALK-1115

Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

Making a (Python) Web App is easy! - posit::conf(2023)

Presented by Marcos Huerta Making Python Web apps using Dash, Streamlit, and Shiny for Python This talk describes how to make distribution-free prediction intervals for regression models via the tidymodels framework. By creating and deploying an interactive web application you can better share your data, code, and ideas easily with a broad audience. I plan to talk about several Python web application frameworks, and how you can use them to turn a class, function, or data set visualization into an interactive web page to share with the world. I plan to discuss building simple web applications with Plotly Dash, Streamlit, and Shiny for Python. Materials: - Comprehensive talk notes here: https://marcoshuerta.com/posts/positconf2023/ - https://www.tidymodels.org/learn/models/conformal-regression/ - https://probably.tidymodels.org/reference/index.html#regression-predictions Corrections: In my live remarks, I said a Dash callback can have only one output: that is not correct, a Dash callback can update multiple outputs. I was trying to say that a Dash output can only be updated by one callback, but even that is no longer true as of Dash 2.9. https://dash.plotly.com/duplicate-callback-outputs"" Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: The future is Shiny. Session Code: TALK-1086

Shiny for Python Shiny tidymodels Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

Magic with WebAssembly and webR - posit::conf(2023)

Presented by George Stagg Earlier this year the initial version of webR was released and users have begun building new interactive experiences with R on the web. In this talk, I'll discuss webR's TypeScript library and what it is able to do. The library allows users to interact with the R environment directly from JavaScript, which enables manipulation tricks that seem like magic. I'll begin by describing how to move objects from R to JS and back again, and discuss the technology that makes this possible. I'll continue with more advanced manipulation, such as invoking R functions from JS and talk about why you might want to do so. Finally, I'll describe how messages are sent over webR's communication channel and explain how this enables webR to work with Shinylive. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: I can't believe it's not magic: new tools for data science. Session Code: TALK-1152

George Stagg

shinylive Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

Large Language Models in RStudio - posit::conf(2023)

Presented by James Wade Large language models (LLMs), such as ChatGPT, have shown the potential to transform how we code. As an R package developer, I have contributed to the creation of two packages -- gptstudio and gpttools -- specifically designed to incorporate LLMs into R workflows within the RStudio environment. The integration of ChatGPT allows users to efficiently add code comments, debug scripts, and address complex coding challenges directly from RStudio. With text embedding and semantic search, we can teach ChatGPT new tricks, resulting in more precise and context-aware responses. This talk will delve into hands-on examples to showcase the practical application of these models, as well as offer my perspective as a recent entrant into public package development. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: I can't believe it's not magic: new tools for data science. Session Code: TALK-1154

rstudio Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

It's All About Perspective: Making a Case for Generative Art - posit::conf(2023)

Presented by Meghan Santiago Harris This talk explores how to create art in the R language while highlighting some similarities between the skills required for creating generative art and those needed to perform data science tasks in R. Because the field of data science is inherently task-oriented, it is no wonder that most people struggle to see the utility of generative art past the bounds of a casual hobby. This talk will invite the participant to learn about generative art while focusing on ""why"" people should create it and its potential place in data science. This talk is suitable for all disciplines and artistic abilities. Furthermore, this talk will aim to expand the participant's perspective on generative art with the following concepts: - What is generative art and how can it be created in R or Python - Justifications for generative art within Data Science - Examples of programming skills that are transferrable between generative art and pragmatic data science projects Materials: - Link to the talk repo: https://github.com/Meghansaha/a_case_for_genart - Link to the slides: https://meghansaha.github.io/a_case_for_genart/#/title-slide - Link to the artpack package site: https://meghansaha.github.io/artpack/ - Personal Site: https://thetidytrekker.com/ Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Developing your skillset; building your career. Session Code: TALK-1109

Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

It's Abstractions All the Way Down... - posit::conf(2023)

Presented by JD Long Abstractions rule everything around us. JD Long talks about abstractions from the board room to the silicon. Over 20 years ago Joel Spolsky famously wrote, "All non-trivial abstractions, to some degree, are leaky." Unsurprisingly this has not changed. However, we have introduced more and more layers of abstraction into our workflows: Virtual Machines, AWS services, WASM, Docker, R, Python, data frames, and on and on. But then on top of the computational abstractions we have people abstractions: managers, colleagues, executives, stakeholders, etc. JD's presentation will be a wild romp through the mental models of abstractions and discuss how we, as technical analytical types, can gain skill in traversing abstractions and dealing with leaks. Materials: https://github.com/CerebralMastication/Presentations/tree/master/2023_posit-conf Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: It's abstractions all the way down .... Session Code: KEY-1161

Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

It's a Great Time to be an R Package Developer! - posit::conf(2023)

Presented by Jenny Bryan and Hadley Wickham (Due to unforeseen circumstances, Hadley Wickham presented this talk "slide karaoke" style, from materials prepared by Jenny Bryan.) In R, the fundamental unit of shareable code is the package. As of March 2023, there were over 19,000 packages available on CRAN. Hadley Wickham and I recently updated the R Packages book for a second edition, which brought home just how much the package development landscape has changed in recent years (for the better!). In this talk, I highlight recent-ish developments that I think have a great payoff for package maintainers. I'll talk about the impact of new services like GitHub Actions, new tools like pkgdown, and emerging shared practices, such as principles that are helpful when testing a package. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Package development. Session Code: TALK-1132

Hadley Wickham, Jenny Bryan

pkgdown Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

Insights in 5-D! (Using magic small-multiples layouts) - posit::conf(2023)

Presented by Matt Dzugan Using Small-Multiples (faceted graphs) is an effective way to compare patterns across many dimensions. In this talk, I'll walk you through some ways to lay out your individual facets according to the underlying data. For example, maybe each facet represents a city or point on a 2D plane - we'll explore ways to organize facets in a grid that mimics the data itself - unlocking your ability to explore patterns in 4+ dimensions. Other solutions to this problem rely on manually-curated lists that map common layouts to a grid, but in this talk, we'll explore solutions that work on EVERYTHING. I'll show you how to incorporate this technique into your viz and how I built the libraries since there are some interesting data science concepts at play. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Lightning talks. Session Code: TALK-1174

Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

In-Process Analytical Data Management with DuckDB - posit::conf(2023)

Presented by Hannes Mühleisen This talks introduces DuckDB, an in-process analytical data management system that is deeply integrated into the R ecosystem. DuckDB is an in-process analytical data management system. DuckDB supports complex SQL queries, has no external dependencies, and is deeply integrated into the R ecosystem. For example, DuckDB can run SQL queries directly on R data frames without any data transfer. DuckDB uses state-of-the-art query processing techniques like vectorised execution and automatic parallelism. DuckDB is out-of-core capable, meaning that it is possible to process datasets far bigger than main memory. DuckDB is free and open source software under the MIT license. In this talk, we will describe the user values of DuckDB, and how it can be used to improve their day-to-day lives through automatic parallelisation, efficient operators, and out-of-core operations. Materials: - https://duckdb.org - https://duckdb.org/docs/api/r.html - https://github.com/duckdb/duckdb-r Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Databases for data science with duckdb and dbt. Session Code: TALK-1099

Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

HTML and CSS for R Users - posit::conf(2023)

Presented by Albert Rapp You can get the most out of popular R tools by combining them with easy-to-learn HTML & CSS commands. It's easy to think that R users do not need HTML and CSS. After all, R is a language designed for data analysis, right? But the reality is that these web standards are everywhere, even in R. In fact, many great tools like {ggtext}, {gt}, {shiny}, and Quarto unlock their full potential when you know a little bit of HTML & CSS. In this talk, I will demonstrate specific examples where R users can benefit from HTML and CSS and show you how to get started with these two languages. Materials: - Here's the link to the video that I mention in the talk: https://youtu.be/QU8wSya-Y9E?si=zw59OSFPl1eJSY7M - Part 1 of this two part series can be found at https://www.youtube.com/watch?v=jX4_Dnzhl0M Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Compelling design for apps and reports. Session Code: TALK-1105

gt Quarto Rapp Shiny Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

How You Get Value as a 1-Person Posit Connect Team - posit::conf(2023)

Presented by Sean Nguyen Sean, a sole Posit Connect developer, shares his experience in delivering business impact. He narrates his transition from crafting one-off reports to developing and deploying robust data science web applications using Python and R with Posit Connect. Despite its common association with large enterprise teams, Sean demonstrates how Posit Connect can be effectively utilized in smaller settings. He presents his work on creating and deploying end-to-end machine learning pipelines in Python, hosting them as APIs, and seamlessly integrating with Shiny apps via Posit Connect. This talk imparts practical strategies and techniques to foster user and executive adoption of Posit Connect within lean (and large) organizations. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Getting %$!@ done: productive workflows for data science. Session Code: TALK-1093

Shiny Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

How to Win Friends and Influence People (With Data) - posit::conf(2023)

Presented by Joe Powers Too many great data science products never go into production. To persuade leaders and colleagues to adopt your data science offering, you must translate your insights into terms that are relevant and accessible to them. Attempts to persuade these audiences with proofs and model performance stats will often fall flat because the audience is left feeling overwhelmed. This talk will demonstrate the data simulation, visualization, and story-telling techniques that I use to influence leadership and the community-building techniques I use to earn the trust and support of fellow analysts. These efforts were successful in persuading Intuit to adopt advanced analytic methods like sequential analysis that cut the duration of our AB tests by over 60%. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Bridging the gap between data scientists and decision makers. Session Code: TALK-1077

Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

How to Keep Your Data Science Meetup Sustainable - posit::conf(2023)

Presented by Ted Laderas Many data science meetup organizers struggle with burnout. It can be daunting to plan a meetup schedule, especially with the added burden of work and life. In this talk, I want to highlight some strategies for keeping your data science meetup sustainable. Specifically, I want to highlight the role of self-care in growing and sustaining your group, as well as low-key activities like a data scavenger hunt, watching videos together, styling plots together, and sharing useful tidyverse functions. By making it easy for your members to contribute and empowering them, it takes a lot of the burden off you as an organizer. You don't need to reinvent the wheel for meetups or have famous guests for each one. Let's start the conversation and make your meetup last. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: It takes a village: building and sustaining communities. Session Code: TALK-1129

tidyverse Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

How to Help Developers Make Apps Users Love - posit::conf(2023)

Presented by Michał Parkoła There are many resources that can help you design better apps. But what if your org creates many apps? Scaling good design to larger groups dials the challenge up to 11. In this talk, I will share how we approach the problem at Appsilon. - I will present our in-house Design Guide. - I will share the successes and failures we've had building it and helping a wide variety of developers use it - I will then share some tips about what you might want to consider if you want to help your org build better apps at scale. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Shiny user interfaces. Session Code: TALK-1127

Shiny Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

How I Learned to Stop Worrying and Love Public Packages - posit::conf(2023)

Presented by Joe Roberts The popularity of R and Python for data science is in no small part attributable to the vast collection of extension packages available for everything from common tasks like data cleaning to highly-specialized domain-specific functions. However, with that ease of sharing packages comes a larger target for bad actors trying to exploit them. We'll explore these security risks along with approaches you can take to mitigate them using Posit Package Manager. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Managing packages. Session Code: TALK-1079

Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

How Data Scientists Broke A/B Testing (And How We Can Fix It) - posit::conf(2023)

Presented by Carl Vogel As data scientists, we care about making valid statistical inferences from experiments. And we've adapted well-established and well-understood statistical methods to help us do so in our A/B tests. Our stakeholders, though, care about making good product decisions efficiently. I'll describe how the way we design A/B tests can put these goals in tension and why that often causes misalignment between how A/B tests are intended to be used and how they are actually used. I'll also talk about how I've used R to implement alternative experimental approaches that have helped bridge the gap between data scientists and stakeholders. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Bridging the gap between data scientists and decision makers. Session Code: TALK-1076

Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

Hitting the Target(s) of Data Orchestration - posit::conf(2023)

Presented by Alexandros Kouretsis We are living at a time when the size of datasets can be overwhelming. Add to this that their process involves linking together different computing systems and software, and integrating dynamically changing reference data, and for sure, you have a problem. Reproducibility, traceability, and transparency have left the building. Here is where Posit Connect along with the vast R ecosystem comes to save the day, allowing the creation of reproducible pipelines. I will share with you my first-hand experience in this presentation. In particular, how we used Targets in Posit Connect combined with AWS technologies in a bioinformatics pipeline. The result? An effective and secure workflow orchestration that is scalable and advances knowledge. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Leave it to the robots: automating your work. Session Code: TALK-1148

Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

Grammar of Graphics in Python with Plotnine - posit::conf(2023)

Presented by Hassan Kibirige {plotnine} brings the elegance of {ggplot2} to the Python programming language. Learn about The Grammar of Graphics and get a feel of why it is an effective way to create Statistical Graphics. ggplot2 is one of the most loved visualisation libraries. It implements a Grammar of Graphics system, which requires one to think about data in terms of columns of variables and how to transform them into geometric objects. It is elegant and powerful. This is a talk about plotnine, which brings the elegance of ggplot2 to the Python programming language. It is an invitation to learn about the Grammar of Graphics system and to appreciate it. It will include some tips on how to avoid common frustrations as you learn the system. Materials: - Website: https://plotnine.org - Source Code: https://github.com/has2k1/plotnine - Slides for this talk: https://github.com/has2k1/my-talks Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Data science with Python. Session Code: TALK-1137

Hassan Kibirige

ggplot2 plotnine Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

Github Copilot integration with RStudio, it's finally here! - posit::conf(2023)

Presented by Tom Mock This talk closes issue #10148, "Github Copilot integration with RStudio", the most upvoted feature request in RStudio's history. Code generating AI tools like Github Copilot‚ promise an "AI pair programmer that offers autocomplete-style suggestions as you code". For the first time, we'll show a native integration of Copilot into RStudio, helping to build on that promise by providing AI-generated "ghost text" autocompletion with R and other languages. I'll also provide a comparison of Copilot's "ghost text" to a chat-style interface in RStudio via the {chattr} package from the Posit MLVerse team. To make the most of these new features, I'll walk through some examples of how sharing additional context, comments, code, and other "prompt engineering" can help you go from code-generating AI tools that feels like an annoying backseat driver to an experienced copilot. We'll close with a robust end-to-end example of how these new RStudio integrations and packages can help you be a more productive developer. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Data science infrastructure for your org. Session Code: TALK-1117

rstudio Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

Getting the Most Out of Git - posit::conf(2023)

Presented by Colin Gillespie Did you believe that Git will solve all of your data science worries? Instead, you've been plunged HEAD~1 first into merging (or is that rebasing?) chaos. Issues are ignored, branches are everywhere, main never works, and no one really knows who owns the repository. Don't worry! There are ways to escape this pit of despair. Over the last few years, we've worked with many data science teams. During this time, we've spotted common patterns and also common pitfalls. While one size does not fit all, there are golden rules that should be followed. At the end of this talk, you'll understand the processes other data science teams implement to make Git work for them. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Getting %$!@ done: productive workflows for data science. Session Code: TALK-1091 -------------------------- Thumbnail from happygitwithr.com, still from Heaven King video

Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

From Novices to Experts: Building a Community of Engaged R Users - posit::conf(2023)

Presented by Natalia Andriychuk At Pfizer, we have over 1500 users with R installed on their machines, along with an R community on MS Teams comprising over a thousand colleagues globally. How can we effectively engage with Pfizer R users and celebrate the successes of this community, while sharing best practices? Additionally, how do we avoid isolated groups duplicating efforts to solve R-related problems across different parts of the organization? To address these challenges, we established the Pfizer R Center of Excellence (CoE) in early 2022. We focus our efforts on bringing together a rapidly growing community of colleagues, providing technical expertise, and offering best-practice guidance. A well-established, maintained and engaged R community promotes an inclusive and supportive learning environment that drives innovation within organizations. Our aim is to help colleagues thrive in their R journey, regardless of their expertise level. During my talk, I will share the techniques we used to build a supportive R community, the tools employed to increase community engagement, and the successes and challenges encountered in building an engaging community of R users. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Pharma. Session Code: TALK-1066

Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

From Journalist to Coder: Creating a Web Publication with Quarto - posit::conf(2023)

Presented by Brian Tarran This is the story of how a Royal Statistical Society writer discovered Quarto, learned how to code (a bit), and built realworlddatascience.net, an online publication for the data science community. In March 2022, I was tasked by the Royal Statistical Society with creating a new online publication: a data science website for data science professionals. I've been a print journalist for 20 years and have worked on websites in that time, but my coding ability began and ended with wrapping href tags around text and images. That is until I discovered Quarto. In this talk, I describe how I explored, learned, and fell in love with the Quarto publishing system, how I used it to build a website -- Real World Data Science (realworlddatascience.net) -- and how the open source community mindset helped shape my thinking about what a new publication could and should be! Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Quarto (1). Session Code: TALK-1071

Quarto Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

From Data Confusion to Data Intelligence - posit::conf(2023)

Presented by Elaine McVey and David Meza Data science teams operate in a unique environment, much different than the IT or software development life cycle. Hope from executives for the impact of data science is extremely high! Understanding of how to make data science efforts successful is very low! This creates an interesting set of organizational challenges for data and analytics teams. These are particularly clear when data science is being introduced at new companies, but plays out at organizations of all sizes. So, how do we navigate this dynamic? We will share some strategies for success. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: From Data Confusion to Data Intelligence. Session Code: KEY-1060

Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

From Concept to Impact: Building and Launching Shiny Apps in the Workplace - posit::conf(2023)

Presented by Tiger Tang Learn to build and launch a Shiny app like you are working on a start-up! Unlock the potential of Shiny apps for your organization! Join Tiger as he shares insights from implementing Shiny apps at his workplace, handling over 160,000 internal requests. Discover a practical mindmap to find, build, and enhance Shiny app use cases, ensuring robustness and improved user engagement. Materials: https://tigertang.org/posit_conf_2023/ Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Bridging the gap between data scientists and decision makers. Session Code: TALK-1074

Shiny Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

FOCAL Point: Utilizing Python, R, and Shiny to Capture, Process, and Visualize Motion - posit::conf

Presented by Justin Markel & Alyssa Burritt One of the fastest movements in modern sports is a golf swing. Capturing this motion using a high-speed camera system creates many unique challenges in processing, analyzing, and visualizing the thousands of data points that are generated. These spatial coordinates can be quickly translated through Python scripts to well-known, industry-specific performance metrics and graphics in Shiny. Down the line, R utilities aid more complicated analyses and optimizations, driving new product innovations. This talk will cover our company's process of implementing these tools into our workflow and highlight key program features that have helped successfully combine these applications for users with a variety of technical backgrounds. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: R or Python? Why not both!. Session Code: TALK-1120

Shiny Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

Field Guide to Writing Your First R Package - posit::conf(2023)

Presented by Fonti Kar I recall writing my first package being an intimidating task. In my talk, I will share how a Biologist's mindset can make R package writing more approachable. This talk is an encouragement and a gentle stroll through the package building process. I will show you ways to be curious when you get stuck and how to prepare for the unexpected. I hope sharing my perspective will help others see package development as wonderful as the natural world and dispel any hesitation to start! Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Package development. Session Code: TALK-1135

Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

Extending Quarto - posit::conf(2023)

Presented by Richard Iannone What are Quarto shortcode extensions? Think of them as powerful little programs you can run in your Quarto docs. I won't show you how to build a shortcode extension during this talk but rather I'm going to take you on a trip across this new ecosystem of shortcode extensions that people have already written. For example, I'll introduce you to the `fancy-text` extension for outputting nicely-formatted versions of fancy strings such as LaTeX and BibTeX; you'll learn all about the `fontawesome`, `lordicon`, `academicons`, `material-icons`, and `bsicons` shortcode extensions that let you add all sorts of icons. This is only a sampling of the shortcode extensions I will present, there will be many other inspiring examples as well. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Quarto (2). Session Code: TALK-1141

fontawesome Quarto Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

epoxy: Super Glue for Data-driven Reports and Shiny Apps - posit::conf(2023)

Presented by Garrick Aden-Buie R Markdown, Quarto, and Shiny are powerful frameworks that allow authors to create data-driven reports and apps. But truly excellent reports require a lot of work in the final steps to get numerical and stylistic formatting just right. {epoxy} is a new package that uses {glue} to give authors templating superpowers. Epoxy works in R Markdown and Quarto, in markdown, LaTeX, and HTML outputs. It also provides easy templating for Shiny apps for dynamic data-driven reporting. Beyond epoxy's features, this talk will also touch on tips and approaches for data-driven reporting that will be useful to a wide audience, from R Markdown experts to the Quarto and Shiny curious. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Elevating your reports. Session Code: TALK-1155

Garrick Aden-Buie

Quarto Shiny Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

Dynamic Interactions: webR to Empower Educators & Researchers with Interactive Quarto Docs

Presented by James Balamuta Full talk title: Dynamic Interactions: Empowering Educators and Researchers with Interactive Quarto Documents Using webR Traditional Quarto documents often lack interactivity, limiting the ability of students and researchers to fully explore and engage with the presented topic. In this talk, we propose a novel approach that utilizes webR, a WebAssembly-powered version of R, to seamlessly embed R code directly within the browser without the need for a server. We demonstrate how this approach can transform static Quarto documents into dynamic examples by leveraging webR's capabilities through standard Quarto code cells, enabling real-time execution of R code and dynamic display of results. Our approach empowers educators and researchers alike to harness the power of interactivity and reproducibility for enhanced learning and research experiences. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Quarto (1). Session Code: TALK-1073

Quarto Quarto Rmd Rmarkdown Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Posit Python Ml

duckplyr: Tight Integration of duckdb with R and the tidyverse - posit::conf(2023)

Presented by Kirill Müller The duckplyr R package combines the convenience of dplyr with the performance of DuckDB. Better than dbplyr: Data frame in, data frame out, fully compatible with dplyr. duckdb is the new high-performance analytical database system that works great with R, Python, and other host systems. dplyr is the grammar of data manipulation in the tidyverse, tightly integrated with R, but it works best for small or medium-sized data. The former has been designed with large or big data in mind, but currently, you need to formulate your queries in SQL. The new duckplyr package offers the best of both worlds. It transforms a dplyr pipe into a query object that duckdb can execute, using an optimized query plan. It is better than dbplyr because the interface is "data frames in, data frames out", and no intermediate SQL code is generated. The talk first presents our results, a bit of the mechanics, and an outlook for this ambitious project. Materials: https://github.com/duckdblabs/duckplyr/ Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Databases for data science with duckdb and dbt. Session Code: TALK-1100

dbplyr dplyr duckplyr tidyverse Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

dplyr 1.1.0 Features You Can't Live Without - posit::conf(2023)

Presented by Davis Vaughan Did you enjoy my clickbait title? Did it work? Either way, welcome! The dplyr 1.1.0 release included a number of new features, such as: - Per-operation grouping with `.by` - An overhaul to joins, including new inequality and rolling joins - New `consecutive_id()` and `case_match()` helpers - Significant performance improvements in `arrange()` Join me as we take a tour of this exciting dplyr update, and learn how to use these new features in your own work! Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Lightning talks. Session Code: TALK-1162

Davis Vaughan

dplyr Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

Diversify Your Career with Shiny for Python - posit::conf(2023)

Presented by Gordon Shotwell A few years ago my company made a sudden shift from R to Python which was quite bad for my career because I didn't really know Python. The main issue was that I couldn't find a niche that allowed me to use my existing knowledge while learning the new language. Shiny for Python is a great niche for R users because none of the Python web frameworks can do what Shiny can do. Additionally, almost all of your knowledge of the R package is applicable to the Python one. This talk will provide an overview of the Python web application landscape and articulate what Shiny adds to this landscape, and then go through the five things that R users need to know before developing their first Shiny for Python application. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Data science with Python. Session Code: TALK-1138

Shiny for Python Shiny Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

Developing a Prototyping Competency in a Statistical Science Organization - posit::conf(2023)

Presented by Daniel Woodie The introduction of new tools, methods, and processes can be a struggle within a statistical science organization. Being deliberate and investing in the creation of a prototyping competency can help in accelerating progress. Prototyping allows organizations to quickly experiment with new ideas, reduce the risk of failure, identify potential issues early, and iterate until the desired outcome is achieved. I will highlight the key areas we have focused on accelerating, our framework for developing this competency, how we use Shiny, and the lessons we've learned along the way. Developing a prototyping competency is crucial for statistical science organizations that wish to stay competitive and innovative in today's rapidly changing landscape. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Building effective data science teams. Session Code: TALK-1059

Shiny Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

Democratizing Access to Education Data - posit::conf(2023)

Presented by Erika Tyagi Learn how the Urban Institute is making high-quality data more accessible through the Education Data Portal. Every year, government agencies release large amounts of data on schools and colleges, but this information is scattered across various websites and is often difficult to use. To make these data more accessible, the Urban Institute built the Education Data Portal, a freely available one-stop shop for harmonized data and metadata for nearly all major federal education datasets. In this talk, we'll demonstrate how the portal works and share lessons we've learned about making data accessible to users with varying technical skills and preferred programming languages. The Urban Institute's Education Data Portal: https://educationdata.urban.org Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: End-to-end data science with real-world impact. Session Code: TALK-1145

Automation Humans Python Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

dbtplyr: Bringing Column-Name Contracts from R to dbt - posit::conf(2023)

Presented by Emily Riederer starts_with(language): Translating select helpers to dbt. Translating syntax between languages transports concepts across communities. We see a case study of adapting a column-naming workflow from dplyr to dbt's data engineering toolkit. dplyr's select helpers exemplify how the tidyverse uses opinionated design to push users into the pit of success. The ability to efficiently operate on names incentivizes good naming patterns and creates efficiency in data wrangling and validation. However, in a polyglot world, users may find they must leave the pit when comparable syntactic sugar is not accessible in other languages like Python and SQL. In this talk, I will explain how dplyr's select helpers inspired my approach to 'column name contracts,' how good naming systems can help supercharge data management with packages like {dplyr} and {pointblank}, and my experience building the {dbtplyr} to port this functionality to dbt for building complex SQL-based data pipelines. Materials: - https://github.com/emilyriederer/dbtplyr - https://emilyriederer.com Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Databases for data science with duckdb and dbt. Session Code: TALK-1098

dplyr pointblank tidyverse Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

Data Visualization with Seaborn - posit::conf(2023)

Presented by Michael Waskom Seaborn is a Python library for statistical data visualization. After nearly a decade of development, seaborn recently introduced an entirely new API that is more explicitly based on a formal grammar of graphics. My talk will introduce this API and contrast it with the classic seaborn interface, sharing insights about the influence of the grammar of graphics on the ergonomics and maintainability of data visualization software. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Data science with Python. Session Code: TALK-1136

Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

Data Science in Production: The Way to a Centralized Infrastructure - posit::conf(2023)

Presented by Oliver Bracht In this talk, the success story of Covestro's posit infrastructure is presented. The problem of the leading German material manufacturer was that no common development environment existed. With the help of eoda and Posit, a replicable, centralized development environment for R and Python was created. Although R and Python represent the core of the infrastructure, multiple languages and tools are unified. In addition to the collaboration of Covestro's data science teams, compliance guidelines could also be better fulfilled. The staging architecture hereby provides developers with a concept for testing and going live with their products. This project presents a best practice approach to a data science infrastructure using Covestro as an example. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Data science infrastructure for your org. Session Code: TALK-1113

Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

CRAN-ial Expansion: Taking Your R Package Development to New Frontiers with R-Universe - posit::conf

Presented by Mo Athanasia Mowinckel Say goodbye to installation headaches and hello to a universe of possibilities with R-Universe! Take your R package development to new frontiers by organizing and sharing packages beyond the bounds of CRAN. R-Universe's reliable package-building process strengthens installation and usage instructions, resulting in fewer support requests and an easy installation experience for users. With webpages and an API for exploring packages, R-Universe creates a streamlined and tidy ecosystem for R-package constellations. Also, you can build a custom toolchain for your users, relieving your workload and empowering users to help themselves. Join me to learn how to explore the vastness of R-Universe and expand your package development possibilities! Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Managing packages. Session Code: TALK-1080

Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

Connect on Kubernetes: Content-level Containerization - posit::conf(2023)

Presented by E. David Aja, not Kelly O'Briant Running Connect with off-host content execution in Kubernetes is very cool and allows you to enable some powerful and sophisticated workflows. The question is, do you really need it? How do you evaluate and decide? Let's have a candid conversation about whether Connect content execution on Kubernetes is right for you and your organization. Moving to Kubernetes will introduce complexity, so it's important to have a strong motivating reason for making the switch. This talk will introduce new Connect features that are made possible by content-level containerization. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Data science infrastructure for your org. Session Code: TALK-1116

Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

Conformal Inference with Tidymodels - posit::conf(2023)

Presented by Max Kuhn Conformal inference theory enables any model to produce probabilistic predictions, such as prediction intervals. We'll demonstrate how these analytical methods can be used with tidymodels. Simulations will show that the results have good coverage (i.e., a 90% interval should include the real point 90% of the time). Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Tidy up your models. Session Code: TALK-1085

Max Kuhn

tidymodels Ai Modelling Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

Commit to Change: How to Increase Accessibility in Your Favorite Open Source Projects - posit::conf

Presented by Rose Franzen Explore accessibility for data scientists by uncovering some common barriers in open source tools with simple fixes that anyone can implement. Dive into the often-overlooked world of accessibility for developers and data scientists! Uncover common accessibility barriers in open source tools, and learn simple steps to address them. Whether you’re a seasoned maintainer or a total novice, you’ll walk away with clear action items to implement right away. Join the movement of individuals championing the next frontier of disability inclusion in technology, shaping a more equitable future for all! Materials: https://github.com/franzenr/commit-to-change Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Package development. Session Code: TALK-1134

Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

Combining R and Python for the Belgian Justice Department - posit::conf(2023)

Presented by Thomas Michem We build a great case on how to combine R and Python in a production environment. So the justice department's back office monitors the smooth processing of all traffic fines in Belgium. They gather that data from all police departments and check if any anomalies occur. The back-office monitors that using a shiny application where they can see traffic signs showing the status of the whole operation and the status is built using Python scripts that perform anomaly detection if the number of fines is in line with what they expect daily. And the results of those checks are delivered to a front-end shiny application with Python flask API. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: R or Python? Why not both!. Session Code: TALK-1122

Shiny Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

Coding Tools for Industry R&D – Development Lessons from an Analytical Lab - posit::conf(2023)

Presented by Camila Saez Cabezas Are you considering or curious about developing code-based tools for scientists? Whether you are an experienced developer or a fellow Posit Academy graduate who might be stepping into this role for the first time, the aim of my story is to inspire you and help you navigate this process. While developing custom R functions, packages, and Shiny apps for diverse analytical capabilities and users in R&D, I learned why it's important to collect certain information at the start before writing any tidying, analysis, visualization, and web application code. In this talk, I will share the essential technical questions that help me define and plan for success. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Lightning talks. Session Code: TALK-1168

Shiny Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

CI/CD Pipelines - Oh, the Places You'll Go! - posit::conf(2023)

Presented by Trevor Nederlof Data scientists are creating incredibly useful data products at an accelerating rate. These products are consumed by others who expect them to be accurate reliable and timely, often promises unfulfilled. In this talk, we will explore how to use common CI/CD pipeline tools already within reach of attendees to automatically test and deploy their apps, APIs, and reports. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Lightning talks. Session Code: TALK-1166

Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

Can I Have a Word? - posit::conf(2023)

Presented by Ellis Hughes Since its release, {gt} has won over the hearts of many due to its flexible and powerful table-generating abilities. However, in cases where office products were required by downstream users, {gt}'s potential remained untapped. That all changed in 2022 when Rich Iannone and I collaborated to add Word documents as an official output type. Now, data scientists can engage stakeholders directly, wherever they are. Join me for an upcoming talk where I'll share my excitement about the new opportunities this update presents for the R community as well as future developments we can look forward to. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Elevating your reports. Session Code: TALK-1156

Rich Iannone

gt Word Data Science Quarto Databricks Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Posit Ml

Building a Flexible, Scaleable Self-Serve Reporting System with Shiny - posit::conf(2023)

Presented by Natalie O'Shea Working in the high-touch world of consulting, our team needed to develop a reporting system that was flexible enough to be tailored to the specific needs of any given partner while still reducing the highly manual nature of populating client-ready slide decks with various metrics and data visualizations. Utilizing the extensive resources developed by the R user community, I was able to create a flexible, scalable reporting system that allows users to populate templated Google slide decks with metrics and professional-grade visualizations using data pulled directly from our database at the time of query. This streamlined approach enables our consultants to spend less time copy-pasting data from one channel to another and instead focus on what they do best: surfacing business-relevant insights and recommendations for our partners. By sharing my approach to customizable self-serve reporting in Shiny, I hope attendees will walk away with new ideas about how to combine parameterized reporting and dashboard development to get the best of both worlds. Additionally, I hope to end by sharing how this project was pivotal in making the business case for procuring Posit products for my broader organization. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Bridging the gap between data scientists and decision makers. Session Code: TALK-1075

Shiny Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

Black Hair and Data Science Have More in Common Than You Think - posit::conf(2023)

Presented by Kari Jordan Data science is often difficult to define because of its many intersections, including statistics, programming, analytics, and other domain knowledge. Would you believe it if I told you Black hair and data science have much in common? This talk is for those considering learning about, studying, or pursuing data science. In it, Dr. Kari L. Jordan draws parallels between approaches to caring for Black hair and approaches to learning data science. We start with the roots and end by picking the right tools and products to maintain our coiffure. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: It takes a village: building and sustaining communities. Session Code: TALK-1131

Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

Becoming an R Package Author (or How I Got Rich Responding to GitHub Issues) - posit::conf(2023)

Presented by Matt Herman The transition from analyzing data in R to making packages in R can feel like a big step. Writing code to clean data or make visualizations seems categorically different from building robust "software" on which other people rely. In this talk, I'll show why that distinction is not necessarily true by discussing my personal experience from learning R in graduate school to reporting bugs on GitHub to becoming a co-author of the tidycensus package and a practicing data scientist. The positive and supportive R community on GitHub, Twitter, and elsewhere contributes to why anyone who writes R code can become a package author. * I have not actually gotten rich but I did get freelance data work based on my package contributions! Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Package development. Session Code: TALK-1133

Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

Automating the Dutch National Flu Surveillance for Pandemic Preparedness - posit::conf(2023)

Presented by Patrick van den Berg The next pandemic may be caused by a flu strain, and with thousands of patients with the flu in Dutch hospitals annually it is important to have accurate and current data. The National Institute for Public Health and the Environment of the Netherlands (RIVM) collects and processes flu data to achieve pandemic preparedness. However, the flu reporting process used to be very laborious, stealing precious time from epidemiologists. In our journey of automating this data pipeline we learned that collaboration was the most important factor in getting to a working system. This talk will be at the cross-section of data science and epidemiology and will provide you with a valuable opportunity to learn from our experiences. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Leave it to the robots: automating your work. Session Code: TALK-1150

Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

AI and Shiny for Python: Unlocking New Possibilities - posit::conf

Presented by Winston Chang In the past year, people have come to realize that AI can revolutionize the way we work. This talk focuses on using AI tools with Shiny for Python, demonstrating how AI can accelerate Shiny application development and enhance its capabilities. We'll also explore Shiny's unique ability to interface with AI models, offering possibilities beyond Python web frameworks like Streamlit and Dash. Learn how Shiny and AI together can empower you to do more, and do it faster. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: I can't believe it's not magic: new tools for data science. Session Code: TALK-1153

Winston Chang

Shiny for Python Shiny Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

Adding a Touch of glitr: Developing a Package of Themes on Top of ggplot - posit::conf(2023)

Presented by Aaron Chafetz and Karishma Srikanth Please note, a power issue cut off the first five minutes of the talk. Explore how our team at the US Agency for International Development (USAID) created our own data viz branding R package on top of ggplot2 and how you can too. How do you create brand cohesion across your large team when it comes to data viz? Inspired by the BBC's bbplot, our team at the US Agency for International Development (USAID) developed a package on top of ggplot2 to create a common look and feel for our team's products. This effort improved not just the cohesiveness of our work, but also trustworthiness. By creating this package, we reduced the reliance on using defaults and the time spent on each project customizing numerous graphic elements. More importantly, this package provided an easier on-ramp for new teammates to adopt R. We share our journey within a federal agency developing a style guide and aim to guide and inspire other organizations who could benefit from developing their own branding package and guidance. Materials: - https://speakerdeck.com/achafetz/adding-a-touch-of-glitr - https://usaid-oha-si.github.io/glitr/ - https://issuu.com/achafetz/docs/oha_styleguide Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Compelling design for apps and reports. Session Code: TALK-1103

ggplot2 Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

A hacker's guide to open source LLMs - posit::conf(2023)

Presented by Jeremy Howard In this deeply informative video, Jeremy Howard, co-founder of fast.ai and creator of the ULMFiT approach on which all modern language models (LMs) are based, takes you on a comprehensive journey through the fascinating landscape of LMs. Starting with the foundational concepts, Jeremy introduces the architecture and mechanics that make these AI systems tick. He then delves into critical evaluations of GPT-4, illuminates practical uses of language models in code writing and data analysis, and offers hands-on tips for working with the OpenAI API. The video also provides expert guidance on technical topics such as fine-tuning, decoding tokens, and running private instances of GPT models. As we move further into the intricacies, Jeremy unpacks advanced strategies for model testing and optimization, utilizing tools like GPTQ and Hugging Face Transformers. He also explores the potential of specialized datasets like Orca and Platypus for fine-tuning and discusses cutting-edge trends in Retrieval Augmented Generation and information retrieval. Whether you're new to the field or an established professional, this presentation offers a wealth of insights to help you navigate the ever-evolving world of language models. (The above summary was, of course, created by an LLM!) For the notebook used in this talk, see https://github.com/fastai/lm-hackers. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Notebooks+LLMs may just be the future of coding. Session Code: KEY-1107

Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

20 questions and AI chat bots - posit::conf(2023)

Presented by Winston Chang Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: I can't believe it's not magic: new tools for data science. Session Code: TALK-1153

Winston Chang

Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

{slushy}: A Bridge to the Future - posit::conf(2023)

Presented by Becca Krouse Scaling the use of R can present complications for environment management, especially in regulated industries with a focus on traceability. One solution is controlled (aka "frozen") environments, which are carefully curated and tested by tech teams. However, the speed of R development means the environments quickly become outdated and users are unable to benefit from the latest advances. Enter {slushy}: a team-friendly tool powered by {renv} and Posit Package Manager. Users can quickly mimic a controlled environment, with the easy ability to time travel between snapshot dates. Attendees will learn how {slushy} bolstered our R adoption efforts, and how this strategy enables tech teams and users to work in parallel towards a common future. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Managing packages. Session Code: TALK-1078

renv Rstudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Python Ml

How the R for Data Science (R4DS) Online Learning Community Made Me a Better Student - posit::conf

Presented by Lydia Gibson Through my participation in R4DS Online Learning Community, I have advanced my R and data science skills, making me a better student than I otherwise would have been through just my studies. As a non-traditional MS Statistics student with an undergraduate background in economics, I had absolutely no experience with the R programming language prior to pursuing my Master's degree. In July 2021, with hopes of getting a headstart on learning R before beginning my degree program, I joined the R4DS Slack Workspace. Along with helping to improve my programming skills, R4DS has connected me with scholarships, mentorship, and other opportunities, and I think that it would be beneficial for other students to know about this great resource. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Developing your skillset; building your career. Session Code: TALK-1110

Databricks Data Science Rstudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Quarto Posit Ml

Quarto Dashboards | Charles Teague | Posit

Quarto has a host of exciting new features in release v1.4, with one of the most anticipated being Quarto Dashboards. In a recent internal presentation, Posit's Charles Teague demonstrated the new capabilities and we wanted to share it with community. Please note that you can use this feature today, but to access it you need to be running on a Quarto prerelease build dated November 6, 2023, or later. We also encourage users to review our pre-release documentation which offers additional information and examples of the dashboard functionality. Quarto Pre-release builds: https://quarto.org/docs/download/prerelease.html Quarto Dashboard Documentation: https://quarto.org/docs/dashboards/

Quarto Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

webR 0.2: Updates to webR's developer API | George Stagg | Posit

More from George Stagg on what’s new in the webR 0.2 release series, this time on webR’s developer API. If you’re a JavaScript or TypeScript developer, give webR a try and see what you might use it for! WebR makes it possible to run R code in the browser without the need for an R server to execute the code: the R interpreter runs directly on the user’s machine. It is possible to work entirely within webR and the R graphics devices, but you might also want to integrate webR into external JavaScript or TypeScript frameworks, including visualization packages. Learn about how to do this and more with webR’s developer API, including plotting data from webR using Observable JS, recent performance improvements, better error handling, worker event messages, and neatly terminating a webR session. 00:15 Performance improvements working with JavaScript 02:50 Example: Plotting data from webR using Observable JS 04:35 Type predicate functions and type conversion 07:30 Handling errors with webRError 09:15 Event messages from webr::canvas() 10:25 Safely handling webR termination 12:30 Links to documentation and resources Website and developer documentation: https://docs.r-wasm.org/ Examples of using webR with Observable JS: - Loading webR and R data manipulation: https://observablehq.com/d/6936259b898a25ce - Bitmap plotting and custom fonts: https://observablehq.com/d/32e2b7e465c0b994

George Stagg

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

webR 0.2: R Packages and Shiny for WebAssembly | George Stagg | Posit

WebR makes it possible to run R code in the browser without the need for an R server to execute the code: the R interpreter runs directly on the user’s machine. But just running R isn't enough, you need the R packages you use every day too. webR 0.2.0 makes many new packages available (10,324 packages - about 51% of CRAN!) and it's now possible to run Shiny apps under webR, entirely client side. George Stagg shares how to load packages with webR, know what ones are available, and get started running Shiny apps in the web browser. There’s a demo webR Shiny app too! 00:15 Loading R packages with webR 01:50 Wasm system libraries available for use with webR 05:30 Tidyverse, tidymodels, geospatial data, and database packages available 08:00 Shiny and httpuv: running Shiny apps under webR 11:05 Example Shiny app running in the web browser 12:05 Links with where to learn more Shiny webR demo app: https://shinylive.io/r/examples/ Website: https://docs.r-wasm.org/ webR REPL example: https://webr.r-wasm.org/latest/ Demo webR Shiny app in this video: https://shiny-standalone-webr-demo.netlify.app/ Source: https://github.com/georgestagg/shiny-standalone-webr-demo/ See the overview of what's new in webR 0.2.0: https://youtu.be/Mpq9a6yMl_w

George Stagg

httpuv Shiny shinylive tidymodels tidyverse Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

webR 0.2 Overview: R in the browser | George Stagg | Posit

WebR is a version of the statistical language R compiled for the browser and Node.js using WebAssembly, via Emscripten. WebR makes it possible to run R code in the browser without the need for an R server to execute the code: the R interpreter runs directly on the user’s machine. Several R packages have also been ported for use with webR, and can be loaded in the usual way using the library() function. George Stagg shares what's new in the 0.2.0 release! 00:23 About WebR and applications of computing with WebAssembly 02:25 Overview of changes in webR 0.2.0 release 04:10 The webR REPL app 07:28 Improved speed in graphics rendering 08:43 Text rendering in plots, including international and accessible fonts 10:27 Updated support for standard bitmap devices 13:05 Lazy virtual file system allows for decreased download size 15:40 Links with where to learn more Website: https://docs.r-wasm.org/ webR REPL example: https://webr.r-wasm.org/latest/

George Stagg

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

5 Reasons R & Python developers LOVE Posit Connect

Posit Connect makes deployment the easiest step in your workflow. Connect saves data scientists time, accelerates productivity, and maximizes the impact data science teams have within their organization. With Posit Connect, you can automate common recurring data science tasks, like: - Sending a daily or weekly report to your boss - Updating a machine learning model - Extracting new data from an API - Loading new data - Updating reports that your stakeholders rely on

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Bite-sized tricks for machine learning with tidymodels | Posit

The tidymodels framework is a collection of R packages for modeling and machine learning using tidyverse principles. This video highlights a number of tidymodels features that could improve your modeling workflows. 0:03 Switching modeling engines is easy 0:21 Never lose your tuning results 0:36 Built-in visualizations for modeling objects 1:03 Grouped resampling 1:16 Case weights 1:32 Select variables based on role and type 2:00 Spatial resampling 2:16 Keep your tidymodels objects small Learn more at https://www.tidymodels.org/

tidymodels tidyverse Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

How does Shiny render things? | Gordon Shotwell

Discussion on the Shiny Python Discord channel from Gordon Shotwell. Gordon talks about how Shiny renders things with reactive programming, how other frameworks work, and how Shiny scales for complex applications. 0:00 - How Shiny renders things - reactivity 2:31 - How do other frameworks work 3:51 - Event driven programming, what's a better way? 6:53 - Runtime tracing 8:25 - Declarative programming 9:20 - Drawing a graph 12:05 - How reactivity scales 13:30 - Reactive calculus 18:45 - Question and answer Shiny for Python: https://shiny.posit.co/py/ Shiny for Python Discord server: https://discord.com/invite/yMGCamUMnS

Shiny for Python Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

The River is our Relative | The Story of the Penobscot Nation

The Penobscot Nation has been around for millennia. On Indian Island, a river gives the Penobscot people life. "The tribe views the river like its own highway. We look at her as a relative, a sister, someone that gives life. Sea Lamprey, Sturgeon, Striper Bass." Jan Paul works for the Penobscot Nation in the Department of Natural Resources and Water Quality. She's using open-source technology, like R and the RStudio IDE, to keep the river healthy, so in turn, it can keep her people healthy. This is Jan's inspiring story, featured for the first time at posit::conf(2023). ------ Credits: Creative Director: Jason Restivo & Scout Studios Art Director & Illustrator: I-Nu Yeh Animator: Theera (Jay) Keeree Content Producer & Writer: Shannon McGarvey Sound Mixer: Caleb Theimer

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

RStudio IDE | Background and Posit Workbench Jobs

This is a great feature for any long running scripts you may have, including, for example, a machine learning training step. It also allows you to run multiple jobs simultaneously. Now the main difference between background jobs and workbench jobs is that background jobs run as a child process within the same session. And workbench jobs run as a new session, either on the local server, or off host if you're running in a multi server or clustered environment

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Teaching the tidyverse in 2023 | Mine Çetinkaya-Rundel

Recommendations for teaching the tidyverse in 2023, summarizing package updates most relevant for teaching data science with the tidyverse, particularly to new learners. 00:00 Introduction 00:46 Using addins to switch between RStudio themes (See https://github.com/mine-cetinkaya-rundel/addmins for more info) 01:40 Native pipe 03:08 Nine core packages in tidyverse 2.0.0 07:15 Conflict resolution in the tidyverse 11:30 Improved and expanded *_join() functionality 22:05 Per operation grouping 27:41 Quality of life improvements to case_when() and if_else() 31:41 New syntax for separating columns 34:51 New argument for line geoms: linewidth 36:08 Wrap up See more in the Teaching the tidyverse in 2023 blog post https://www.tidyverse.org/blog/2023/08/teach-tidyverse-23

Mine Çetinkaya-Rundel

rstudio tidyverse tidyverse.org Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

What does superseded mean? Package development lifecycle process and the meaning of superseded.

An important part of the process of package lifecycle and package development is not just adding new functions. It's is equally important to remove functions. Hadley Wickham shares about the package lifecycle process and what 'supersede' means for functions. See the full video about the purrr 1.0 release: https://youtu.be/EGAs7zuRutY More about the package lifecycle stages: https://lifecycle.r-lib.org/articles/stages.html Maintaining the house that tidyverse built: https://youtu.be/izFssYRsLZs

Hadley Wickham

purrr tidyverse Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

What does deprecated mean? Package lifecycle and the process of deprecation.

An important part of the process of package lifecycle and package development is not just adding new functions. It's equally important to remove functions. Hadley Wickham shares about the package lifecycle process and what 'deprecation' means for functions. See the full video about the purrr 1.0 release: https://youtu.be/EGAs7zuRutY More about the package lifecycle stages: https://lifecycle.r-lib.org/articles/stages.html Maintaining the house that tidyverse built: https://youtu.be/izFssYRsLZs

Hadley Wickham

purrr tidyverse Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Posit Cloud Essentials | Ep 1: Getting Started

On the last Tuesday of every month, we host an event – Posit Cloud Essentials – where we explore the ins and outs of Posit Cloud, diving into its key features, valuable tips, and real-world use cases. The event is open to all and hosted on YouTube with a live Q&A during each month’s event. This month, Alex Chisholm, Product Manager for Posit Cloud, walks through how to get started with a free Posit Cloud account. Enabling you to conduct analysis, generate insights, and share findings from your web browser. What is Posit Cloud? Posit Cloud makes it easy to move your entire workflow into a unified online experience, complete with project management and publishing capabilities. Use your favorite coding languages and environments and share your work seamlessly with others, all from the comfort of your own web browser. No registration is required to join the events. Simply add the event to your calendar using the link below. Create a free Posit Cloud account → https://posit.cloud/ Explore the source code used in this demo → https://posit.cloud/spaces/394911/join?access_code=v-iZm0epL-n-vNHpht4JAfH47gmqWdg0cM6hyll7 Add future Posit Cloud Essential events to your calendar → http://evt.to/adahaeuow Q&A from this demo can be found here → https://app.sli.do/event/q2aLBPfVRAvUCFryNs9YuL

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

What Exactly Is Digital Transformation? | Posit + PING

Doug Linsmeyer, VP of Digital Technology at PING, clarifies how he thinks about digital transformation, adopting new technologies, and how Posit Connect helps data scientists at PING continue to innovate. Read and watch the full story here: https://posit.co/about/customer-stories/ping/ Learn more about Posit Connect: https://posit.co/products/enterprise/connect/

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Why RStudio is now Posit (J.J. Allaire | Posit CEO) - KNN Ep. 158

Today, I had the pleasure of interviewing J.J. Allaire. J.J. is the founder of RStudio and the creator of the RStudio IDE. He is an author of several packages in the R Markdown publishing ecosystem including rmarkdown, flexdashboard, learnr, and distill, and also worked extensively on the R interfaces to Python, Spark, and TensorFlow. J.J. is now leading the Quarto project, which is a new Jupyter-based scientific and technical publishing system. In this episode, we learn about why RStudio has now repositioned itself as Posit, how it maximizes its open-source nature as a B Corp, and how J.J. as an open-source advocate views the private nature of many LLMs. I really enjoyed this conversation, and I hope you will as well! Posit - https://posit.co/ Podcast Sponsors, Affiliates, and Partners: - Pathrise - http://pathrise.com/KenJee | Career mentorship for job applicants (Free till you land a job) - Taro - http://jointaro.com/r/kenj308 (20% discount) | Career mentorship if you already have a job - 365 Data Science (57% discount) - https://365datascience.pxf.io/P0jbBY | Learn data science today - Interview Query (10% discount) - https://www.interviewquery.com/?ref=kenjee | Interview prep questions Listen to Ken's Nearest Neighbors on all the main podcast platforms! On Apple Podcasts: https://podcasts.apple.com/us/podcast/kens-nearest-neighbors/id1538368692 (Please rate if you enjoy it!) On Spotify: https://open.spotify.com/show/7fJsuxiZl4TS1hqPUmDFbl On Google: https://podcasts.google.com/feed/aHR0cHM6Ly9mZWVkcy5idXp6c3Byb3V0LmNvbS8xNDMwMDQxLnJzcw?sa=X&ved=0CAMQ4aUDahcKEwjQ2bGBhfbsAhUAAAAAHQAAAAAQAQ MORE DATA SCIENCE CONTENT HERE: My Twitter - https://twitter.com/KenJee_DS LinkedIn - https://www.linkedin.com/in/kenjee/ Kaggle - https://www.kaggle.com/kenjee Medium Articles - https://medium.com/@kenneth.b.jee Github - https://github.com/PlayingNumbers My Sports Blog - https://www.playingnumbers.com ️ 66DaysOfData Discord Server - https://discord.com/invite/4p37sy5muZ

flexdashboard learnr Quarto rmarkdown rstudio tensorflow Ken Jee Ken Jee Podcast Kjp Podcast Data Science Data Science Podcast Knn Ken's Nearest Neighbors Knn Podcast Ken's Nearest Neighbors Podcast Jj Allaire Jj Allaire Interview Rstudio Posit Coding Languages Programming Language Wars Founder R Markdown Quarto Jupyter Open Source Llms Open Ai Sports Data Bill James Open Source Software

[84] Reproducible Publications with Python and Quarto (Thomas Mock)

Join our Meetup group: https://www.meetup.com/data-umbrella Tom Mock: Reproducible Publications with Python and Quarto ## Resources - slides: https://thomasmock.quarto.pub/python-umbrella/#/ ## Full transcript https://blog.dataumbrella.org/quarto-blog ## About the Event Quarto is an open-source scientific and technical publishing system that builds on standard markdown with features essential for scientific communication. The system has support for reproducible embedded computations, equations, citations, crossrefs, figure panels, callouts, advanced layouts, and more. In this talk we'll explore the use of Quarto with Python, describing both integration with IPython/Jupyter and the Quarto VS Code extension. Users can author Jupyter notebooks or documents as plain text markdowns with code in Python, R, Julia or Observable. Quarto includes the ability to publish high-quality articles, reports, presentations, websites, blogs, and books in HTML, PDF, MS Word, ePub, Reveal.js and more. ## Timestamps 00:00 Data Umbrella introduction 03:41 Introduce the speaker, Thomas Mock 04:14 Thomas begins 05:14 RStudio is now Posit 05:55 What is Quarto? 07:13 Origins of Quarto 08:31 Goal: Computation Document 09:09 Goal: Scientific Markdown 10:03 Goal: Single Source Publishing 10:33 Simple example of what Quarto looks like (YAML, Markup, Markdown, code chunks) 12:29 Simple example: multi-format (output formats: html, pdf, docx, epub, pptx, revealjs) 13:16 List of what is possible with Quarto 14:02 So, what is Quarto: quarto is a language-agnostic command line interface (CLI) 15:27 Basic Quarto workflow 16:43 Difference between "render" and "preview" 17:16 IPython 18:43 Stored/frozen computation and reproducibility 20:36 A *.qmd is a plain text file 21:28 Quarto doesn't have to be plain text 22:12 Rendering pipeline 22:57 What to do with my existing .ipynb? 24:23 Comfort of your own workspace: JupyterLab, Visual Studio Code, 25:00 Auto-completion in RStudio + VSCode 26:01 Quarto Extensions and Visual / Live Editor 27:19 Quarto, unified document layout 29:54 Quarto, unified syntax across Markdown and code 31:11 Built-in vs Custom 33:01 Extending Quarto with Extensions 33:51 Interactivity, Jupyter Widgets (with plots, matplotlib, etc) 34:15 Interactivity, Observable 35:01 Interactivity, on the fly Observable "widgets" 36:24 Parameters - one source, many outputs 37:36 Rendering with parameters 38:27 Quarto Publish 38:57 Quarto, crafted with love and care (the team) 39:30 Quarto Resources (installation) 39:44 Quarto resources: video tutorials 40:13 Q: Can Quarto documents be shared like Overleaf docs and can users import article templates for specific journals into Quarto? 41:39 new! Manuscript option to bundle an entire project together (bundle can be shipped to a journal) 42:48 Q: Is Quarto git friendly? 43:28 Q: Has Quarto already been used in published scientific work? 44:14 publishing books with Quarto 44:22 Q: Any general suggestions for outputting to docx (Word)? 45:20 Q: Any tips on how Quarto can help conda users? 46:14 Q: Can you use GitHub Actions with Quarto? 47:18 Q: Can you have individual environments for each blog post? 49:50 Download CLI (command line interface) for Quarto 51:10 Example Gallery 51:44 nbdev project 53:14 Quarto blog, Shinylive extension 55:12 Q: How can I use Quarto to write scientific papers? ## About the Speaker: Tom Mock - Twitter: https://twitter.com/thomas_mock - GitHub: https://github.com/jthomasmock #python #quarto #rstats

Python support in Posit Connect

Posit Connect: Deploy everything you create in R & Python, including interactive applications (Shiny, Streamlit, Dash), documents, notebooks, and dashboards. Learn more at https://posit.co/products/enterprise/connect/

Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Live Q&A Session - Ep 3. Scheduling a Quarto Doc (with custom branding) on Posit Connect

Live Q&A Session for the End-to-End Workflow Demo on June 28th *Please note this was the live Q&A portion of the event. You can view the demo recording here: https://youtu.be/V82BBU9ldcM Ep 3. Scheduling a Quarto Doc (with custom branding) on Posit Connect | End-to-end workflows Follow-up links: * Demo recording: https://youtu.be/V82BBU9ldcM * Posit Team: https://posit.co/products/enterprise/team/ * Talk to us directly: https://posit.co/schedule-a-call/?booking_calendar__c=RST_YT_Demo * Follow-along blog post: To be added * Source code for example: To be added * Posit Team demo resources: pos.it/demo-resources

Quarto Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Open Source in Drug Development | Thomas Nietmann and Posit

Thomas Neitmann, Associate Director at Denali Therapeutics, sat down with Posit to talk about open source in clinical trials, his work at Roche, the creation of admiral, career beginnings, and his future predictions for data science in the pharma space. Posit's work in Pharma: https://posit.co/solutions/pharma/

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Roche Roche Clinical Trials Thomas Neitmann

RStudio + Amazon SageMaker | Build Beyond Your Laptop

Did you know that you can use RStudio, the best IDE for R and Python users, with Amazon Sagemaker? RStudio on Amazon SageMaker makes it easy for R users to quickly and easily get started coding in RStudio on AWS from their browser, no server setup required, by using a new integration with Posit Workbench. In this webinar, Posit team members will show you how to get started with RStudio on Amazon SageMaker to analyze your organization’s data in S3 and train ML models. As a fully managed offering on Amazon SageMaker, this release makes it easy for DevOps teams and IT Admins to administer, secure, and scale their organization’s centralized data science infrastructure with familiar AWS tools and frameworks. Learn more at: https://posit.co/products/cloud/sagemaker/ Talk to us about using RStudio and SageMaker: https://posit.co/schedule-a-call/?booking_calendar__c=Sagemaker

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Posit Presents: A Game of Numbers with PING

The innovators at PING use Posit solutions such as Posit Connect to impact how golf data is analyzed and shared with golfers worldwide. High-level data, once only available to professional players, is now in the hands of everyday golfers thanks to public-facing apps like Ballnamic, which allows golfers to choose the golf ball that best suits their game. Through the use of Shiny apps, Posit Workbench APIs, and Posit Connect, the team at PING can now innovate faster, accelerate product development, and advance the democratization of golf data. Learn more at https://posit.co/about/customer-stories/ping/

Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss PING PING Golf

Python made easier with Posit

Tom Mock, Workbench Product Manager at Posit, discusses our commitment to Python from open-source projects to enterprise support. Specifically, Tom discusses Posit Team, the all-in-one solution for your data science team to build, share and maintain your R & Python data products. Posit Team comprises our three professional products, Posit Workbench, Connect, and Package Manager. Posit Workbench give your data scientists access to their favorite development tools in a centralized environment. Enabling them to work in the language they prefer, all while sharing IT resources and collaborating to find actionable insights. Posit Connect is where your team can securely host all of their data products and share them with the push of a button. Schedule reports to update automatically and give users access to only the insights they need. Posit Package Manager allows your team to deliver reproducible data science without the burden of manual package management. Organize and centralize R and Python packages across your team, department, or organization. Posit Team: https://posit.co/products/enterprise/team/ Posit Workbench: https://posit.co/products/enterprise/workbench/ Posit Connect: https://posit.co/products/enterprise/connect/ Posit Package Manager: https://posit.co/products/enterprise/package-manager/

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Quarto for Academics | Mine Çetinkaya-Rundel

This video highlights some of Quarto's features that are especially useful for academics, as educators and as researchers. 00:00 Introduction 01:06 Linking to documentation from code with code-link 02:27 Informative YAML errors and YAML completion 03:41 Creating Quarto slides with revealjs 04:20 PDF export of HTML slides 05:03 Annotating slides with chalkboard 06:40 Advancing slides for your audience with multiplex 07:38 Revealing code in slides with echo 08:16 Highlighting code with code-line-numbers 09:37 Customizing output location with output-location 10:42 Showing code chunk fences with echo: fenced 12:03 Code annotation 14:11 Authoring manuscripts with Quarto templates 16:49 Inserting citations from Zotero or from a DOI with the RStudio Visual Editor 19:59 Wrap up and learning resources

Mine Çetinkaya-Rundel

Quarto revealjs rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Posit Connect | Provide Authenticated Access to Your Data Products

Posit Connect is all about getting your data products into the hands of stakeholders and collaborators. Here, we demonstrate how to provide authenticated access to a Dash application. For more information about Posit Connect, go to: https://posit.co/products/enterprise/connect/

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Get started with Quarto | Mine Çetinkaya-Rundel

This video walks you through creating documents, presentations, and websites and publishing with Quarto. The video features authoring Quarto documents with executable R code chunks using the RStudio Visual Editor (https://quarto.org/docs/visual-editor/). 00:00 Introduction 00:34 Authoring a document with Quarto 01:13 Using the RStudio visual editor 04:13 Code chunks and chunk options 06:31 Inserting cross references to figures and tables (https://quarto.org/docs/authoring/cross-references.html) 08:56 Adding a citation from a DOI (https://quarto.org/docs/visual-editor/technical.html#citations) 10:10 Seamlessly switching between output formats 10:58 Creating Quarto presentations (https://quarto.org/docs/presentations/) 14:36 Customizing the output location of code in presentations (https://quarto.org/docs/presentations/revealjs/#output-location) 16:09 Creating a website from scratch (https://quarto.org/docs/websites/) 19:19 Creating multi-format documents (https://quarto.org/docs/output-formats/html-multi-format.html) 20:22 Publishing the website to QuartoPub (https://quarto.org/docs/publishing/quarto-pub.html)

Mine Çetinkaya-Rundel

Quarto revealjs rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Why Shiny for Python? - Posit PBC

Learn how Shiny for Python's design philosophy sets it apart from Streamlit, Dash, and traditional web development frameworks. With Shiny for Python out of alpha as of April, many have wondered how it stacks up against other popular alternatives. In this video, Gordon Shotwell -- developer advocate on the Shiny team at Posit -- explores the design philosophy behind Shiny for Python and how it compares to other frameworks for developing data science web applications. If you are a data scientist working mostly in Python, we hope this motivates you to take a serious look at Shiny for Python. Learn more on Posit blog, https://posit.co/blog/why-shiny-for-python/ 00:00 What is Shiny for Python? 03:05 What is Reactivity? 04:19 How does Shiny compare to Streamlit? 05:14 What are the main differences between Streamlit and Shiny for Python? 07:05 Why should I prefer Shiny over Streamlit? 09:09 How does Shiny compare to Dash? 10:22 What is the difference between a stateless and a stateful application? 12:19 Why consider Shiny for Python over Dash? 13:37 Shiny for Python's Design Philosophy

Shiny Team

Shiny for Python Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

posit::conf(2023) Workshop:Enhancing Communication & Collaboration with Quarto and Jupyter Notebooks

Register now: http://pos.it/conf Instructor: Hamel Husain Workshop Duration: 1-Day Workshop This workshop is for you if you: • have some experience with Python and Jupyter and want to learn how Quarto can support and enhance your workflows • want to learn about turning your notebooks to websites and publications • want to learn how to write python packages with Jupyter notebooks and Quarto with the help of nbdev The workshop will assume some prior experience with Python and Jupyter Notebooks. Sharing knowledge through writing is a critical aspect of scientific activity, including data science. It allows researchers to communicate their findings and insights to a wider audience, build upon existing work, and collaborate with others in their field. However, until recently, there have been limited options for publishing long-form writing and expository analyses authored in Jupyter Notebooks, a popular medium for data scientists. Enter Quarto - an innovative, open-source scientific and technical publishing system compatible with Jupyter Notebooks and other popular mediums. Quarto provides data scientists with a seamless way to publish their work in a high-quality format that is easily accessible and shareable. With Quarto, researchers can turn their Jupyter Notebooks into professional-looking publications in a variety of formats, including web pages, books, and slides. In this workshop, we will demonstrate how Quarto enables data scientists to turn their work products into professional, high-quality publications, websites, blog posts, and other shareable artifacts. As a bonus, we will also discuss how you can create and document Python packages using Jupyter notebooks and Quarto with the help of nbdev. The learning outcomes for the workshop include: • examine case studies where sharing scientific knowledge has greatly improved the efficacy of data science teams • author documents in plain text markdown or Jupyter notebooks with equations, citations, crossrefs, figure panels, callouts, and advanced layouts • learn how to author content in IPython/Jupyter and the Quarto VS Code extension • leverage Quarto for creating different types of publications, including personal blogs, knowledge management for teams, notes, books, websites, and presentation slides • extend Quarto with notebook filters and extensions • host websites and publications on platforms like GitHub Pages, QuartoPub, and Netlify • test notebooks and documentation with Quarto’s execution options • create and document Python packages with nbdev and Quarto

Quarto Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

posit::conf(2023) Workshop: What They Forgot to Teach You About R

Register now: http://pos.it/conf Instructors: Shannon Pileggi and David Aja Workshop Duration: 1-Day Workshop This course is for you if you answer yes to these questions: • Have you been using R for a while and feel there might be better ways to organize your R life, but don’t know what they are? • Do you want to put programming on pause and learn about actionable programming-adjacent workflows for streamlining analysis in R? • Are you willing to feel a bit of (git) pain to leverage the benefits of version control for collaboration and time travel? This 1-day What They Forgot (WTF) To Teach You About R workshop is for experienced R and RStudio users who want to (re)design their R lifestyle via project-oriented workflows and version control for data science (Git/GitHub). At the conclusion of the workshop, you will have strategies for organizing data science projects and workflows, employing robust file paths, constructing human and machine-readable file names, and facilitating collaboration with yourself or others via version control

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

posit::conf(2023) Workshop: Web Design for Shiny Developers

Register now: http://pos.it/conf Instructors: Maya Gans and David Granjon This course is for you if: • you are an R developer with basic Shiny knowledge • you want to quickly test new business ideas • you want to increase the reach of your apps and websites Website design and development is one of the most critical factors contributing to whether the user has a good or poor experience while browsing your site, directly influencing the overall impression of your brand. Besides, bad design decisions can significantly impact app performances. By exposing you to common governing rules of design, this course will walk you through the entire design process, from ideation to execution. These rules will help you to become a better collaborator to design teams, and enable you to create beautiful front-end experiences for Shiny

Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

posit::conf(2023) Workshop: Tidy time series and forecasting in R

Register now: http://pos.it/conf Instructor: Rob J Hyndman Workshop Duration: 2-Day Workshop This course is for you if you: • already use the tidyverse packages in R such as dplyr, tidyr, tibble and ggplot2 • need to analyze large collections of related time series • would like to learn how to use some tidy tools for time series analysis including visualization, decomposition and forecasting It is common for organizations to collect huge amounts of data over time, and existing time series analysis tools are not always suitable to handle the scale, frequency and structure of the data collected. In this workshop, we will look at some packages and methods that have been developed to handle the analysis of large collections of time series. On day 1, we will look at the tsibble data structure for flexibly managing collections of related time series. We will look at how to do data wrangling, data visualizations and exploratory data analysis. We will explore feature-based methods to explore time series data in high dimensions. A similar feature-based approach can be used to identify anomalous time series within a collection of time series, or to cluster or classify time series. Primary packages for day 1 will be tsibble, lubridate and feasts (along with the tidyverse of course). Day 2 will be about forecasting. We will look at some classical time series models and how they are automated in the fable package, and we will explore the creation of ensemble forecasts and hybrid forecasts. Best practices for evaluating forecast accuracy will also be covered. Finally, we will look at forecast reconciliation, allowing millions of time series to be forecast in a relatively short time while accounting for constraints on how the series are related

dplyr ggplot2 lubridate tibble tidyr tidyverse Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

posit::conf(2023) Workshop: Teaching Data Science Masterclass

Register now: http://pos.it/conf Instructor: Dr. Mine Çetinkaya-Rundel Workshop Duration: 1-Day Workshop This course is for you if you: • you want to learn / discuss curriculum, pedagogy, and computing infrastructure design for teaching data science with R and RStudio using the tidyverse and Quarto • you are interested in setting up your class in Posit Cloud • you want to integrate version control with git into your teaching and learn about tools and best practices for running your course on GitHub This masterclass is aimed primarily at participants teaching data science in an academic setting in semester-long courses, however much of the information and tooling we introduce is applicable for shorter teaching experiences like workshops and bootcamps as well. Basic knowledge of R is assumed and familiarity with the tidyverse and Git is preferred. There has been significant innovation in introductory statistics and data science courses to equip students with the statistical, computing, and communication skills needed for modern data analysis. Success in data science and statistics is dependent on the development of both analytical and computational skills, and the demand for educators who are proficient at teaching both these skills is growing. The goal of this masterclass is to equip educators with concrete information on content, workflows, and infrastructure for painlessly introducing modern computation with R and RStudio within a data science curriculum. In a nutshell, the day you’ll spend in this workshop will save you endless hours of solo work designing and setting up your course. Topics will cover teaching the tidyverse in 2023, highlighting updates to R for Data Science (2nd ed) and Data Science in a Box as well as present tooling options and workflows for reproducible authoring, computing infrastructure, version control, and collaboration. The workshop will be comprised of four modules: • Teaching data science with the tidyverse and Quarto • Teaching data science with Git and GitHub • Organizing, publishing, and sharing of course materials • Computing infrastructure for teaching data science Throughout each module we’ll shift between the student perspective and the instructor perspective. The activities and demos will be hands-on; attendees will also have the opportunity to exchange ideas and ask questions throughout the session. In addition to gaining technical knowledge, participants will engage in discussion around the decisions that go into developing a data science curriculum and choosing workflows and infrastructure that best support the curriculum and allow for scalability. We will also discuss best practices for configuring and deploying classroom infrastructures to support these tools

Mine Çetinkaya-Rundel

Quarto rstudio tidyverse Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

posit::conf(2023) Workshop: Steal like an Rtist: Creative Coding in R

Register now: http://pos.it/conf Instructors: Ijeamaka Anyene Fumagalli & Sharla Gelfand Workshop Duration: 1-Day Workshop This workshop is for you if you: • are comfortable with R and RStudio, experience with tidyverse and ggplot2 • are interested in applying data visualization skills more creatively, but may not know where to start or how to develop style/inspiration • are an artist interested in exploring code as another medium for creating their work R is a tool for data analysis but also can be used for self-expression. This workshop will be an introduction to creative coding in R in order to make visual art. We will take an inspiration-first approach, using compelling pieces to discuss and learn the techniques that shape the work. This workshop takes guidance from its namesake, the book “Steal Like An Artist” by Austin Kleon - once we have identified and learned to recreate existing works, we will cover how to take this inspiration and transform, remix, or reinterpret it in the pursuit of developing our own work and artistic styles. This workshop is hands-on and will cover color theory and manipulation, a reintroduction of the data frame as the foundation for creating art (instead of just for analyzing data!), using ggplot2 as an artistic canvas, creating basic and specialized shapes, tiling and pattern making, developing your own functions and using iteration. We will also discuss how to use controlled randomness to convert a standalone piece into a generative art system that can produce many distinct outputs. Creative coding may seem a world apart from data analysis, but we see a large overlap and intersection of the skills used in both, not to mention the creative muscles that are already used in data visualization

ggplot2 rstudio tidyverse Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

posit::conf(2023) Workshop: Shiny in Production: Tools and Techniques

Register now: http://pos.it/conf Instructors: Eric Nantz and Mike Thomas Workshop Duration: 1-Day Workshop This course is for you if you: • had a Shiny application work just fine on your machine, but encounters critical issues after deployment • are eager to prospectively apply techniques before deployment to plan for the unexpected • want to know the benefits and trade-offs between various ways of hosting Shiny applications Shiny brings tremendous possibilities to share innovative data science workflows with others inside an intuitive web interface. Many in the Shiny community have shared effective development techniques for building a robust application. Even with the best intentions during application development, a myriad of issues can arise once it leaves the confines of your machine. In this one-day workshop, you will implement core techniques to account for common scenarios that arise once your application is used in production, such as accounting for thousands of simultaneous users, how effective profiling can address performance bottlenecks, and ensuring your application is doing as little as possible to ensure a smooth and responsive experience. This course assumes intermediate knowledge of building Shiny applications in R and prior experience deploying an application to a platform such as the shinyapps.io service or products like Posit Connect

Shiny shinyapps Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

posit::conf(2023) Workshop: Shiny Dashboards

Register now: http://pos.it/conf Instructor: Colin Rundel Workshop Duration: 1 Day Workshop This course is for you if you: • have some experience with Shiny and want to improve your skills, • are interested in building dashboards for reporting, and • want to learn about styling and theming your dashboard. In this workshop we will explore all of the interesting and variety of ways you can use Shiny: from adding dynamic elements to your existing RMarkdown / Quarto documents to building and deploying dashboards for reporting, and customizing the appearance and themeing of the app (and your outplots like plots and tables). This workshop assumes that you have a basic familiarity with Shiny (e.g. the ability to write simple apps and basics of reactivity)

Quarto rmarkdown Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

posit::conf(2023) Workshop: Machine Learning and Deep Learning with Python

Register now: http://pos.it/conf Instructor: Sebastian Raschka Workshop Duration: 1-Day Workshop In this workshop, you will learn the machine and deep learning fundamentals using a modern open-source stack. We’ll start with a brief introduction to Python’s scientific computing libraries, including NumPy, Pandas, and Matplotlib, which provide the foundation for data analysis and visualization. From there, we will dive into the scikit-learn API, a user-friendly, open-source library for machine learning in Python. You will learn how to use it to create machine learning classifiers and apply tree-based models like random forests, gradient boosting, and XGBoost. In the second part of this workshop, we will also cover deep learning concepts and introduce PyTorch, the most widely used deep learning research library. You will also learn about training multi-layer neural networks efficiently using multi-GPU and mixed-precision techniques. Finally, we will explore how to use a pretrained large language transformer with scikit-learn and fine-tune it on a custom downstream task using PyTorch. By the end of this workshop, you will have a good understanding of the fundamental principles of machine learning and be able to construct advanced classification pipelines for tabular data using scikit-learn. Additionally, you will gain experience in image classification and natural language processing techniques using PyTorch and be able to implement them in your own predictive modeling projects effectively

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

posit::conf(2023) Workshop: It's Not Just Code: Managing an Open Source Project

Register now: http://pos.it/conf Instructor: Tracy Teal Workshop Duration: 1-Day Workshop This workshop is for you if you: • are involved in maintaining an open source project and struggling to feel like it’s sustainable, or are looking for practice or guidance • are starting out or interested in being involved in maintaining an open source project, and want to learn how to set up the project for the most effective engagement from contributors and users • are interested in learning more about the people side of open source project maintenance and connecting with other maintainers

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

posit::conf(2023) Workshop: Introduction to tidymodels

Register now: http://pos.it/conf Instructors: Hannah Frick, Simon Couch, Emil Hvitfeldt Workshop Duration: 1-Day Workshop This workshop is for you if you: • have intermediate R knowledge, experience with tidyverse packages, and either of the R pipes • can read data into R, transform and reshape data, and make a wide variety of graphs • have had some exposure to basic statistical concepts such as linear models, random forests, etc. Intermediate or expert familiarity with modeling or machine learning is not required. This workshop will teach you core tidymodels packages and their uses: data splitting/resampling with rsample, model fitting with parsnip, measuring model performance with yardstick, and basic pre-processing with recipes. Time permitting, you’ll be introduced to model optimization using the tune package. You’ll learn tidymodels syntax as well as the process of predictive modeling for tabular data

Emil Hvitfeldt, Hannah Frick, Simon Couch

parsnip rsample tidymodels tidyverse yardstick Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

posit::conf(2023) Workshop: Introduction to Quarto with R + RStudio

Register now: http://pos.it/conf Instructor: Andrew Bray Workshop Duration: 1-Day Workshop This course is for you if you: • have a basic knowledge of how to use the RStudio IDE • have some familiarity with markdown, or • are excited to author flexible single documents like technical reports and slide presentations Seasoned users of R Markdown will get more out of the Advanced Quarto with R and RStudio: Projects, Websites, Books, and More workshop, which is focused on projects, a distinct strength of Quarto in authoring work that spans multiple documents. This workshop will prepare you to author a rich array of documents in Quarto, the next generation of R Markdown. Quarto is an open-source scientific and technical publishing system that offers multilingual programming language support to create dynamic and static documents, books, presentations, blogs, and other online resources. The focus for this workshop will be on single documents. You will learn to create static documents, to add interactivity to them with Shiny and htmlwidgets, or steer them in the direction of sophisticated scientific documents. In the afternoon you’ll take the same authoring approaches to create slide presentations in various formats such as reveal.js, beamer, and pptx

Quarto rstudio Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

posit::conf(2023) Workshop: Introduction to Data Science with R and Tidyverse

Register now: http://pos.it/conf Instructors: Posit Academy Instructors Workshop Duration: 2-Day Workshop This course is ideal for: • those new to R or the Tidyverse • anyone who has dabbled in R, but now wants a rigorous foundation in up-to-date data science best practices • SAS and Excel users looking to switch their workflows to R This is not a standard workshop, but a six-week online apprenticeship that culminates in two in-person days at posit::conf(2023). Begins August 7th, 2023. No knowledge of R required. Visit posit.co/academy to learn more about this uniquely effective learning format. Here, you will learn the foundations of R and the Tidyverse under the guidance of a Posit Academy mentor and in the company of a close group of fellow learners. You will be expected to complete a weekly curriculum of interactive tutorials, and to attend a weekly presentation meeting with your mentor and fellow students. Topics will include the basics of R, importing data, visualizing data with ggplot2, wrangling data with dplyr and tidyr, working with strings, factors, and date-times, modelling data with base R, and reporting reproducibly with quarto

dplyr ggplot2 Quarto tidyr tidyverse Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

posit::conf(2023) Workshop: Introduction to Data Science with Python

Register now: http://pos.it/conf Instructors: Posit Academy Instructors Workshop Duration: 1 Day Workshop This course is ideal for: • those new to Python • anyone who has dabbled in Python, but is not sure how to use Python to do data science • R users who want to work more closely with Python users on their team This is not a standard workshop, but a six-week online apprenticeship that culminates in one in-person day at posit::conf(2023). Begins August 7th, 2023. No knowledge of Python required. Visit posit.co/academy to learn more about this uniquely effective learning format. Here, you will learn the foundations of Python and data analysis under the guidance of a Posit Academy mentor and in the company of a close group of fellow learners. You will be expected to complete a weekly curriculum of interactive tutorials, and to attend a weekly presentation meeting with your mentor and fellow students. Topics will include importing packages and datasets, visualizing data with plotnine, wrangling data with pandas, writing and applying functions, and reporting reproducibly with quarto

plotnine Quarto Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

posit::conf(2023) Workshop: Getting Started with Shiny for R

Register now: http://pos.it/conf Instructor: Colin Rundel Workshop Duration: 1-Day Workshop This course is for you if you: • are comfortable with the basics of R, such as writing functions, indexing vectors and lists, debugging simple errors, and working with data structures like data frames • are interested in creating interactive web applications • have no or minimal experience with Shiny for R Shiny is an R package that makes it easy to build interactive web apps straight from R. This workshop will start at the beginning: designing and creating user interfaces, learning and mastering the reactive model that connects your R code to the interface, and deploying apps publicly and privately. We will wrap up with some intermediate-level tools: debugging and modularizing your apps and implementing dynamic user interfaces. In the end, you’ll be a confident Shiny user, able to design interactive apps to achieve your purpose and produce a polished and professional implementation. If you have a bit of experience, you’ll see things in a new way. If you don’t, we’ll get you started on the right footing

Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

posit::conf(2023) Workshop: Fundamentals of Package Development

Register now: http://pos.it/conf Instructor: Andy Teucher Workshop Duration: 1-Day Workshop This workshop is for you if: • You have written several R scripts and find yourself wondering how to reuse or share the code you’ve written • You know how to write functions in R • You are looking for a way to take the next step in your R programming journey We will be demonstrating some workflows using Git and GitHub. Knowledge of these tools is not required, and you will absolutely be able to complete the workshop without them, but some of the lessons will be more rewarding to you if you are prepared to try them out. If you are looking to get started with Git and GitHub, we recommend you register for the “What they forgot to teach you about R” workshop on Day 1, and join us for this workshop on Day 2. We are often faced with the need to share our code with others, or find ourselves writing similar code over and over again across different projects. In R, the fundamental unit of reusable code is a package, containing helpful functions, documentation, and sometimes sample data. This workshop will teach you the fundamentals of package development in R, using tools and principles developed and used extensively by the tidyverse team - specifically the ‘devtools’ family of packages including usethis, testthat, and roxygen2. These packages and workflows help you focus on the contents of your package rather than the minutiae of package structure. You will learn the structure of a package, how to organize your code, and workflows to help you develop your package iteratively. You will learn how to write good documentation so that users can learn how to use your package, and how to use automated testing to ensure it is functioning the way you expect it to, now and into the future. You will also learn how to check your package for common problems, and how to distribute your package for others to use. This will be an interactive 1-day workshop, and we will be using the RStudio IDE to work through the materials, as it has been designed to work well with the development practices we will be featuring

devtools roxygen2 rstudio testthat tidyverse usethis Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

posit::conf(2023) Workshop: From R User to R Programmer

Register now: http://pos.it/conf Instructors: Emma Rand and Ian Lyttle Workshop Duration: 1-Day Workshop This course is for you if you: • have experience equivalent to an introductory data science course using tidyverse • feel comfortable with the Whole game chapter of R for Data Science This is a one-day, hands-on workshop for those who have embraced the tidyverse and want to improve their R programming skills and, especially, reduce the amount of duplication in their code. The two main ways to reduce duplication are creating functions and using iteration. We will use a tidyverse approach to cover function design and iteration with {purrr}. • Master the art of writing functions that do one thing well, adhere to existing conventions and can be fluently combined together to solve more complex problems. • Learn how to perform the same action on many objects using code which is succinct and easy to read

purrr tidyverse Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

posit::conf(2023) Workshop: Engaging and Beautiful Data Visualizations with ggplot2

Register now: http://pos.it/conf Instructor: Cédric Scherer Workshop Duration: 1-Day Workshop This course will be appropriate for you if you: • already know how to create basic graphics with the ggplot2 package • aim to improve the design of your ggplot outputs • want to learn how to create more complex charts which feature multiple layers, annotations, text styling, custom themes, and more Creating effective and easily accessible data visualizations of high quality in an efficient and preferably reproducible way is an essential skill for everyone working in a data-related field. Luckily, by leveraging the functionality of ggplot2, the most famous package for data visualization with R, and related extension packages one can create highly customized data visualization without the need for post-processing. This workshop provides everything one needs to know to create and customize numerous chart types with ggplot2. Participants will learn the most important steps and helpful tips to create visually appealing and informative graphics with a code-only approach. The power of ggplot2 and related extension packages will be illustrated with advanced real–life examples that help to understand useful coding tricks and the process of creating engaging and effective visualizations. The workshop will particularly focus on more advanced tasks with ggplot2 such as styling labels and titles, customizing themes and visual aesthetics, and using less-common chart types

ggplot2 Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

posit::conf(2023) Workshop: DevOps for Data Scientists

Register now: http://pos.it/conf Instructor: Rika Gorn Workshop Duration: 2-Day Workshop This workshop is for you if you: • are a data scientist or analyst who wants to put their work into production, • want to learn a more about Docker containers and virtual machines, or • want to better understand importance of networking, security, storage, and different server architectures to deploying data apps. In this workshop, we will discuss ways to better containerize, deploy, and scale your data products. We'll use both R and shell scripting to virtualize Posit products in the cloud. You'll learn about servers, networking, security, and authorization to be dangerous -- or at least to understand your DevSecOps teams. By the end of the workshop you will have the tools to start deploying your own data science assets into production

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

posit::conf(2023) Workshop: Designing Data Visualizations to Successfully Tell a Story

Register now: http://pos.it/conf Instructor: Cédric Scherer Workshop Duration: 1-Day Workshop This course is for you if you: • want to understand and learn the art of communicating data with impactful visualizations • aim to improve your data visualization design to create effective and informative graphics • are willing to spend a bit more time to choose the right chart, proper color palettes, and suitable fonts along with additional elements to guide the viewer Communicating data through meaningful and easily accessible visualization is a critical competence for most data-related roles including data scientists, analysts, scientific researchers, and managers. A well-designed graphic is able to inform, spark engagement, explain patterns, and drive decisions and actions. At the same time, poor choices in the design process can complicate interpretation or even, intentionally or unintentionally, mislead the audience. The aim of this course is to demystify the creative processes of data visualization design to turn data into a meaningful story. Participants will learn helpful tips and tricks to create appealing and informative data visualizations that are not “just showing the numbers” but successfully tell a story. We will cover principles of good data visualization design, explore different options to display data, and discuss ways to guide and engage viewers with the aim to create impactful graphics. The course also features sessions on picking suitable yet beautiful colors, what to consider when choosing and pairing typefaces, and the layout of graphics, also in the context of dashboard building

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

posit::conf(2023) Workshop: Deploy and Maintain Models with vetiver

Register now: http://pos.it/conf Instructor: Julia Silge Workshop Duration: 1-Day Workshop This workshop is for you if you: • have intermediate R or Python knowledge (this will be a “choose your own adventure” workshop where you can work through the exercises in either R or Python) • can read data from CSV and other flat files, transform and reshape data, and make a wide variety of graphs • can fit a model to data with your modeling framework of choice We expect participants to have exposure to basic modeling and machine learning practice, but NOT expert familiarity with advanced ML or MLOps topics. Many data scientists understand what goes into training a machine learning or statistical model, but creating a strategy to deploy and maintain that model can be daunting. In this workshop, learn what MLOps (machine learning operations) is, what principles can be used to create a practical MLOps strategy, and what kinds of tasks and components are involved. We’ll use vetiver, a framework for MLOps tasks in Python and R, to version, deploy, and monitor the models you have trained and want to deploy and maintain in production reliably and efficiently

Julia Silge

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

posit::conf(2023) Workshop: Data Science Workflows with Posit Tools — R Focus

Register now: http://pos.it/conf Instructor: Ryan Johnson and Katie Masiello Workshop Duration: 1-Day Workshop This course is for you if you: • Build finished data products starting from raw data and are looking to improve your workflow • Are looking to expand your knowledge of Posit open source and professional tools • Want to improve interoperability between data products in your work or on your team • Have experience developing in R. An analogous course with a Python focus is also offered In this R-focused workshop, we will discuss ways to improve your data science workflows! During the course, we will review packages for data validation, alerting, modeling, and more. We’ll use Posit’s open source and professional tools to string all the pieces together for an efficient workflow. We’ll discuss environments, managing deployed content, working with databases, and interoperability across data products

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

posit::conf(2023) Workshop: Data Science Workflows with Posit Tools — Python Focus

Register now: http://pos.it/conf Instructors:Gagan Singh and Sam Edwardes Workshop Duration: 1-Day Workshop Workshop Duration: 1-Day Workshop This course is for you if you: • Build finished data products starting from raw data and are looking to improve your workflow • Are looking to expand your knowledge of Posit open source and professional tools • Want to improve interoperability between data products in your work or on your team • Have experience developing in Python. An analogous course with an R focus is also offered In this Python-focused workshop, we will discuss ways to improve your data science workflows! During the course, we will review packages for data validation, alerting, modeling, and more. We’ll use Posit’s open source and professional tools to string all the pieces together for an efficient workflow. We’ll discuss environments, managing deployed content, working with databases, and interoperability across data products

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

posit::conf(2023) Workshop: Causal Inference with R

Register now: http://pos.it/conf Instructors: Malcolm Barrett and Travis Gerke This course is for you if you: • know how to fit a linear regression model in R • have a basic understanding of data manipulation and visualization using tidyverse tools • are interested in understanding the fundamentals behind how to move from estimating correlations to causal relationships In this workshop, we’ll teach the essential elements of answering causal questions in R through causal diagrams, and causal modeling techniques such as propensity scores and inverse probability weighting. In both data science and academic research, prediction modeling is often not enough; to answer many questions, we need to approach them causally. In this workshop, we’ll teach the essential elements of answering causal questions in R through causal diagrams, and causal modeling techniques such as propensity scores and inverse probability weighting. We’ll also show that by distinguishing predictive models from causal models, we can better take advantage of both tools. You’ll be able to use the tools you already know--the tidyverse, regression models, and more--to answer the questions that are important to your work

tidyverse Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

posit::conf(2023) Workshop: Big Data with Arrow

Register now: http://pos.it/conf Instructors: Nic Crane and Stephanie Hazlitt Workshop Duration: 1-Day Workshop This course is for you if you: • want to learn how to work with tabular data that is too large to fit in memory using existing R and tidyverse syntax implemented in Arrow • want to learn about Parquet and other file formats that are powerful alternatives to CSV files • want to learn how to engineer your tabular data storage for more performant access and analysis with Apache Arrow Data analysis pipelines with larger-than-memory data are becoming more and more commonplace. In this workshop you will learn how to use Apache Arrow, a multi-language toolbox for working with larger-than-memory tabular data, to create seamless “big” data analysis pipelines with R. The workshop will focus on using the the arrow R package—a mature R interface to Apache Arrow— to process larger-than-memory files and multi-file data sets with arrow using familiar dplyr syntax. You’ll learn to create and use interoperable data file formats like Parquet for efficient data storage and access, with data stored both on disk and in the cloud, and also how to exercise fine control over data types to avoid common large data pipeline problems. This workshop will provide a foundation for using Arrow, giving you access to a powerful suite of tools for performant analysis of larger-than-memory data in R

dplyr tidyverse Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

posit::conf(2023) Workshop: Advanced tidymodels

Register now: http://pos.it/conf Instructor: Max Kuhn, Software Engineer, Posit Workshop Duration: 1-Day Workshop This workshop is for you if you: • have used tidymodels packages like recipes, rsample, and parsnip • are comfortable with tidyverse syntax (e.g. piping, mutates, pivoting) • have some experience with resampling and modeling (e.g., linear regression, random forests, etc.), but we don’t expect you to be an expert in these In this workshop, you will learn more about model optimization using the tune and finetune packages, including racing and iterative methods. You’ll be able to do more sophisticated feature engineering with recipes. Time permitting, model ensembles via stacking will be introduced. This course is focused on the analysis of tabular data and does not include deep learning methods. Participants who have completed the “Introduction to tidymodels” workshop will be well-prepared for this course. Participants who are new to tidymodels will benefit from taking the Introduction to tidymodels workshop before joining this one

Max Kuhn

finetune parsnip rsample tidymodels tidyverse Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

posit::conf(2023) Workshop: Advanced Quarto with R + RStudio

Register now: http://pos.it/conf Instructor: Andrew Bray Workshop Duration: 1-Day Workshop This course is for you if you: • have a basic knowledge of how to use the RStudio IDE • have experience working with single R Markdown and/or Quarto files • are excited to author multi-document projects like books, websites, and blogs Participants who are new to computational documents will benefit from taking Intro to Quarto with R and RStudio: Documents and Presentations before joining this workshop. This workshop will prepare you to author a rich array of documents in Quarto, the next generation of R Markdown. Quarto is an open-source scientific and technical publishing system that offers multilingual programming language support to create dynamic and static documents, books, presentations, blogs, and other online resources. The focus for this workshop will be on projects that weave together multiple documents and allow you to write books and build websites. You will also learn various ways to deploy and publish your Quarto projects on the web

Quarto rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

posit::conf Workshop:Leveraging & Contributing to the Pharmaverse for Clinical Trial Reporting in R

Register now: http://pos.it/conf Instructors: Pawel Rucki, Christina Fillmore, Thomas Neitmann Workshop Duration: 1-Day Workshop You should take this workshop if you are interested in the future of clinical trial reporting, and would like to join this shift to between company collaboration. In this workshop we will introduce the pharmaverse, a collection of open source R packages that provide the next generation backbone for clinical trial reporting. As of March 2023, 123 people from more than 10 companies have contributed to the pharmaverse packages - and this workshop is about how you can both leverage their work to date and get involved to help evolve this important collection of efforts. We will create ADaM datasets, prepare tables and figures as well as interactive shiny apps. This course will also give a grounding in how to navigate pharmaverse R packages, and their repositories, and understand how you can contribute

Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

[79] Create a Python Web App Using Shiny (Gordon Shotwell)

Join our Meetup group for more events! https://www.meetup.com/data-umbrella ## Resources - website for presentation: https://shiny.rstudio.com/py/ - https://shiny.rstudio.com/py/docs/reactive-mutable.html - https://www.shinyapps.io/ - https://huggingface.co/new-space - https://shiny.rstudio.com/py/docs/shinylive.html - https://shiny.rstudio.com/py/api/reactive.poll.html#shiny.reactive.poll ## About the Event Shiny makes it easy to build interactive web applications with the power of Python’s data and scientific stack. If you want to develop a python web application you usually need to choose between simple, limited frameworks like Streamlit and more extensible frameworks like Dash. This can cause a lot of problems if you get started with a simple framework but then discover that you need to refactor your application to accommodate the next user request. Shiny for Python differs from other frameworks because it has tremendous range. You can build a small application in a few minutes with the confidence that the framework can handle much more complex problems. In this workshop we will go through the core limitations of Streamlit, and build a Shiny app which avoids those limitations. ## Timestamps 00:00 Welcome 00:23 Reshama introduces Data Umbrella 03:45 Reshama introduces Gordon Shotwell 04:21 Gordon Shotwell begins 04:29 The motivation to develop Shiny for Python 06:05 The main strength of both the R and Python library 06:56 What Gordon Shotwell will build during his presentation 07:25 Shiny documentation website 08:01 QuickStart for R users showing differences between the R and Python libraries 08:44 All the function reference in Shiny 09:08 Demo starts 09:50 Virtual environment 10:36 How to start shiny app in the terminal 11:15 Install shiny extension in VS Code which makes it easier to preview the web app 11:36 How the output function works on the preview app to execute 12:22 Penguin dataset description for the demo 12:45 Modules/submodules shiny app is built on 13:04 How to add a sidebar layout (sidebar, panel sidebar and panel main) 13:43 How to read in the data and the output functions 14:31 How to define some server logic 14:59 The conventional shiny rule 16:30 Use of slide input 17:50 Where the reactive magic comes in 19:30 Important note on what can really slow down your shiny app 20:14 Importance of Python data copy method when using external dataset 21:01 Important note to avoid dependency inside the render function 21:30 Q&A 29:35 Adding a plot to the output: The UI sides 30:12 Adding a plot to the output: The render sides 32:16 The core principle of reactivity in which you do not want to repeat yourself 33:26 Reactivate calculation concept which allows you to store intermediate values in one place 37:24 Q&A 38:53 Reactive calculations and rendering functions 39:30 Side-effects or user effect. Another class of interactions 41:18 How to tell reactive effect what it should respond to or what events to watch before executing 41:53 How to update the data filter in the side-effect function 42:22 The second important pattern for shiny 43:00 One of the important things to pay attention to once you start learning/using shiny 44:45 Series of Q&A until the end of the video. Some response includes live demo 01:01:03 Gordon Shotwell ends his presentation 01:01:17 Reshama closes the session ## About the Speaker Gordon Shotwell is a Software Engineer at Posit. He's been using Shiny to solve business problems for the past ten years. - LinkedIn: https://www.linkedin.com/in/gshotwell/ ## Key Links - Transcript: https://github.com/data-umbrella/event-transcripts/blob/main/2023/78-gordon-shiny.md - Meetup Event: https://www.meetup.com/data-umbrella/events/292848290/ - Video: https://youtu.be/pXidQWYY14w #python #deployment

Deploying a Python application with Posit Connect

Posit Connect is our flagship publishing platform for data products built in R and Python. Learn more: https://posit.co/products/enterprise/connect/ Book a demo of Connect: https://posit.co/schedule-a-call/?booking_calendar__c=RSC_YT_Demo With Connect, you can deploy, manage, and share your R and Python content, including Shiny applications, Dash, Streamlit, and Voilà applications, R Markdown reports, Jupyter Notebooks, Quarto documents, dashboards, APIs (Plumber, Flask), and more. Give stakeholders authenticated access to the content they need, and schedule reports to update automatically

plumber Quarto Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Launch different development environments and manage cluster options with Posit Workbench

Posit Workbench: https://posit.co/products/enterprise/workbench/ Data scientists should be able to use the language and development environment they prefer. Jupyter Notebook, JupyterLab, VS Code, and RStudio are all available development environments within Posit Workbench. Workbench is also exceptional for managing compute resources. Use Kubernetes and Slurm and adjust the CPU and memory to match the job you're trying to run

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

What is Posit Team? [in less than 2 minutes]

Posit Team is the bundle of our most popular products including: Workbench, Connect, and Package Manager. Together, this bundle delivers an end-to-end toolchain for data science teams committed to R and Python, improving every step of a data scientist's workflow, from developing insights, to deploying data products, to managing environments. Learn more at https://posit.co/products/enterprise/team/

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Running multiple R & Python sessions at once on Posit Workbench

Posit Workbench is the premier development experience for professional data scientists who use R and Python. One of the many great features Workbench offers is the ability to run multiple sessions at once, across different languages and development environments. In this video, we give you a glimpse of that feature. To watch the full demo of our professional software, go here: https://youtu.be/1KEX3gZTQnE Learn more about Posit Workbench: https://posit.co/products/enterprise/workbench/

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

How to deploy a Dash application from VS Code to Posit Connect

Episode 1: Publishing a Dash application to Posit Connect Led by: Ryan Johnson, Data Science Advisor Follow-up links: * Posit Team: https://posit.co/products/enterprise/team/ * Talk to us directly: https://posit.co/schedule-a-call/?booking_calendar__c=RST_YT_Demo * Follow-along blog post: https://posit.co/blog/deploying-a-dash-application-to-posit-connect/ * Source code for example: https://github.com/sol-eng/python-examples * Posit Team demo resources: pos.it/demo-resources Timestamps: 00:53 - What is Posit Team? (Posit Workbench, Posit Connect, Posit Package Manager) 3:25 - JumpStart Examples in Posit Connect 4:00 - Opening up a VS Code Session in Posit Workbench 8:03 - Downloading the Dash JumpStart example in Posit Connect 9:40 - Open up the JumpStart example in VS Code in Posit Workbench 11:18 - Install the required Python packages using pip 12:55 - Python packages on Posit Package Manager 15:12 - Defining the packages you want to install (latest, specific date, etc.) 19:12 - Creating an API key in Posit Connect 20:29 - Deployment process from VS Code to Posit Connect 21:36 - Dash application hosted on Posit Connect 22:23 - Access controls in Posit Connect (sharing with the people who need to see your app) 24:10 - Adjusting the runtime of an application in Posit Connect (controlling compute resources) 26:35 - Tags in Posit Connect 26:48 - Vars (if your content requires privileged access) 27:15 - Recap of what was covered in the demo On the last Wednesday of every month, we host a Posit Team demo and Q&A session that is open to all. You can use this to add the event to your own calendar. Who are these monthly demos for? Everyone is welcome to join us - regardless of industry, background, or experience! We will discuss topics that will speak to: * Data scientists and administrators new to Posit Team or are looking to grow their understanding of our toolchain, * Teams searching for a new analytic platform built to support open-source data science, * And, those that are just curious about Posit Team! What you can expect from the monthly Posit Team demo: During the session, we will walk through an end-to-end data science workflow and demo the core functionality of Posit Team while highlighting some of our latest features! While each session's content will vary slightly, here are a few core topics we will address each month: * Open Source Analytics: The future of data science is open source. We'll discuss methods for leveraging open-source tools and packages in a secure and scalable way! * Deployment: How to share the amazing data science assets your Team has built, including web applications, machine learning models, APIs, and more! * Data Access: Data comes in various forms and is stored in various ways. We'll discuss best practices for accessing, reading, and writing data! * Job Scheduling: Do you have recurring data science jobs? We'll show you how to automate these processes using Posit Connect. What is Posit Team? Posit Team is a bundle of our popular professional software (Posit Workbench, Posit Connect, and Posit Package Manager) for developing data science projects, publishing data products, and managing packages. Registration is not required. The event will be streamed through YouTube Premiere

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Dash

The Evolution of Shiny with Posit's CTO, Joe Cheng

Posit’s Joe Cheng talks about the evolution of Shiny and the future of Shiny for Python. Joe is one of the original architects of Shiny and leads Posit’s Open Source software development. Learn more about Shiny for Python at shiny.posit.co/py

Joe Cheng

Shiny for Python Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

RStudio on Amazon SageMaker

Working with analysis or a data set that exceeds the capabilities of your local workstation? One simple option for scaling up is RStudio on Amazon SageMaker. See how you can get started quickly. Link to learn more: https://docs.aws.amazon.com/sagemaker/latest/dg/rstudio.html

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Hadley Wickham | {purrr} 1.0: A complete and consistent set of tools for functions and vectors

{purrr} has reached the 1.0 milestone, with new features like progress bars, improvements to the map family, and tools for list flattening and simplification. 0:00 Introduction 0:11 What is purrr? 00:32 What is functional programming? 03:08 Announcing purrr 1.0 03:58 Progress bars 05:18 Better error messages 07:18 New map function: map_vec() 09:58 New list_* functions 12:04 Flattening and simplification 17:40 Breaking Changes 22:34 How the tidyverse handles deprecation 24:41 An overview of functional programming 26:22 Closing, resources to help with deprecation, how to submit issues See more in the {purrr} 1.0.0 release blog post! https://www.tidyverse.org/blog/2023/03/tidyverse-2-0-0/

Hadley Wickham

purrr tidyverse tidyverse.org Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Open Source in Pharma with Phil Bowsher

Why are open-source languages like R and Python becoming the default ways for pharmaceutical companies to complete clinical trials? Phil Bowsher sheds insight on this trend, including how we got here and what it means for major pharmaceutical companies moving forward. Phil Bowsher leads the Life Sciences practice at Posit and has helped almost every major pharma around the globe successfully adopt open source. Learn more at posit.co/pharma

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Embracing R and Python

Listen to Posit's Chief Scientist Hadley Wickham talk about the future of Posit. Visit www.posit.co to learn more

Hadley Wickham

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Python in Posit Workbench | Launch in Your Native Development Environment

Posit Workbench makes it easier than ever to code in Python. Launch the development environment you prefer, including VS Code, Jupyter, and the RStudio IDE. Learn more at Posit.co/workbench Professional data scientists using both R and Python love Posit Workbench because of its enterprise-friendly benefits, including collaboration, centralized management, security, and commercial support

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Embracing Open Source in Pharma || GSK || Posit

Many statistical and clinical programmers have spent their entire careers programming in one language. Major pharmaceutical companies like GSK are collaborating with each other to bring open-source into clinical workflows. Learn more at pos.it/pharma

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Posit Package Manager | Manage R and Python Packages Across Your Organization

Posit Package Manager is a repository management server to organize and centralize R and Python packages across your organization. Learn more here: https://posit.co/products/enterprise/package-manager/ Use it to provide full mirrors of CRAN, Bioconductor, and PyPI. Restrict access to potentially harmful public packages by curating your own custom repository with only the packages you need. Support air-gapped environments by providing offline access to your package repository

air Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Posit Workbench | Build Data Products in R & Python Using Jupyter, VSCode, and RStudio IDE

More computing power. More possibilities. Less hassle. Posit Workbench is the preferred development environment for R and Python developers. Code in R. Code in Python. Develop in Jupyter, VSCode, and of course, the RStudio IDE. Build all of the data products you can imagine: Reports, Dashboards, Applications, and APIs, with more computing power and no additional strain on IT. For more information: Posit.co/products/enterprise/workbench

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Hadley Wickham: Thank you, from Open Source at Posit

The thing that’s made RStudio (now Posit) so amazing, is the community. We love and value our open source community, and we’re so appreciative of everyone who has helped make Posit what it is today. And our community, is… YOU. You live and work on every continent. You use R for school or work or fun; answering questions that are important to you. You create and share thousands of resources to help others. You contribute to code and documentation on hundreds of open source projects. You’re involved in the community, sharing with each other in organizations and user groups around the world. And, of course, you write millions of lines of code. We thank you, and wish you all the best in the coming year!

Hadley Wickham

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Rich Iannone | What's new and exciting in gt 0.8.0 | Posit

With the gt package, anyone can make wonderful-looking tables using the R programming language. Rich Iannone, maintainer of gt, shows what's new and improved in gt 0.8.0! 00:00 Introduction 00:42 Find/Replace values with sub_values() 02:46 Find values and style them with tab_style_body() 05:00 Place a cell in your Quarto/RMarkdown doc with extract_cells() 07:13 Make numbers more readable with cols_align_decimal() 08:54 See column id info with tab_info() 11:03 Date and time formatting improvements For more details: • Demo script in this video: https://pos.it/gt8 • Read the blog post on gt 0.8.0: https://posit.co/blog/new-features-upgrades-in-gt-0-8-0/ • Learn more at https://gt.rstudio.com/ • See a full list of new features and improvements at https://gt.rstudio.com/news/index.html#gt-080

Rich Iannone

gt Quarto rmarkdown rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Quarto with the Quarto Team | An Open-Source Chat

Join Al Manning, Carlos SchIidegger, & Charles Teague, members of the Quarto Team, as they take our questions. Quarto is an open-source tool for scientific and technical publishing. Create dynamic content with Python, R, Julia, and Observable. Author documents as plain text markdown or Jupyter notebooks. Publish high-quality articles, reports, presentations, websites, blogs, and books in HTML, PDF, MS Word, ePub, and more. Author with scientific markdown, including equations, citations, crossrefs, figure panels, callouts, advanced layout, and more. Key Resources: ⬡ Learn more and get started with Quarto at quarto.org Contact ⬡ Bug reports and feature requests - https://github.com/quarto-dev/quarto-cli/issues ⬡ Need help? Github discussions - https://github.com/quarto-dev/quarto-cli/discussions Introduction Videos for Quarto ⬡ Mine and Julia talk, https://www.youtube.com/watch?v=p7Hxu4coDl8 ⬡ Quarto Series, 1️⃣ Welcome to Quarto Workshop led by Tom Mock: https://www.youtube.com/watch?v=yvi5uXQMvu4 2️⃣ Building a Blog with Quarto led by Isabella Velásquez: https://www.youtube.com/watch?v=CVcvXfRyfE0&feature=youtu.be 3️⃣ Beautiful reports and presentations with Quarto led by Tom Mock: https://www.youtube.com/watch?v=hbf7Ai3jnxY&feature=youtu.be Timestamps 00:00:00 Introductions 2:55 Why open source? 6:20 Can we expect to see Quarto available to R-users via CRAN any time soon? 9:10 Quarto and Google Docs? 9:49 Lua filters/shortcodes. Advice for a good development environment for prototyping and debugging? 14:59 Is there a single documentation page for ALL the quarto-specific YAML options? https://quarto.org/docs/reference 16:15 Navigating Quarto's documentation. 18:00 Is there something like Observable SQL cells on the roadmap? 20:10 Is there something closer to {bookdown} for Quarto? What is the best way to retain data and environment objects in a quarto book? Is there any path to enabling this? See Includes, https://quarto.org/docs/authoring/includes.html 24:20 Flexdashboard? Coming soon. 26:30 A big challenge in the adoption is that Quarto is competing with ipython notebooks for mindspace, what does the Quarto team think about that? Quarto and Jupyter Notebooks will hopefully be thought of as complementary to one another, with Quarto helping a lot with narrative, layout, and appearance for publication and sharing. 30:10 Where should I go to contact you about an issue? What if the issue isn't just Quarto, say, Quarto + Jupyter? 31:50: What is the Quarto team hoping to see the community produce? Feedback, reporting in github issues. Quarto Extensions. 34:05 Custom styling, configuring grid options. Any tips or anything in the roadmap that will help users finetune the look and feel of their output? 38:40 Terminology question; what do we call a published Quarto doc? (or webpage, blog, etc.?) 40:00 How do I stay up to date with Quarto? Getting the latest release and learning about what is new? See what's up on quarto.org. Look under get-started and under downloads for pre-releases

bookdown cli finetune flexdashboard Quarto Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Posit Presents: R Around the World

From Kenya to Kentucky to outer space. Open source is changing the world. And so can you. Learn more at Posit.co

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

What is Posit Connect | Deploy All of Your R & Python Content

Posit Connect is our flagship professional product for data scientists that want to share their data products with other. Learn more at https://posit.co/products/enterprise/connect/ Whether you're building a Shiny application, a Dash application, a Quarto report, or dashboard, you can host it on Posit Connect. Posit Connect supports all the languages that data scientists love, as well as the development environments they love to build in

Quarto Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

RStudio is now Posit!

Our name changed. Our mission stayed the same. Everyone in the R community has contributed to the success of open source and the journey was made richer because of supporters like you. You have challenged and inspired us to create products that enable data scientists to generate knowledge and use that knowledge to create a lasting impact in their communities and organizations. Outside of the powerful tools we have created together, we are proud to be part of such a passionate, supportive, and diverse community of users from around the world. Over the last few years, we ruminated on how we can better enable and support the changing needs of our customers and community. The result is our rebrand to Posit. We are excited at the chance to bring what we all love so much about the R community to everyone. Finally, our new website posit.co is now live. Please check out our new home and let us know what you think!

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Open Source Chat - {gt} with Rich Iannone

Join Rich Iannone, maintainer of the {gt} package, as he takes questions from the community about the latest in {gt} v0.7.0, and building great looking data display tables with R. Key Resources: ⬡ Get started with {gt} - https://gt.rstudio.com Reach out: 38:48 - How do I ask Rich about {gt}, feature requests, bug reports, how to solve a problem via {gt}? Rich and the {gt} team would love to hear from you. ⬡ Feature requests & bug reports with GitHub Issues, https://github.com/rstudio/gt/issues ⬡ GitHub Discussions, https://github.com/rstudio/gt/discussions ⬡ Ask the community a question, https://community.rstudio.com/tag/gt ⬡ Follow {gt} on Twitter, feel free to reach out and ask questions, https://twitter.com/gt_package Timestamps Rich Iannone Introduction. 03:52 - Why {gt}? - What does {gt} bring to the table? Why so much effort into static, data display tables? 05:50 - Why open source? Why is {gt} open source and why have you dedicated your career to develop open source software? 08:30 - {gt} v0.7.0, Tell us about those new vector formatting functions in {gt}. Why did you include them? Could you show us some examples? {gt}'s vector formatting functions help you customize the styling, look and feel of your values. Converting the output values R gives you, and making them look exactly the way you want them to can be tricky. A lot of work was put into {gt} to give nice value formatting options. You can now access all these outside of a gt table; e.g. in text, in a plot, etc. 22:35 - Could you provide an example or two with the new styling function called `opt_stylize()`? What kinds of tables can you make with that? Can you extend that with your own tweaks? 28:15 - Can you make your own themes and share them? "How do I create my own custom theme for my table? A theme I can share with the rest of my organization?" 31:58 - What is the distinction between tab_options and the opt_* functions? Why would a function be in opt_* and not tab_options? 34:00 - sub_values() function, to find and replace certain values in your table. 36:50 - What is the current support for latex in {gt} at the moment? "Personally, I much prefer HTML, but for scientific publications, we are asked to provide a LaTeX file." 42:50 - "In my work, I often produce A4 output in PDF, mainly with ggplot2 content. It would be nice to be able to combine ggplot + gt tables in a similar way {patchwork} works. Having the plot and the table next to it is very useful sometimes." 44:30 - Interactive Tables with {gt}? 47:45 - "Any plans to make applying of same style to several columns easier? Unless I'm mistaken, the locations argument of tab_style requires one to specify an individual column. See here: https://gt.rstudio.com/reference/tab_style.html#examples." Yes, supply a vector of columns or use tidyselect functions. 49:15 - "Excel output with {gt}? Would be a huge improvement. I often have to produce tabular output that can be easily reused. Usually it means Excel tables. So far I have mainly done this with Python and openpyxl or PyWin32 (through COM). A simple solution in R would be great." 50:20 - Support for additional output formats with {gt}? Excel, PowerPoint, etc.? 50:25 - {pointplank}, a package to methodically validate your data whether in the form of data frames or as database tables., https://rich-iannone.github.io/pointblank/. Check out the workshop materials at https://github.com/rich-iannone/pointblank-workshop 55:50 - "Are there ways to have grouped rows? I mean when repeated rows have same characters can we merge them to one?" 58:00 - "Is there an ability to add 'battleship coordinates' (e.g. column letters & row numbers) to a gt object? This is a standard for table across my org and I’ve been trying to figure out how to implement it." 59:59 "Do you have suggestions or examples of building out & applying corporate formatting to gt tables (e.g. adding a company logo, company colors, etc.)?" 01:04:30 - "With PDF/LaTeX output for wide tables, it does not shrink the table."

Rich Iannone

ggplot2 gt pointblank rstudio tidyselect Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Mine Çetinkaya-Rundel & Julia Stewart Lowndes | Hello Quarto: Share, Collaborate, Teach, Reimagine

What is Quarto? In this talk, in four acts, we will discuss how Quarto unifies and extends the R Markdown ecosystem. Through our work with NASA Openscapes, Duke University, and RStudio, we will share our experiences sharing, teaching, collaborating, and reimagining with Quarto. Full of demos with Quarto in RStudio and Jupyter and weaving together narrative and code, this talk will help you get the most out of Quarto regardless of your background with R Markdown, the computing language(s) you may use, and your tool of choice for authoring. https://quarto.org/ Session: Keynote

Mine Çetinkaya-Rundel

Quarto rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Joe Cheng | The Past and Future of Shiny | RStudio (2022)

Shiny is a package that makes it easy to create interactive web apps using R. It's hard to believe, but it's been ten whole years since the launch of Shiny! I'll mark the occasion by sharing some stories about the creation of Shiny, the conditions that have made it a success over the years, and our plans for the near future. https://shiny.rstudio.com/ Session: Keynote

Joe Cheng

rstudio Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

What's New in {gt} 0.7.0?

gt 0.7.0 was just released. Rich Iannone, maintainer of gt, dives into the 7 new features added. For more details, ⬢ Read the blog post on gt 0.7 https://www.rstudio.com/blog/all-new-things-in-gt-0-7-0/. ⬢ Learn more about gt at https://gt.rstudio.com/. ⬢ Follow the gt twitter account, https://twitter.com/gt_package. 00:07 The new Word table output format, .docx output. 00:34 A whole new family of vector formatting functions (vec_fmt_*()) has been added. 01:03 Table presets/themes styling with the new opt_stylize() function. 01:50 The new tab_stub_indent() for superfine control over row label indentation (in the stub) 02:26 The new fmt_duration() function for formatting of time duration values. 03:32 An upgraded gtsave() that uses {webshot2}, .png output looks better. 04:14 Accessibility enhancements for HTML table outputs

Rich Iannone

gt rstudio webshot2 Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

rstudio::conf(2022) All things R & RStudio

July 2022 in Washington DC

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Plot Outputs in Shiny for Python || Winston Chang || RStudio

Shiny makes it easy to build interactive web applications with the power of Python’s data and scientific stack. You can try out Shiny for Python without installing a single thing… All in the browser. Learn more about Shiny for Python: https://shiny.rstudio.com/py/ Check out our interactive Shiny for Python examples: https://shinylive.io/py/examples/ Content: Winston Chang (@winston_chang)

Winston Chang

rstudio Shiny for Python Shiny shinylive Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Multiple Inputs in Shiny for Python || Winston Chang || RStudio

Shiny makes it easy to build interactive web applications with the power of Python’s data and scientific stack. You can try out Shiny for Python without installing a single thing… All in the browser. Learn more about Shiny for Python: https://shiny.rstudio.com/py/ Check out our interactive Shiny for Python examples: https://shinylive.io/py/examples/ Content: Winston Chang (@winston_chang)

Winston Chang

rstudio Shiny for Python Shiny shinylive Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Getting Started with Shiny for Python - in the browser! || Winston Chang || Posit

Shiny makes it easy to build interactive web applications with the power of Python’s data and scientific stack. You can try out Shiny for Python without installing a single thing… All in the browser. Learn more about Shiny for Python: https://shiny.rstudio.com/py/ Check out our interactive Shiny for Python examples: https://shinylive.io/py/examples/ Content: Winston Chang (@winston_chang)

Winston Chang

rstudio Shiny for Python Shiny shinylive Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Submitting Your Work to the Table Contest | 2022 Table Contest

Rich Iannone walks through how to submit a table to the 2022 Table Contest. He explains considerations for each field, and how to update & edit your entry afterwards. Learn more about the 2022 Table Contest at https://www.rstudio.com/blog/rstudio-table-contest-2022/

Rich Iannone

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Let's Create a Quarto Doc & Share it on Quarto.Pub | Table Contest 2024

Rich Iannone creates a gt table with Quarto, and publishes it on quarto.pub. You'll see how to set up your .qmd file in the RStuido IDE, preview your table, publishing it to Quarto Pub, some admin settings, and how to share your table made in Quarto to the world

Rich Iannone

gt Quarto Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Create & Publish a Quarto Blog on Quarto Pub in 100 Seconds | Quarto Pub

Thomas Mock, Quarto Product Manager, walks you through how to build a simple blog with Quarto and share it with the world on quarto.pub, all in less than two minutes. Quarto is the multi-language publishing system. It also allows you to publish executable code blocks to include R, Python, Julia, or Observable JS output in your blog posts (and many other formats). Quarto websites and blogs are particularly excellent ways to develop your technical skills and share your learnings with the world. Resources, ⬡ Creating a Quarto Blog, https://quarto.org/docs/websites/website-blog.html ⬡ Publishing to Quarto Pub, https://quarto.org/docs/publishing/quarto-pub.html ⬡ Customize your Quarto blog or Website. This example creates and deploys a simple Quarto blog template, but there are ways to customize and style your content. Isabella Velásquez walks through this in detail at the Sept 2022 meetup, https://youtu.be/CVcvXfRyfE0 ⬡ Learn more about Quarto at quarto.org. Requirements, - To publish from the RStudio IDE, you'll need to be working on a recent version of RStudio, v2022.07.1 or later. - You may also work from Jupyter Labs, VS Code, or a notebook integrated with the Quarto CLI

cli Quarto rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Wrangling data for a Shiny app in Python || Michael Chow || Posit

Shiny makes it easy to build interactive web applications with the power of Python’s data and scientific stack. Learn more about Shiny for Python: https://shiny.rstudio.com/py/ Check out our interactive Shiny for Python examples: https://shinylive.io/py/examples/ Content: Michael Chow (@chowthedog) Producer: Jesse Mostipak (@kierisi) Editing and Motion Design: Tony Pelleriti (@TonyPelleriti)

Michael Chow

rstudio Shiny for Python Shiny shinylive Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging RStats Open Source Oss Reticulate Pyshiny Shiny for Python Jupyter Michael Chow Tony Pelleriti Posit

Shinywidgets - An Overview || Carson Sievert || RStudio

Shiny makes it easy to build interactive web applications with the power of Python’s data and scientific stack. Shinywidgets lets you use ipywidgets in Shiny for Python applications. We called it ipyShiny during development, but we're launching as Shinywidgets! Learn more about how to integrate them into your Shiny for Python apps. . Learn more about Shiny for Python: https://shiny.rstudio.com/py/ Check out our interactive Shiny for Python examples: https://shinylive.io/py/examples/ Content: Carson Sievert (@cpsievert) Producer: Jesse Mostipak (@kierisi) Editing and Motion Design: Tony Pelleriti (@TonyPelleriti)

Carson Sievert

rstudio Shiny for Python Shiny shinylive Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Gt Lazy Evaluation Tidymodels Statistics Debugging RStats Open Source Oss Reticulate Shiny for Python Pyshiny Ipyshiny Carson Sievert Tony Pelleriti Posit

Shiny UI Editor Project Walkthrough || Nick Strayer || RStudio

The Shiny UI Editor is a dynamic drag-and drop interface to help you design beautiful Shiny apps. The Shiny UI Editor is a visual tool for building the UI portion of a Shiny application that generates clean and human-readable code. The goal of the Shiny UI Editor is to allow people to build the broad-level UI for their Shiny app without writing code. The editor is intended for those who may not be comfortable with the HTML-style code of Shiny’s UI functions or who simply don’t want to fiddle with sizes to get things laid out correctly. Learn more about the Shiny UI Editor here: https://rstudio.github.io/shinyuieditor/ And read up on GridLayout here: https://rstudio.github.io/gridlayout Content: Nick Strayer (@NicholasStrayer) Producer: Jesse Mostipak (@kierisi) Editing and Motion Design: Tony Pelleriti (@TonyPelleriti)

Nick Strayer

gridlayout rstudio Shiny shinyuieditor Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging RStats Open Source Oss Reticulate Drag and Drop Shiny Ui Shiny Ui Editor Nick Strayer Tony Pelleriti Posit

Shiny UI Editor Feature Tour || Nick Strayer || Posit (RStudio)

The Shiny UI Editor is a dynamic drag-and drop interface to help you design beautiful Shiny apps. The Shiny UI Editor is a visual tool for building the UI portion of a Shiny application that generates clean and human-readable code. The goal of the Shiny UI Editor is to allow people to build the broad-level UI for their Shiny app without writing code. The editor is intended for those who may not be comfortable with the HTML-style code of Shiny’s UI functions or who simply don’t want to fiddle with sizes to get things laid out correctly. Learn more about the Shiny UI Editor here: https://rstudio.github.io/shinyuieditor/ And read up on GridLayout here: https://rstudio.github.io/gridlayout Content: Nick Strayer (@NicholasStrayer) Producer: Jesse Mostipak (@kierisi) Editing and Motion Design: Tony Pelleriti (@TonyPelleriti)

Nick Strayer

gridlayout rstudio Shiny shinyuieditor Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging RStats Open Source Oss Reticulate Shiny Ui Shiny Ui Editor Drag and Drop Nick Strayer Tony Pelleriti Posit

Shiny Programming Practices || Joe Cheng || Posit

Have you ever wanted to sit down and talk with Joe Cheng, the creator of Shiny and CTO of Posit (RStudio) and ask him how he approaches programming? Look no further - we've got that conversation for you right here! Shiny makes it easy to build interactive web apps straight from R or Python. You can host standalone apps on a webpage or embed them in Markdown-style documents or build dashboards. You can also extend your Shiny apps with CSS themes, htmlwidgets, and JavaScript actions. Learn more about Shiny: https://shiny.rstudio.com/ Check out Shiny for Python: https://shiny.rstudio.com/py/ Explore our interactive Shiny for Python examples: https://shinylive.io/py/examples/ Content: Joe Cheng (@jcheng) Producer: Jesse Mostipak (@kierisi) Editing and Motion Design: Tony Pelleriti (@TonyPelleriti)

Joe Cheng

rstudio Shiny for Python Shiny shinylive Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging RStats Open Source Oss Reticulate Pyshiny Shiny for Python Posit Joe Cheng Tony Pelleriti

Hey Shiny Team, what are some of your biggest learnings from 2022? || Shiny Developers || RStudio

BIG THINGS happened on the Shiny team in 2022! Our team built out a new Shiny UI Editor, Shiny for Python, and Shiny for Python in the browser using WebAssembly. So we asked some of our Developers what their biggest learnings have been from building these products! Learn more about Shiny for Python: https://shiny.rstudio.com/py/ Content: Winston Chang (@winston_chang), Carson Sievert (@cpsievert), Nick Strayer (), Michael Chow (@chowthedog) Producer: Jesse Mostipak (@kierisi) Video editing + motion design: Tony Pelleriti (@TonyPelleriti)

Carson Sievert, Michael Chow, Nick Strayer, Shiny Team, Winston Chang

rstudio Shiny for Python Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown CRAN Interoperability Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Plumber Blogdown Gt Tidymodels Statistics Debugging RStats Open Source Oss Winston Chang Nick Strayer Carson Sievert Michael Chow Shiny for Python Shiny Ui Editor Posit Drag and Drop Pyshiny Tony Pelleriti

Hello, World! A Quick Tour of Shiny for Python || Carson Sievert || Posit

Shiny makes it easy to build interactive web applications with the power of Python’s data and scientific stack. Learn more about Shiny for Python: https://shiny.rstudio.com/py/ Check out our interactive Shiny for Python examples: https://shinylive.io/py/examples/ Content: Carson Sievert (@cpsievert) Producer: Jesse Mostipak (@kierisi) Editing and Motion Design: Tony Pelleriti (@TonyPelleriti)

Carson Sievert

rstudio Shiny for Python Shiny shinylive Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging RStats Open Source Oss Reticulate Shiny for Python Getting Started Carson Sievert Tony Pelleriti Posit

Getting Started with {shinytest2} Part I || Example + basics || RStudio

00:00 Introduction 00:48 Overview of the demo Shiny app 03:00 Running record_test() 04:44 Results from record_test() 07:18 A note on .png files created during testing 08:52 Debugging with shinytest2 09:32 Using app$view() to open a visual representation of a headless browser Part 2 - Exporting values: https://youtu.be/7KLv6HdIxvU Part 3 - Using shiny.testmode: https://youtu.be/xDxa_mDwN04 Manually testing Shiny applications is often laborious, inconsistent, and doesn’t scale well. Whether you are developing new features, fixing bug(s), or simply upgrading dependencies on a serious app where mistakes have real consequences, it is critical to know when regressions are introduced. shinytest2 provides a streamlined toolkit for unit testing Shiny applications and seamlessly integrates with the popular testthat framework for unit testing R code. shinytest2 uses chromote to render applications in a headless Chrome browser. chromote allows for a live preview, better debugging tools, and/or simply using modern JavaScript/CSS. By simply recording your actions as code and extending them to test the more particular aspects of your application, it will result in fewer bugs and more confidence in future Shiny application development. Read up on shinytest2 here: https://rstudio.github.io/shinytest2/ Learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Barret Schloerke (@schloerke) Motion design and editing: Jesse Mostipak (@kierisi) Theme song: Brad PKL by Blue Dot Sessions (https://app.sessions.blue/browse/track/113507)

Barret Schloerke

chromote rstudio Shiny shinytest2 testthat Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Shinytest2 Barret Schloerke

Getting Started with {shinytest2} Part 3 || Using shiny.testmode in {shinytest2} || RStudio

00:00 Introduction 00:15 Testing production apps Part 1 - Getting started: https://youtu.be/SS1Na3c8lhk Part 2 - Exporting values: https://youtu.be/7KLv6HdIxvU Manually testing Shiny applications is often laborious, inconsistent, and doesn’t scale well. Whether you are developing new features, fixing bug(s), or simply upgrading dependencies on a serious app where mistakes have real consequences, it is critical to know when regressions are introduced. shinytest2 provides a streamlined toolkit for unit testing Shiny applications and seamlessly integrates with the popular testthat framework for unit testing R code. shinytest2 uses chromote to render applications in a headless Chrome browser. chromote allows for a live preview, better debugging tools, and/or simply using modern JavaScript/CSS. By simply recording your actions as code and extending them to test the more particular aspects of your application, it will result in fewer bugs and more confidence in future Shiny application development. Read up on shinytest2 here: https://rstudio.github.io/shinytest2/ Learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Barret Schloerke (@schloerke) Motion design and editing: Jesse Mostipak (@kierisi) Theme song: Brad PKL by Blue Dot Sessions (https://app.sessions.blue/browse/track/113507)

Barret Schloerke

chromote rstudio Shiny shinytest2 testthat Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Barret Schloerke Shinytest2

Getting Started with {shinytest2} Part 2 || Exporting values || RStudio

00:00 Introduction 00:29 Exporting reactives 03:28 Using exportTestValues() Part 1 - Getting started: https://youtu.be/SS1Na3c8lhk Part 3 - Using shiny.testmode: https://youtu.be/xDxa_mDwN04 Manually testing Shiny applications is often laborious, inconsistent, and doesn’t scale well. Whether you are developing new features, fixing bug(s), or simply upgrading dependencies on a serious app where mistakes have real consequences, it is critical to know when regressions are introduced. shinytest2 provides a streamlined toolkit for unit testing Shiny applications and seamlessly integrates with the popular testthat framework for unit testing R code. shinytest2 uses chromote to render applications in a headless Chrome browser. chromote allows for a live preview, better debugging tools, and/or simply using modern JavaScript/CSS. By simply recording your actions as code and extending them to test the more particular aspects of your application, it will result in fewer bugs and more confidence in future Shiny application development. Read up on shinytest2 here: https://rstudio.github.io/shinytest2/ Learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Barret Schloerke (@schloerke) Motion design and editing: Jesse Mostipak (@kierisi) Theme song: Brad PKL by Blue Dot Sessions (https://app.sessions.blue/browse/track/113507)

Barret Schloerke

chromote rstudio Shiny shinytest2 testthat Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Shinytest2 Barret Schloerke

Data visualization and plotting with Shiny for Python || Carson Sievert || RStudio

Shiny makes it easy to build interactive web applications with the power of Python’s data and scientific stack. Learn more about Shiny for Python: https://shiny.rstudio.com/py/ Check out our interactive Shiny for Python examples: https://shinylive.io/py/examples/ Content: Carson Sievert (@cpsievert) Producer: Jesse Mostipak (@kierisi) Editing and Motion Design: Tony Pelleriti (@TonyPelleriti)

Carson Sievert

rstudio Shiny for Python Shiny shinylive Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging RStats Open Source Oss Reticulate Shiny for Python Pyshiny Carson Sievert Tony Pelleriti Posit

An Interview with Winston Chang: Building a Wordle App with Shiny for Python || RStudio

Shiny makes it easy to build interactive web applications with the power of Python’s data and scientific stack. Learn more about Shiny for Python: https://shiny.rstudio.com/py/ Check out our interactive Shiny for Python examples: https://shinylive.io/py/examples/ Content: Winston Chang (@winston_chang) + Jesse Mostipak (@kierisi) Producer: Jesse Mostipak (@kierisi) Editing and Motion Design: Tony Pelleriti (@TonyPelleriti)

Winston Chang

rstudio Shiny for Python Shiny shinylive Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging RStats Open Source Oss Reticulate Shiny for Python Pyshiny Wordle Winston Chang Jesse Mostipak Tony Pelleriti Posit

A Beginner's Guide to Shiny for Python || Winston Chang || Posit

Shiny makes it easy to build interactive web applications with the power of Python’s data and scientific stack. Learn more about Shiny for Python: https://shiny.rstudio.com/py/ Check out our interactive Shiny for Python examples: https://shinylive.io/py/examples/ Content: Winston Chang (@winston_chang) Producer: Jesse Mostipak (@kierisi) Editing and Motion Design: Tony Pelleriti (@TonyPelleriti)

Winston Chang

rstudio Shiny for Python Shiny shinylive Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging RStats Open Source Oss Reticulate Shiny for Python Pyshiny Wasm Posit Winston Chang Tony Pelleriti

Programming Games with Shiny || Roll the Dice: with Quosures! || RStudio

00:00 Introduction 03:44 The pain of copy + paste 07:28 Going on a helper function adventure! 18:09 Ready for rlang 28:17 !! + enquo() 37:57 Benefits of the rlang approach 38:46 Embracing the embrace operator 41:33 Visualizing what's happening using reactlog You've most likely used Shiny to build a web app that displays data, but you can also use Shiny to build games! In this video series, Jesse and Barret pair program simply games in Shiny as a way to uncover and explore new features. Read up on the embrace operator here: https://rlang.r-lib.org/reference/embrace-operator.html Learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Barret Schloerke (@schloerke) and Jesse Mostipak (@kierisi) Animation, motion design, and editing: Jesse Mostipak (@kierisi) Theme song: Hakodate Line by Blue Dot Sessions (https://app.sessions.blue/browse/track/111291")

Barret Schloerke

reactlog rlang rstudio Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Viz Ggplot Technology Coding Connect Shiny Package Manager CRAN Interoperability Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Quosures Embrace Metaprogramming Tidy Evaluation Reactlog Rlang Jesse Mostipak Barret Schloerke

Rich Iannone || New features in {gt} 0.6.0! || RStudio

00:00 Introduction 00:18 sub_missing() 03:51 Markdown formatting in sub_missing() 04:51 sub_zero() 07:34 sub_small_vals() 13:08 sub_large_vals() 16:25 final thoughts A new version of the R package {gt} has been released! We are now at version `0.6.0` and there are now even more features that'll make your display/summary tables look and work much, much better. Let's run through some of the bigger changes and see the benefits they can bring! New functions for substituting cell data We now have four new functions that allow you to make precise substitutions of cell values with perhaps something more meaningful. They all begin with `sub_` and that's short for substitution! sub_missing() (formerly known as fmt_missing()) Here's something that's both old and new. The sub_missing() function (for replacing NAs with... something) is new, but it's essentially replacing a function that is old (fmt_missing()). The missing_text replacement of "---" is actually an em dash (the longest of the dash family). This can be downgraded to an en dash with "--" or we can go further with "-", giving us a hyphen replacement. Or, you can use another piece of text. If you're using and loving fmt_missing(), it's okay! You'll probably receive a warning about it when you upgrade to {gt} 0.6.0 though. Best to just substitute fmt_missing() with sub_missing() anyway! sub_zero() The sub_zero() function allows for substituting zero values in the table body. sub_small_vals() Next up is the sub_small_vals() function. Ever have really, really small values and really just want to say they are small? With sub_small_vals() we can reformat smaller numbers using the default threshold of 0.01. Small and negative values can also be handled but they are handled specially by the sign parameter. Setting that to "-" will format only the small, negative values. You don't have to settle with the default threshold value or the default replacement pattern (in small_pattern). This can be changed and the "x" in small_pattern (which uses the threshold value) can even be omitted. sub_large_vals() Okay, there's one more substitution function to cover, and this one's for all the large values in your table: sub_large_vals(). With this you can substitute what you might consider as too large values in the table body. Large negative values can also be handled but they are handled specially by the sign parameter. Setting that to "-" will format only the large values that are negative. You don't have to settle with the default threshold value or the default replacement pattern (in large_pattern). This can be changed and the "x" in large_pattern (which uses the threshold value) can even be omitted. Final thoughts We are always trying to improve the gt package with a mix of big features (some examples: improving rendering, adding new families of functions) and numerous tiny features (like improving existing functions, clarifying documentation, etc.). It's hoped that the things delivered in gt 0.6.0 lead to improvements in how you create and present summary tables in R. If there are features you *really* want, always feel free to: File an issue: https://github.com/rstudio/gt/issues) Talk about your ideas on the Discussions page: https://github.com/rstudio/gt/discussions Learn more about the gt package here: https://gt.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Rich Iannone (@riannone) Motion Design & editing: Jesse Mostipak Music: Nu Fornacis by Blue Dot Sessions https://app.sessions.blue/browse/track/98983

Rich Iannone

gt rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Coding Server Pro Shiny Rmarkdown CRAN Interoperability Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Gt 0.6.0 Rich Iannone Gt Update Sub_missing Sub_small_vals Sub_large_vals Sub_zero Tables

{gt} Table Battles || Eurovision || RStudio

00:00 Introduction 00:07 Jesse's gt table, with a focus on flag emoji and interactivity via a Shiny app 09:50 Rich's gt table, with a focus on CSS and embedded animations Code: https://github.com/kierisi/rstudio_videos/tree/main/gt/table-battles Learn more about the gt package here: https://gt.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Rich Iannone (@riannone) & Jesse Mostipak (@kierisi) Motion Design & editing: Jesse Mostipak Music: Gemeni City by Blue Dot Sessions https://app.sessions.blue/browse/track/113567

Rich Iannone

gt rstudio Shiny Rstudio Data Science Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Rich Iannone Jesse Mostipak Table Battles

Programming Games with Shiny || Roll the Dice || RStudio

00:00 Introduction 01:40 Rolling with eventReactive( ) 06:26 Reducing eventReactive( ) to reactive( ) + isolate( ) 16:23 Combining reactive( ) and bindEvent( ) 20:11 Reviewing our reactives 21:23 Writing a function to de-duplicate dice rolls You've most likely used Shiny to build a web app that displays data, but you can also use Shiny to build games! In this video series, Jesse and Barret pair program simply games in Shiny as a way to uncover and explore new features. Read up on tabset panels here: https://shiny.rstudio.com/reference/shiny/0.14/tabsetPanel.html Learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Barret Schloerke (@schloerke) and Jesse Mostipak (@kierisi) Animation, motion design, and editing: Jesse Mostipak (@kierisi) Theme song: Hakodate Line by Blue Dot Sessions (https://app.sessions.blue/browse/track/111291")

Barret Schloerke

rstudio Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate EventReactive Isolate Reactive BindEvent Barret Schloerke Jesse Mostipak

{gt} Table Battles || Crosswords || RStudio

00:00 Introduction 00:34 Rich's gt table, with a focus on creating audio within a table 07:28 Jesse's gt table, with a focus on sentiment analysis Learn more about the gt package here: https://gt.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Rich Iannone (@riannone) & Jesse Mostipak (@kierisi) Motion Design & editing: Jesse Mostipak Music: Nu Fornacis by Blue Dot Sessions https://app.sessions.blue/browse/track/98983

Rich Iannone

gt rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Sentiment Analysis Audio Rich Iannone Jesse Mostipak

Posit Package Manager || Link Your Package Manager Repo to Your RStudio IDE || Posit

In this video Jeremey Allen walks through connecting Posit Package Manager to Workbench for fast and secure access to the organization’s R and Python libraries. Posit Package Manager is a repository management server to organize and centralize packages across your team, department, or entire organization. Get offline access to CRAN, PyPI, and Bioconductor, share local packages, restrict package access, find packages across repositories, and more. Experience reliable and consistent package management, optimized for data science. Learn more about Posit Package Manager here: https://www.rstudio.com/products/package-manager/ Got questions? Check out the Posit Package Manager Frequently Asked Questions page: https://docs.rstudio.com/rspm/admin/getting-started/faq/

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Katie Masiello || Build a Codenames app using {pins} and Shiny! || RStudio

00:00 Introduction 00:05 Project outline 03:56 Create a codename generator (using RMarkdown) 09:35 Publish to RStudio Connect 10:38 Create a Shiny app 18:15 A little bit of troubleshooting 18:18 Ta-da! Learn more about the pins package here: https://pins.rstudio.com/ Learn more about Shiny here: https://shiny.rstudio.com/ And learn more about RStudio Connect here: https://www.rstudio.com/products/connect/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Katie Masiello (@katieontheridge) Animation, motion design, and editing: Jesse Mostipak (@kierisi) Theme song: Contrarian by Blue Dot Sessions (https://app.sessions.blue/browse/track/64281)

rmarkdown rstudio Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Pins Codenames Katie Masiello

RStudio's {pins} package: what it is, how it works, and what it can do for you! || RStudio

00:00 Introduction 00:09 What is the pins package? 01:49 pins - not just for RStudio Connect! 02:31 pins use cases 04:47 How to use pins instead of final_final_01_noreallyfinal.xls 06:37 How do pin boards work? 08:55 Getting started with pins 10:42 Versioning with pins at the board or pin level 11:47 pins and caching 12:13 Things you shouldn't pin 14:00 Major functions in the pins package 17:21 Using pin_upload( ) and pin_download( ) 19:52 pins and Google Cloud 21:27 pins and modelops with the vetiver package Learn more about the pins package here: https://pins.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Katie Masiello (@katieontheridge) and Jesse Mostipak (@kierisi) Animation, motion design, and editing: Jesse Mostipak (@kierisi) Theme song: Contrarian by Blue Dot Sessions (https://app.sessions.blue/browse/track/64281)

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Pins Katie Masiello Jesse Mostipak

Programming Games with Shiny || Guess the Number || RStudio

00:00 Introduction 00:35 Setting up our app UI 06:19 Using observeEvent() 10:02 Writing an if else statement as part of our feedback mechanism 13:40 Testing our app and deciding which bugs to fix first 14:20 Check yourself before you req() yourself 17:10 Using tabset panels to control what the user sees You've most likely used Shiny to build a web app that displays data, but you can also use Shiny to build games! In this video series, Jesse and Barret pair program simply games in Shiny as a way to uncover and explore new features. Read up on tabset panels here: https://shiny.rstudio.com/reference/shiny/1.5.0/tabsetPanel.html Learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Barret Schloerke (@schloerke) and Jesse Mostipak (@kierisi) Animation, motion design, and editing: Jesse Mostipak (@kierisi) Theme song: Hakodate Line by Blue Dot Sessions (https://app.sessions.blue/browse/track/91291)

Barret Schloerke

rstudio Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Stringr Tidyr Purrr Github Data Wrangling Odbc Plumber Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Pgws Programming Games With Shiny Barret Schloerke Jesse Mostipak

{gt} Table Battles || Digital Publications || RStudio

00:00 Introduction 00:32 Jesse's gt table, with a focus on changing background cell color 07:11 Rich's gt table, which uses three different tables to create a fixed-size scrollable gt table You can find the code for each table here: https://github.com/kierisi/rstudio_videos/tree/main/gt/table-battles/01_round-01_digital-publications Learn more about the gt package here: https://gt.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Rich Iannone (@riannone) & Jesse Mostipak (@kierisi) Motion Design & editing: Jesse Mostipak Music: Nu Fornacis by Blue Dot Sessions https://app.sessions.blue/browse/track/98983

Rich Iannone

gt rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Rich Iannone Jesse Mostipak TidyTuesday Table Battle

Rich Iannone || {gt} Intendo Game Data Project Walkthrough || RStudio

00:00 Introduction 00:11 Setting up our environment 01:21 Importing data 01:56 Data preparation using the tidyverse 14:12 Basic gt table 16:25 Specifying row order with row_group_order() 17:20 Formatting currency with fmt_currency() 18:10 Formatting missing values with fmt_missing() 18:55 Creating row groups with tab_options() 19:50 Relabel column names with cols_label() 20:41 Creating tab spanners with tab_spanner() 23:00 Creating a table title and subtitle with tab_header() 24:40 Aligning table title and subtitle with opt_align_table_header() 25:16 Creating a stubhead label with tab_stubhead() 26:00 Format all table cell text using tab_style() 27:25 Automatically format data color based on value using data_color() 30:45 Creating Markdown-friendly source notes using tab_source_note() 32:45 Creating Markdown-friendly footnotes using tab_footnote() 39:28 Adjust table column width using cols_width() 40:55 Adjust cell padding using opt_horizontal_padding() and opt_vertical_padding() 42:22 Change row group headers using tab_style() 43:40 Convert all table text to small caps using opt_all_caps() 43:58 Change all table text font using opt_table_font() 44:28 Changing table, table heading, footnotes, and source notes background color using tab_options() 46:41 Add a table "cap" at the top and bottom using table.border.top.width() and table.border.bottom.width() 47:23 Use multiline formatting with footnotes using footnotes.multiline() 47:34 Change line style using table_body.hlines.style() 47:55 Change table title and subtitle font sizes using heading.title.font.size() and heading.subtitle.font.size() 48:11 Checking out our final table! Code to recreate the table from the video: https://github.com/kierisi/rstudio_videos/blob/main/gt/rich-intendo-project-walkthrough/intendo-30032022.R Learn more about the gt package here: https://gt.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Rich Iannone (@riannone) Motion design and editing: Jesse Mostipak (@kierisi) Music: Nu Fornacis by Blue Dot Sessions https://app.sessions.blue/browse/track/98983

Rich Iannone

gt rstudio tidyverse Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Rich Iannone

"A Stroke of Innovation," a Posit Film

This snippet is from Posit's short film, "A Stroke of Innovation," detailing the incredible work done by the data science team at the City of Reykjavík. You can watch the whole film and read the entire story here: https://posit.co/about/customer-stories/iceland/

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Posit Presents: A Stroke of Innovation

Introducing "A Stroke of Innovation," a short film made possible by a team of innovators looking to make the world a better place. Visit https://posit.co/about/customer-stories/iceland/ for the full web experience

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Programming Games with Shiny || Dragon Realm || RStudio

00:00 Introduction 00:05 Fun dragon facts 00:35 Describing the Dragon Realm game 01:20 Outlining our approach 04:38 Coding the basics of our app 10:15 Programming our action buttons 14:10 A note on coding objects "outside" of Shiny 15:27 Programming cave choice logic 20:29 Connecting action buttons to our consequences function 29:40 Creating separate pages using tabsetPanel() 39:35 Conclusion You've most likely used Shiny to build a web app that displays data, but you can also use Shiny to build games! In this video series, Jesse and Barret pair program simply games in Shiny as a way to uncover and explore new features. And because we know you'll ask, Jesse is using the Woodland theme from the base16 palette. You can get it - and other themes - from the {rsthemes} package: https://github.com/gadenbuie/rsthemes Read up on tabset panels here: https://shiny.rstudio.com/reference/shiny/1.5.0/tabsetPanel.html Learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Barret Schloerke (@schloerke) and Jesse Mostipak (@kierisi) Animation, motion design, and editing: Jesse Mostipak (@kierisi) Intro music: RGift by Blue Dot Sessions (https://app.sessions.blue/browse/track/91282) Theme song: Hakodate Line by Blue Dot Sessions (https://app.sessions.blue/browse/track/91291)

Barret Schloerke

rstudio Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown CRAN Interoperability Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Dragon Realm Barret Schloerke Jesse Mostipak

Carson Sievert || Using tagQuery() from {htmltools} to modify HTML snippets in R || RStudio

00:00 Introduction 00:45 Motivating example - enabling front-facing camera as an input for fileInput() 01:55 Breaking down the return value of fileInput() 04:16 Design philosophy of fileInput() 07:27 tagAppendAttributes() overview 11:05 tagQuery() basics 12:00 Quick overview of the htmltools package 13:18 How tagQuery() is used to append attributes 20:54 How tagQuery() is used to append children 23:45 Using tagQuery() on an actionButton() Learn more about tagQuery here: https://rstudio.github.io/htmltools/articles/tagQuery.html Read up on tagAppendAttributes() here: https://shiny.rstudio.com/reference/shiny/latest/tagAppendAttributes.html And learn more about the htmltools package here: https://rstudio.github.io/htmltools/index.html Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Developer (@winston_chang) Animation, design, and editing: Jesse Mostipak (@kierisi)

Carson Sievert

htmltools rstudio Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Htmltools Carson Sievert Tagquery Tagquery()

James Blair || Getting Started with {plumbertableau} || RStudio

00:00 Introduction 01:19 Setting up the problem - capitalizing text with a custom function 02:18 Using Plumber to create an API for our function 04:08 Using Run API + Swagger from the RStudio IDE 05:44 Giving Tableau access to the function with PlumberTableau 09:16 Reviewing what we've done so far 09:47 Comparing results between Plumber and PlumberTableau 10:12 Overview of what PlumberTableau does 14:27 Centralized hosting with RStudio Connect 15:17 Looking at our API in RStudio Connect 18:14 How to access the deployed API from Tableau 21:03 Overview of RStudio Connect, Tableau, and PlumberTableau process 21:52 More in-depth example using sample sales data 22:36 Example with the Python equivalent of PlumberTableau, FastAPITableau 25:15 Overview of how these Tableau extension packages work 27:21 Setting up a connection between Tableau and RStudio Connect Read more about the plumbertableau package here: https://rstudio.github.io/plumbertableau/ And learn about the fastapitableau package here: https://rstudio.github.io/fastapitableau/ If you’re unfamiliar with Plumber, this Quickstart guide gives a good overview of the package: https://www.rplumber.io/articles/quickstart.html And you can learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: James Blair (@Blair09M) Design and editing: Jesse Mostipak (@kierisi) Music: Borough by Blue Dot Sessions https://app.sessions.blue/browse/track/89821

plumber plumbertableau rstudio Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Plumbertableau James Blair

Carson Sievert || Customizing Navigation Items in Shiny using {bslib} || RStudio

00:00 Introduction 00:15 Linking inside navbarPage 01:19 Replacing tabPanel with navbarPage, and navbarMenu 02:32 nav_spacer() 03:41 Adding header and//or footer content 04:07 Replacing tabsetPanel with navs_tab and navs_pill 04:32 navs_tab_card() and navs_pill_card() variants 04:40 Demo of all of the nav_*() functions The bslib R package provides tools for customizing Bootstrap themes directly from R, making it much easier to customize the appearance of Shiny apps & R Markdown documents. bslib’s primary goals are: - To make custom theming as easy as possible. - Custom themes may even be created interactively in real-time. - Also provide easy access to pre-packaged Bootswatch themes. - Make upgrading from Bootstrap 3 to 4 (and beyond) as seamless as possible. (Shiny and R Markdown default to Bootstrap 3 and will continue to do so to avoid breaking legacy code.) - Serve as a general foundation for Shiny and R Markdown extension packages. (Extensions such as flexdashboard, pkgdown, and bookdown already fully support bslib’s custom theming capabilities.) You can read more about bslib here: https://rstudio.github.io/bslib/articles/bslib.html And you can learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Carson Sievert (@cpsievert) Design & editing: Jesse Mostipak (@kierisi)

Carson Sievert

bookdown bslib flexdashboard pkgdown rstudio Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Bslib Carson Sievert

Carson Sievert || Developing Shiny Custom Themes in Real Time Using {bslib}| RStudio

00:00 Introduction 00:09 The magic of bs_theme_preview() 01:43 The interactive widget provided by bs_theme_preview() 02:12 Using Bootswatch themes 02:57 Using the interactive widget to adjust your theme in real time 03:38 Integration with Google Fonts 04:22 Thematic is enabled in bs_theme_preview() 04:45 DT tables is enabled in bs_theme_preview() 05:30 Going from the interactive widget to your R code 07:03 Using interactive theming on your own Shiny app 09:01 Interactive theming with R Markdown documents The bs_theme_preview() function launches an example shiny app via run_with_themer() and bs_theme_dependencies(). This is useful for getting a quick preview of the current theme setting as well as an interactive GUI for tweaking some of the main theme settings. Link to docs: https://rstudio.github.io/bslib/reference/bs_theme_preview.html You can read more about the bslib package here: https://rstudio.github.io/bslib/ And you can learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Carson Sievert (@cpsievert) Design and editing: Jesse Mostipak (@kierisi)

Carson Sievert

bslib DT rstudio Shiny thematic Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Bslib Carson Sievert

Carson Sievert || Custom Theming with {bslib} in Shiny and R Markdown using bs_theme() || RStudio

00:00 Introduction 01:15 Jumping right in with the theme argument in Shiny 01:31 Shiny's classic navbarPage using bs_theme() 01:46 Specifying your Bootstrap version 02:31 Using the theme argument in R Markdown 03:17 Custom theming in R Markdown using bs_theme() 04:10 bslib templates provided by RStudio 05:33 Review of common arguments for the theme parameter 08:47 Tips for working with dark themes 10:34 Introduction to the thematic package, which styles plots 12:04 How thematic handles fonts 13:09 Using fonts with bslib in R Markdown 14:36 Moving a theme from an R Markdown document into a Shiny app 16:51 Setting warnings for contrast ratios 18:42 A quick tour of Bootstrap 4 and Bootstrap 5 Sass variables 20:35 A quick overview of writing custom HTML in Shiny 22:15 How bslib automatically handles color contrast ratios bs_theme() allows you to creates a Bootstrap theme object, where you can choose a (major) Bootstrap version, choose a Bootswatch theme (optional), customize main colors and fonts via explicitly named arguments (e.g., bg, fg, primary, etc), and customize other, lower-level, Bootstrap Sass variable defaults via .... You can read more about the bslib package here: https://rstudio.github.io/bslib/ And you can learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Carson Sievert (@cpsievert) Design and editing: Jesse Mostipak (@kierisi)

Carson Sievert

bslib rstudio Shiny thematic Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Bslib Bs_theme() Bs_theme Carson Sievert

Barret Schloerke || Maximize computing resources using future_promise() || RStudio

00:00 Introduction 01:45 Setting up a multisession using the future package 02:05 Simulation using two workers 04:14 Simulation using 10 workers 05:20 What happens when we run out of workers? 05:35 How Shiny handles future processes like promises 07:16 Introduction to future_promise() 07:45 Demo of the promises package 09:21 Setting the number of workers 10:40 Demo of processing without future_promise() 14:11 Wrapping a slow calculation in a future() 14:53 Demo of processing using Plumber 16:25 Considerations on the number of cores to use 17:21 What happens if we run out of workers? 19:44 Decrease in execution times using future_promise() In an ideal situation, the number of available future workers (future::nbrOfFreeWorkers()) is always more than the number of future::future() jobs. However, if a future job is attempted when the number of free workers is 0, then future will block the current R session until one becomes available. The advantage of using future_promise() over future::future() is that even if there aren’t future workers available, the future is scheduled to be done when workers become available via promises. In other words, future_promise() ensures the main R thread isn’t blocked when a future job is requested and can’t immediately perform the work (i.e., the number of jobs exceeds the number of workers). You can read more about the promises package here: https://rstudio.github.io/promises/articles/shiny.html And you can learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Barret Schloerke (@schloerke) Design and editing: Jesse Mostipak (@kierisi)

Barret Schloerke

plumber rstudio Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Promises Barret Schloerke Future_promise()

Nick Strayer || Part IV: Styling a Shiny Wordle App with CSS || RStudio

00:00 Introduction 00:44 Switching from verbatimTextOutput to uiOutput 01:42 Switching from renderText to HTML DOM elements 03:17 In-line styling with divs 07:30 Converting individual letters from block elements to adjacent grids with CSS grid 08:56 Adding CSS at the head of the UI variable in Shiny with tags$head (and wrapping with HTML!) 10:36 CSS targeting of the background color 12:24 Link: Complete Guide to CSS Grid 14:05 Moving text position within each individual div using CSS classes 16:48 Creating a gap between grid elements 17:13 Rounding border edges for letter grids 19:00 Formatting letter grid background color to indicate result "correctness" 21:30 Increasing font size 23:37 Updating the legend to use color, not text indicators 26:40 Adjusting padding to improve app aesthetic 28:08 Formatting the app UI with justified centering 31:56 Adjusting the text input and Go button 34:07 Why Flexbox is the right tool for this task 35:09 Exploring Flexbox Dev Tools in Chrome 39:14 Adjusting the colors of letter grids using Inspect Element 40:40 Making text bold with font-weight 41:04 Hint on how to approach formatting the keyboard In final installment of this four-part series, RStudio's Nick Strayer walks through using CSS to stylize our Shiny Wordle app. Code + word list: https://github.com/wch/shiny-wordle Check out the full Shiny app here: https://winston.shinyapps.io/wordle/ You can learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Nick Strayer (@NicholasStrayer) Animation, design, and editing: Jesse Mostipak (@kierisi) Music: Lakal by Blue Dot Sessions Wordle: https://www.powerlanguage.co.uk/wordle/

Nick Strayer

rstudio Shiny shinyapps Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate CSS Wordle Nick Strayer

Barret Schloerke || {reactlog} Rundown || RStudio

00:00 Introduction to Reactlog 00:44 Viewing Reactlog using an Old Faithful Shiny app 02:07 The Reactlog interface 04:31 Walking through a reactive graph with Reactlog 05:14 Downstream dependency invalidation in Shiny 06:43 How Shiny "grabs" data 09:41 How the Reactlog timeline works 10:46 Switching between idle states in Reactlog 11:58 Reactlog interactivity - clicking a single item 13:21 Reactlog with the Pythagoras Theorem app 15:45 Adding a UI and server value to add Reactlog to your Shiny app 18:05 Walking through the reactive graph using the Pythagorean Theorem app 21:07 Append-only behavior of Reactlog 21:18 Marking a time point in Reactlog 23:17 Using Reactlog to debug reactivity 26:55 Resetting our app and testing logic changes 28:01 Reactlog with a large Shiny app, CRANwhales 34:10 Freezing reactive values 36:19 Calculating click count in a Shiny app 37:10 Click the button, render the plot is bad - see why Shiny is an R package from RStudio that makes it incredibly easy to build interactive web applications with R. Behind the scenes, Shiny builds a reactive graph that can quickly become intertwined and difficult to debug. reactlog provides a visual insight into that black box of Shiny reactivity. After logging the reactive interactions of a Shiny application, reactlog constructs a directed dependency graph of the Shiny’s reactive state at any time point in the record. The reactlog dependency graph provides users with the ability to visually see if reactive elements are: - Not utilized (never retrieved) - Over utilized (called independently many times) - Interacting with unexpected elements - Invalidating all expected dependencies - Freezing (and thawing), preventing triggering of future reactivity There are many subtle features hidden throughout reactlog. Here is a short list quickly describing what is possible within reactlog: - Display the reactivity dependency graph of your Shiny applications - Navigate throughout your reactive history to replay element interactions - Highlight reactive family trees - Filter on reactive family trees - Search for reactive elements You can read more about reactlog here: https://rstudio.github.io/reactlog/articles/reactlog.html And you can learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Barret Schloerke (@schloerke) Design & editing: Jesse Mostipak (@kierisi)

Barret Schloerke

cranwhales reactlog rstudio Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Reactlog Reactivity

Winston Chang || Part III: Adding a Keyboard to a Wordle Shiny App || RStudio

00:00 Introduction 00:25 Setting up a keyboard 00:54 Using an HTML p tag to print out letter indicators 01:56 Back to our keyboard! 03:44 Setting up a search and replace 06:32 Removing letters using regular expressions 08:43 Making guesses a reactiveVal() 11:00 Avoiding an infinite loop with reactiveVal() In Part III of this four-part series, Winston walks through how to build a keyboard in a Shiny Wordle app. Code + word list: https://github.com/wch/shiny-wordle Check out the full Shiny app here: https://winston.shinyapps.io/wordle/ You can learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Developer (@winston_chang) Animation, design, and editing: Jesse Mostipak (@kierisi) Wordle: https://www.powerlanguage.co.uk/wordle/

Winston Chang

rstudio Shiny shinyapps Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Wordle Winston Chang

Daniel Petzold || RStudio Team: Building and Sharing Jupyter Notebooks || RStudio

Learn more about RStudio Team here. https://www.rstudio.com/products/team/ Find the code for this example here. https://github.com/danielpetzold/space-tracker Read our blog post here. https://www.rstudio.com/blog/build-and-share-jupyter-notebooks-on-rstudio-team/ Timecodes 0:00 - Intro 0:07 - Build Jupyter Notebooks to analyze and visualize data 2:47 - Publish directly from RStudio Workbench to your content hub 5:13 - Share With Your Stakeholders on RStudio Connect Jupyter Notebooks are interactive documents for code, outputs, and text. However, they’re often stuck in data scientists’ local computing environments. Collaborating can be difficult and sharing can be tedious. To live up to their fullest potential, data science teams need a way to scale their development securely and efficiently — while providing stakeholders easy access to their output and visualizations. RStudio Team, made up of RStudio Workbench, RStudio Connect, and RStudio Package Manager, brings everything together to help data scientists create, reproduce, and share insights from their Jupyter Notebooks. Let’s dive into a real-life example by exploring data from NASA’s Center for Near-Earth Objects (NEOs). Daniel Petzold walks us through his data analysis and reporting. Want to explore the report yourself? Check out the published report on RStudio Connect here. https://colorado.rstudio.com/rsc/space-tracker/space_tracker.html On RStudio Workbench, you have a choice of editors: the RStudio IDE, JupyterLab, Jupyter Notebook, or VS Code. Choose your preference. From here, you can explore your dataset, embed HTML directly in your document, create visualizations, and more. Once you've run your analyses and created insightful visualizations, you want to be able to share them with your team. RStudio Workbench allows you to publish to RStudio Connect, the content platform from RStudio. You have multiple options: push-button deployment from Jupyter Notebook or using terminal commands from JupyterLab. It’s not enough to publish your work. Once on RStudio Connect, you can share with end-users. Make your analysis accessible to specific users or more generally with different authentication measures. In addition, you can schedule the document to run at a certain time and send out an email with refreshed data. Click the links below to learn more about these offerings. RStudio Workbench: https://www.rstudio.com/products/workbench/ RStudio Connect: https://www.rstudio.com/products/connect/

RStudio Team

rstudio Rstudio Data Science Machine Learning Python Stats Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tidyr Github Data Wrangling Tidy Data Odbc Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Jupyter JupyterLab Jupyter Notebooks RStudio Workbench Rstudio Connect

Winston Chang || Part II: Handling Duplicate Letters in a Shiny Wordle App || RStudio

00:00 Introduction 00:52 Setting up the problem with duplicate letters 02:08 Coding the first pass for exact matches in the correct position 06:29 Re-evaluating how to approach the problem 12:28 Removing only one instance of a letter 13:56 Testing our code 14:54 Setting up the second pass 19:08 Scoping with a double arrow 19:52 Debugging with a browser() statement 21:28 Checking our code In Part II of this four-part series, Winston walks through how to handle duplicate letters when building your Shiny Wordle app. Code + word list: https://github.com/wch/shiny-wordle Check out the full Shiny app here: https://winston.shinyapps.io/wordle/ You can learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Developer (@winston_chang) Animation, design, and editing: Jesse Mostipak (@kierisi) Wordle: https://www.powerlanguage.co.uk/wordle/

Winston Chang

rstudio Shiny shinyapps Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Wordle Winston Chang

Winston Chang || Part I: Build a Basic Wordle App with Shiny || RStudio

00:00 Introduction 00:12 What is Wordle? 00:36 The Wordle app we'll build by the end of this four-part series 01:08 How to approach the problem 01:38 Word list (link to file below) 01:52 UI function with fluidPage() 02:24 Print out what player guesses using verbatimTextOutput() 03:36 Run app in Viewer Panel 04:04 Adding an action button with actionButton() 04:29 Using bindEvent() with actionButton() 06:02 Limiting guesses to words with five characters 07:40 Using req() and cancelOutput() 08:54 Incorporating the word list 10:13 Matching player guess to word list 11:06 Matching player guess to target word 13:50 Writing a function to match guess to target word with feedback 18:15 Checking word length between guess and target 23:02 Why we're using intermediary functions 28:51 Printing formatted letter information In Part I of this four-part series, Winston walks through how to build a basic Wordle app using Shiny! Code + word list: https://github.com/wch/shiny-wordle Check out the full Shiny app here: https://winston.shinyapps.io/wordle/ You can learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Developer (@winston_chang) Animation, design, and editing: Jesse Mostipak (@kierisi) Wordle: https://www.powerlanguage.co.uk/wordle/

Winston Chang

rstudio Shiny shinyapps Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Wordle Winston Chang

What is Posit Workbench? || Posit

Rich Iannone || Making Beautiful Tables with {gt} || RStudio

00:00 Introduction 00:37 Adding a title with tab_header() (using Markdown!) 01:47 Adding a subtitle 02:48 Aligning table headers with opt_align_table_header() 03:48 Using {dplyr} with {gt} 06:03 Create a table stub with rowname_col() 07:35 Customizing column labels with col_label() 09:45 Formatting table numbers with fmt_number() 12:10 Adjusting column width with cols_width() 15:39 Adding source notes with tab_source_note() 16:55 Adding footnotes with tab_footnote() 18:55 Customizing footnote marks with opt_footnote_marks() 19:10 Demo of how easy managing multiple footnotes is with {gt} 23:41 Customizing cell styles with tab_style() 27:07 Adding label text to the stubhead with tab_stubhead() 28:15 Changing table font with opt_table_font() 29:25 Automatically scaling cell color based on value using data_color() With the gt package, anyone can make wonderful-looking tables using the R programming language. The gt philosophy: we can construct a wide variety of useful tables with a cohesive set of table parts. These include the table header, the stub, the column labels and spanner column labels, the table body, and the table footer. It all begins with table data (be it a tibble or a data frame). You then decide how to compose your gt table with the elements and formatting you need for the task at hand. Finally, the table is rendered by printing it at the console, including it in an R Markdown document, or exporting to a file using gtsave(). Currently, gt supports the HTML, LaTeX, and RTF output formats. The gt package is designed to be both straightforward yet powerful. The emphasis is on simple functions for the everyday display table needs. You can read more about gt here: https://gt.rstudio.com/articles/intro-creating-gt-tables.html And you can learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Rich Iannone (@riannone) Design & editing: Jesse Mostipak (@kierisi)

Rich Iannone

dplyr gt rstudio Shiny tibble Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Rich Iannone

An inclusive solution for teaching and learning R during the COVID pandemic

The COVID pandemic has shaken our teaching and learning approaches in many different ways all over the world. Nonetheless, it has also provided opportunities for bringing creativity into the classroom. In this talk, I will discuss how I have used RStudio Cloud in my teaching during the pandemic and how I capitalized on the opportunities that RStudio Cloud offers to deal with the crucial issues of software installation. Introducing RStudio Cloud in the units has allowed me to work effectively in an online environment to engage, motivate and empower students through their learning process while removing the troubles and hurdles of software installation which is generally particularly challenging in first-year cohorts without prior coding experience. I used RStudio Cloud in a data science introductory unit at Monash University and as a tool to present the usage of R and RStudio for reproducible reporting in another unit on Reproducible and Collaborative Practises. In the latter, I introduced RStudio Cloud during the first few weeks to get the students up to speed before transitioning to using R and RStudio Cloud in their own local machines while using the command line interface, Git, and GitHub as a version control tool for reproducible reporting. I will also discuss how I organized and managed the unit's RStudio Cloud account so that my research associates were also an integral part of the unit delivery to ensure the success of the units. Read Dr. Menéndez's guest post on the RStudio Blog: https://www.rstudio.com/blog/rstudio-cloud-an-inclusive-solution-for-learning-r/ Read more in the follow-up blog post: https://www.rstudio.com/blog/teaching-data-science-in-the-cloud/

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

To Our Community: Thank You | RStudio Open Source (2021)

This year, among many challenges, we have been so grateful for our community who have continued to show up in so many different ways. As the year comes to a close, we wanted to say thank you. We know folks have adjusted where they spend their time and energy, and we just want to say thank you for continuing to be here and learn together, wherever you are right now. Thank you to our maintainers. Our package maintainers not only contribute code, but also steward projects, welcome new contributors, and answer many questions. Thank you for continuing to set a positive tone and fostering a positive community for our contributors and users! Thank you to our contributors. Package contributors contribute code, ideas, conversation, documentation, and tests. You are the ones trying things out, figuring out what’s wrong, and sharing ideas and fixes. Thank you for pushing our code to grow and evolve and to help ever more people! Thank you to our educators. Educators work in classrooms, with friends and colleagues, and respond to email lists from someone around the world. Thank you for sharing your enthusiasm for data science with others and expanding the group of people who make sense of data with code! Thank you to everyone who’s used R to solve a problem, create aRt, write a blog post, share insights or any of the thousands of ways we can create with R. Thank you to all of you who have contributed in some way, no matter how big or small. Without you, there would be no point to writing open source code. Thank you for all the work you do in the world, using data to improve our lives and our knowledge of the world in such incredibly diverse ways. Music: Basketliner by Bitters, published on Blue Dot Sessions - https://app.sessions.blue/browse/track/81258 Editing & motion design: Jesse Mostipak

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Leveraging the Cloud for Analytics Instruction at Scale: Challenges and Opportunities

Data science and programming languages like R and Python are some of the most in demand skills in the world. Students interested in analytics and professors facilitating curriculums deserve to use industry-leading tools to acquire these skills. However, it’s challenging to enable this experience in an educational setting, especially at scale. The traditional tools to facilitate learning analytics simply aren’t great. Students and professors often spend way too much time troubleshooting systems and software, things that are a complete waste of time and detract from the learning experience. Additionally, there are seemingly endless IT hurdles and requirements. That’s part of the reason we created RStudio Cloud, a brilliantly simple but powerful solution for teaching and learning analytics, especially at scale. RStudio Cloud solves many of the technical and financial challenges associated with teaching analytics. It’s also a joy to use for professors, students, and IT administrators. In this presentation, Dr. Brian Anderson will discuss the challenges and opportunities associated with leveraging the cloud to deliver analytics instructions at the undergraduate and graduate levels at scale. Our hope is that you walk away inspired to think about ways you can leverage RStudio and the Cloud to enhance your students' experiences with learning analytics. Read more in the follow-up blog post: https://www.rstudio.com/blog/teaching-data-science-in-the-cloud/

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

RStudio Team Deep Dive | In A Hosted Environment

You probably know that RStudio makes a free, open-source development environment for data scientists. It’s made with love and used by millions of people around the world. What you might not know is that we also make a professional platform, called RStudio Team. In this Live Session, Tom will walk you through our Rstudio Team Trial, where you can learn how to best test drive.... - Scaling your data science work - Seamlessly managing open-source data science environments - Automate repetitive tasks - Rapidly share key insights and data science products securely to your entire organization. - And, optionally integrate some of your favorite open-source packages into the trial experience Leading organizations like NASA, Janssen Pharmaceuticals, The World Health Organization, financial institutions, government agencies and insurance organizations around the globe use RStudio’s professional products to tackle world-changing problems and we’re inviting you to learn how. You’ll learn how RStudio Team gives professional data science teams superpowers, with all of the bells and whistles that enterprises need. If you don't have your own trial instance of Rstudio Team to follow along (not required), feel free to request yours here: https://www.rstudio.com/products/team/evaluation3/ Additional resources here: https://docs.google.com/document/d/1HGt7LSohhyxpCvETvVEFHugrdaSnTcZaXbI0jV5g9ok/edit?usp=sharing

RStudio Team

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Building R packages with devtools and usethis | RStudio

Package building doesn't have to be scary! The tidyverse team has made it easy to get started with RStudio and the devtools/usethis packages. This hour long presentation will walk you through the basics of R package building, and hopefully leave you prepared to go out and build your own package! Slides: https://colorado.rstudio.com/rsc/pkg-building/ Source Code: https://github.com/jthomasmock/pkg-building devtools: https://devtools.r-lib.org/ usethis: https://usethis.r-lib.org/ R Packages book: https://r-pkgs.org/index.html

devtools rstudio tidyverse usethis Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

RStudio Team Demo | Build & Share Data Products Like The World’s Leading Companies

You probably know that RStudio makes a free, open-source development environment for data scientists. It’s made with love and used by millions of people around the world. What you might not know is that we also make a professional platform, called RStudio Team. Learn How RStudio Team Can... - Help you scale your data science work - Seamlessly manage open-source data science environments - Automate repetitive tasks - And, rapidly share key insights and data science products securely to your entire organization. Timecodes 0:00 - Intro 4:18 - Hard truth of data science 10:22 - Serious Data Science 16:46 - Model management with R and Python 18:48 - Live Demo / RStudio Workbench 23:09 - RStudio support for Jupyter Notebooks 24:40 - Live Demo / RStudio Connect 28:01 - RStudio support for VS Code 30:05 - R and Python within RStudio 32:33 - Scale and share data science results 36:55 - Sharing previous versions of presentations 38:16 - Data Science team knowledge sharing 40:36 - Scheduling snd emailing data science content 43:55 - Live demo / RStudio Package Manager 48:09 - Data Science stories 49:37 - RStudio Team 52:59 - What makes RStudio different? 55:12 - Q/A - Learn More Leading organizations like NASA, Janssen Pharmaceuticals, The World Health Organization, financial institutions, government agencies and insurance organizations around the globe use RStudio’s professional products to tackle world-changing problems and we’re inviting you to learn how. You’ll learn how RStudio Team gives professional data science teams superpowers, with all of the bells and whistles that enterprises need. You can try RStudio Team free here: https://www.rstudio.com/products/team/evaluation2/ If you'd like to access presentation slides, sign up for future events, provide feedback and/or ask additional questions we've bundled everything together for you here: https://docs.google.com/document/d/1HGt7LSohhyxpCvETvVEFHugrdaSnTcZaXbI0jV5g9ok/edit?usp=sharing

RStudio Team

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

How to Prioritize Projects | Data Science Hangout Highlights

RStudio is joined by Moody Hadi, Manager of New Product Development and Financial Engineering at S&P Intelligence, to discuss how data scientists can become leaders within their organizations. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.rstudio.com LinkedIn: https://www.linkedin.com/company/rstudio-pbc Twitter: https://twitter.com/rstudio

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Data Science Hangout | Moody Hadi at S&P Global | Unlocking Business Value with Data Science

We want to help data science leaders become better. The Data Science Hangout is a weekly, free-to-join open conversation for current and aspiring data science leaders. An accomplished leader in the space will join us each week and answer whatever questions the audience may have. We were recently joined by Moody Hadi, Manager of New Product Development and Financial Engineering at S&P Intelligence ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.rstudio.com LinkedIn: https://www.linkedin.com/company/rstu... Twitter: https://twitter.com/rstudio

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

The Importance of Understanding Your Business Users | Data Science Hangout Highlights

RStudio is joined by Tori Oblad, Data Officer at WaFd bank, to discuss how data scientists can become leaders within their organizations. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.rstudio.com LinkedIn: https://www.linkedin.com/company/rstudio-pbc Twitter: https://twitter.com/rstudio

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Dr. Julia Silge | RStudio Voices | RStudio

Julia Silge recently sat down with Michael Demsko Jr for an interview, the first in a new Voices of RStudio PBC series. In this excerpt, Julia discusses where she sees the most value created in the data science lifecycle--and it's not advanced machine learning models. Read the full interview at https://blog.rstudio.com/tags/rstudio-voices/

Julia Silge

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Measuring the Impact of Data Science | Data Science Hangout Highlights

RStudio is joined by Frank Corrigan, Director of Decision Intelligence, to discuss how data scientists can become leaders within their organizations. Watch the full recording: https://www.youtube.com/watch?v=KBs4b3Q2n8Y ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.rstudio.com LinkedIn: https://www.linkedin.com/company/rstudio-pbc Twitter: https://twitter.com/rstudio

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Teaching and learning with RStudio Cloud | RStudio

Learn about RStudio Cloud and most recent developments, particularly with respect to teaching with it. Slides are posted at https://rstd.io/tl-rscloud. ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud ### ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Cloud RStudio Cloud Teaching Remote Teaching Online Education

Make Sure You Communicate Value | Data Science Hangout Highlights

RStudio is joined by Tori Oblad, Data Officer at WaFd bank, to discuss how data scientists can become leaders within their organizations. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.rstudio.com LinkedIn: https://www.linkedin.com/company/rstudio-pbc Twitter: https://twitter.com/rstudio

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Data Scientists vs. Business Analysts | Data Science Hangout Highlights

RStudio is joined by Frank Corrigan, Director of Decision Intelligence, to discuss how data scientists can become leaders within their organizations. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.rstudio.com LinkedIn: https://www.linkedin.com/company/rstudio-pbc Twitter: https://twitter.com/rstudio

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Data Science Hangout | Frank Corrigan, Target | Understanding the Impact of Data Science

We want to help data science leaders become better. The Data Science Hangout is a weekly, free-to-join open conversation for current and aspiring data science leaders. An accomplished leader in the space will join us each week and answer whatever questions the audience may have. We were recently joined by Frank Corrigan, Director of Decision Intelligence at Target. 6:38 - Problem formation - trying to find the unknown unknowns and bringing them to the business 8:20 - Integrating your data science team into your company's business objectives (ex. newsletter) 10:50 - What is the divide between a business analyst and a data scientist, in your eyes? How business analysts and data scientists differ 15:32 - What is the biggest mistake you’ve made in your role and what did you learn from this mistake? 19:15 - Onboarding new team members effectively 25:00 - The importance of motivating non-data scientists 26:42 - Resources for data scientists 32:30 - Challenges when using different tools across a data science team 49:28 - Analytical thinking vs critical thinking skills 57:30 - Embracing the 80/20 Rule & the importance of Focus Time 1:02:48 - Two frameworks to be more effective with stakeholders 1:06:47 - Rebranding to "decision intelligence" 1:08:35 - Quantitatively measuring impact from data science insights ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Join the Data Science Hangout Live every Thursday from 12-1 ET: https://www.addevent.com/event/Qv9211919 Follow Us Here: Website: https://www.rstudio.com LinkedIn: https://www.linkedin.com/company/rstudio-pbc Twitter: https://twitter.com/rstudio

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

RStudio Cloud | Viewing Learning Work | Instructor View

As an Admin (or Moderator) of your course space, you and your fellow instructors have access to all projects in your space. You can open student projects from any projects listing, or to see all the projects of a given student, go to their profile page by clicking on their name from the Members page. Note that if you do open a student's project while they also have it open, they will be temporarily disconnected from the project. You can also view how much time your students have spent using Cloud. Simply visit the Usage area of your space, where you can see aggregate usage data for the entire class, or for an individual student and their projects. ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud VIDEO CREDITS: Monitor icon made by xnimrodx from flaticon.com Cloud icon made by Freepik from flaticon.com Tiny Putty Music from Blue Dot Sessions: https://app.sessions.blue/browse/track/52046 ### ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Cloud Teaching RStudio Cloud

RStudio Cloud | Tuning Resources | Instructor View

By default, each project is allocated 1 GB of RAM and 1 CPU, and can execute in the background for up to 1 hour. If your plan allows it, you can increase the memory, CPU or background execution time allocated to a project. To do so, open Project settings, go to the Resources panel and adjust the allocation. Note that copies of a project will inherit its resource settings. However, the effective allocation for the copy will be limited by the maxiumum allowed by the account that "owns" the new copy. The usage hours consumed by a project in a given amount of time depend on the resources allocated to the project, according to the following formula: (RAM + CPUs allocated) / 2 x hours. On disk, each project is allocated up to 20GB for its files, data, and packages. The maximum size of a file that can be uploaded to a project is 500MB. ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud VIDEO CREDITS: Monitor icon made by xnimrodx from flaticon.com Cloud icon made by Freepik from flaticon.com Tiny Putty Music from Blue Dot Sessions: https://app.sessions.blue/browse/track/52046 ### ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Cloud Teaching RStudio Cloud

RStudio Cloud | Troubleshooting | Instructor View

Relaunch Project If you are working on a project, and it fails to load or becomes completely unresponsive, you can relaunch the project. Depending on the underlying problem, relaunching the project may fix it and let you continue. Allocate Additional Memory memory-gaugeRunning out of memory while working on a project may be the cause of a variety of errors. The project memory gauge will give you an idea of what percentage of available memory your project is currently using. The gauge updates roughly every ten seconds, so it may not show the exact usage at the current moment. Note that reclaiming unused memory is controlled by R and the operating system - you may see uncanny fluctuations in the gauge as the system manages memory. ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud VIDEO CREDITS: Monitor icon made by xnimrodx from flaticon.com Cloud icon made by Freepik from flaticon.com Tiny Putty Music from Blue Dot Sessions: https://app.sessions.blue/browse/track/52046 ### ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Cloud Teaching RStudio Cloud

RStudio Cloud | Setting Up a Base Project | Instructor View

You can make all projects in a space begin with a default set of files and packages. You do this by defining a Base Project for the space. Create a new project and add any packages or files you want all projects created in the space to start with. Set the project's access so that everyone in the space can view the project. Go to the Space Settings page and select the project as the Base Project. Once you select a project as your Base Project, it will no longer be included in the projects listing for the space. To access it, choose the Edit command from the Space Settings page. Changes to the Base Project are not retroactive. Changes will not be applied to any projects already created - the changes will only apply to future projects created via the New Project action. ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud VIDEO CREDITS: Monitor icon made by xnimrodx from flaticon.com Cloud icon made by Freepik from flaticon.com Tiny Putty Music from Blue Dot Sessions: https://app.sessions.blue/browse/track/52046 ### ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Teaching Cloud RStudio Cloud

RStudio Cloud | Roles | Instructor View

Each member of a space is assigned a role to determine what they are able to do within the space. The available roles are: Admin: can manage users, view, edit and manage all projects and view space usage data. Moderator: can view, edit and manage all projects, and view space usage data. Contributor: can create, edit and manage their own projects. This is the default. Viewer: can view projects shared with everyone in the space. A viewer cannot create or save copies of projects. Members are assigned an initial role when they are invited to or join a space, but roles can be changed by an Admin at any time. When you invite an individual member to a Invitation Required space, you set the initial role in the Add Member dialog box. When someone joins via a sharing link, their initial role is set to the current Initial Role setting in the members options panel. Admins can update a user’s role via the role selector in the members list. Changing permissions lets you fine-tune the Contributor and Viewer roles. The permissions are: Contributors can see the members list: enables Contributors to see who can access the space. Contributors can make their projects visible to all members: enables Contributors to share their projects with everyone in the space. Contributors can change project resources: enables Contributors to change RAM and CPU settings on their projects. Viewers can see the members list: enables Viewers to see who can acess the space. ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud VIDEO CREDITS: Monitor icon made by xnimrodx from flaticon.com Cloud icon made by Freepik from flaticon.com Tiny Putty Music from Blue Dot Sessions: https://app.sessions.blue/browse/track/52046 ### ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Cloud RStudio Cloud Teaching

RStudio Cloud | Reusing a Workspace | Instructor View

One approach to reusing a space is to remove the current cohort of students from the space, and reuse the same space with the next cohort. If you are an Admin of a space, you can remove any member from that space. Go to the Members area of the space. For the member you would like to remove, press the Delete icon. You will be prompted to confirm that you would like to remove them from the space, and to choose whether to leave their projects behind in the space, or move them to their personal space. You can also remove members programatically via the Cloud API using the rscloud package. Another option is to create a new space from your current course space using the Copy Space action. If you'd like to re-use materials from a space, e.g. to teach a new cohort of students, use the Copy Space command. All projects shared with everyone in the original space will be copied over to the new space. Members of the original space are not copied over - you will be the only initial member of the new space. ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud VIDEO CREDITS: Monitor icon made by xnimrodx from flaticon.com Cloud icon made by Freepik from flaticon.com Tiny Putty Music from Blue Dot Sessions: https://app.sessions.blue/browse/track/52046 ### ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Teaching Cloud RStudio Cloud

RStudio Cloud | Project Types | Instructor View

A project is the fundamental unit of work on RStudio Cloud. It encapsulates your R code, packages and data files and provides isolation from other analyses. If you are familiar with projects in the desktop RStudio IDE, an RStudio Cloud project is the same thing, plus some additional metadata for access and sharing. To create a new project from scratch, simply press the New Project button from the Projects area. Your new project will open in the RStudio IDE. To create a new project from an existing git repository, press the down arrow on the right side of the New Project button, and choose ‘New Project from Git Repo’ from the menu that appears. Note that your git credentials need to be entered each time you create a new project and are only cached for 15 minutes by default. To create a Jupyter Project, press the down arrow on the right side of the New Project button, and choose ‘New Jupyter Project’ from the menu that appears. A new Jupyter project will be created and deployed. Once deployed, you will see the Jupyter hub tree view with a welcome.ipynb notebook that contains information about getting started with Jupyter. ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud VIDEO CREDITS: Monitor icon made by xnimrodx from flaticon.com Cloud icon made by Freepik from flaticon.com Tiny Putty Music from Blue Dot Sessions: https://app.sessions.blue/browse/track/52046 ### ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Teaching Cloud RStudio Cloud

RStudio Cloud | Overview | Instructor View

ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud VIDEO CREDITS: Monitor icon made by xnimrodx from flaticon.com Cloud icon made by Freepik from flaticon.com Tiny Putty Music from Blue Dot Sessions: https://app.sessions.blue/browse/track/52046 ### ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Teaching RStudio Cloud Cloud

RStudio Cloud | Inviting Co-instructors and Teaching Assistants | Instructor View

After creating a space, go to the Members area to manage its membership. You can add members to your space in three ways: To add members one by one, choose the Invitation Required option, and send invitations to each person you’d like to add to the space via the Add Member button. Note that for security reasons, the invitation is good for 7 days from the time that it is sent. To allow many people to join the space, choose the Sharing Link option, copy the sharing link and then share that link with all the people you’d like to join the space, either via an email or by posting the link on a web page. Keep in mind that anyone with that link will be able to join your space. If you would like to disable a link you previously shared, choose Reset Sharing Link; this will prevent any additional people from joining the space using that link. Space membership can also be managed programatically. See API Access from R in the Advanced Topics section below for details. Note that you can switch the access option at any time - a common approach is to initially set the access option to Sharing Link, post the sharing link, then after all the people you want to join the space initially have done so, switch access to Invitation Only. The original sharing link will no longer allow additional people to join the space, but you can add any new members individually. ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud VIDEO CREDITS: Monitor icon made by xnimrodx from flaticon.com Cloud icon made by Freepik from flaticon.com Tiny Putty Music from Blue Dot Sessions: https://app.sessions.blue/browse/track/52046 ### ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Teaching Cloud RStudio Cloud

RStudio Cloud | Invite Learners | Instructor View

As you did to invite your co-instructors, go to the space's Members area to invite your students to your course space. The easiest approach is to enable access via a sharing link. - Click on the Sharing link option in the Access section - Set the Initial Role to Contributor - Click on the Copy Sharing Link action You can then distribute the sharing link to all your students via a course web page or email. Once all your students have joined the space, you can either reset the sharing link (which will disable access via the previous sharing link), or set Access to "Invitation Required" to ensure that nobody joins the class later without your permission. See the Members section in Shared Spaces above for more details and alternate methods for adding students to your space. Note that each student must have their own RStudio Cloud account. When they attempt to access your course space for the first time, they will be prompted to log in, or to sign up for an account if they don't already have one. If your organization is using SSO, account creation will happen automatically when they log in the first time. At this point, you and your students should be all set. ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud VIDEO CREDITS: Monitor icon made by xnimrodx from flaticon.com Cloud icon made by Freepik from flaticon.com Tiny Putty Music from Blue Dot Sessions: https://app.sessions.blue/browse/track/52046 ### ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Cloud Teaching RStudio Cloud

RStudio Cloud | Exporting Projects | Instructor View

The contents of a project can be downloaded without opening the project. Press the Export action to the right of the project that you wish to download. A dialog box will appear showing the progress of your export. The process can take anywhere from a few seconds to a couple of minutes, depending on the size of the project, and how recently it was opened. Once the export is complete, press the Download button in the dialog box to download your project. ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud VIDEO CREDITS: Monitor icon made by xnimrodx from flaticon.com Cloud icon made by Freepik from flaticon.com Tiny Putty Music from Blue Dot Sessions: https://app.sessions.blue/browse/track/52046 ### ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

RStudio Cloud | Creating an Assignment | Instructor View

A good way to create assignments for your students is to make a project for each assignment, and populate it with the files and packages you would like each student to have when they begin the assignment. When you are ready to reveal an assignment to your students, open the project and do the following: - Click on the Project Settings button (the gear in the upper right) - Select the Access panel - Set the project access so it can be viewed by everyone in the space - Check "Make this project an assignment" When the student opens an assignment project (either via the projects listing, or via a direct link to the project), RStudio Cloud will automatically make a copy for them. To convey this special behavior, assignments are displayed a bit differently, both for you and for your students. You can see the students’ copies of the assignment by clicking on the "View n derived projects" link that will appear with your original project. Note that changes you make to the original assignment will not be applied to any student copies already created. The changes will only apply to future copies of the assignment. ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud VIDEO CREDITS: Monitor icon made by xnimrodx from flaticon.com Cloud icon made by Freepik from flaticon.com Tiny Putty Music from Blue Dot Sessions: https://app.sessions.blue/browse/track/52046 ### ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Cloud Teaching RStudio Cloud

RStudio Cloud | {rscloud} Package | Instructor View

You can access RStudio Cloud's API to manage space members programatically using the rscloud package. You will need to create client credentials to use the package. To do so, click on your icon/name in the header to reveal the User panel, then click on Credentials. This will take you to the Credentials page of RStudio User Settings, where you can create and manage your client credentials. {rscloud} package repo: https://github.com/rstudio/rscloud ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud VIDEO CREDITS: Monitor icon made by xnimrodx from flaticon.com Cloud icon made by Freepik from flaticon.com Tiny Putty Music from Blue Dot Sessions: https://app.sessions.blue/browse/track/52046 ### ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Rscloud RStudio Cloud Api Teaching Cloud

Posit Cloud | Creating a Shared Space | Instructor View

A shared space is an area where a group of people can collaborate together - only the members of a shared space can access the space and its contents. To create a shared space, go to the navigation sidebar (click the menu icon at the upper left if needed) and choose New Space, then follow the on-screen instructions. ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud VIDEO CREDITS: Monitor icon made by xnimrodx from flaticon.com Cloud icon made by Freepik from flaticon.com Tiny Putty Music from Blue Dot Sessions: https://app.sessions.blue/browse/track/52046 ### ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Teaching Cloud RStudio Cloud

Data Science Hangout | Tori Oblad, WaFd Bank | Getting Executives to Support Data Science

We want to help data science leaders become better. The Data Science Hangout is a weekly, free-to-join open conversation for current and aspiring data science leaders. An accomplished leader in the space will join us each week and answer whatever questions the audience may have. We were recently joined by Tori Oblad, Enterprise Data & Analytics Officer at WaFd Bank. Here are a few snippets from our conversation: 1:14 - Start of session 3:00 - How to build an internal data science community 11:40 - Showing the art of the possible 14:00 - How do you get others to lead topics and foster engagement? 26:17 - Writing starter scripts for new users 35:55 - When to use R or Python versus BI 36:38 - Building toy models in Excel to explain it to people / to build relationships with business 38:33 - Avoiding vendor lock-in, being technology agnostic 43:35 - How to build confidence with IT and compliance 49:15 - Working with business users and creating business value 53:21 - Getting business and executive support 1:22:30 - What data scientists should focus on when communicating with stakeholders: value ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.rstudio.com LinkedIn: https://www.linkedin.com/company/rstudio-pbc Twitter: https://twitter.com/rstudio

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

How to Improve Your Communication Skills | Data Science Hangout Highlights

RStudio is joined by Elaine McVey, VP of Data Science at The Looma Project, to discuss how data scientists can become leaders within their organizations. Watch the full recording here: https://www.youtube.com/watch?v=IkqItgPSPro&feature=youtu.be ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.rstudio.com LinkedIn: https://www.linkedin.com/company/rstudio-pbc Twitter: https://twitter.com/rstudio

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

How to Communicate Value | Data Science Hangout Highlights

RStudio is joined by Jonathan Regenstein, Head of Data and Quantamental Research at Truist Securities, to discuss how data scientists can become leaders within their organizations. Watch the full recording here: https://www.youtube.com/watch?v=pNTENrov020 ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.rstudio.com LinkedIn: https://www.linkedin.com/company/rstudio-pbc Twitter: https://twitter.com/rstudio

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

How to Speak to Executives | Data Science Hangout Highlights

RStudio is joined by Elaine McVey, VP of Data Science at The Looma Project, to discuss how data scientists can become leaders within their organizations. Watch the full recording here: https://www.youtube.com/watch?v=IkqItgPSPro ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.rstudio.com LinkedIn: https://www.linkedin.com/company/rstudio-pbc Twitter: https://twitter.com/rstudio

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Data Science Hangout | Jonathan Regenstein, Truist | Relationships with IT and Non-Data Scientists

We want to help data science leaders become better. The Data Science Hangout is a weekly, free-to-join open conversation for current and aspiring data science leaders. An accomplished leader in the space will join us each week and answer whatever questions the audience may have. We were recently joined by Jonathan Regenstein, Head of Data and Quantamental Research at Truist Securities. Working with IT and building relationships was a focus in our conversation with Jonathan and he included a few tips for building relationships with non-data scientist colleagues. Find a partner within the IT organization and talk to that person at least once a week. IT can help you communicate value proposition along the way as well. "It sounds crazy to say this in the world of data science, but relationship building was critical to what we did, especially at a bank. Thousands of request for new technology. There's no way to avoid going through all the security scans and check marks that we have to go through. We want to make sure we have a good partner who is going to help us do that" 0:48 - Start of session 10:52 - How should data science leaders work with IT? 46:20 - How far out Data Science Leaders should be planning projects with IT 48:20 - How do you become a champion of data science within your organization? 1:02:11 - Your responsibility as a data science leader is to work cross functionally 1:04:17 - Data Science Leaders: Your business cares about the value, not how you got there. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio-pbc Twitter: https://twitter.com/rstudio

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Data Science Hangout | Elaine McVey at the Looma Project | Communicating the Value of Data Science

The Data Science Hangout is a weekly, free-to-join open conversation for current and aspiring data science leaders. An accomplished leader in the space will join us each week and answer whatever questions the audience may have. We were recently joined by Elaine McVey, VP of Data Science at the Looma Project. 21:30 - How to approach experimentation and running tests 43:30 - How do you communicate the value of data science to executives 52:40 - Ways to improve your communication skills as a data scientist 57:15 - How to package insights to executives 1:01:03 - What data scientists get wrong when communicating insights ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.rstudio.com LinkedIn: https://www.linkedin.com/company/rstu... Twitter: https://twitter.com/rstudio To join future data science hangouts, more info here: rstd.io/datasciencehangout

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Art Steinmetz | Open Source Data Science in the Enterprise | RStudio

Art Steinmetz is the former Chairman, CEO and President of Oppenheimer Funds. In this interview, Art gives his unique perspective on the value and suitability of open source, code-first data science for the enterprise. Timecodes 0:00 Intro 0:10 What are some of the advantages to adopting open source software within an organization? 2:23 Is open source software appropriate for enterprise-level data science? 4:29 How do you build support for open source software within an organization? 5:56 Do executives need to know how to code in order to manage data science teams? 7:56 How and why did you get started with R, and why? 9:55 Any other advice for an aspiring Data Science Leader? For a more in-depth view of Art's perspective on Open Source Data Science in Investment Management, see this blog post https://blog.rstudio.com/2020/10/13/open-source-data-science-in-investment-management/ To learn more about RStudio's professional products, and how they can help you scale, secure and operationalize your open source data science, see www.rstudio.com

Nicolas Nguyen - ZEISS | Supply Chain Management Meetup | RStudio

0 - 50:20 Presentation 50:20 - 56:52 Q&A Presentation by Nicolas Nguyen, Digital Supply Chain and Global S&OP Leader for Carl Zeiss Meditec Abstract: In demand & supply planning, we often need to calculate projected inventories and replenishment plans - sometimes for hundreds or thousands of SKUs, and through different levels of the distribution network. In the sales & operations planning (S&OP) process, we might need to run some scenarios to balance demand and supply to support sales: changing inventories plans, sales plans, delivery lead times, production plans, product mix, etc. Using Shiny, we can design simple, powerful, scalable, and reproducible apps for demand & supply planning as well as the S&OP process. In this talk, you will learn about real-life examples of web applications, which can be deployed in a few minutes to perform some popular demand & supply planning operations. __ Thank you to Nicolas for an awesome presentation on how he is using Shiny today. With all the interest from the community in this topic, we’d love to continue the discussion and connect us all through: Future meetups: https://www.meetup.com/RStudio-Enterprise-Community-Meetup/ R for Data Science Online Learning Community (“chat-supply_chain”): r4ds.io/join RStudio Community: community.rstudio.com

rstudio Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Supply Chain Demand Planning Meetup

Managing COVID vaccine distribution in West Virginia | RStudio

With a little help from open source software Learn more: Data Driven West Virginia: https://business.wvu.edu/research-outreach/data-driven-wv DDWV PPE forecasting: https://wvutoday.wvu.edu/stories/2020/04/27/wvu-business-experts-partner-with-the-national-guard-to-forecast-ppe-needs DDWV inventory management system: https://wvutoday.wvu.edu/stories/2021/03/22/a-different-kind-of-science-wvu-chambers-college-data-scientists-propel-west-virginia-s-acclaimed-vaccine-strategy-with-digital-inventory-management-system West Virginia National Guard: https://www.wv.ng.mil/ Shiny: https://shiny.rstudio.com/ West Virginia leading nation at start of vaccine rollout: https://www.vox.com/first-person/2021/3/4/22313540/covid-19-vaccine-west-virginia *** In the United States, approximately 2.5 million doses of COVID vaccines are being delivered each day, and how these doses go from the manufacturer to a shot in someone’s arm varies by state, often with mixed results. But early on in the vaccine distribution process, one state led the pack in terms of using the majority of vaccine doses it had been allotted. That state? West Virginia. Part of what has made West Virginia successful is the creation of an inventory management system using Shiny, an open source framework for building interactive web applications. The system was built by Data Driven West Virginia, part of the John Chambers College of Business and Economics at West Virginia University, in collaboration with the West Virginia Army National Guard. Using Shiny has provided visibility into each component of the vaccine supply chain, leading to the creation of distribution plans that are able to quickly and efficiently match supply with demand, getting vaccines to the right people in the right location at the right time. *** RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

rstudio Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Plumber Blogdown Gt Tidymodels Statistics Debugging RStats Open Source Oss Data Driven West Virginia Katherine Kopp Brad Price Major Ryan Coss Covid Covid Vaccine Vaccine Distribution

Katherine Kopp | COVID vaccine distribution Shiny app walkthrough (mock data) | RStudio

Learn more: Data Driven West Virginia: https://business.wvu.edu/research-outreach/data-driven-wv DDWV PPE forecasting: https://wvutoday.wvu.edu/stories/2020/04/27/wvu-business-experts-partner-with-the-national-guard-to-forecast-ppe-needs DDWV inventory management system: https://wvutoday.wvu.edu/stories/2021/03/22/a-different-kind-of-science-wvu-chambers-college-data-scientists-propel-west-virginia-s-acclaimed-vaccine-strategy-with-digital-inventory-management-system West Virginia National Guard: https://www.wv.ng.mil/ Shiny: https://shiny.rstudio.com/ West Virginia leading nation at start of vaccine rollout: https://www.vox.com/first-person/2021/3/4/22313540/covid-19-vaccine-west-virginia *** To understand just how hard it is to get vaccines to the population, it helps to understand where it can go wrong. This starts with how vaccines are packed into containers. To fill up a container, Pfizer places 195 vials into a tray, and up to 5 trays into a single container. Moderna puts 10 vials into a small box, and then combines a minimum of 10 small boxes into a single container. In most states Pfizer and Moderna ship directly to the organization that will be administering the vaccine to the population. This could be a hospital, a pharmacy, or any place where trained professionals will be putting shots into arms. But what happens when a pharmacy receives a full container from Pfizer, 975 vials, but only needs 600? West Virginia has removed this complication by shipping directly to five hubs strategically located throughout the state. Within each of these hubs, containers of vaccine vials are broken down into smaller components and then either picked up or shipped directly to the hospital, pharmacy, or organization that will be administering the vaccine. These hubs are managed by the Joint Interagency Task Force (JIATF), a team of teams composed of public, private, and governmental organizations as well as the National Guard. The Joint Interagency Task Force is responsible for drawing up a weekly distribution plan for each hub, in alignment with CDC allocations, and matching vaccine supply with demand. By using a statewide system managed by a central organization, there’s a level of agility and fluidity that allows each hub to adjust to a variety of changes in order to maximize the number of vaccines that are being administered to the population each week. *** RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com. *** Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

rstudio Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging RStats Open Source Oss Reticulate Data Driven West Virginia Covid Covid Vaccine Vaccine Distribution Katherine Kopp Brad Price Major Ryan Coss

Julia Silge | Monitoring Model Performance | RStudio

0:00 Project introduction 1:50 Overview of the setup code chunk 3:05 Getting new data 4:05 Getting model from RStudio Connect using httr and jsonlite 6:20 Bringing in metrics 9:45 Using the pins package 10:50 Using boards on RStudio Connect 13:30 Benefits of using pins 14:00 Visualizations using ggplot and plotly 17:00 Knitting the flexdashboard 18:10 Project takeaways You can read Julia's blogpost, Model Monitoring with R Markdown, pins, and RStudio Connect, here: https://blog.rstudio.com/2021/04/08/model-monitoring-with-r-markdown/ Modelops playground GitHub repo: https://github.com/juliasilge/modelops-playground pins package documentation: https://pins.rstudio.com/ flexdashboard documentation: https://rmarkdown.rstudio.com/flexdashboard/ tidymodels documentation: https://www.tidymodels.org/

Julia Silge

flexdashboard httr rmarkdown rstudio tidymodels Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Modelops Julia Silge Model Performance Pins

Tom Mock | RStudio Connect in Production

https://rstudio.com/resources/webinars/rstudio-connect-in-production/ In part 2 of this 3 part series, Tom covers: Communicating results can be the most challenging part of Data Science: many insights never leave the laptops where they are discovered. In this webinar, we will show you how to use RStudio Connect to deploy your results in a production environment. You’ll learn how to automate publishing, schedule updates, and provide consumers with self-service access to your work. RStudio Connect is a revolutionary new way to host executable Data Science content. About Tom: Thomas is involved in the local and global data science community, serving as Outreach Coordinator for the Dallas R User Group, as a mentor for the R for Data Science Online Learning Community, as co-founder of #TidyTuesday, attending various Data Science and R-related conferences/meetups, and participated in Startup Weekend Fort Worth as a data scientist/entrepreneur

rstudio webinars Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Kelly O'Briant | Interactivity in Production | RStudio (2019)

https://rstudio.com/resources/webinars/interactivity-in-production/ In part 3 of this 3 part series, Kelly covers: Interactive products take your data science to a new level, but they require new coding decisions. This webinar will give you clear guidelines on when and how to add interactivity to your work. Here you’ll learn: when to use off-the-shelf interactive products like parameterized R Markdown and htmlwidgets, when to create bespoke interactivity with Shiny, how to make your Shiny apps as fast as possible, how to support interactivity in production, and much more. About Kelly: Kelly is Solutions Engineer for RStudio and also an organizer of the Washington DC chapter of R-Ladies Global. It’s an R users group for lady-folk and friends

rstudio Shiny webinars Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Garrett Grolemund | Reproducibility in Production | RStudio (2019)

https://rstudio.com/resources/webinars/reproducibility-in-production/ In part 1 of this 3 part series, Garrett covers the following: Computational documents offer limitless opportunities for your business. With them, your consumers can rerun your report with new parameters, apply your analysis to new data, or schedule future, automatic updates to your work—all with the click of a button. This is the first in a three part webinar series that will describe this new form of reproducibility. Here, we begin by showing you how to write executable R Markdown documents for a production environment. About Garrett: Garrett is the author of Hands-On Programming with R and co-author of R for Data Science and R Markdown: The Definitive Guide. He is a Data Scientist at RStudio and holds a Ph.D. in Statistics, but specializes in teaching. He's taught people how to use R at over 50 government agencies, small businesses, and multi-billion dollar global companies; and he's designed RStudio's training materials for R, Shiny, R Markdown and more. Garrett wrote the popular lubridate package for dates and times in R and creates the RStudio cheat sheets

lubridate rstudio Shiny webinars Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Webinar Summary | Avoid Dashboard Fatigue | RStudio (2020)

0:00 Introduction 0:07 The Problem 1:05 The Solution 3:20 Real Life Success Stories 5:27 Demo (with code) Don't have an hour to watch a webinar? We've made a summary video that covers the main points of our "Avoid Dashboard Fatigue" webinar from Sean Lopp and Rich Iannone. The full webinar covered: Data science teams face a challenging task. Not only do they have to gain insight from data, they also have to persuade others to make decisions based on those insights. To close this gap, teams rely on tools like dashboards, apps, and APIs. But unfortunately data organizations can suffer from their own success - how many of those dashboards are viewed once and forgotten? Is a dashboard of dashboards really the right solution? And what about that pesky, precisely formatted Excel spreadsheet finance still wants every week? In this webinar, we’ll show you an easy way teams can solve these problems using proactive email notifications through the blastula and gt packages, and how RStudio pro products can be used to scale out those solutions for enterprise applications. Dynamic emails are a powerful way to meet decision makers where they live - their inbox - while displaying exactly the results needed to influence decision-making. Best of all, these notifications are crafted with code, ensuring your work is still reproducible, durable, and credible. We’ll demonstrate how this approach provides solutions for data quality monitoring, detecting and alerting on anomalies, and can even automate routine (but precisely formatted) KPI reporting. Webinar materials: https://rstudio.com/resources/webinars/avoid-dashboard-fatigue/ About Sean: Sean has a degree in mathematics and statistics and worked as an analyst at the National Renewable Energy Lab before making the switch to customer success at RStudio. In his spare time he skis and mountain bikes and is a proud Colorado native. About Rich: My background is in programming, data analysis, and data visualization. Much of my current work involves a combination of data acquisition, statistical programming, tools development, and visualizing the results. I love creating software that helps people accomplish things. I regularly update several R package projects (all available on GitHub). One such package is called DiagrammeR and it's great for creating network graphs and performing analyses on the graphs. One of the big draws for open-source development is the collaboration that comes with the process. I encourage anyone interested to ask questions, make recommendations, or even help out if so inclined!

Rich Iannone

blastula gt rstudio webinars Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Tom Mock & Shannon Haggerty | Theming Shiny and RMarkdown with {thematic} & {bslib} | RStudio

From rstudio::global(2021) Shiny X-Sessions, sponsored by Appsilon: this presentation covers the basics of how the thematic and bslib packages can be used to consistently style all the components of a shiny app at once. About Tom Mock: Thomas is involved in the local and global data science community, serving as Outreach Coordinator for the Dallas R User Group, as a mentor for the R for Data Science Online Learning Community, as co-founder of #TidyTuesday, attending various Data Science and R-related conferences/meetups, and participated in Startup Weekend Fort Worth as a data scientist/entrepreneur. About Shannon Haggerty: Shannon is on RStudio’s Customer Success team working with teams across the Life Sciences and Healthcare. In her free time, she likes to bake, hang out with her dogs, and explore new hobbies. Learn more about the rstudio::global(2021) X-Sessions: https://blog.rstudio.com/2021/01/11/x-sessions-at-rstudio-global/

bslib rmarkdown rstudio Shiny thematic Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Bslib Thematic

Pedro Silva | Styling Shiny with CSS & SASS and Speeding Up Shiny Apps | Posit

From rstudio::global(2021) Shiny X-Sessions, sponsored by Appsilon: in the first part of this talk I will discuss how to use CSS to give your application a fresh and unique look, while keeping your codebase clean and organized with SASS. During the second half I will discuss how to leverage Shiny update functions, proxy objects and JavaScript messages to speed up your dashboards. About Pedro Silva: Pedro has nearly a decade of experience combining frontend and backend technologies, and is an expert on augmenting R Shiny dashboards with CSS and JavaScript. Learn more about the rstudio::global(2021) X-Sessions: https://blog.rstudio.com/2021/01/11/x-sessions-at-rstudio-global/

rstudio Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Appsilon

Jenny Bryan | Help me help you: creating reproducible examples | RStudio (2018)

What is a reprex? It’s a reproducible example. Making a great reprex is both an art and a science and this webinar will cover both aspects. A reprex makes a conversation about code more efficient and pleasant for all. This comes up whenever you ask someone for help, report a bug in software, or propose a new feature. The reprex package (https://reprex.Tidyverse.org) makes it especially easy to prepare R code as a reprex, in order to share on sites such as https://community.rstudio.com, https://github.com, or https://stackoverflow.com. The habit of making little, rigorous, self-contained examples also has the great side effect of making you think more clearly about your programming problems. Webinar materials: https://rstudio.com/resources/webinars/help-me-help-you-creating-reproducible-examples/ About Jenny: Jenny is a software engineer on the tidyverse team. She is a recovering biostatistician who takes special delight in eliminating the small agonies of data analysis. Jenny is known for smoothing the interfaces between R and spreadsheets, web APIs, and Git/GitHub. She’s been working in R/S for over 20 years and is a member of the R Foundation. She also serves in the leadership of rOpenSci and Forwards and is an adjunct professor at the University of British Columbia

Jenny Bryan

reprex rstudio tidyverse tidyverse.org webinars Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Reprex

Damian Rodziewicz | Scaling Shiny to Thousands of Users | RStudio

From rstudio::global(2021) Shiny X-Sessions, sponsored by Appsilon: in this talk I will discuss how to scale Shiny dashboards to thousands of users. About Damian Rodziewicz: Damian is one of the four co-founders of Appsilon. Before founding Appsilon he worked at Accenture, UBS, Microsoft and Domino Data Lab. Learn more about the rstudio::global(2021) X-Sessions: https://blog.rstudio.com/2021/01/11/x-sessions-at-rstudio-global/

rstudio Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Appsilon

Dominik Krzemiński | Appsilon's Guide to Working With Open Source Shiny | RStudio

From rstudio::global(2021) Shiny X-Sessions, sponsored by Appsilon: There is no need to praise Shiny for its influence on interactive data visualisation. As with many other technology stacks, Shiny could benefit from community contributions for the further development of the package itself and the growth of independent packages that add new features. In this talk, I present some of the most popular Shiny extensions and explain what are the ways to help with developing Shiny-related tools. About Dominik Krzemiński: Dominik is the Open Source Tech Lead at Appsilon where he enjoys contributing to open source tools, mainly in R and Python. He created shiny.i18n, shiny.semantic, and the TODOr package for R. He also participated in the Google Summer of Code, where he developed tools supporting neuroscience analyses. He’s also a fan of all kinds of board sports and capoeira. Learn more about the rstudio::global(2021) X-Sessions: https://blog.rstudio.com/2021/01/11/x-sessions-at-rstudio-global/

rstudio Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Appsilon

Olga Mierzwa-Sulima | Best Practices for Developing Shiny Apps | RStudio

From rstudio::global(2021) Shiny X-Sessions, sponsored by Appsilon: Best practices for developing Shiny apps presentation covers organizing app's code with modules and R6 classes, setting up development environment, and testing. About Olga Mierzwa-Sulima: Olga is experienced in production applications of analytical solutions, especially for FMCG companies. Recently she developed a price elasticity model for Unilever. Learn more about the rstudio::global(2021) X-Sessions: https://blog.rstudio.com/2021/01/11/x-sessions-at-rstudio-global/

R6 rstudio Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Olga Mierzwa-Sulima

Filip Stachura & Marek Rogala | Empowering Data Scientists to Build Spectacular Shiny Apps | RStudio

From rstudio::global(2021) Shiny X-Sessions, sponsored by Appsilon: in this talk, Appsilon's CEO and CTO show their vision of challenges facing Shiny app authors and what is crucial to achieving success. They announce 3 key initiatives that Appsilon undertakes to empower data scientists to build spectacular Shiny Apps, including the {shiny.fluent} package. About Filip Stachura: Filip is a CEO and a Co-founder of Appsilon. He holds a double degree in Applied Mathematics and Computer Science from the University of Warsaw. He started his professional career at Microsoft in California. Passionate about data analysis, elegant visualisations and tackling hard algorithmic and analytical problems. About Marek Rogala: Marek Rogala is the CTO at Appsilon, where he drives innovation in R and Shiny as well as Machine Learning. He previously did software engineering at Google and at Domino Data Lab, where he worked on enabling data scientists to experiment and collaborate effectively.: Learn more about the rstudio::global(2021) X-Sessions: https://blog.rstudio.com/2021/01/11/x-sessions-at-rstudio-global/

rstudio Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Appsilon

Nathan Stephens | Best Practices for Administering RStudio in Production | RStudio (2019)

Most organizations are unfamiliar with the R programming language. As a result they often struggle to onboard and manage R in production. In this webinar we introduce the RStudio Quickstart which makes it easy to try RStudio professional products on your desktop for free. We also outline best practices for using R and RStudio in production. Webinar materials: https://rstudio.com/resources/webinars/best-practices-for-administering-rstudio-in-production/ About Nathan: Nathan has a background in analytic solutions and consulting. He has experience building data science teams, architecting analytic infrastructure, and delivering innovative data products. He is a long time user of R

rstudio webinars Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Nathan Stephens

Tom Mock | A Gentle Introduction to Tidy Statistics in R | RStudio (2019)

R is a fantastic language for statistical programming, but making the jump from point and click interfaces to code can be intimidating for individuals new to R. In this webinar I will gently cover how to get started quickly with the basics of research statistics in R, providing an emphasis on reading data into R, exploratory data analysis with the Tidyverse, statistical testing with ANOVAs, and finally producing a publication-ready plot in ggplot2. Use the code presented instantly on RStudio Cloud! RStudio Cloud: rstudio.cloud Webinar materials: https://rstudio.com/resources/webinars/a-gentle-introduction-to-tidy-statistics-in-r/ About Thomas: Thomas is involved in the local and global data science community, serving as Outreach Coordinator for the Dallas R User Group, as a mentor for the R for Data Science Online Learning Community, as co-founder of #TidyTuesday, attending various Data Science and R-related conferences/meetups, and participated in Startup Weekend Fort Worth as a data scientist/entrepreneur

ggplot2 rstudio tidyverse webinars Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Thomas Mock

Sean Lopp | Posit Investments in Pharma | Posit

From rstudio::global(2021) Pharma X-Sessions, sponsored by ProCogia: R/Pharma is an organization of R enthusiasts who work in the pharma and biotech industries. This presentation summarizes the group and presents some goals for 2021. More about Sean Lopp: Sean has a degree in mathematics and statistics and worked as an analyst at the National Renewable Energy Lab before making the switch to customer success at RStudio. In his spare time he skis and mountain bikes and is a proud Colorado native. Learn more about the rstudio::global(2021) X-Sessions: https://blog.rstudio.com/2021/01/11/x-sessions-at-rstudio-global/

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Pharma Rpharma Biotech

Marly Gotti | Risk Assessment Tools: R Validation Hub Initiatives | Posit

From rstudio::global(2021) Pharma X-Sessions, sponsored by ProCogia: we will present some of the resources and tools the R Validation Hub has been working on to aid the biopharmaceutical industry in the process of using R in a regulatory setting. In the talk, you will learn about the {riskmetric} R package, which measures the risk of using R packages, and you will also see a demo of the Risk Assessment Shiny application, which is an advanced user interface for {riskmetric}. About Marly Gotti: Marly Gotti is a Senior Data Scientist at Biogen and a former RStudio intern. She is also an executive committee member of the R Validation Hub, where she advocates for the use of R within a biopharmaceutical regulatory setting. Learn more about the rstudio::global(2021) X-Sessions: https://blog.rstudio.com/2021/01/11/x-sessions-at-rstudio-global/

rstudio Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Riskmetric Pharma Marly Gotti

Harvey Lieberman | R/Pharma | Posit

From rstudio::global(2021) Pharma X-Sessions, sponsored by ProCogia: R/Pharma is an organization of R enthusiasts who work in the pharma and biotech industries. This presentation summarizes the group and presents some goals for 2021. About Harvey Lieberman: Harvey Lieberman works at Novartis and has been a member of R/Pharma since 2017. For more on R/Pharma: https://www.pharmar.org/ Learn more about the rstudio::global(2021) X-Sessions: https://blog.rstudio.com/2021/01/11/x-sessions-at-rstudio-global/

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Rpharma Pharma

Ellis Hughes | R Package Validation Framework | Posit

From rstudio::global(2021) Pharma X-Sessions, sponsored by ProCogia: in this talk I discuss the process developed for validating internally generated R packages at SCHARP (Statistical Center for HIV/AIDS Research and Prevention) - the R Package Validation Framework. I cover the elements of the framework and basics of applying it with some examples. By using tools native to the R package building infrastructure, validation can become an integrated part of your package development, improving the quality of both the package and validation. About Ellis Hughes: I am a statistical Programmer at Fred Hutch Cancer Research Center where I work on a team that evaluates potential HIV vaccine candidates. Having graduated from Washington State University with a degree in Bioengineering, I found a passion for programming in R. I now organize the Seattle UseR group, and enjoy building packages to automate my workflows. Learn more about the rstudio::global(2021) X-Sessions: https://blog.rstudio.com/2021/01/11/x-sessions-at-rstudio-global/

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Package Validation Ellis Hughes Vaccine Development

Nathan Stephens | Make PowerPoint Presentations with R Markdown | RStudio (2018)

Data scientists use R Markdown documents to create reproducible code that can be rendered in a variety of output types. Some of the most common output types include HTML, Word, and PDF, but new improvements make it possible to create PowerPoint presentations as well. PowerPoint presentations are still common currency for sharing insights in most organizations today. This webinar demonstrates how to create feature rich PowerPoint presentations from R Markdown and how to use these presentations to share insights, visualizations, Shiny apps, and more. About Nathan: Nathan has a background in analytic solutions and consulting. He has experience building data science teams, architecting analytic infrastructure, and delivering innovative data products. He is a long time user of R

rstudio Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Nathan Stephens

Edgar Ruiz | Programación con R | RStudio (2019)

Hay ocasiones que, cuando trabajamos en un análisis en R, necesitamos dividir nuestros datos en grupos, y después tenemos que correr la misma operación sobre cada grupo. Por ejemplo, puede ser que los datos que tenemos contienen varios países, y necesitamos crear un modelo por cada país. Otro caso sería el de correr múltiples operaciones sobre los mismos datos. Estos casos requieren que sepamos cómo usar iteraciones con R. Este webinar se concentrará en cómo utilizar el paquete llamado purrr para ayudarnos a resolver este tipo de problema. Descargar materiales: https://rstudio.com/resources/webinars/programacio-n-con-r/ About Edgar: Edgar Ruiz es un Ingeniero de Soluciones en RStudio. Es el administrador de los sitios oficiales de sparklyr y de R para bases de datos. También es autor de los paquetes de R: dbplot, tidypredict y modeldb, y co-autor de el paquete dbplyr

Edgar Ruiz

dbplyr modeldb purrr rstudio tidypredict webinars Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Edgar Ruiz

Mike Garcia | R in Pharma: Intro to Shiny | Posit

Slides: https://garciamikep.github.io/rstudioglobal-2021-shiny-slides/slides.html#1 From rstudio::global(2021) Pharma X-Sessions, sponsored by ProCogia: in this introduction to Shiny app development, we begin with a quick review of visualization with {ggplot2} and then cover core concepts in app structure and reactive programming. After building several Shiny apps of increasing complexity, we wrap up with a demonstration of how to include your Shiny app in a dashboard using the {flexdashboard} package. About Mike Garcia: Mike is a Data Science Consultant with ProCogia, with a background in Biostatistics and experience in clinical trial design and public health research. If not geeking out on data with a cup of coffee and spreading his passion for R, he's probably out enjoying the outdoors. Learn more about the rstudio::global(2021) X-Sessions: https://blog.rstudio.com/2021/01/11/x-sessions-at-rstudio-global/ _______________________________________________________________________________________________ To hear more about how other major pharmaceutical companies are transitioning to open source data science you can watch talks from this year’s R in Pharma conference: https://www.youtube.com/@RinPharma/playlists At Posit, we have a dedicated Pharma team to help organizations migrate and utilize open source for drug development. To learn more about our support for life sciences, please see our dedicated Pharma page where you can book a call with our team. (https://posit.co/solutions/pharma)

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Volha Tryputsen | R in Janssen Drug Discovery Statistics | Posit

From rstudio::global(2021) Pharma X-Sessions, sponsored by ProCogia: this talk discusses how R is utilized in the Janssen drug discovery statistics workflow. About Volha: Volha is the Principal Statistician in the Translational Medicine and Early Development Statistics (TMEDS) group in the Quantitative Sciences Department of Janssen R&D. Learn more about the rstudio::global(2021) X-Sessions: https://blog.rstudio.com/2021/01/11/x-sessions-at-rstudio-global/

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Pharma Janssen Drug Discovery

Posit Presents: Saving the Coral Reefs

Data scientists are changing the world. For a real-world view of how data scientists work to solve hard, complex, and valuable problems, watch Pim Bongaerts from the California Academy of Sciences speak about using data science to help save coral reefs. About Pim: Pim Bongaerts is the McCosker Chair of Aquatic Biology, Assistant Curator at the California Academy of Sciences, where he applies genomics and field ecology to his research into coral reef biodiversity and evolution

Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown CRAN Interoperability Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Pim Bongaerts California Academy of Sciences Coral Reefs Ocean Conservation

Lou Bajuk & Kevin Bolger | Why Data Science in the Cloud? | RStudio (2020)

As business and organizational needs expand, a centralized ecosystem such as the cloud is needed to securely store and access data, conduct analyses, and share results. We’ll share some examples of what it means to do data science in the cloud and discuss some problems that users may face along the way, and the solutions that RStudio products can provide. We’ll also discuss best practices for migrating to a cloud environment. What you’ll learn: - What are the benefits of working in a cloud environment? - What are the different cloud environments available? - How do I learn which is the best fit for my organization? - What should I consider when migrating my data science infrastructure to the cloud? Webinar materials: https://rstudio.com/resources/webinars/why-data-science-in-the-cloud/ About Lou: Lou is a passionate advocate for data science software, and has had many years of experience in a variety of leadership roles in large and small software companies, including product marketing, product management, engineering and customer success. In his spare time, his interests includes books, cycling, science advocacy, great food and theater. About Kevin: After finishing his education in the University of Limerick, Ireland – Kevin’s passion for data science was cemented. Focusing primarily on data analytics and modelling, he went on to spend the first years of his career working at a biopharmaceutical company, where he led the data team on multiple products. Since moving to Seattle with his Washington native wife, Kevin has spent his spare time enjoying the beautiful PNW and playing ‘hurling’, an ancient gaelic field sport with the Seattle Gaels. He now leads the Data Science team at ProCogia as the Director of Data Solutions – where he works with clients from Biotech to Telecom

rstudio webinars Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Forcats RStats Open Source Oss Reticulate Lou Bajuk Kevin Bolger Cloud-Based Data Science Cloud

Andy Nicholls & Michael Rimler | Using R to Drive Agility in Clinical Reporting | RStudio (2020)

The R language is used extensively throughout the pharmaceutical industry. But its use within the tightly regulated clinical reporting workflows has remained limited. GSK Biostatistics has embarked upon a journey to embed R as a primary statistical analysis tool for clinical reporting. Enabling R within a global department of over 600 Statisticians, Programmers and Data Scientists is challenging! It requires planning, patience, and a strong foundation that enables consistency across the enterprise. We invite you to learn more about how we achieved this at GSK. You'll learn about our tidyverse-centric training program, a future-ready Working Area for R Programming (WARP) environment, and a leading-edge R for Clinical Reporting (R4CR) initiative. The goal: help embed R in every-day clinical reporting output. About Andy: Andy Nicholls has a long history with the R language and in Data Science, authoring the book ‘R in 24 Hours’. He is currently Head of Statistical Data Sciences within GSK’s Biostatistics department. One of his team’s main objectives is to embed the R language within Biostatistics; developing training materials, overseeing various adoption initiatives and provisioning a world-class environment for R . He is also the lead for the cross-industry R Validation Hub initiative that aims to support the use of R for regulatory work. About Michael: Michael Rimler is a clinical programmer in GSK Biostatistics and passionate about influencing the evolving role of open source technologies and data science capabilities on clinical data analytics. He is involved with numerous internal initiatives aimed at moving the organization in this direction, including leading the effort to fully integrate R into the clinical reporting process. Michael is a co-lead of the Open Source Technologies in Clinical Research PHUSE working group project, has chaired a PHUSE US Single Day Event on Data Visualization, and will serve as a co-chair for the 2021 PHUSE US Connect

rstudio tidyverse Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Forcats RStats Open Source Oss Reticulate Clinical Reporting GSK Pharma Andy Nicholls Michael Rimler

Hadley Wickham | testthat 3.0.0 | RStudio (2020)

In this webinar, I'll introduce some of the major changes coming in testthat 3.0.0. The biggest new idea in testthat 3.0.0 is the idea of an edition. You must deliberately choose to use the 3rd edition, which allows us to make breaking changes without breaking old packages. testthat 3e deprecates a number of older functions that we no longer believe are a good idea, and tweaks the behaviour of expect_equal() and expect_identical() to give considerably more informative output (using the new waldo package). testthat 3e also introduces the idea of snapshot tests which record expected value in external files, rather than in code. This makes them particularly well suited to testing user output and complex objects. I'll show off the main advantages of snapshot testing, and why it's better than our previous approaches of verify_output() and expect_known_output(). Finally, I'll go over a bunch of smaller quality-of-life improvements, including tweaks to test reporting and improvements to expect_error(), expect_warning() and expect_message(). Webinar materials: https://rstudio.com/resources/webinars/testthat-3/ About Hadley: Hadley Wickham is the Chief Scientist at RStudio, a member of the R Foundation, and Adjunct Professor at Stanford University and the University of Auckland. He builds tools (both computational and cognitive) to make data science easier, faster, and more fun. You may be familiar with his packages for data science (the tidyverse: including ggplot2, dplyr, tidyr, purrr, and readr) and principled software development (roxygen2, testthat, devtools, pkgdown). Much of the material for the course is drawn from two of his existing books, Advanced R and R Packages, but the course also includes a lot of new material that will eventually become a book called "Tidy tools"

Hadley Wickham

devtools dplyr ggplot2 pkgdown purrr readr roxygen2 rstudio testthat tidyr tidyverse waldo webinars Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Testthat Hadley Wickham

Mine Çetinkaya-Rundel | Teaching R online with RStudio Cloud | RStudio (2020)

RStudio Cloud is a lightweight and easy to set up / use solution to teaching R online, in the browser. In this webinar we will walk you through the steps of setting up your course on RStudio Cloud, highlighting the various functionalities for teachers and students. We will also discuss best practices and provide an opportunity for the audience to experience the setup first hand. Additionally, we highlight a suite of ready to use resources for teaching an introduction to data science and statistical thinking course using R. Webinar materials: https://rstudio.com/resources/webinars/teaching-r-online-with-rstudio-cloud/ About Mine: Mine Çetinkaya-Rundel is Professional Educator and Data Scientist at RStudio as well as Senior Lecturer in the School of Mathematics at University of Edinburgh (on leave from Department of Statistical Science at Duke University). Mine’s work focuses on innovation in statistics and data science pedagogy, with an emphasis on computing, reproducible research, student-centered learning, and open-source education as well as pedagogical approaches for enhancing retention of women and under-represented minorities in STEM. Mine works on integrating computation into the undergraduate statistics curriculum, using reproducible research methodologies and analysis of real and complex datasets. She also organizes ASA DataFest and works on the OpenIntro project. She is also the creator and maintainer of datasciencebox.org and she teaches the popular Statistics with R MOOC on Coursera

Mine Çetinkaya-Rundel

rstudio webinars Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown CRAN Interoperability Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Rayshader Plumber Blogdown Lazy Evaluation Tidymodels Statistics Programming Education Forcats RStats Open Source Oss Reticulate Mine Çetinkaya-Rundel RStudio Cloud Teaching Online Remote Education Distance Learning

A quick tour of RStudio 1.4 | RStudio

HD version here: https://youtu.be/oCR_LB3H73M 0:00 Introduction 0:20 R Markdown Visual Editor 0:46 Insert citations in R Markdown 1:09 Python support in Environment pane 2:05 Python environment selection 2:25 Rainbow parentheses 2:43 Monospace font support 2:54 Support for multiple source columns 3:10 Command palette 3:27 Customize data and configuration storage (users and servers) 3:55 RStudio Pro edition features 4:08 Authenticate RStudio Server Pro using SAML 4:25 Project sharing with Launcher 4:48 Request a GPU with SLURM 5:00 Run Visual Studio Code sessions (beta) What's new with RStudio 1.4: A visual markdown editor that provides improved productivity for composing longer-form articles and analyses with R Markdown. New Python capabilities, including display of Python objects in the Environment pane, viewing of Python data frames, and tools for configuring Python versions and conda/virtual environments. The ability to add source columns to the IDE workspace for side-by-side text editing. A new command palette (accessible via Ctrl+Shift+P) that provides easy keyboard access to all RStudio commands, add-ins, and options. Support for rainbow parentheses in the source editor (enabled via Options, then Code, then Display). New citation support that allows you to include document citations from your document bibliography, personal or group libraries, and several other sources. Integration with a host of new RStudio Server Pro features including project sharing when using Launcher, Microsoft Visual Studio Code support (currently in beta), SAML authentication, and local launcher load-balancing. Read more on our blog: https://blog.rstudio.com/2021/01/19/announcing-rstudio-1-4/

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Visual Editor

Greg Wilson | Teaching Online at Short Notice | RStudio (2020)

So here you are: you planned to teach your class or deliver your workshop in person, and now you have to do it online or not at all. Nobody is giving you time or money to make the change, and a hundred other things also need your attention. Where should you start, and what can you realistically hope to achieve? This one-hour webinar will present answers from people who have found themselves in this situation before, and will recommend a handful of techniques that you can apply right away. Webinar materials: https://rstudio.com/resources/webinars/teaching-online-at-short-notice/ About Greg: Dr. Greg Wilson has worked for 35 years in both industry and academia, and is the author or editor of several books on computing and two for children. He is best known as the co-founder of Software Carpentry, a non-profit organization that teaches basic computing skills to researchers, and is now part of the education team at RStudio

rstudio webinars Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Greg Wilson Teaching Remote Learning

Alison Hill & Desirée De Leon | How to Get Your Materials Online With R Markdown | RStudio (2020)

Full title: Sharing on Short Notice: How to Get Your Teaching Materials Online With R Markdown Educators create a lot of files for teaching- slides, exercises, solutions, assignments, data, figures- that all ultimately need to be shared with other people. Having a link for sharing your teaching materials can save you time and pain, but it is hard to get started if you’ve never shared your resources online before. In this webinar, we’ll give a tour of the R Markdown ecosystem for educators that you can start to use right away. We’ll show how it can help you make your teaching more shareable, reproducible, and resilient. About Alison: I studied psychology and quantitative methods, receiving my Ph.D. from Vanderbilt University (2008). For eight years, I was a professor and scientist at Oregon Health & Science University, where my research was funded by the National Institutes of Health, the Oregon Clinical and Translational Research Institute, and Autism Speaks. I have written numerous scientific journal articles and book chapters on autism and neurodevelopmental disorders. I have developed and delivered workshops, graduate-level courses, and curricula based on teaching R, the tidyverse, and literate programming. You can follow my current work for RStudio Education on GitHub. About Desirée: I am neuroscience PhD student at Emory University and also a former summer intern at RStudio. This past summer, I worked with Alison Hill to develop a handbook filled with practical advice and resources for educators who teach with R and RStudio. I enjoy spending my time on collaborative projects that involve coding, teaching, and illustration

rstudio tidyverse Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Alison Hill Desiree De Leon Reproducibility

James Blaire & Barret Schloerke | Integrating R with Plumber APIs | RStudio (2020)

Full title: Expanding R Horizons: Integrating R with Plumber APIs In this webinar we will focus on using the Plumber package as a tool for integrating R with other frameworks and technologies. Plumber is a package that converts your existing R code to a web API using unique one-line comments. Example use cases will be used to demonstrate the power of APIs in data science and to highlight new features of the Plumber package. Finally, we will look at methods for deploying Plumber APIs to make them widely accessible. Webinar materials: https://rstudio.com/resources/webinars/expanding-r-horizons-integrating-r-with-plumber-apis/ About James: James is a Solutions Engineer at RStudio, where he focusses on helping RStudio commercial customers successfully manage RStudio products. He is passionate about connecting R to other toolchains through tools like ODBC and APIs. He has a background in statistics and data science and finds any excuse he can to write R code. About Barret: I specialize in Large Data Visualization where I utilize the interactivity of a web browser, the fast iterations of the R programming language, and large data storage capacity of Hadoop

Barret Schloerke

odbc plumber rstudio webinars Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate

Enabling Remote Data Science Teams | RStudio (2020)

Whatever happens in the coming months, remote work is here to stay. The goal of this webinar is to provide data scientists and data science team leaders with the knowledge and tools to succeed as a distributed team. Some of the topics we will cover include: - Setting up a remote and collaborative R environment - Version control and Scrum - Using Shiny and RStudio Connect to share apps within and across teams - How to improve the UI and appearance of Shiny dashboards - How to scale Shiny dashboards to hundreds of users - How to build and grow a remote data science team About Alex: Alex is a Solutions Engineer at RStudio, where he helps organizations succeed using R and RStudio products. Before coming to RStudio, Alex was a data scientist and worked on economic policy research, political campaigns, and federal consulting. About Olga: Olga is experienced in production applications of analytical solutions, especially for FMCG companies. Recently she developed a price elasticity model for Unilever. About Damian: Damian is one of the four co-founders of Appsilon. Before founding Appsilon he worked at Accenture, UBS, Microsoft and Domino Data Lab. About Pedro: Pedro has nearly a decade of experience combining frontend and backend technologies, and is an expert on augmenting R Shiny dashboards with CSS and JavaScript

rstudio Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Forcats RStats Open Source Oss Reticulate Version Control Scrum Rstudio Connect Dashboards Remote Work

Roche & Novartis: Effective Visualizations for Data Driven Decisions || Posit (2020)

Effective visual communication is a core task for all data scientists including statisticians, epidemiologists, machine learning experts, bioinformaticians, etc. By using the right graphical principles, we can better understand data, highlight core insights and influence decisions toward appropriate actions. Without it, we can fool ourselves and others and pave the way to wrong conclusions and actions. While numerous solutions exist to analyze data, these often require many manual steps to convert them into visually convincing and meaningful reports. How do we put this in practice in an accurate, transparent and reproducible way? In this webinar we will introduce an open collaborative effort, currently undertaken by Roche and Novartis, to develop solutions for effective visual communication with a focus on reporting medical and clinical data. The aim of the collaboration is to develop a user-friendly, fit for purpose, open source package to simplify the use of good graphical principles for effective visual communication of typical analyses of interventional and observational data encountered in clinical drug development. We will introduce the initial visR package design which easily integrates into a typical tidyverse workflow. The package provides guidance and meaningful default parameters covering all aspects from the design, implementation and review of statistical graphics. Webinar materials: https://posit.co/resources/videos/effective-visualizations-for-data-driven-decisions/ About Charlotta: Charlotta is a computational biologist by training and works as a data scientist in the Personalized Healthcare department at Roche where she uses R to untap the wealth of information coming from healthcare data collected in real-world settings to support the development of new medicines. About Diego: Diego is a data scientist specializing in applied machine learning at Roche Personalized Healthcare since March 2019. He has developed models to perform various tasks and analyze diverse data sources. Currently, his main applications of interest are in onocology and clinico-genomics. About Mark: Mark is a methodologist supporting the clinical development and analytics department at Novartis. He has a focus on data visualization working on a number of internal and external initiatives to improve the reporting of clinical trials and observational studies. About Marc: Marc is a biostatistics group head at Novartis. He is interested in advancing the methods and practice of clinical development, for instance through effective use of graphics. https://graphicsprinciples.github.io/

tidyverse Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Roche Novartis Pharma

Sean Lopp & Lou Bajuk | R & Python: A Data Science Love Story | RStudio (2020)

Many Data Science teams today leverage both R and Python in their work, but struggle to use them together. Data Science leaders and their business partners find it difficult to make key data science content easily discoverable and available for decision-making, while IT Admins and DevOps engineers grapple with how to efficiently support these teams without duplicating infrastructure. Even experienced data scientists familiar with both languages often struggle to combine them without painful context switching and manual translations. In this webinar, you will learn how RStudio helps organizations tackle these challenges, with a focus on some of the recent additions to our products that have helped deepen the happy relationship between R and Python: - Easily combine R and Python in a single Data Science project using a single IDE. - Leverage a single infrastructure to launch and manage Jupyter Notebooks, JupyterLab, VSCode and the RStudio IDE, while giving your team easy access to Kubernetes and other resources. - Share and manage access to R- and Python-based interactive applications, dashboards, and APIs, all in a single place. Webinar materials: https://rstudio.com/resources/webinars/r-python-a-data-science-love-story/ About Lou: Lou is a passionate advocate for data science software, and has had many years of experience in a variety of leadership roles in large and small software companies, including product marketing, product management, engineering and customer success. In his spare time, his interests includes books, cycling, science advocacy, great food and theater. About Sean: Sean has a degree in mathematics and statistics and worked as an analyst at the National Renewable Energy Lab before making the switch to customer success at RStudio. In his spare time he skis and mountain bikes and is a proud Colorado native

rstudio webinars Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate

Sean Lopp & Rich Iannone | Avoid Dashboard Fatigue | RStudio (2020)

Data science teams face a challenging task. Not only do they have to gain insight from data, they also have to persuade others to make decisions based on those insights. To close this gap, teams rely on tools like dashboards, apps, and APIs. But unfortunately data organizations can suffer from their own success - how many of those dashboards are viewed once and forgotten? Is a dashboard of dashboards really the right solution? And what about that pesky, precisely formatted Excel spreadsheet finance still wants every week? In this webinar, we’ll show you an easy way teams can solve these problems using proactive email notifications through the blastula and gt packages, and how RStudio pro products can be used to scale out those solutions for enterprise applications. Dynamic emails are a powerful way to meet decision makers where they live - their inbox - while displaying exactly the results needed to influence decision-making. Best of all, these notifications are crafted with code, ensuring your work is still reproducible, durable, and credible. We’ll demonstrate how this approach provides solutions for data quality monitoring, detecting and alerting on anomalies, and can even automate routine (but precisely formatted) KPI reporting. Webinar materials: https://rstudio.com/resources/webinars/avoid-dashboard-fatigue/ About Sean: Sean has a degree in mathematics and statistics and worked as an analyst at the National Renewable Energy Lab before making the switch to customer success at RStudio. In his spare time he skis and mountain bikes and is a proud Colorado native. About Rich: My background is in programming, data analysis, and data visualization. Much of my current work involves a combination of data acquisition, statistical programming, tools development, and visualizing the results. I love creating software that helps people accomplish things. I regularly update several R package projects (all available on GitHub). One such package is called DiagrammeR and it's great for creating network graphs and performing analyses on the graphs. One of the big draws for open-source development is the collaboration that comes with the process. I encourage anyone interested to ask questions, make recommendations, or even help out if so inclined!

Rich Iannone

blastula gt rstudio webinars Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Forcats RStats Open Source Oss Reticulate Dynamic Emails Reporting Sean Lopp Rich Iannone

Lou Bajuk & Sean Lopp | RStudio: A Single Home for R & Python| RStudio

Many Data Science teams today are bilingual, leveraging both R and Python in their work. While both languages are tremendously powerful, teams frequently struggle to use them together. We’ve heard from our customers how even experienced data scientists familiar with both languages often struggle to combine them without painful context switching and manual translations. Data Science leaders and business stakeholders find it difficult to make key data science content easily discoverable and available for decision-making, and IT Admins and DevOps engineers grapple with how to efficiently support these teams. In this webinar, you will learn how RStudio helps Data Science teams tackle all these challenges, and make the Love Story between R and Python a happier one: - Easily combine R and Python in a single Data Science project - Leverage a single infrastructure to launch and manage Jupyter Notebooks and JupyterLab environment, as well as the RStudio IDE - Organize and share Jupyter Notebooks alongside your work in R and your mixed R and Python projects This webinar will show examples of all these capabilities, and discuss the benefits of leveraging R and Python. About Lou: Lou is a passionate advocate for data science software, and has had many years of experience in a variety of leadership roles in large and small software companies, including product marketing, product management, engineering and customer success. In his spare time, his interests includes books, cycling, science advocacy, great food and theater. About Sean: Sean has a degree in mathematics and statistics and worked as an analyst at the National Renewable Energy Lab before making the switch to customer success at RStudio. In his spare time he skis and mountain bikes and is a proud Colorado native

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Lou Bajuk Sean Lopp Webinar

Kevin Bolger & Oisin Bates | Architecting RStudio Products in the Cloud | RStudio (2020)

Part of RStudio's Data Science in the Cloud Webinar Series. About Kevin: After finishing his education in the University of Limerick, Ireland – Kevin’s passion for data science was cemented. Focusing primarily on data analytics and modelling, he went on to spend the first years of his career working at a biopharmaceutical company, where he led the data team on multiple products. Since moving to Seattle with his Washington native wife, Kevin has spent his spare time enjoying the beautiful PNW and playing ‘hurling’, an ancient gaelic field sport with the Seattle Gaels. He now leads the Data Science team at ProCogia as the Director of Data Solutions – where he works with clients from Biotech to Telecom. About Oisin: Oisin is a data science and AWS Cloud Architect for ProCogia

rstudio Rstudio Data Science Machine Learning Python Stats Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Forcats RStats Open Source Oss Reticulate Kevin Bolger Oisin Bates Cloud Cloud-Based Data Science Data Science Cloud

Alex Gold | Managing Packages for Open Source Data Science | RStudio

What You'll Learn: With over 15,000 R packages on CRAN, over 230,000 on PyPI, and more arriving every day, the task of managing a package environment for data science in R and Python can be daunting. In this webinar, you will learn about the most current strategies and tooling for creating and maintaining a reproducible package environment. Whether you’re an individual data scientist or the administrator of an entire RStudio Team cluster, you’ll better understand how you can enhance your ability to work easily and safely with open-source data science packages. About Alex: Alex is a Solutions Engineer at RStudio, where he helps organizations succeed using R and RStudio products. Before coming to RStudio, Alex was a data scientist and worked on economic policy research, political campaigns, and federal consulting

RStudio Team

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Alex Gold

Podcast | Not So Standard Deviations Episode 100 | RStudio (2020)

Featuring: Hilary Parker & Roger Peng In episode 100 of Not So Standard Deviations, the first ever episode prepared in advance, Hilary and Roger discuss creativity, its role in data science, and how it can be fostered through conversation. Also, follow up on coffee and oat milk. About Hilary: Hilary Parker is a Data Scientist on the styling recommendations team at Stitch Fix, a personal styling service that uses a combinations of human stylists and algorithmic recommendations to help people find what they love. At Stitch Fix, she focuses on what sorts of data to collect from clients in order to optimize clothing recommendations, as well as building out prototypes of algorithms or entirely new products based on new data sources. She is also a co-founder of the Not So Standard Deviations podcast, a bi-weekly data science podcast with Roger Peng that has over half a million downloads. Their topics of discussion include the R ecosystem, recent developments in the data science and statistics field, reproducibility and the "how" of how data scientists and statisticians work. Hilary recently authored the paper Opinionated Analysis Development based on discussions from the podcast. Prior to her career in the tech field, Hilary received her PhD in Biostatistics from Johns Hopkins School of Public Health. She lives at the San Francisco Zen Center with her partner, a Soto Zen Priest. In her free time, she enjoys exploring her home of 2 years, San Francisco. About Roger: Roger D. Peng is a Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health where his research focuses on the development of statistical methods for addressing environmental health problems. He is the author of the popular book R Programming for Data Science and nine other books on data science and statistics. He is also the co-creator of the Johns Hopkins Data Science Specialization, the Simply Statistics blog where he writes about statistics for the public, the Not So Standard Deviations podcast with Hilary Parker, and The Effort Report podcast with Elizabeth Matsui. Roger is a Fellow of the American Statistical Association and is the recipient of the Mortimer Spiegelman Award from the American Public Health Association, which honors a statistician who has made outstanding contributions to public health. He can be found on Twitter and GitHub at @rdpeng

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Hilary Parker Roger Peng

Panel: Career Advice for Data Scientists | RStudio (2020)

Featuring: Gabriela de Queiroz, David Keyes, Sydeaka Watson, and Jen Hecht This panel will be focused on how you build a career around R! Our panelists are all passionate about R and have each taken a different path to build a career around that passion. We'll be touching on topics like the different stages of career growth and how you find a community you can go to for support. If you're just getting started in your career, looking to make a transition, or interested in learning some great career-building skills, be sure to join us! About Gabriela de Queiroz: Gabriela de Queiroz is a Sr. Engineering & Data Science Manager at IBM where she manages and leads a team of developers working on Data & AI Open Source projects. Her team contributes to projects such as TensorFlow, PyTorch, Apache Arrow, Apache Spark, and several other open source projects inside IBM. She works to democratize AI by building tools and launching new open source projects. She is passionate about making data science available to everybody and is actively involved with several organizations to foster an inclusive community. She is the founder of AI Inclusive, a global organization that is helping increase the representation and participation of gender minorities in Artificial Intelligence. She is also the founder of R-Ladies, a worldwide organization for promoting diversity in the R community with more than 180 chapters in 50+ countries. About David Keyes: David Keyes is the founder of R for the Rest of Us, which teaches people and individuals to use R through online courses, custom trainings, and public workshops. With stops as an elementary school teacher, PhD social scientist, and program evaluator, David brings his unconventional career trajectory to his current role, helping those with who don't think of themselves as typical R users embrace the power of R. About Sydeaka Watson: Dr. Sydeaka Watson is a native of New Orleans, Louisiana and currently lives in Dallas, Texas. She is Founder and Owner of Korelasi Data Insights, LLC and a Senior Data Scientist at Elicit Insights, LLC. In these roles, Sydeaka uses predictive analytics and visual tools to draw insights from diverse datasets. Sydeaka earned a Ph.D. in Statistics from Baylor University and has several years of teaching experience. In her 5 years as Research Assistant Professor in The University of Chicago Biostatistics Laboratory, she consulted with over 110 biomedical research teams in The University of Chicago Medical Center, specializing in statistical analysis and experimental design for clinical research studies. Her current research interests include applications of (1) image recognition for computer vision and (2) data science for social justice. Sydeaka currently serves as Organizer of the R-Ladies Dallas Chapter. She also volunteers in the Dallas chapters of Girls Who Code and Black Girls Code. About Jen Hecht: Jen Hecht is the VP of People Operations at RStudio. She was first introduced to R in 2013, as a non-programmer seeking better ways to manage analytical projects - a quest which was aided both by the RStudio toolchain and the welcoming support of R Ladies, R meetup groups, and other wonderful open resources. Ever since, she has been captivated by open data science tools and the communities that build them. Before joining RStudio in 2018, Jen held HR and People Analytics roles in a variety of industries, including financial services, biotech, and used record shops. Outside of work, Jen loves books and music of all kinds, and is a novice fly caster

rstudio tensorflow Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Data Science Career Career

Fernanda Viegas & Martin Wattenberg | Data, visualization, and designing AI | RStudio (2020)

Recent progress in machine learning has raised a series of urgent questions: How can we train and debug deep learning models? How can we understand what is going on inside a neural network? And, perhaps most important, how can we design systems that serve people best? We'll show a series of examples from the People+AI Research (PAIR) initiative at Google--ranging from data visualizations for researchers, to tools for medical practitioners, to guidelines for designers--that illustrate how thinking carefully about data can lead to better tools, more effective design, and help humans and AI work together. About Fernanda: Fernanda Viégas and Martin Wattenberg co-lead Google’s PAIR (People+AI Research) initiative, part of Google Brain. Their work in machine learning focuses on transparency and interpretability, as part of a broad agenda to improve human/AI interaction. They are well known for their contributions to social and collaborative visualization, and the systems they’ve created are used daily by millions of people. Viégas and Wattenberg are also known for visualization-based artwork, which has been exhibited in venues such as the Museum of Modern Art in New York, London Institute of Contemporary Arts and the Whitney Museum of American Art. Their artwork has influenced contemporary design practice: for instance, the techniques in their wind map are now used by many major media companies to display the weather. About Martin: Fernanda Viégas and Martin Wattenberg co-lead Google’s PAIR (People+AI Research) initiative, part of Google Brain. Their work in machine learning focuses on transparency and interpretability, as part of a broad agenda to improve human/AI interaction. They are well known for their contributions to social and collaborative visualization, and the systems they’ve created are used daily by millions of people. Viégas and Wattenberg are also known for visualization-based artwork, which has been exhibited in venues such as the Museum of Modern Art in New York, London Institute of Contemporary Arts and the Whitney Museum of American Art. Their artwork has influenced contemporary design practice: for instance, the techniques in their wind map are now used by many major media companies to display the weather

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Ai Google Deep Learning

Alex Gold | Deploying End-To-End Data Science with Shiny, Plumber, and Pins | RStudio

It’s easier than ever to craft a complete R-centric data science pipeline thanks to packages like Shiny, Plumber, and Pins. In this talk, you’ll learn how to use R to bring your modeling and visualization work into production. You’ll walk away with recipes, tips, and tricks to deploy data, models, and apps to ensure your work is as impactful as possible. About Alex: Alex is a Solutions Engineer at RStudio, where he helps organizations succeed using R and RStudio products. Before coming to RStudio, Alex was a data scientist and worked on economic policy research, political campaigns, and federal consulting

plumber rstudio Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Alex Gold

ZJ | Easy larger-than-RAM data manipulation with {disk.frame} | RStudio

Learn how to handle 100GBs of data with ease using {disk.frame} - the larger-than-RAM-data manipulation package. R loads data in its entirety into RAM. However, RAM is a precious resource and often do run out. That's why most R user would have run into the "cannot allocate vector of size xxB." error at some point. However, the need to handle larger-than-RAM data doesn't go away just because RAM isn't large enough. So many useRs turn to big data tools like Spark for the task. In this talk, I will make the case that {disk.frame} is sufficient and often preferable for manipulating larger-than-RAM data that fit on disk. I will show how you can apply familiar {dplyr}-verbs to manipulate larger-than-RAM data with {disk.frame}. About ZJ: ZJ is a machine learning developer based in Melbourne, Australia. He regularly contributes to open source projects. He has more than 10 years of experience in banking before joining the tech sector. In his free time, he enjoys playing Go/Baduk/Weiqi

dplyr rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate ZJ Disk.frame

Wolfram King | Lifelong Learning with R Weekly | RStudio

R Weekly is a weekly newsletter with many great R blogs post, tutorials, and other formats of resources. R Weekly wants to keep track of these great things in the R community and make it more accessible to everyone. This is a warm and welcoming place. The team welcomes everyone who wants to contribute to the R community. In this talk I will cover these 6 topics: 1. How to use the R Weekly website 2. Why I created R Weekly 3. How to Contribute to R Weekly 4. How to release a new post 5. How to join the team 6. Learning from building the community About Wolfram: Wolfram King is the founder of the R Weekly project. He is an active member of the R community and has several popular R open-source projects on GitHub

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Wolfram King

Simon Couch | tidymodels/stacks: A Grammar for Stacked Ensemble Modeling | RStudio

Full title: tidymodels/stacks, Or, In Preparation for Pesto: A Grammar for Stacked Ensemble Modeling Through a community survey conducted over the summer, the RStudio tidymodels team learned that users felt the #1 priority for future development in the tidymodels package ecosystem should be ensembling, a statistical modeling technique involving the synthesis of multiple learning algorithms to improve predictive performance. This December, we were delighted to announce the initial release of stacks, a package for tidymodels-aligned ensembling. A particularly statistically-involved pesto recipe will help us get a sense for how the package works and how it advances the tidymodels package ecosystem as a whole. About Simon: Simon Couch is an R developer and statistics student at Reed College, where he is entering the final semester of his undergraduate degree. He co-authors and maintains R packages including broom, infer, and stacks, leads trainings and workshops as an RStudio-certified tidyverse trainer, and researches in algorithmic data privacy. He interned on the RStudio tidymodels team in summer 2020, and is currently applying to doctoral programs in statistics

Simon Couch

infer rstudio tidymodels tidyverse Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Simon Couch Stacks

Richard Vogg | Examples of simulated datasets that bring value to a data-driven company | RStudio

Full title: How I became a Data Composer – examples of simulated datasets that bring value to a data-driven company How can I get the buy-in from business partners to use more advanced techniques? What can I do to make a data project involving several teams more efficient? And how can I train analysts who do not (yet) have access to sensitive data? A good data composer is skilled at creating suitable data quickly and efficiently. R has many functions and packages that help with simulating independent variables and composing those in a meaningful way. In this talk, I will share how I started creating data and how this skill helped me with solving some of the issues described above. Showing a few examples – of small, medium-sized, and large data composition – I want to encourage attendees to simulate data and enrich their data skillset. About Richard: Richard Vogg studied mathematics at TU Kaiserslautern, Germany, where he focused on statistics and obtained a Master’s degree. He worked as a Senior Business Analyst at Evalueserve in Chile for the last years, analyzing data for a major US bank. At the end of 2020, he moved back to Germany. Richard is a fan of applied statistics and storytelling with data. Outside of R, he enjoys playing the ukulele, trumpet, and didgeridoo

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Richard Vogg Simulated Data

Pamela E. Pairo | An easy and friendly way to build your multilingual website | RStudio

Having a personal website is a great way to share our experiences with other people, that also allows us to improve our communication skills and expand our networking groups. Besides, if the website is multilingual, the scope will be extended considerably by facilitating the exchange of ideas. I will give the key steps, some tips, and important considerations to bear in mind when creating a multilingual website using Blogdown, Hugo, and Netlify. Although having a multilingual website demands more effort, R enables us to build a website easily and keep it updated. I aim to help and encourage others to build their website to promote exchange experiences among people from different native languages. About Pamela: Pamela E. Pairo is a Ph.D. in Biological Sciences of the University of Buenos Aires, with expertise in community ecology. One of her research lines focuses on analyzing the impact of human activities on the diversity and composition of biological communities with particular interest in arthropods. In addition, she is interested in studying the spatio-temporal patterns of dengue disease in Argentina. She also is a teaching assistant in statistics at the Argentine University of Enterprise (UADE)

blogdown rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Pamela E. Pairo

Nicholas Pylypiw | Racial Equity Dashboard: Unpacking Systemic Inequity | RStudio

At Cape Fear Collective, we’re redefining what a town square looks like in our community, serving as a place where all people, organizations, and ideas can come together to effect real, lasting, and systemic change. By merging cutting edge data science with an emphasis on equity and the lived experience of our most marginalized communities, Cape Fear Collective supports Southeastern North Carolina’s front line organizations in combating poverty, racism, poor health and education outcomes, and socio-economic disparities. This talk is about how we bring that model to life through our Racial Equity Dashboard, from data sourcing, to modeling and, ultimately, action. About Nick: Nick is the Director of Data Science at Cape Fear Collective, a non profit which supports Southeastern North Carolina’s front line organizations in combating poverty, racism, poor health and education outcomes, and socio-economic disparities. Prior to CFC, he honed his data science and consulting skills in the marketing analytics space, transforming the way Fortune 500+ companies (Lowe's Southwest Airlines, P&G, and many others) think about their customer strategy and value proposition. He lives in Raleigh, North Carolina

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Nicholas Pylypiw Dashboard

Michael Chirico | Making .pot-ery with R: Translations in R Packages | RStudio

The R community is globally distributed and R itself is available with messages in 14 languages. Adding translations for non-native English-speaking users of your package can ease their experience and empower them to build better things with less frustration (though please note that ""object of type 'closure' is not subsettable"" is equally inscrutable in all human languages). In this talk, I will cover translations in R packages -- how to implement them, why to do so, and how to maintain them. This will summarize and extend learnings based on our experience adding Mandarin translations to data.table and culminating in the potools package. About Michael: Michael Chirico is a data scientist working on compute memory efficiency at Google. Before that he worked at Grab in Singapore and earlier got his PhD in Economics at the University of Pennsylvania. He is passionate about making tools to empower others who work with data (most of this energy is directed towards data.tab ≤) and loves learning languages (at various middling levels of proficiency in Japanese, Spanish, and Mandarin, with goals to learn Cantonese, Hokkien, Vietnamese and Bahasa)

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Michael Chirico

Marie Vendettuoli | Lessons learned developing a library of validated packages | RStudio

Full title: Towards an integrated {verse}: lessons learned developing a library of validated packages Developing R packages as a unified {verse} – a set of packages that work well together but with each focusing on individual tasks – is an efficient strategy to structure support for complex workflows. The ongoing challenge becomes managing the growth of related packages in a holistic manner. This is especially problematic in industries with a heavy emphasis on stability, for example if packages need to be validated prior to use in production. In this talk, I will discuss a paradigm for developing and maintaining validated R packages, emphasizing the following areas: 1. Strategies for organizing packages to prevent excessive re-work 2. Facilitating responsive, iterative development and 3. Empathy for developer and user experiences About Marie: Marie Vendettuoli is a Senior Statistical Programmer at Statistical Center for HIV/AIDS Research and Prevention (SCHARP - https://www.fredhutch.org/en/research/divisions/vaccine-infectious-disease-division/research/biostatistics-bioinformatics-and-epidemiology/statistical-center-for-hiv-aids-research-and-prevention.html) @ FredHutch. She holds a PhD from Iowa State University in Human Computer Interaction and started developing R packages for use within regulatory frameworks while working as a Data Scientist at USDA Center for Veterinary Biologics (https://www.aphis.usda.gov/aphis/ourfocus/animalhealth/veterinary-biologics/sa_about_vb/ct_vb_about). Before discovering R, Marie worked in a CBER (https://www.fda.gov/about-fda/fda-organization/center-biologics-evaluation-and-research-cber)-regulated laboratory. Her main interest is developing analytical infrastructure to facilitate scientific analysis for fellow data scientists working in a regulatory environment

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Marie Vendettuoli

Lucy D'Agostino McGowan | Designing Randomized Studies using Shiny | RStudio

This talk will walk through building a self-contained randomized study using Shiny and learnr modules. We will discuss building informed consent, the randomization process, demographic surveys, and R-based studies into a single online framework to allow users to seamlessly enroll and participate in randomized studies via a single URL. The talk will include both practical recommendations as well as technical code snippets. About Lucy: Lucy D'Agostino McGowan is an assistant professor of statistics at Wake Forest University, where she leads the WFU Data Science Lab. Her research focuses on causal inference, human-data interaction, and statistical communication

learnr rstudio Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Lucy D'Agostino McGowan

John Helveston | Using formr to create R-powered surveys with individualized feedback | RStudio

This talk demonstrates how the formr study framework extends the power and flexibility of R to surveys. Using R and RMarkdown code, researchers and teachers can use the formr platform to generate both simple surveys and complex studies with individualized feedback. The platform is built on a web-based application programming interface for R via OpenCPU, enabling complex features such as automated email and text message reminders, adaptive testing, graphical and interactive feedback, and integration with external data sources. In this talk, I introduce some of the formr basics and showcase two examples of how I have used it, including making conjoint surveys with randomized images and timed, randomized quizzes for my students. About John: John Paul Helveston is an Assistant Professor in the Engineering Management and Systems Engineering Department at the George Washington University. He studies technological change, with a particular interest in accelerating the transition to environmentally sustainable and energy-saving technologies. His research centers around how consumer preferences, market dynamics, and policy affect the emergence of critical technologies, such as electric vehicles and solar energy. He is an expert on China's rapidly emerging electric vehicle industry as well as the critical relationship between the US and China in developing and mass producing low carbon energy technologies. He applies an interdisciplinary approach to research, with expertise in discrete choice modeling and conjoint analysis as well as interview-based case studies. He has conducted extensive fieldwork in China, collaborating with colleagues at Tsinghua university, Beijing Normal University, and China's State Information Center on past projects. He is a fluent speaker of Mandarin Chinese and also an award-winning swing dancer. John holds a Ph.D. and M.S. in Engineering and Public Policy from Carnegie Mellon University and a B.S. in Engineering Science and Mechanics from Virginia Tech

rmarkdown rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate John Helveston Formr

Garrick Aden-Buie | xaringan Playground: Using xaringan to learn web development | RStudio

xaringan is a quirky package that extends R Markdown to create beautiful web-based HTML slides. Some of xaringan’s quirks come from the JavaScript library it uses, remarkjs, and some of it from the unusual naming scheme xaringan uses for its functions. But under this quirky exterior lies a powerful tool for learning and practicing web development, especially when combined with infinite_moon_reader() for immediate feedback. In this talk I'll cover some basic web concepts that illustrate how fun and rewarding it can to learn HTML, CSS and JavaScript while building awesome slides in R Markdown. About Garrick: The workshop will be led by friend of RStudio, Garrick Aden-Buie, R developer and educator and Data Scientist at Moffitt Cancer Center, where he uses Shiny to enable and accelerate cancer research

Garrick Aden-Buie

rstudio Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Garrick Aden-Buie Xaringan HTML

Dean Marchiori | A retrospective on a year of commercial data science projects in R | RStudio

Full title: How reproducible am I? A retrospective on a year of commercial data science projects in R Reproducibility is a critical aspect in science to enable trust & communication. In R, many tools exist to bring in the best practices of reproducibility into the hands of data scientists. However, outside of a research setting, how does reproducibility hold up in commercial data science projects? In this talk I take an honest retrospective of my own commercial R projects in the last year. I look at the various types of analyses completed, and which workflows were selected and why. Through this process we can learn how workflow choices may help in the short term but hinder in the long term. More importantly what can be done strike the balance between progress and perfection when doing data science in the wild? About Dean: Dean Marchiori is a Statistician based in Sydney, Australia. He currently works with Endeavour Energy as a Senior Data Scientist modelling bushfire and vegetation risk on the electricity network. Dean's career started in finance as an equities trader before moving into advanced analytics, where he has worked with some of Australia’s largest organisations. His professional interests are in geospatial analysis, time series modelling and the R programming language. In 2019, Dean was named one of the top 10 analytics leaders in Australia by IAPA. He is also recognised as an Accredited Statistician with the Statistical Society of Australia. He holds a Bachelor of Science in Mathematics (awarded with University Medal) and a Masters degree in Applied Finance. Outside of work Dean enjoys bodysurfing, running and spending time with his wife and two boys

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Dean Marchiori

Danielle Smalls-Perkins & Dorris Scott | Reflections on creating the Mi-R community | RStudio

Full title: Your R is My R too: Reflections on creating the Mi-R community While the R community has made strides in increasing the representation and participation for women and users from underrepresented regions, there are still members of the R community that have expressed desires for a more inclusive space in addition to these strides. In addition, there are unique challenges that underrepresented R users experience in their respective workspaces or academic environments. In late February of 2020, Danielle Smalls-Perkins and Dorris Scott created Mi-R (Minorities in R) as a result of their various experiences both in and outside the R community. The purpose of this talk is to reflect on the challenges, highlights, and future directions of the first six months since the creation of Mi-R. About Danielle: Danielle Smalls-Perkins co-founded MiR Community with the hope that the R community would continue to encourage the inclusion and recognition of contributions made from R users of diverse backgrounds. She loves to use R for understanding and storytelling. Danielle currently works as a Senior Strategist in Google's Trust and Safety Team. She advocates for model fairness, interpretability, and reducing harmful outcomes of algorithmic decision-making on vulnerable populations. About Dorris: Dorris Scott is the GIS Librarian and Social Science Data Curator at Washington University – St. Louis, where she provides consultation on projects that use geospatial data along with providing training in various GIS software, programming applications of geospatial data, and data management. She also serves as a liaison between Washington University Libraries and social science departments assisting faculty with their data needs such as data management and data curation. Dorris received her PhD in Geography from the University of Georgia, with a specialization in GIS applications for public health

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Dorris Scott Danielle Smalls-Perkins

Danielle Oberdier | How Content Makes the Data Go 'Round | RStudio

What makes a successful data science community thrive across industries? A recent Aflac WorkForces Report showed that professionals who are engaged in a community within their industry are 70% more likely to be satisfied with their work. I believe anyone can and should create content about data. In this talk, I will direct your attention towards 1) the ways that content creation can lead to heightened data science opportunities 2) how to know which type/s of content mediums (podcasts, blogs, video) are right for you 3) how to leverage social media and networking connections to make your content reach the right audiences. I hope to inspire listeners to create their own content and online brands as resources for fellow R community members

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Danielle Oberdier

Colin Rundel | parsermd - parsing R Markdown for fun and profit | RStudio

parsermd is a new R package for parsing and programmatically interacting with R Markdown (Rmd) documents. This package implements a formal grammar for Rmd documents in C++ using Boost's Spirit X3 library and provides additional user facing functions for the resulting abstract syntax tree. In this talk we will provide background on the structure and grammar of Rmd documents as well as discuss the ways in which the parsing of these documents enables a variety of automatable tasks. Specifically, we will focus on demonstrating how these tools can be used to provide automated feedback on student submissions in a statisical programming course. About Colin: Colin is a lecturer in Statistics and Data Science at the University of Edinburgh. He has been teaching statistics and data science courses, with a focus on computing and spatial modeling, for the last 8 years

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Colin Rundel

Chelsea Parlett-Pelleriti | Hands-on ways to remotely teach data science are invaluable | RStudio

Full title: With more learning occurring virtually or in hybrid mode, hands-on ways to remotely teach data science are invaluable With more learning occurring virtually or in hybrid mode, hands-on ways to remotely teach DS are invaluable. Guided simulation exercises in R allow learners to explore concepts deeply, on their own time, and with others. They can also experiment with the simulations, try out edge cases, and challenge their assumptions, leading to more fruitful discussions. The comparison between coefficient estimates in regular, LASSO, and RIDGE regression, or how PCA performs when data are related are great examples of concepts where guided simulations can encourage learners to build intuitive knowledge. This talk explores how to use simulation exercises in R to help learners explore DS concepts and provides examples. About Chelsea: Chelsea Parlett-Pelleriti is a PhD Candidate and full-time instructional faculty teaching Data Science at Chapman University. Her research centers around how we can use statistics and machine learning to improve the way we analyze behavioral data. In her free time, you can find Chelsea on Twitter making stats memes or statsTikTok's. She also writes about statistics, machine learning, and using R for various blogs

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Chelsea Parlett-Pelleriti

Carson Sievert | Custom theming in Shiny & R Markdown with bslib & thematic | RStudio

Custom theming in Shiny and R Markdown often requires writing styling rules in both CSS and R. In particular, styles for HTML content (e.g., actionButton(), tabsetPanel(), titlePanel(), etc) derive from Bootstrap CSS, so customization is traditionally done by overwriting that CSS, which is difficult to do 100% correctly. The {bslib} package helps solve this problem by making it easy to customize (any version of) Bootstrap CSS defaults from R. However, this only solves part of the problem since CSS doesn't necessarily effect output(s) rendered by R, such as plotOutput(). The {thematic} package helps solve this problem by providing auto theming of plotOutput()s (based on CSS) as well as a simple interface for styling any R graphic for any output format. About Carson: Carson is a software engineer at RStudio working on R packages such as shiny, shinymeta, and plotly. His book "Interactive data visualization with R, plotly, and shiny", published by CRC Press, is also freely available online at plotly-r.com

Carson Sievert

bslib rstudio Shiny shinymeta thematic Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Carson Sievert Bslib Thematic

Athanasia M. Mowinckel | Make a package - Make some friends | RStudio

In 2017, I had never exposed my code to anyone other than a select few before, and I was terrified. I had some functions made from a colleagues script that I thought might be useful for others, and dared myself to make a package and push it to github. In stead of the dreaded ridiculing of poor code and development, people embraced the package and helped us make it better. Within just a couple of days, pull requests came from others to help us improve the code, implement tests, and improve documentation. I learned so much just by looking through the PRs and seeing how others worked. Rather than make me shy off development, the R neuro community's positive feedback has helped me find a new interest and joy in developing tools. About Athanasia: Athanasia M. Mowinckel is a staff scientist at the Center for Lifespan Changes in Brain and Cognition, at the University of Oslo. She has a background on cognitive psychology, and uses R for almost everything. She goes by the nickname "Mo" (closer to 'Mou' than 'Moe'), and is a member of the R-Ladies Global team

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Athanasia M. Mowinckel Package Development

Alex Cookson | The Power of Great Datasets | RStudio

There are a few classic datasets, like mtcars, nycflights, or Titanic passengers. They're okay, but they leave something to be desired for folks learning R: they're kind of boring. There's a big difference between "Okay Datasets" and "Great Datasets". Great Datasets prompt you to exclaim, "That's so cool!" They get your blood pumping and mind racing with questions you want answered. They give tremendous motivation to answer those questions. And in answering those questions, you'll probably learn some R. I want you to curate Great Datasets. You'll contribute to the richness of our community, you'll learn some R yourself, and you'll feel fantastic when someone finds your Great Dataset and exclaims, "That's so cool!" About Alex: Alex Cookson helps the Customer Intelligence team at the Royal Canadian Mint make the most of their data. When he's not working on A/B testing, recommendation engines, or exploratory data analysis at the Mint, he can be found participating in Tidy Tuesday or thinking up cool datasets to explore. And when he's not doing that, he's probably cycling around Toronto or doting on his two cats, Tom Tom and Ruby

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Package Development Alex Cookson

Alan Feder | Categorical Embeddings: New Ways to Simplify Complex Data | RStudio

When building a predictive model in R, many of the functions (such as lm(), glm(), randomForest, xgboost, or neural networks in keras) require that all input variables are numeric. If your data has categorical variables, you may have to choose between ignoring some of your data and too many new columns. Categorical embeddings are a relative new method, utilizing methods popularized in Natural Language Processing that help models solve this problem and can help you understand more about the categories themselves. While there are a number of online tutorials on how to use Keras (usually in Python) to create these embeddings, this talk will use embed::step_embed(), an extension of the recipes package, to create the embeddings. About Alan: Alan Feder is a Principal Data Scientist at Invesco, where he uses as much R as possible to solve problems and build products throughout the company. Previously, he worked as a data scientist at AIG and an actuary at Swiss Re. He studied statistics and mathematics at Columbia University. He is unreasonably excited to spread the word about categorical embeddings. Alan lives in New York City with his wife, Ashira, and two children, Matan and Sarit

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Alan Feder

Yanina Bellini Saibene | On programming, teaching, and building tutorials with learnr | RStudio

Full title: On programming, teaching, and building interactive tutorials with learnr Teaching R is part of my activities as a community organizer, an RStudio Certified Instructor, a conference chair, and a researcher. Since 2019, I use the learnr package to generate interactive tutorials to teach R synchronously and asynchronously. The addition of the Tutorials panel in RStudio IDE and the need for virtual classes made the use of this package even more interesting. In this talk, I will tell you how to generate interactive tutorials, how to add pedagogical tools to them, what other packages you can use with {learnr} and show multilingual examples. About Yanina: Yanina Bellini Saibene is a researcher at INTA (National Institute of Agricultural Technology) dedicated to applying data science to the agricultural sector and a professor in several regional specializations about Digital Agriculture and Data Analysis. Yanina is formally trained as a Licenciate in Information System with a Master degree in Data Mining and Knowledge Management. She is an active member of the R Community as an R-Ladies organizer and part of the R-Ladies Global Team. She also is a co-founder and co-chair of LatinR and part of the organizing team of useR!2020 and user!2021 (co-chair). She is also co-founder of MetaDocencia, a open, free, volunteer-lead, not-for-profit educational organization and part of the teams that translate educational and technical material to Spanish

learnr rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Yanina Bellina Saibene Learnr

Winston Chang | Making Shiny apps faster with caching | RStudio

Shiny's 1.6 has a new function, bindCache(), which makes it easy to dramatically speed up reactive expressions and output rendering functions. This allows many applications to scale up to serve several times more users without an increase in server resources. Note: Shiny 1.6.0 isn't yet on CRAN, but will be in the next few days. In the meantime, you can install it with: remotes::install_github(""rstudio/shiny@rc-v1.6.0"") About Winston: Winston is a software engineer at RStudio. He holds a Ph.D. in psychology from Northwestern University and is the author of R Graphics Cookbook, published by O’Reilly Media

Winston Chang

rstudio Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Winston Chang BindCache() BindCache

Vicki Boykis | Your public garden | RStudio

Vicki will discuss how that as people who can write code and analyze data, we have a lot of input and power over what our digital and work worlds looks like, and therefore can act as agents of change and repair. About Vicki: Vicki Boykis is a machine learning engineer at Automattic, the company behind Wordpress.com. She works mostly in Python, R, Spark, and SQL, and really enjoys building end-to-end data products. Outside of work she publishes the Normcore Tech newsletter (https://vicki.substack.com) and blogs at https://veekaybee.github.io/. In her "spare time", she blogs, reads, and writes terrible joke tweets about data

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Vicki Boykis

Sophie Beiers | Trial and Error in Data Viz at the ACLU | RStudio

Visualizing data the “right” way requires many considerations — the topic, the quality of your data, your audience, your time frame, and the various channels of (sometimes conflicting) feedback you received. In this presentation, I’ll introduce some reflections on these considerations and ways I’ve incorporated feedback (or not) into my work as Data Journalist at the ACLU. Lastly, I’ll present some of the sillier trials and errors I’ve made that were arguably necessary to my process in creating effective data visualizations using R. About Sophie: Sophie Beiers works on the ACLU's Analytics team as a Data Journalist where she analyzes and visualizes data for their lawyers’ legal arguments and for external advocacy pieces. Prior to her time at the ACLU, she received her master’s degree in Quantitative Methods in Social Sciences at Columbia University where she also TA’d at the Lede Program for Data Journalism. Before NYC, she kicked off her career in analytics in San Francisco at the education nonprofit "YouthTruth." Sophie is a Bay Area native but currently lives in Portland, OR and enjoys running, hiking, and making pottery in her free time

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Sophie Beiers

Shirbi Ish-Shalom | Using R to Up Your Experimentation Game | RStudio

Have you ever cut an A/B test short? Maybe because of traffic constraints, your antsy boss, or early successful results. In reality, cutting your test short can be catastrophic, making your business decision no better than a coin flip. Learn some R-driven tips & tricks to get meaningful results quickly with a statistically rigorous methodology called sequential testing, an A/B testing enhancement my team employs at Intuit. Key Takeaways: 1) What is sequential testing and how to use it. 2) How to learn (and fail!) quickly by taking big metric swings 3) How I used R to share my learnings & make them useful for anyone (even non-data scientists!) at my company About Shirbi: Shirbi Ish-Shalom is a human person

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Shirbi Ish-Shalom

Shelmith Kariuki | rKenyaCensus Package | RStudio

The rKenyaCensus package contains the results of the 2019 Kenya Population Census. The census exercise was carried out in August 2019, and the results were released in February 2020. Kenya leveraged on technology to capture data during cartographic mapping, enumeration and data transmission, making the 2019 Census the first paperless census to be conducted in Kenya. The data was published in four different pdf files (Volume 1 - Volume 4) which can be found in the Kenya National Bureau of statistics website. The data in its current form was open and accessible, but not usable and so there was need to convert it into a machine readable format. This data can be used by the government, non-governmental organizations and any other entities for data driven policy making and development. During the talk, I will explain the reasons behind development of the package, take you through the steps I took during the process and finally showcase analysis of certain aspects of the data. About Shelmith: Shelmith Kariuki is a Senior Data Analyst based in Nairobi, Kenya. She is an RStudio Certified Tidyverse trainer (https://education.rstudio.com/trainers/), currently working as a Data Analytics consultant with UN DESA. She previously worked as a Research Manager at Geopoll, and as a Data Analyst at Busara Center for Behavioral Economics. She also worked as an assistant lecturer in various Kenyan universities, teaching units in Statistics and Actuarial Science. She has extensive experience in data analysis using R. She co-organizes a community of R users in Nairobi (https://www.linkedin.com/feed/hashtag/nairobir/) and in Africa (https://twitter.com/AfricaRUsers). One of the missions of her community work is to make sure that there is an increased number of R adopters, in Africa. She is very passionate about training and using data analytics to drive development projects in Africa

education.rstudio.com rstudio tidyverse Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Shelmith Kariuki RKenyaCensus

Sean Lopp | R & Python: Going Steady | RStudio

While there has been a lot of excitement about the R and Python love story, there are still misconceptions that individuals, teams, or organizations must pick between R or Python. This talk will explain why this false choice exists, debunk the myths that cause teams to be stuck with only one tool, and clarify how data scientists can use both languages to be more effective. We will explore this love story's blossoming relationship by looking at updates to RStudio's packages and products that make it easier to develop and collaborate in R and Python. This talk is for individuals who want to uncover the benefits of multilingual data science, IT professionals who are skeptical their life can get better by supporting more languages, and data science managers interested in enabling their teams instead of forcing their data superheros to be subservient to particular tools. About Sean: Sean has a degree in mathematics and statistics and worked as an analyst at the National Renewable Energy Lab before making the switch to customer success at RStudio. In his spare time he skis and mountain bikes and is a proud Colorado native

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Sean Lopp

Riva Quiroga | Learning to program in R with a "communicative approach" | RStudio

Full title: How to do things with words: learning to program in R with a "communicative approach" Textbooks for learning a new language always start the same: you learn to say hello, to introduce yourself, and some simple and useful sentences to communicate with others. In language teaching, this is called a “communicative approach”, and is based on the idea that learning a language successfully comes through having to communicate real meaning to real people. This is what I expected to find when I first tried to learn R seven years ago. Sadly, I got stuck in resources that started with definitions of abstract concepts and no real examples of how to say things with data. In this talk I will discuss the benefits of adopting a communicative approach and how to implement it when teaching/learning R, writing documentation, and writing code that will be read by other human beings. About Riva: I like to organize R related things, like meetups (RLadies Santiago & RLadies Valparaíso), conferences (LatinR, satRday Santiago), book translations (R4DS in Spanish), and data projects (#DatosDeMiercoles). I am an editor at The Programming Historian, and I am currently pursuing a PhD in Linguistics

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Riva Quiroga

Rika Gorn | From Zero to Hero: Best practices for setting up Rstudio Team in the Cloud | RStudio

Learn best practices for setting up the entire Rstudio team infrastructure - Server Pro, Connect, Package Manager from the perspective of a data scientist and for a data science audience - especially those who have never worked with servers, AWS, or bash. This talk will also be applicable to data scientists looking to start on an engineering project outside of Rstudio as well. I started out as a complete novice, & throughout my learning experience I noticed a distinct lack of resources for non-engineers. This talk will focus on best practices for AWS architecture and cloud formation, key security issues such as SSL and https, server configurations, deployment errors, and most importantly resources that are understandable for data scientists just getting into the data engineering or devops space. About Rika: Rika Gorn is the Manager of Business Intelligence at Spring Health - a mental healthcare tech start-up that provides comprehensive mental healthcare benefits. Previously, she worked on quality assurance for a mobile mental health team at Coordinated Behavioral Care, data analytics at Covenant House International, strategic management and evaluation at TCC Group, and program analysis at the Vera Institute of Justice. Rika received her Bachelors in Political Science from Hunter College and her Masters in Public Administration at the NYU Wagner School of Public Service. Rika is also a proud board member of R-Ladies NYC

RStudio Team

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Devops Rika Gorn

Nicole Kramer | A New Paradigm for Multifigure Coordinate-Based Plotting in R | RStudio

R is unparalleled in its ability to transform raw data into a wide array of beautiful graphics, all within the same environment. However, when it comes to complex, multi-paneled plots, users rely on 3rd party graphic design software to arrange plots. Here I present the new world of programmatic, coordinate-based multi-figure plotting in R. Employing grid Graphics and drawing from the paradigms of base plotting and ggplot2, I am developing a package that will revolutionize the way plots are laid out in R. Not only will individual plots be aesthetically customizable and tailored for speed, users will also be offered exquisite control over all aspects of page layout, plot placement, and arrangements. Come join me in changing how we plot in R! About Nicole: Nicole Kramer is a third year [Bioinformatics and Computational Biology](https://bcb.unc.edu/) graduate student at the University of North Carolina at Chapel Hill. She works in the [lab of Dr. Doug Phanstiel](http://phanstiel-lab.med.unc.edu/), where her and her colleagues use experimental and computational techniques to study human genomics. Prior to grad school, Nicole received her B.S. in Biological Engineering from MIT in 2018. When not doing science, you can find Nicole petting dogs, admiring giraffes, or knitting tiny animals!

ggplot2 rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Nicole Kramer

Neal Richardson | Bigger Data With Ease Using Apache Arrow | RStudio

The Apache Arrow project enables data scientists using R, Python, and other languages to work with large datasets efficiently and with interactive speed. Arrow is so fast at some workflows that it seems to defy reality--or at least the limits of R's capabilities. This talk examines the unique characteristics of the Arrow project that enable it to redefine what is possible in R. The talk also highlights some of the latest developments in the arrow R package, including how you can query and manipulate multi-file datasets, and it presents strategies for speeding up workflows by up to 100x. About Neal: Currently Director of Engineering at Ursa Labs / RStudio. Previously led product and engineering at Crunch.io. Ph.D. in Political Science from the University of California, Berkeley

Neal Richardson

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Forcats RStats Open Source Oss Reticulate Neal Richardson Apache Apache Arrow Big Data

Mine Çetinkaya-Rundel | Feedback at scale | RStudio

As enrollments in statistics and data science courses grow and as these courses become more computational, educators are faced with an interesting challenge -- providing timely and meaningful feedback, particularly with online delivery of courses. The simplest solution is using assignments that are easier to auto-grade, e.g. multiple-choice questions, simplistic coding exercises, but it is impossible to assess mastery of the entire data science cycle using only these types of exercises. In this talk I will discuss writing effective learnr exercises, providing useful and motivating feedback with gradethis, distributing them at scale online and as an R package, and collecting student data for formative assessment with learnrhash. About Mine: Mine Çetinkaya-Rundel is Professional Educator and Data Scientist at RStudio as well as Senior Lecturer in the School of Mathematics at University of Edinburgh (on leave from Department of Statistical Science at Duke University). Mine’s work focuses on innovation in statistics and data science pedagogy, with an emphasis on computing, reproducible research, student-centered learning, and open-source education as well as pedagogical approaches for enhancing retention of women and under-represented minorities in STEM. Mine works on integrating computation into the undergraduate statistics curriculum, using reproducible research methodologies and analysis of real and complex datasets. She also organizes ASA DataFest and works on the OpenIntro project. She is also the creator and maintainer of datasciencebox.org and she teaches the popular Statistics with R MOOC on Coursera

Mine Çetinkaya-Rundel

gradethis learnr rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Mine Çetinkaya-Rundel Feedback Learnr Learnrhash

Michael Chow | Bringing the Tidyverse to Python with Siuba | RStudio

Last January I left my job to spend a year developing siuba, a python port of dplyr. At its core, this decision was driven by a decade of watching python and R users produce similar analyses, but in very different ways. In this talk, I'll discuss 3 ways siuba enables R users to transfer their hard-earned programming knowledge to python: (1) leveraging the power of dplyr syntax, (2) options to generate SQL code, and (3) working with the plotnine plotting library. Looking back, I'll consider two critical pieces that have helped me develop siuba: using it to livecode TidyTuesday analyses, and building an interactive tutorial for absolute beginners. About Michael: Michael Chow is a data scientist and learning researcher. He serves as a co-director at Code for Philly. In past lives, he worked on adaptive assessment tools in ed tech, and received a PhD in cognitive psychology from Princeton University

Michael Chow

dplyr plotnine rstudio tidyverse Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Siuba Michael Chow Sql

Megan Beckett | Aesthetically automated figure production | RStudio

Automation, reproducibility, data driven. These are not normally concepts one would associate with the traditional publishing industry, where designers normally manually produce every artefact in proprietary software. And, when you have 1000s of figures to produce and update for a single textbook, this becomes an insurmountable task, meaning our textbooks quickly become outdated, especially in our rapidly advancing world. With R and the tidyverse in our back pocket, we rose to the challenge to revolutionize this workflow. I will explain how we collaborated with a publishing group to develop a system to aesthetically automate the production of figures for a textbook including translations into several languages. I think you’ll find this talk interesting as it shows how we applied tools that are familiar to us, but in an unconventional way to fundamentally transform a conventional process. About Megan: Megan Beckett is a Data Scientist at Exegetic Analytics, where she consults, develops and leads several analytical projects across a wide range of fields and industries. *"Scientifically creative; creatively scientific."* This aptly describes her philosophy and approach in her work and life. Megan helped co-found and organises the Cape Town R-Ladies chapter and is a co-organiser of the satRday events in South Africa. She loves to paint, with her most recent work exploring the [biodiversity of southern Africa](https://mappamundi.netlify.app/portfolio/), and running is her passion, whether on the road or the trail

rstudio tidyverse Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Forcats RStats Open Source Oss Reticulate Megan Beckett Publishing Automation Reproducibility Data Driven

Maya Gans | Starting an R Book Club: Cooking Up Friendships in Isolation | RStudio

Amidst a global pandemic there’s been one consistency in my life: every Tuesday a group of friends meet to discuss Hadley Wickham’s Advanced R. I crowdsourced interest using the R4DS Slack and the results were magical: a group of incredibly curious and generous people motivated to learn and teach one another emerged. The meetings evolved from a group of strangers giving timid presentations to a safe space where we share and improve upon personal applications. The 1 club has grown to 3 regional cohorts, and became a model for discussing other books too. This talk will go over the structure of our meetings in hopes of empowering others to start their own book clubs, showcasing a different way people can create and engage in communities. About Maya: I am a mycologist turned data scientist. I love statistics, data visualization, and all things JavaScript. I am currently an intern at RStudio designing a visual block-based programming language. I create music-related infographics for JamBase.com. When I’m not coding, I’m climbing tall mountains

Hadley Wickham

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Maya Gans Book Club Advanced R

Max Kuhn | What's new in tidymodels? | RStudio

tidymodels is a collection of packages for modeling using a tidy interface. In the last year there have been numerous improvements and extensions. This talk gives an overview of additional tuning methods, new extension packages for models and recipes, and other features. About Max: Max Kuhn is a software engineer at RStudio. He is currently working on improving R's modeling capabilities. He was a Director of Nonclinical Statistics at Pfizer Global R&D in Connecticut. He was applying models in the pharmaceutical and diagnostic industries for over 18 years. Max has a Ph.D. in Biostatistics. Max is the author of numerous R packages for techniques in machine learning and reproducible research and is an Associate Editor for the Journal of Statistical Software. He, and Kjell Johnson, wrote the book Applied Predictive Modeling, which won the Ziegel award from the American Statistical Association, which recognizes the best book reviewed in Technometrics in 2015. Their latest book, Feature Engineering and Selection, was published in 2019

Max Kuhn

rstudio tidymodels Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Max Kuhn

Matt Thomas & Mike Page | How the Tidyverse helped the British Red Cross respond to COVID | RStudio

Full title: Cognitive speed: How the Tidyverse helped the British Red Cross respond quickly to COVID-19 We will discuss the importance of cognitive speed, defined here as the rate in which an idea can be translated into code, and why the Tidyverse excels in this domain. We will demonstrate this idea in relation to a suite of tools we were required to rapidly develop at the British Red Cross in order to respond effectively to the COVID-19 pandemic. To do this, we will exhibit how elements of the unifying design principles outlined in the ‘tidyverse design guide - Tidyverse team’ relate to the notion of cognitive speed, giving specific examples for various design considerations. We believe this talk will encourage reflection on better design practices for future R developers, using the design principles of the tidyverse as the guiding beacon. About Matt: Dr. Matt Thomas is Head of Strategic Insight and Foresight at the British Red Cross. Matt's team aims to help the Red Cross become more anticipatory and proactive by producing insights and tools including the Vulnerability Index (https://britishredcrosssociety.github.io/covid-19-vulnerability/) and Resilience Index (https://britishredcross.shinyapps.io/resilience-index/). He holds a PhD in Evolutionary Anthropology and, prior to joining the British Red Cross, was researching topics including reindeer herders in the Arctic, hunter-gatherers in the Philippines, and witches in China. Outside of work, Matt writes a column for an anthropology magazine (https://www.sapiens.org/column/machinations/) as well as fiction. About Mike: Mike Page is a data scientist on the Strategic Insight and Foresight team at the British Red Cross. Here, he helps to develop a suite of open source tools and dashboards including the Vulnerability Index (https://britishredcrosssociety.github.io/covid-19-vulnerability/) and Resilience Index (https://britishredcross.shinyapps.io/resilience-index/). Mike is also the author of several R packages including mortyr and newsrivr. In his spare time you can find him rock climbing around the Alps

rstudio shinyapps tidyverse Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Forcats RStats Open Source Oss Reticulate Matt Thomas Mike Page Covid Covid-19 Pandemic Red Cross

Marcus Adams | Putting a GMP Shiny App into Production | RStudio

Full title: Not The App We Deserve. The App We Need: Putting a GMP Shiny App into Production In February 2020, the Digital Proactive Process Analytics (DPPA) group within Merck’s manufacturing division officially launched a Shiny app to automate the creation of Continuous Process Verification (CPV) reports into production. That’s right – the almighty, mysterious, coveted production. From a technical perspective, the app is nothing particularly special (except other than getting LaTeX successfully installed to support the use of R Markdown). Users enter a few parameters and out pops a PDF with a series statistical analyses of a product’s quality testing data. The R blogosphere is filled with examples of similar Shiny apps. What mattered was the app was in production, and furthermore it was approved for GMP use. This meant these reports could be submitted to the FDA and other regulatory agencies. This meant the data could be used to support product release decisions. This meant Merck’s engineers were about to save thousands of hours per year in compiling data, generating charts, and calculating summary statistics. This was the app manufacturing sites needed. Most of the work in getting this app into production was not implementing the top-level features. Sorry, no discussion of fancy statistical process control methods here. Instead this talk will discuss some of the many things the development team (none of which came from a software development background) needed to learn in order to create a robust, secure, and maintainable production application. About Marcus: Marcus Adams is an Associate Director, Engineering at the biopharmaceutical company Merck. He earned his BEng and MS in Chemical Engineering from the University of Delaware and Villanova University, respectively. His more than decade of experience at Merck spans the bio-pharmaceutical spectrum and includes experience in pre-clinical PK/PD modeling, product commercialization, in-line technology support, procurement, and vaccine distribution technology development. Currently, he works as a part of the Digital Proactive Process Analytics team, leveraging Merck’s Big Data Platform in the development of manufacturing information data models, report automation tools, and integrated-systems analysis applications. His professional interests include effective digital visualization, reproducible research/analysis, and convincing his coworkers of the diverse, flourishing world beyond Microsoft Excel

rstudio Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Marcus Adams GMP

Malcolm Barrett | You're Already Ready: Zen and the Art of R Package Development | RStudio

R packages make it easier to write robust, reproducible code, and modern tools in R development like usethis make it easy to work with packages. When you write R packages, you also unlock a whole ecosystem of tools that will make it easier to test, document, and share your code. Despite these benefits, many believe package development is too advanced for them or that they have nothing to offer. A fundamental belief in Zen is that you are already complete, that you already have everything you need. I’ll talk about why your project is already an R package, why you’re already an R package developer, and why you already have the skills to walk the path of development. About Malcolm: Malcolm Barrett is Clinical Research Data Scientist at Teladoc Health, an epidemiologist, and an R developer. He is also an organizer for the Los Angeles R Users Group. Malcolm is the author of several R packages, including ggdag and precisely. Previousy, he was an intern at RStudio and spent two years of service in AmeriCorps. In 2013 and 2014, while serving in AmeriCorps, Malcolm lived in the Zen Center of New York City, where he is still a student

rstudio usethis Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Malcolm Barrett Package Development

Kate Hertweck | R training and documentation for different levels of expertise | RStudio

Full title: Making the jump from learning to applying: R training and documentation for different levels of expertise How does someone make the leap from learning R to actively applying R in professional work? At what point (if ever!) do we get to call ourselves "experts in R? This talk explores what differentiates novice, practitioner, and expert R programmers, and how transitions between these stages occur. I'll discuss the type of support required for R users to move from one level of expertise to the next, and how different types of training and documentation can support R users at each level. Understanding variable levels of education among R practitioners supports our own professional work, from collaborative coding to package development, and helps build a bigger, more inclusive R community. About Kate: Kate Hertweck is the bioinformatics training manager at Fred Hutchinson Cancer Research Center, where they develop and teach courses on reproducible computational methods as a part of fredhutch.io. Kate's graduate training at University of Missouri in genomic evolution of plants was followed by a postdoctoral fellowship at the National Evolutionary Synthesis Center (NESCent) at Duke University, where they fell in love with R and began working exclusively in computational biology. Kate then spent four years as an assistant professor teaching bioinformatics, genomics, and plant taxonomy before transitioning to biomedical research training. Kate has been involved in The Carpentries, a non-profit organization that teaches reproducible computational methods, since 2014, serving as a leader in community governance since 2016. When not being an overenthusiastic instructor, Kate likes to spend their time doing fiber arts (knitting, crochet) and enjoying all things science fiction

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Kate Hertweck

Kara Woo | Always look on the bright side of plots | RStudio

Everyone who creates visualizations in R is bound to make mistakes that prevent their plots from looking as they should. Sometimes, these mistakes create beautiful "accidental aRt", though other times they're just plain frustrating. Either way, however, there's something to be learned. This talk will draw on years of watching both the ggplot2 issue tracker and the @accidental__aRt twitter account to highlight some common plot foibles and explain what they can teach us about how ggplot2 works. About Kara: Kara Woo is a research scientist in data curation at Sage Bionetworks, where she builds tools to help researchers document and share their data. Kara is a core developer of ggplot2 and collects data visualizations gone beautifully wrong on a blog called accidental aRt

ggplot2 rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Kara Woo Accidental Art

JooYoung Seo | Accessible Data Science Beyond Visual Models | RStudio

Full title: Accessible Data Science Beyond Visual Models: Non-Visual Interactions with R and RStudio Packages Data science is full of vision-dominant practices, and most data scientists rely heavily on visual models. However, data science itself should require insight and computational thinking beyond what is just seen by eyes. JooYoung Seo, who is a blind data scientist and who was working for RStudio's accessibility projects over the summer 2020, will talk about his experience with some non-visual techniques to interact with data. If you would like to know more about various ways of making data science accessible via R, and new accessibility features introduced in RStudio IDE and Shiny, his demonstration without sight will be thought-provoking. About JooYoung: JooYoung Seo is a Ph.D candidate in the Learning, Design, and Technology program at the Pennsylvania State University, and internationally certified accessibility professional whose research and development focuses on accessible computing for all. As an RStudio's double-certified data science instructor (i.e., Tidyverse + Shiny), who is blind, he is committed to making data science ecosystem more accessible to people with and without dis/abilities using R. To this end, he has been actively contributing to R open-source projects including Shiny, RMarkdown, bookdown, and distill for accessibility, and interned on the RStudio IDE and Shiny team as an accessibility engineer in summer 2020

Shiny Team

bookdown rmarkdown rstudio Shiny tidyverse Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate JooYoung Seo Accessibility

John Burn-Murdoch | Reporting on and visualising the pandemic | RStudio

John will discuss the lessons he's learned reporting on and visualising the pandemic, including the world of difference between making charts for a technical audience and making charts for a mass audience. You'll learn from his experience navigating the highly personal and political context within which people consume and evaluate graphics and data, and how that can help us better design and communicate with visualisations down the pipeline for the future. About John: John Burn-Murdoch is the Financial Times’ senior data visualisation journalist, and creator of the FT’s coronavirus trajectory tracker charts. He has been leading the FT’s data-driven coverage of the pandemic, exploring its impacts on health, the economy and wider society. When pandemics are not happening, he also uses data and graphics to tell stories on topics including politics, economics, climate change and sport, and is a visiting lecturer at the London School of Economics

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Forcats RStats Open Source Oss Reticulate John Burn-Murdoch FT Financial Times Covid Covid-19 Pandemic Journalism

Jeroen Ooms | Monitoring health and impact of open-source projects | RStudio

At rOpenSci, we have come to realize that in order to help researchers get the most out of R, we need better tooling to monitor the quality, health, and impact of R packages. This applies both to our internal projects, as well as other packages in the R ecosystem. But what exactly makes a good R package? In this talk we discuss various aspects of open-source software that are not always immediately obvious, and that you may want to consider when depending on an R package. We identify several categories of indicators you could look for, ranging from the role in the dependency network, to expectations around maintenance and participation. Finally we introduce an ambitious new rOpenSci project called R-universe: an open platform, where we will experiment with showing metrics and other background information about packages, that may reveal something about the health and the impact of the project, and also facilitate discovery of other software. About Jeroen: Postdoc hacker for @ropensci at UC Berkeley

Jeroen Ooms

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Jeroen Ooms Ropensci

Javier Luraschi | Using pins with Python and JavaScript | RStudio

Last year, pins got released as a brand new R package to pin, discover and cache remote resources for R users. This package has matured to support many use cases; from caching remote URLs, and easily sharing datasets with other R users, to building automated pipelines. However, in order to truly collaborate in multi-disciplinary data-driven teams, one needs to consider how to collaborate beyond R. How can we share resources with designers and machine learning experts who happen to use different programming languages like Python and JavaScript? This talk will introduce the pinsjs project, a cross-language community project which has the goal of bringing pins to the broader open source community to enable rich workflows across larger data-driven teams. About Javier: Javier is the author of “Mastering Spark with R”, pins, sparklyr, mlflow and torch. He holds a double degree in Math and Software Engineer and decades of industry experience with a focus on data analysis. Javier is currently working on a project of his own; and previously worked in RStudio, Microsoft Research and SAP

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Javier Luraschi JavaScript Pins

Irene Steves | The dynamic duo: SQL & R | RStudio

There's a point in every data wranglers' career in which their full dataset can no longer fit into just CSV files, and the journey to database-world begins. I reached this point about two years ago, when I transitioned from ecological research to the world of eCommerce fraud prevention. My calls to read_csv became scarcer as I came to rely more and more on databases. In this talk, I'll demonstrate how I use R and SQL to access database tables, and how I incorporate both into my daily workflow, aided by features in RStudio IDE. I'll also discuss our company’s "riskiconn" package for handling database connections and queries, which includes customizations to simplify day-to-day data querying. About Irene: Irene holds an M.Sc. in Ecology and a B.A. in Integrative Biology, through which she first discovered R and data science. Her interest in data led her to the Arctic Data Center at the University of California Santa Barbara, a summer internship at RStudio, and ultimately to the Research & Data Science department at Riskified, where she now explores the complex patterns of fraud in eCommerce. In her free time, she studies Hebrew through podcasts and dubbed kids' movies

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Sql EDA

Hadley Wickham | Maintaining the house the tidyverse built | RStudio

Hadley will talk about how the tidyverse has evolved since its creation (just five years ago!). You'll learn about our greatest successes, learn from our biggest failures, and get some hints of what's coming down the pipeline for the future. About Hadley: Hadley Wickham is the Chief Scientist at RStudio, a member of the R Foundation, and Adjunct Professor at Stanford University and the University of Auckland. He builds tools (both computational and cognitive) to make data science easier, faster, and more fun. You may be familiar with his packages for data science (the tidyverse: including ggplot2, dplyr, tidyr, purrr, and readr) and principled software development (roxygen2, testthat, devtools, pkgdown). Much of the material for the course is drawn from two of his existing books, Advanced R and R Packages, but the course also includes a lot of new material that will eventually become a book called "Tidy tools"

Hadley Wickham

devtools dplyr ggplot2 pkgdown purrr readr roxygen2 rstudio testthat tidyr tidyverse Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Hadley Wickham

Grant Fleming | Fairness and Data Science: Failures, Factors, and Futures | RStudio

In recent years, numerous highly publicized failures in data science have made evident that biases or issues of fairness in training data can sneak into, and be magnified by, our models, leading to harmful, incorrect predictions being made once the models are deployed into the real world. But what actually constitutes an unfiar or biased model, and how can we diagnose and address these issues within our own work? In this talk, I will present a framework for better understanding how issues of fairness overlap with data science as well as how we can improve our modeling pipelines to make them more interpretable, reproducible, and fair to the groups that they are intended to serve. We will explore this new framework together through an analysis of ProPublica's COMPAS recidivism dataset using the tidymodels, drake, and iml packages. About Grant: Grant Fleming is a Data Scientist at Elder Research, co-author of the Wiley book _Responsible Data Science_ (2021), and contributor to the O'Reilly book _97 Things About Ethics Everyone in Data Science Should Know_. His professional focus is on machine learning for social science applications, model explainability, and building tools for reproducible data science. Previously, Grant was a research contractor for USAID

rstudio tidymodels Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Ethics Propublica Tech

Eric Cronstrom | How we made the switch: a case study on automating a complex report | RStudio

The Center for Charter Schools at Central Michigan University produces annual reports for about 60 schools. The reporting process used to be a cumbersome blend of many technologies. The Center used to use a blend of SQL, Excel, inDesign, and VBScript that would all culminate in a nice looking, branded report for each school. 2 years ago, staff turnover allowed the data team to rethink the process, having had experience in R from graduate work the team at the Center decided to go all in on R Studio and R Markdown for report production a mere 1 month before the reports were due to be published. This talk will be a case study of how we as an organization adopted RStudio tools to streamline a cumbersome process to fantastic results. About Eric: Eric is responsible for administering a wide range of day to day program functions associated with the performance and accountability of CMU partner schools. He ensures that the data the Center utilizes to evaluate school performance is accurate and stored within a sound data infrastructure. He also leverages his wide range of technical skills to lead the development and production of reports and respond to questions regarding school performance and demographic context. Prior to joining the Center, Eric was a database administrator for Central Michigan University Libraries. He has also served as a lecturer at Central Michigan University teaching courses in web design, database design and programming. He earned a master’s degree in information systems management and a bachelor’s degree in computer science from Central Michigan University. He is also pursuing an additional master’s degree in applied statistics at Central Michigan University

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate K12 Charter Schools Education

Emily Riederer | oRganization | RStudio

Many case studies demonstrate the benefits of organizations developing internal R packages. But how do you move your organization from individual internal packages to a coherent internal ecosystem? This talk applies the jobs-to-be-done framework to consider the different roles that internal tools can play, from unblocking IT challenges to democratizing tribal knowledge. Beyond technical functionality, we will explore design principles and practice that make internal packages good teammates and consider how these deviate from open-source standards. Finally, we will consider how to exploit the unique challenges and opportunities of developing within an organization to make packages that collaborate well -- both with other packages and their human teammates. About Emily: Emily Riederer is an Analytics Manager at Capital One. Emily leads a team that is focused on building internal analytical tools and data products, including a suite of R packages and Shiny apps, and cultivating an innersource community of practice for analysts. Emily is an active member of the R community. In 2019, she co-organized satRday Chicago and the Chicago R unconference. You can find her {projmgr} R package on CRAN and her blog at emilyriederer.netlify.com. Previously, Emily earned degrees in Mathematics and Statistics at UNC Chapel Hill and worked as a research assistant in emergency department simulation and optimization

rstudio Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate

Barret Schloerke | plumber + future: Async Web APIs | RStudio

plumber is an R package that allows users to create web APIs by decorating R functions using roxygen2-like comments. In the latest release, asynchronous code (using future or promises) may be inserted at any stage of a plumber route execution, enabling parallel processing using multiple workers. In this talk, I will go through how you can set up your own asynchronous plumber API to leverage your full computing potential. About Barret: I specialize in Large Data Visualization where I utilize the interactivity of a web browser, the fast iterations of the R programming language, and large data storage capacity of Hadoop

Barret Schloerke

plumber roxygen2 rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Api

Andrew Tran | The Opioid Files: Turning big pharmacy data over to the public | RStudio (2021)

Through a community survey conducted over the summer, the RStudio tidymodels team learned that users felt the #1 priority for future development in the tidymodels package ecosystem should be ensembling, a statistical modeling technique involving the synthesis of multiple learning algorithms to improve predictive performance. This December, we were delighted to announce the initial release of stacks, a package for tidymodels-aligned ensembling. A particularly statistically-involved pesto recipe will help us get a sense for how the package works and how it advances the tidymodels package ecosystem as a whole. About Andrew: Andrew is a data reporter on the rapid-response investigative team at The Washington Post who has analyzed how covid-19 has disproportionately impacted certain communities, the spread of opioids across the country, and the rise of right-wing violence. He shared in winning the Pulitzer Prize for Investigative Reporting in 2018. He's an advocate for open data and reproducibility in journalism

rstudio tidymodels Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Andrew Tran Wapo

Allison Horst | R Art Lessons | RStudio

Art can be a welcoming bridge for learners and users to engage with and learn tools and skills in R. As RStudio’s first Artist-in-Residence, my goal has been to make the R landscape more welcoming for a broader community of users through engaging, didactic artwork. In this R, art, and heart-filled talk, I’ll share the motivation behind my R artwork and some lessons learned over the past year as Artist-in-Residence, including: Learning to embrace cute and credible artwork Art to help students engage with, learn and remember R skills Art for community building and support I hope this talk inspires viewers to use, create and share more artwork, so that together we can make the R landscape feel even brighter. About Allison: Allison Horst PhD teaches data analysis, statistics, and presentation skills at the Bren School of Environmental Science and Management (UC Santa Barbara). In addition to courses, she leads interdepartmental R/RStudio workshops for incoming graduate students, created and teaches an online R-refresher workshops for alumni, and is a co-founder of R-Ladies Santa Barbara. In 2018 she earned the student-selected Distinguished Teaching Award at the Bren School, and in 2019 was awarded the campus-wide Distinguished Teaching Award by the UCSB Academic Senate. For her graduate research, Allison studied toxicity of engineered nanoparticles in environmental microorganisms. She is also a landscape painter, illustrator and designer

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Allison Horst

Ahmadou Dicko | Humanitarian Data Science with R | RStudio

Humanitarian actors are increasingly using data to drive their decisions. Since the Haiti 2010 earthquake, the volume of data collected and used by humanitarians has been growing exponentially and organizations are now relying on data specialists to turn all this data into life-saving data products. These data products are created by teams using proprietary point and click software. The process from the raw data to the final data product involves a lot of clicking, copying and pasting and is usually not reproducible. Another approach to humanitarian data science is possible using R. In this talk, I will show how to seamlessly develop reproducible, reusable humanitarian data products using the tidyverse, rmarkdown and some domain-focused R packages. About Ahmadou: Ahmadou Dicko is a statistics and data analysis officer at the United Nations High Commissioner for Refugees (UNHCR) where he uses statistics and data science to help safeguard the rights and well-being of refugees in West and Central Africa. He has an extensive experience in the use of statistics and data science in development and humanitarian projects. Ahmadou was the lead of the OCHA Center for Humanitarian Data team for West and Central Africa and has worked with several humanitarian and development organizations such as IFRC, FAO, IAEA, OCHA. Ahmadou is a RStudio trainer (https://education.rstudio.com/trainers/) and he is passionate about the R community. He is currently co-organizing the Dakar R User Group (https://www.meetup.com/DakaR-R-User-Group/) and co-leading the AfricaR initiative (https://africa-r.org/)

education.rstudio.com rmarkdown rstudio tidyverse Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Ahmadou Dicko Data for Good

Aaron Jacobs | Introducing xrprof: A New Way to Profile R | RStudio (2021)

Tracking down performance issues in R code usually means using R's built-in Rprof() profiler or one of the packages built around it. But the changing nature of the R community (towards more deployed applications) makes local profiling workflows frustrating, which is why I have written a new profiler: xrprof. xprof is compatible with existing R tools, but unlike them it can be used to profile R code that is already running -- in fact, it is designed to be safe to point at R code running ""in production"". xrprof also works seamlessly when R is run inside Docker, and can even be run in complex environments like Kubernetes clusters. Taking inspiration from the {jointprof} package, xrprof can also show function calls at the C/C++ level alongside those from R. This can be immensely useful for diagnosing problems in packages that make heavy use of compiled code. About Aaron: Aaron Jacobs is a Senior Data Scientist on the R&D team at Crescendo, a technology company in the sports betting space with a large internal R ecosystem. Prior to Crescendo he worked in Canadian public policy research. Aaron has a strong interest in the engineering side of data science and the emerging use of R "in production". He is the author of several CRAN and GitHub packages, as well as xrprof -- a new R profiling tool

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate

Tracy Teal | Teaching R using inclusive pedagogy: Carpentries workshops lessons learned | RStudio

Talk from rstudio::conf(2019) The Carpentries is an open, global community teaching researchers the skills to turn data into knowledge. Since 2012 we have taught 700+ R workshops & trained 1600+ volunteer instructors. Our workshops use evidence-based teaching, focus on foundational and relevant skills and create an inclusive environment. Teaching the Tidyverse allows learners to start working with data quickly, and keeps them motivated to begin and sustain their learning. Our assessment show that these approaches have been successful in attracting diverse learners, building confidence & increasing coding usage. Through our train-the-trainer model and open, collaborative lessons, this approach scales globally to reach more learners and further democratize data. About Tracy Teal: Executive Director of The Carpentries (https://carpentries.org) and co-founder of Data Carpentry (http://www.datacarpentry.org), a non-profit organization that develops and runs workshops training researchers in effective data analysis and visualization to enable data-driven discovery. Manages projects, operations and finances. Leads lesson development and volunteer coordination and is responsible for strategic and business planning

rstudio tidyverse Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Tracy Teal

Sean Lopp | Announcing Posit Package Manager | RStudio (2019)

Posit Package Manager is the newest professional product that helps teams, departments, and entire enterprises organize and centralize package management. If you've ever struggled with IT to get access to a new (any?) R package, reproduce an old result, or share your code with others, Posit Package Manager can help! We'll introduce the new product, discuss how R repositories can be used to solve problems and take a sneak peek at what is coming in 2019. VIEW MATERIALS https://github.com/slopp/rspm-rstudioconf About the Author Sean Lopp Sean has a degree in mathematics and statistics and worked as an analyst at the National Renewable Energy Lab before making the switch to customer success at RStudio. In his spare time he skis and mountain bikes and is a proud Colorado native. *Posit Package Manager, formerly known as RStudio Package Manager

rstudio Sean Lopp Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Mike K Smith | Using rmarkdown and parameterised reports | RStudio

My brain is lazy, shallow and easily distracted. Learn how I use notebooks to keep my present-self organised, my future-self up to speed with what I was thinking months ago, and also how I use parameterised reports to share results for both quantitative and non-quantitative audiences across multiple endpoints. I can update and render outputs for a variety of outputs from a single markdown notebook or report. I’ll show you how I organise my work using the Tidyverse, use child documents with parameterisation and also how this is served out to my colleagues via RStudio Connect. About Mike K Smith: I have 25 years experience of working in the Pharmaceutical industry (Pfizer), with more than 15 years working on modelling and simulation projects. I am a keen advocate of smarter drug development with a particular interest in Bayesian methods, dose-response, reproducible research and knowledge management. My particular expertise is in the use of simulation methodology to predict drug outcomes, find efficient trial designs, assess decision criteria and evaluate analysis methodologies. My current role at Pfizer is as specialist in computation and modeling solutions - evaluating and deploying new tools and training colleagues. I am an RStudio certified tidyverse trainer

rmarkdown rstudio tidyverse Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Mike Smith

Mary Rudis | How R and Posit are revolutionizing Stats Education in Community Colleges | Posit

Talk from rstudio::conf(2019) There is no doubt that Posit has had an impact on how introductory statistics is taught in colleges today. When we consider the sheer dominance that giants like Texas Instruments, IBM, and Pearson Publishing have had in academic curriculum development, it’s no small wonder that tools like R and Python have been able to gain a foothold. Projects like DataCamp, ModernDive.com, “Introductory Statistics with Randomization and Simulation” courtesy of openintro.org, Wickham’s “R for Data Science” and Peng’s “R Programming for Data Science” are great resources for the student who has already some fundamental math or statistical background and has become comfortable around computing and applications-driven computational exercises. But many of us know that Data Science cannot simply be relegated to the privileged few that stumble into it by virtue of circumstance. My passion, and the purpose of my talk, is to provide educators with a digestible guidebook that would be appropriate for introduction to statistical concepts in high school, college, and under-resourced schools looking for ways to increase diversity in STEM. Organized in small, adaptable activities designed to be the amuse-esprit enticing both the timid and the skeptical to the proverbial banquet table that is Posit, this exploration into the world of statistics education should be of interest to a wide audience. My hope is to increase data literacy in real world context – with primary emphasis on descriptive statistics and distributions. About Mary Rudis: After graduating from Lehigh University in Bethlehem, PA with a degree in Mathematics, Mary began as a high school mathematics and computer science teacher, developing technology infrastructure for a small, private high school in Pennsylvania. Throughout her career, she brings innovative approaches and enjoys the role of trailblazer. Mary's most recent accomplishments as math department chair included developing mathematics curriculum and coordinating engineering, bioengineering and data science degrees at Great Bay Community College in Portsmouth, NH. Mary's primary interests are learning and instruction, developing data science curricula for two-year colleges and 4-year liberal arts colleges, and working with area high school students in STEM at the University of NH Tech Camp each summer

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Mary Rudis Education

Kelly Nicole Bodwin | Intro stats with R: Easing the transition to software for beginners | RStudio

Talk from rstudio::conf(2019) In this talk, we will present our approach to incorporating R and RStudio into a 10-week introductory statistics course for non-majors Cal Poly. Our primary contribution will be to share a series of Shiny Apps, created to ease students with no statistical or coding background into the philosophy of using programming tools to explore data. Our program was recently used in 3 sections of 35 students each this Fall, during which students were surveyed regularly for their reactions to the approach. We will demonstrate our new tools, discuss our successes and failures, share student-generated output, and summarize the results of our Fall survey. Kelly teaches at Cal Poly in San Luis Obispo, California, where she takes great joy in forcing statistical thought and R skills upon unsuspecting undergraduates. Her research is in clustering methods, digital humanities, and R tools for education. In her free time, she hikes and camps, plays board games, and tweets too much about R

rstudio Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Kelly Bodwin Beginner

Jesse Mostipak | R4DS online learning community: self-taught data science & DEI | RStudio (2019)

The first iteration of the R4DS Online Learning Community was created as an online space for learners and mentors to gather and work through the "R for Data Science" text in a collaborative and supportive environment. The creation of this group was inspired by my own success in transitioning to a career in data science coupled with the resources that I wanted to see in the R programming space. This talk will go through the learnings of creating an online learning space focused on R programming for data science, and how future iterations of similar groups can more proactively center on bringing about diversity, equity, and inclusion to data science spaces. About Jesse Mostipak: Molecular biologist turned public school teacher who eventually fell in love with non-profit data science. Harvard SDP, Datanaut, and perpetual #rstats noob

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate Jesse Mostipak

JD Long | Empathy in action: Building community of practice for analytics in a global corp | RStudio

Talk from rstudio::conf(2019) JD Long, Vice President of Risk Management & Data Philosophy at the global reinsurer Renaissance Re will share his experience with creating a "Community of Practice" for analytics inside of a global corporation. The theme of "empathy" will be recurring as he discusses how he worked to create a supportive learning environment focused on helping analysts "kick ass" regardless of their tool set. This means creating a community that's supportive of Excel, SQL, Python, and, of course, R. About JD Long: I build models. And according to George E. P. Box, my models are wrong. My skill is understanding when and where my models are useful. I'm an experienced risk and data scientist with a background in insurance, reinsurance, market risk, and stochastic modeling. I'm the guy who can build a Monte Carlo model, help parallelize the model to run on Amazon's cloud services and then stand in front of a general audience and put the work in context where everyone understands. My super power is thinking probabilistically, understanding risk, and communicating clearly. I have a history forming bridges between IT and business teams

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats RStats Open Source Oss Reticulate JD Long

Heather & Jacqueline Nolis | Push straight to prod: API development with R and Tensorflow | RStudio

Talk from rstudio::conf(2019) When tasked with creating the first customer-facing machine learning model at T-Mobile, we were faced with a conundrum. We had been told time and time again to deploy machine learning models in production you had to use Python, but our very best data scientists were fluent in building neural networks in R with Keras and TensorFlow. Determined to avoid double work, we decided to use R in production for our machine learning models. After months of work, wrangling our containers to meet cloud security compliance, and conforming to DevOps standards, we succeeded in creating a containerized API solution using the keras and plumber R packages and Docker. Today R is actively powering tools that our customers directly interact with and we have open sourced our methods. In this talk, we'll walk through how to deploy R models as container-based APIs, the struggles and triumphs we've had using R in production, and how you can design your teams to optimize for this sort of innovation. About Heather Nolis: Heather Nolis is a founding member of the AI @ T-Mobile team, focusing the conversion of cutting-edge analyses to real-time, scalable data-driven products. She began her career in neuroscience but once realized how heavily that field relied on software built by other people, she pivoted - deciding to make software herself. You can find her @heatherklus on Twitter, where she speaks about diversity in technology, the ethical implications of data, and cats. About Jacqueline Nolis: Dr. Jacqueline Nolis is a co-founder of Nolis, LLC, a data science consulting firm. She has over a decade of experience using data to help companies including DSW, Union Bank, Microsoft, and Airbnb. She has a PhD from Arizona State University where her research focused on electric vehicle route optimization. For fun she likes to use machine learning for humor

plumber rstudio tensorflow Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate TensorFlow Tmobile

Jake Thompson | Branding and Packaging Reports with R Markdown | RStudio (2020)

The creation of research reports and manuscripts is a critical aspect of the work conducted by organizations and individual researchers. Most often, this process involves copying and pasting output from many different analyses into a separate document. Especially in organizations that produce annual reports for repeated analyses, this process can also involve applying incremental updates to annual reports. It is important to ensure that all relevant tables, figures, and numbers within the text are updated appropriately. Done manually, these processes are often error prone and inefficient. R Markdown is ideally suited to support these tasks. With R Markdown, users are able to conduct analyses directly in the document or read in output from a separate analyses pipeline. Tables, figures, and in-line results can then be dynamically populated and automatically numbered to ensure that everything is correctly updated when new data is provided. Additionally, the appearance of documents rendered with R Markdown can be customized to meet specific branding and formatting requirements of organizations and journals. In this presentation, we will present one implementation of customized R Markdown reports used for Accessible Teaching, Learning, and Assessment Systems (ATLAS) at the University of Kansas. A publicly available R package, ratlas, provides both Microsoft Word and LaTeX templates for different types of projects at ATLAS with their own unique formatting requirements. We will discuss how to create brand-specific templates, as well as how to incorporate the templates into an R package that can be used to unify report creation across an organization. We will also describe other components of branding reports beyond R Markdown templates, including customized ggplot2 themes, which can also be wrapped into the R package. Finally, we will share lessons learned from incorporating the R package workflow into an existing reporting pipeline. https://rstudio.com/resources/rstudioconf-2020/branding-and-packaging-reports-with-r-markdown/

ggplot2 rstudio Rstudio::conf(2020) Jake Thompson Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Max Kuhn | Total Tidy Tuning Techniques | RStudio (2020)

Many models have structural parameters that cannot be directly estimated from the data. These tuning parameters can have a significant effect on model performance and require some mechanism for finding reasonable values. The tune and workflow packages enable tidymodels users to optimize these parameters using a variety of efficient grid search methods as well as with iterative search techniques (such as Bayesian optimization)

Max Kuhn

rstudio tidymodels Rstudio::conf(2020) Max Kuhn Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Joe Cheng | Styling Shiny apps with Sass and Bootstrap 4 | Posit (2020)

Customizing the style–fonts, colors, margins, spacing–of Shiny apps has always been possible, but never as easy as we’d like it to be. Canned themes like those in the shinythemes package can easily make apps look slightly less generic, but that’s small consolation if your goal is to match the visual style of your university, corporation, or client. In theory, one can “just” use CSS to customize the appearance of your Shiny app, the same as any other web application. But in practice, the use of large CSS frameworks like Bootstrap means significant CSS expertise is required to comprehensively change the look of an app. Relief is on the way. As part of a round of upgrades to Shiny’s UI, we’ve made fundamental changes to the way R users can interact with CSS, using new R packages we’ve created around Sass and Bootstrap 4. In this talk, we’ll show some of the features of these packages and tell you how you can take advantage of them in your apps. Resources: https://speakerdeck.com/jcheng5/styling-shiny

Joe Cheng

Shiny shinythemes Rstudio::conf(2020) Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Joe Cheng

Eduardo Ariño de la Rubia | Value in Data Science Beyond Models in Production | RStudio (2020)

ML in production is one of the most obvious ways that data science organizations create value in business. However, these models are at the very end of a long story of how quantitative research changes and enhances organizations. In this talk I will discuss how I have found DS organization to be truly transformative outside of ML in the loop. Bio: Eduardo Ariño de la Rubia is a DS manager and educator. He loves R and RStudio. He has a Masters in Negotiation, Conflict Resolution and Peacebuilding, which is probably the most useful training he could have received

rstudio Rstudio::conf(2020) Eduardo Ariño De La Rubia Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Dewey Dunnington | Best practices for programming with ggplot2 | RStudio (2020)

The ggplot2 package is widely acknowledged as a powerful, dynamic, and easy-to-learn graphics framework when used in an interactive environment

ggplot2 rstudio Rstudio::conf(2020) Dewey Dunnington Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Alicia Schep | Auto-magic Package Development | RStudio (2020)

Vega-lite is a high-level grammar of interactive graphics implemented in Javascript; it renders interactive visualizations in the browser based on a JSON specification. In Python and Javascript, the Altair and vega-lite-api packages have demonstrated how the development of APIs to build Vega-Lite graphics can be partially automated based on the Vega-Lite JSON schema, which describes the required format for a Vega-Lite JSON specification. This talk will describe the development of the ‘vlbuildr’ package for building Vega-Lite specifications in R and the ‘vlmetabuildr’ package for building the ‘vlbuildr’ package. The ‘vlbuildr’ package seeks to provide a pipe-friendly, “R-like” functional interface for building up simple to complex specifications for Vega-Lite graphics, which can in turn be rendered as an HtmlWidget by the ‘vegawidget’ R package. Building such an API in a fully automated way from the Vega-Lite schema presents considerable challenges, so the approach taken here was to rely on partial automation. Human judgement dictates the basic contours of the API, such as what groups of functions to include and how various types of building blocks will go together. The part that is automated is filling in many details such as the different variants of a group of functions, the exact parameters needed for each function, and the documentation of those parameters -- the parts that would be extremely tedious to port over!

rstudio Rstudio::conf(2020) Alicia Schep Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Desiree De Leon | Of Teacups, Giraffes, and R Markdown | RStudio (2020)

How do you make your R Markdown lessons feel friendly for learners you’ll never meet? How do you make it engaging so they sit and stay a while? How do you make it memorable so they come back to visit again? In this talk, I’ll share lessons learned from my experience of making a series of online statistics modules (co-authored by Hasse Walum) that feel accessible and fun-- housed entirely in an R Markdown site, complete with a whimsical, illustrated narrative about teacup giraffes. I’ll show how adding good characters with your audience in mind, good design, and good play helped me make the most of HTML output. To help you get started, I’ll share resources that Alison Hill and I have developed--including a series of cookbooks and out-of-the-box templates-- so that you will have a leg up on applying these ideas to R Markdown collections of your own

rstudio Rstudio::conf(2020) Desiree De Leon Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Namita Nandakumar | R + Tidyverse in Sports | RStudio (2020)

This talk will use a case study, most likely in hockey, to showcase the many ways in which R and the Tidyverse can be used to analyze sports data as well as the unique priorities and considerations that are involved in applying statistical tools to sports problems

rstudio tidyverse Rstudio::conf(2020) Namita Nandakumar Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Sports

Gordon Shotwell | Technical Debt is a Social Problem | Posit (2020)

Technical debt is a big problem for the R community. Even though R has excellent support for testing, documentation and packaging code it has the reputation that it is not suitable for production applications because data scientists don’t pay enough attention to technical debt within their codebases. Most people think of technical debt as an engineering problem. We choose to make our current work cheaper at the expense of needing to do more work down the road. But when you look closely at the root causes of technical debt they are almost always about interpersonal relationships. Developers have trouble empathizing with other users of their code and so don’t spend the time to make that code easy for future developers to use and understand. In this talk I argue that we should think about technical debt as a social problem because it gives us insight into why it’s so hard to pay back. I then provide a practical roadmap of how to introduce best practices into your data science team

Rstudio::conf(2020) Mr. Gordon Shotwell Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Ellis Hughes | Approaches to Assay Processing Package Validation | RStudio (2020)

In this talk I will discuss the steps that have been created for validating internally generated R packages at SCHARP (Statistical Center for HIV/AIDS Research and Prevention) and the lessons learned while creating packages as a team. Housed within Fred Hutch, SCHARP is an instrumental partner in the research and clinical trials surrounding HIV prevention and vaccine development. Part of SCHARP’s work involves analyzing experimental biomarkers and endpoints which change as the experimental question, analysis methods, antigens measured, and assays evolve. Maintaining a validated code base that is rigid in its output format, but flexible enough to cater a variety of inputs with minimal custom coding has proven to be important for reproducibility and scalability. SCHARP has developed several key steps in the creation, validation, and documentation of R packages that take advantage of R’s packaging functionality. First, the programming team works with leadership to define specifications and lay out a roadmap of the package at the functional level. Next, statistical programmers work together to develop the package, taking advantage of the rich R ecosystem of packages for development such as roxygen2, devtools, usethis, and testthat. Once the code has been developed, the package is validated to ensure it passes all specifications using a combination of testthat and rmarkdown. Finally, the package is made available for use across the team on live data. These procedures set up a framework for validating assay processing packages that furthers the ability of Fred Hutch to provide world-class support for our clinical trials

devtools rmarkdown roxygen2 rstudio testthat usethis Rstudio::conf(2020) Ellis Hughes Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Dr. Travis Gerke | UnicoRns are real | RStudio (2020)

Common advice from experienced data scientists to job-seekers is to avoid job postings that describe a "data science unicorn": someone who has experience performing an unrealistically large array of technical and business-related job duties. Seeking a unicorn is viewed as a potential indicator that the company fails to understand their data science needs, and that new hires will not be poised for success due to lacking support and resources [Robinson & Nolis, 2019]. The R language, particularly when used with RStudio products, has evolved to enable production-level activities in the areas of data wrangling, reporting/dashboarding, database/software engineering, machine learning, and web application development. It is increasingly plausible that a data scientist will be able to efficiently perform a wide variety of job functions with experience only in a single language (R). Indeed, even entry level R users may tread into "unicorn" territory. Current standards for data scientist job descriptions and salaries do not accommodate this nuance, leaving both job-seekers and hiring managers unable to distinguish job requirements which should be read as warning signs from listings which are idyllic matches for the modern R unicorn. In this talk, we present data aggregated from several large compensation analytics companies which summarize current benchmarks for data science job descriptions and corresponding salary ranges. We then suggest job description language to target modern R users, considering both job duty compatibility and job post findability. These descriptions are presented with likely salary range pairings. Attention is given to deviations from traditional degree requirements, years of experience, and demands for multiple programming language literacy which may lack relevance for the R unicorn. Our overarching goal is to provide job description templates which encourage optimal matchmaking between R job seekers and organizations in need of their talents

rstudio Rstudio::conf(2020) Dr. Travis Gerke Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Brandon Farr | 15 Years of R in Quantitative Finance | Posit (2020)

Use of R in the investment industry is established and growing. This talk will discuss changes seen in 15 years of practice within asset management firms. I hope discussion of lessons learned and recommendations will benefit those currently in finance and those interested in hearing how the flexibility of R manifests in the financial world

Rstudio::conf(2020) Brandon Farr Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Quant

Kevin Ushey | renv: Project Environments for R | RStudio (2020)

The renv package helps you create reproducible environments for your R projects. With renv, you can make your R projects more: - Isolated: Installing a new or updated package for one project won’t break your other projects, and vice versa. - Portable: Easily transport your projects from one computer to another, even across different platforms. renv makes it easy to install the packages your project depends on. - Reproducible: renv records the exact package versions you depend on, and ensures those exact versions are the ones that get installed wherever you go. In this presentation, I'll introduce renv and some of its main workflows

renv rstudio Rstudio::conf(2020) Kevin Ushey Renv Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Evangeline Reynolds | Flipbooks | RStudio (2020)

Good examples facilitate accomplishing new or unpracticed tasks in a programmatic workflow. Tools for communicating examples have improved in recent years. Especially embraced are tools that show code and its resultant output immediately thereafter --- the case of Jupytr notebooks and Rmarkdown documents. But creators using these tools often must choose between big-picture or narrow-focus demonstration; creators tend to either demo a complete code pipeline that accomplishes a realistic task or instead demonstrate a minimal example which makes clear the behavior of a particular function, but how it might be used in a larger project isn't clear. Flipbooks help address this problem, allowing the creator to present a full demonstration which accomplishes a real task, and gives the viewer the opportunity to focus on unfamiliar steps. A set of flipbook building functions parse code in a data manipulation or visualization pipeline and then build it back up incrementally. Aligned superimposition of new code and output atop previous code and output makes it easy to identify how each code change triggers changes in output. The presentation will guide attendees in creating their own Flipbooks (with Xaringan slides) or mini Flipbooks (gif output)

rmarkdown rstudio Rstudio::conf(2020) Evangeline Reynolds Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Danielle Navarro | Toward a grammar of psychological experiments | RStudio (2020)

Why does a psychological scientist learn a programming language? While motivations are many and varied the two most prominent are data analysis and data collection. The R programming language is well placed to address the first need, but there are fewer options for programming behavioural experiments within the R ecosystem. The simplest experimental designs can be recast as surveys, for which there are many options, but studies in cognitive psychology, psychophysics or developmental psychology typically require more flexibility. In this talk I outline the design principles behind xprmntr, an R package that provides wrappers to the a javascript library (jsPsych) for constructing web based psychology experiments and uses the plumber package to call server side R code as needed. In doing so, I discuss limitations to the current implementation and what a "grammar of experiments" might look like

plumber rstudio Rstudio::conf(2020) Danielle Navarro Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Colin Gillespie | How to win an AI Hackathon, without using AI | RStudio (2020)

Anyone reading a newspaper or listening to the news is led to believe that AI is the solution to all problems. From self-driving cars to detecting disease to catching fraud, there doesn’t seem to be a situation that AI can’t tackle. Once “big data” is thrown into the mix, the AI solution is all but certain. But is AI always needed? Over the last eighteen months, Jumping Rivers has entered (and won) four Hackathons. All Hackathons were characterised with “big data” and the need to improve prediction. All Hackathons were won without using AI (or any sort of machine learning). This talk will focus on one particular competition around reducing leakage at Northumbrian Water. Using a combination of R, Shiny, and Tidyverse (and a few other tricks), we were able to demonstrate within the short Hackathon time frame that clear presentation of data to the front line engineers was more likely to reduce leakage, than simply providing vague estimates of a potential future leak

rstudio Shiny tidyverse Rstudio::conf(2020) Colin Gillespie Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Braulio Cuandon & Ana Alyeska Santos | Using R to Create Reproducible Engineering Test Reports

Talk from rstudio::conf(2020) Engineers at Biosense Webster, a Johnson and Johnson medical device company that specializes in diagnosing and treating cardiac arrhythmias, write multiple test reports to comply with FDA regulatory standards. These intricate reports require 36 hours of an engineer’s time on average, constraining the engineers from completing investigations and studies in a timely matter. Writing scripts in R that create reproducible reports can significantly reduce the time spent by an engineer creating these reports allowing them to do a much thorough investigation with a larger scope. Through Shiny, engineers could conveniently have their parameters and recorded data processed and stored in a database by accessing a web link and filling out the required information within a user-friendly interface. Upon the generation of the report, accurate and properly formatted test reports, compliant to both the company and FDA regulatory standards, are produced through Rmarkdown and knitr knitting all the outputs with complete data analysis tools such as normality plots and process capability measurements to a word document that follows company required headers, footers, and headings. The reproducible report creation shown in this report can be extended to other types of test reports and protocols. The pilot phase that has been conducted has shown that complete report production has been decreased from 36 hours to an hour

rmarkdown rstudio Shiny Rstudio::conf Rstudio::conf(2020) Braulio Cuandon Ana Alyeska Santos Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Andrew Mangano | Growth Hacking - Product Analytics at Scale using R and RStudio | RStudio (2020)

Salesforce is not only a cloud software solution out of the box, but also a highly customizable platform that can be modified for a wide range of use cases. In addition to complexity, customer trust is our #1 company value and customer data privacy is abstracted from everyone outside of the customer. Product and Growth Analytics is an emerging field separate from business analytics and data science and focuses on building software product that improve user retention and engagement. Companies like Facebook and AirBnB have robust data science teams focused on product analytics. At Salesforce however, given the scale, customization, and privacy values, product data science is not so straightforward. Utilizing R and Rstudio tools for collaboration and reproducible analytics, the Data Intelligence team is able to solve complex problems at enterprise scale. This talk will preview anonymized predictive and growth analytics work while also highlighting how we work and collaborate cross platform and languages (Python via reticulate)

reticulate rstudio Rstudio::conf(2020) Andrew Mangano Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Ian Lyttle | Small Team, Big Value: Using R to Design Visualizations | RStudio (2020)

Many R users can feel isolated due to the prevalence of Python or Tableau at their institutions. This talk will focus on how we use R to develop reference implementations of visualizations (using ggplot2), and to develop corporate-themed color maps (using the colorspace package) to bring value to the entire institution. Color maps can be translated into variety of formats, for Tableau, Qlik Sense, d3, etc., and deployed independently from R. For visualizations, our goal is to translate ggplot2 objects to Vega-Lite specifications, using a package we are developing: ggvega. Vega-Lite visualizations are web-native, and are rendered independently from R. Specifications can be designed to be extensible to new data, allowing them serve as templates, to be deployed and updated for use outside of R. Of course, despite isolation within an institution, our work with the larger R open-source communities provides a foundation on which to build; in fact, we have a lot of company and are having a lot of fun

ggplot2 rstudio Rstudio::conf(2020) Ian Lyttle Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Mine Çetinkaya-Rundel | Making the Shiny Contest | RStudio (2020)

In January 2019 RStudio launched the first-ever Shiny contest to recognize outstanding Shiny applications and to share them with the community. We received 136 submissions for the contest and reviewing them was incredibly inspiring and humbling. In this talk, we shine a spotlight on the backstage: the inspiration behind the contest, the process of evaluation, what we learned about Shiny developers and how we can better support them, and what we learned about running contests and how we hope to improve the Shiny Contest experience. We also highlight some of the winning apps as well as the newly revamped Shiny Gallery, which features many noteworthy contest submissions. Finally, we introduce the new process for submitting your apps to the Shiny Gallery and, of course, to Shiny Contest 2020! https://rstudio.com/resources/rstudioconf-2020/making-the-shiny-contest/

Mine Çetinkaya-Rundel

rstudio Shiny Rstudio::conf(2020) Mine Çetinkaya-Rundel Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Thomas Lin Pedersen | Extending your ability to extend ggplot2 | RStudio (2020)

The ggplot2 package continue to be one of the most used frameworks for producing graphics in R. While being extremely flexible, the package itself can be constrained by the different types of graphic elements and statistic transformations available. Instead of continuing to add new features, the development in recent years have focused on making ggplot2 extensible by other packages, thus distributing development and maintenance. Despite the best of intentions, ggplot2 can feel daunting to extend, due unusual idiosyncrasies, a foreign object system, and a partly obscured rendering model. This talk intend to remove the mystery of extending ggplot2, by describing the basic ways that it can be extended and showcasing a couple of simple extensions that can be build with very little code. Lastly, it will include discussions of some best practices and gotchas that may come in handy when you start out

Thomas Lin Pedersen

ggplot2 rstudio Rstudio::conf(2020) Thomas Lin Pedersen Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Tyson Barrett | List-columns in data.table | RStudio (2020)

The use of list-columns in data frames and tibbles is well documented (e.g. Bryan, 2018), providing a cognitively efficient way to organize results of complex data (e.g. several statistical models, groupings of text, data summaries, or even graphics) with corresponding data. For example, one can store student information within classrooms, player information within teams, or analyses within groups. This allows the data to be of variable sizes without overly complicating or adding redundancies to the structure of the data. In turn, this can improve the reliability to appropriately analyze the data. Because of its efficiency and speed, being able to use data.table to work with list-columns would be beneficial in many data contexts (e.g. to reduce memory usage in large data sets). Herein, I demonstrate how one can create list-columns in a data table using the by argument in data.table and purrr::map(). I compare the behavior of the data.table approaches to the dplyr::group_nest() function and tidyr::unnest(), two of the several powerful Tidyverse nesting and unnesting functions. Results using bench::mark() show the speed and efficiency of using data.table to work with list-columns

dplyr purrr rstudio tidyr tidyverse Rstudio::conf(2020) Tyson Barrett Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Henrik Bengtsson | Future: Simple Async, Parallel & Distributed Processing in R | RStudio (2020)

Future is a minimal and unifying framework for asynchronous, parallel, and distributed computing in R. It is designed for robustness, consistency, scalability, extendability, and adoptability - all in the spirit of "developer writes code once, user runs it anywhere". It is being used in production for high-performance computing and asynchronous UX, among other things. In this talk, I will discuss common feature requests, recent progress we have made, and what is the pipeline

rstudio Rstudio::conf(2020) Henrik Bengtsson Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Dr. Carson Sievert | Reproducible Shiny apps with shinymeta | RStudio (2020)

Shiny makes it easy to take domain logic from an existing R script and wrap some reactive logic around it to produce an interactive webpage where others can quickly explore different variables, parameter values, models/algorithms, etc. Although the interactivity is great for many reasons, once an interesting result is found, it’s more difficult to prove the correctness of the result since: (1) the result can only be (easily) reproduced via the Shiny app and (2) the relevant domain logic which produced the result is obscured by Shiny’s reactive logic. The R package shinymeta provides tools for capturing and exporting domain logic for execution outside of a Shiny runtime (so that others can reproduce Shiny-based result(s) from a new R session)

Carson Sievert

rstudio Shiny shinymeta Rstudio::conf(2020) Dr. Carson Sievert Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Ben Joaquin | Data Science in Meatspace | RStudio (2020)

"The Data Science community is dominated by folks doing amazing work with data that starts in and never leaves cyberspace. This talk is about best practices and playbooks for doing data science that involves meatspace (the opposite of cyberspace) and why R is such a great language for working with data that originated in the physical world. While the concrete examples in this talk will mostly come from the manufacturing space, where I have the most experience, I believe the themes are relevant to many meatspace workflows. We'll talk through effective playbooks that can help you navigate common tasks throughout the life-cycle of a project. We’ll also weave in how R’s glorious package ecosystem, including Tidyverse, can be combined with other languages like python, and with enterprise products like RStudio Connect to great effect. Specifically, we'll discuss practices in these areas: best practices for data collection in meatspace the importance of quantifying measurement system error collecting the correct data for training computer vision models the rarely discussed cost of maintaining models in production"

rstudio tidyverse Rstudio::conf(2020) Ben Joaquin Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Aymen Waqar | Building an iPad dashboard using plumber & RStudio Connect in Pharma | RStudio (2020)

As companies are becoming aware of the need to embrace data-driven solutions, R has gained a huge momentum over recent years. Getting the insights to users has become a very important factor of Data Scientist work. While our world has advanced there is a need to build not only web applications, but also applications on mobile that are available offline. We would like to share with you how within months we have gone from nothing to a production-ready application that handles 500 concurrent users in healthcare. There are plenty of challenges to solve including restricted environments, internal processes and users availability. We will show you how to overcome them and iterate fast, navigating through complex infrastructure and integrating with proxy architecture to serve applications to end users in compliant manner. With RStudio Connect and Plumber you can deploy a scalable REST API that can feed insights to your users. This allows you to go one step further and implement native applications for tablets and smartphones. With the right tools, mindset and priorities you can achieve personal success by introducing a digital transformation within your organization, starting with something as small as converting a business critical Excel file that is slow, difficult to edit and maintain, to a robust application. Step by step your organization will evolve and become empowered by your insights uncovering even more untapped potential

plumber rstudio Rstudio::conf(2020) Aymen Waqar Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Karl Feinauer | Using Jupyter with RStudio Server Pro | RStudio (2020)

This talk is for R admins who want to learn how to set up Jupyter notebooks on RStudio Server Pro. We'll cover prerequisites, basic configuration, best practices for management, Jupyter Lab, and more

rstudio Rstudio::conf(2020) Karl Feinauer Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

David Smith | MLOps for R with Azure Machine Learning | RStudio (2020)

David Smith | January 31, 2020 Azure Machine Learning service (Azure ML) is Microsoft’s cloud-based machine learning platform that enables data scientists and their teams to carry out end-to-end machine learning workflows at scale. With Azure ML's new open-source R SDK and R capabilities, you can take advantage of the platform’s enterprise-grade features to train, tune, manage and deploy R-based machine learning models and applications. In this talk, the attendees will learn how to: •Carry out ML workflows using the authoring experience of their choice, from no-code to code-first options that include Azure ML’s drag-and-drop visual interface for defining workflows and RStudio Server on the Data Science Instance, a hosted VM workstation, for using the Azure ML R SDK from the RStudio browser-based interface. •Use the Azure ML R SDK to manage cloud resources and train, hyperparameter tune, and log and visualize metrics for their models at scale on Azure compute. •Build ML Pipelines in R for defining and orchestrating reusable and reproducible ML workflows. •Deploy, manage, and monitor their R ML models and applications as web services on Azure Container Instance and Azure Kubernetes Service, with an emphasis on robust DevOps and CI/CD for orchestrating and streamlining their end-to-end data science development lifecycle

rstudio Rstudio::conf(2020) David Smith Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Ron Keizer | Building a Medical Device with R | Posit (2020)

The InsightRX precision dosing platform tailors in-patient drug doses to individual patients' characteristics and biomarkers, leveraging pharmacological models of drug metabolism and drug effects. These models are implemented in R, exposed through APIs, and called from a cloud-based web application

Rstudio::conf(2020) Ron Keizer Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Neal Richardson | Accelerating Analytics with Apache Arrow | RStudio (2020)

The Apache Arrow project is a cross-language development platform for in-memory data designed to improve system performance, memory use, and interoperability. This talk presents recent developments in the 'arrow' package, which provides an R interface to the Arrow C++ library. We'll cover the goals of the broader Arrow project, how to get started with the 'arrow' package in R, some general concepts for working with data efficiently in Arrow, and a brief overview of upcoming features

Neal Richardson

rstudio Rstudio::conf(2020) Neal Richardson Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging RStats Open Source Oss Reticulate Apache Arrow

Ryan Timpe | Learning R with humorous side projects | RStudio (2020)

What should you name a new dinosaur discovery, according to neural networks? Which season of The Golden Girls should you watch when playing a drinking game? How can you build a LEGO set for the lowest price? R is constantly evolving, so as users, we’re constantly learning. Over the past few years, I’ve found that working on side projects is great for hands-on learning - and for me, the more absurd the project, the better. Side projects provide a safe, low-stakes environment to learn new packages and methodologies before using them in work or in production. Sharing those projects can help publicize the package and increase its accessibility, benefiting both the original author and future users. In this talk, I’ll share my experiences with side projects for learning state-of-the-art data science tools and growing as an R user, including how one project helped me land my dream job

rstudio Rstudio::conf(2020) Ryan Timpe Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Jay Campanell | A high school student’s journey to bring R into the classroom | Posit (2020)

My 8th-grade capstone project introduced me to R. The project was a data visualization about breakfast tacos. I used R and other web-based tools. My lightning talk will focus on my experience about using R for class projects and getting support from my parents to help integrate R into the classroom. I will show how students can get started when they don't know how to use R. I will talk about the project’s toolkit, which includes Posit Cloud, Google Sheets, Chromebook, measurement tools, my phone, and how R is being used in my school. A 5-minute presentation in our Lightning Talks series

Rstudio::conf(2020) Jay Campanell Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Javier Luraschi | Updates on Spark, MLflow, and the broader ML ecosystem | RStudio (2020)

Originally posted at https://rstudio.com/resources/rstudioconf-2020/updates-on-spark-mlflow-and-the-broader-ml-ecosystem/

rstudio Rstudio::conf(2020) Javier Luraschi Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Bryan Lewis | Parallel computing with R using foreach, future, and other packages | RStudio (2020)

Steve Weston's foreach package defines a simple but powerful framework for map/reduce and list-comprehension-style parallel computation in R. One of its great innovations is the ability to support many interchangeable back-end computing systems so that *the same R code* can run sequentially, in parallel on your laptop, or across a supercomputer. Recent new packages like future package define elegant new programming approaches that can use the foreach framework to run across a wide variety of parallel computing systems. This talk introduces the basics of foreach and future packages with examples using a variety of back-end systems including MPI, Redis and R's default parallel package clusters

rstudio Rstudio::conf(2020) Bryan Lewis Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Gergely Daroczi | Getting things logged | RStudio (2020)

One of the greatest strengths of R is the ease and speed of developing a prototype (let it be a report or dashboard, a statistical model or rule-based automation to solve a business problem etc), but deploying to production is not a broadly discussed topic despite its importance. This hands-on talk focuses on best practices and actual R packages to help transforming the prototypes developed by business analysts and data scientist into production jobs running in a secured and monitored environment that is easy to maintain -- discussing the importance of logging, securing credentials, effective helper functions to connect to database, open-source and SaaS job schedulers, dockerizing the run environment and scaling infrastructure

rstudio Rstudio::conf(2020) Gergely Daroczi Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Dr. Sydeaka Watson | Neural Networks for Longitudinal Data Analysis | RStudio (2020)

Longitudinal data (or panel data) arise when observations are recorded on the same individuals at multiple points in time. For example, a longitudinal baseball study might track individual player characteristics (team affiliation, age, height, weight, etc.) and outcomes (batting average, stolen bases, runs, strikeouts, etc.) over multiple seasons, where the number of seasons could vary across players. Neural network frameworks such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs) can flexibly accommodate this data structure while preserving and exploiting temporal relationships. In this presentation, we highlight the use of neural networks for longitudinal data analysis with tensorflow and keras in R

rstudio tensorflow Rstudio::conf(2020) Dr. Sydeaka Watson Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Rob Hyndman | How Rmarkdown changed my life | RStudio (2020)

Over the last few years, Rmarkdown seems to have taken over my life, or at least my written communication. These days I use Rmarkdown to maintain my website, write my blog, write textbooks, write academic papers, prepare slides for talks, keep my CV up-to-date, help my students write theses, prepare university policy documents, write letters, prepare exams, write reports for clients, and more. I haven't quite got to the point of using it for shopping lists, but perhaps that's my next Rmarkdown template. I will reflect on the journey in getting to this point, what I've lost and what I've gained. I will also speculate on what might be next in the Rmarkdownification of my life

rmarkdown rstudio Rstudio::conf(2020) Rob Hyndman Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Therese Anders | Peer review in data science courses | RStudio (2020)

Peer review enables instructors of large data science classes to provide substantive feedback to students beyond what is feasible with standard code review via automated grading and continuous integration. It facilitates peer learning, which is shown in literature to have positive learning outcomes, and can reduce the burden of grading by course staff. The ghclass package provides a suite of functions to manage courses via GitHub repositories. The package has recently been supplemented with the functionality to implement peer review. Developed during my 2019 summer internship with RStudio in collaboration with my mentor Mine Çetinkaya-Rundel, the peer review functions in ghclass interface with the GitHub API to create review repositories, move files between authors and reviewers, submit feedback, and collect grades. In this presentation, I will give a demonstration of the peer review functions in ghclass. A set of six functions allows instructors to 1) create a random review roster, 2) set up the review repository infrastructure within a GitHub organization, 3) move assignments from authors to reviewers, 4) collect grades, 5) return the feedback, and 6) obtain a rating of the review from the authors. I reflect on the pedagogy of implementing peer review in introductory data science classes and talk about lessons learned from a real-world test run of the package in the Fall semester 2019 at the University of Edinburgh, conducted by Mine Çetinkaya-Rundel. The presentation highlights ghclass as an R command-line based, open source, low profile, and powerful solution to enable peer review in classes ranging from a size of two to approximately 400 students. A 5 minute presentation in our Lightning Talks series

Mine Çetinkaya-Rundel

rstudio Rstudio::conf(2020) Therese Anders Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Frank Harrell | R for Graphical Clinical Trial Reporting | RStudio (2020)

For clinical trials a good deal of effort goes into producing both final trial reports and interim reports for data monitoring committees, and experience has shown that reviewers much prefer graphical to tabular reports. Interactive graphical reports go a step further and allow the most important information to be presented by default, while inviting the reviewer to drill down to see other details. The drill-down capability, implemented by hover text using the R plotly package, allows one to almost entirely dispense with tables because the hover text can contain the part of a table that pertains to the reviewer's current focal point in the graphical display, among other things. Also, there are major efficiency gains by having a high-level language for producing common elements of reports related to accrual, exclusions, descriptive statistics, adverse events, time to event, and longitudinal data. This talk will overview the hreport package, which relies on R, RMarkdown, knitr, plotly, Hmisc, and HTML5. RStudio is an ideal report development environment for using these tools

rmarkdown rstudio Rstudio::conf(2020) Frank Harrell Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Dr. Tyler Morgan-Wall | 3D ggplots with rayshader | RStudio (2020)

Learn how a single line of code can transform your data visualizations into stunning 3D using the rayshader package. In this talk, I will show how you can use rayshader to create beautiful 3D figures and animations to help promote your research and analyses to the public. Find out how to use principles of cinematography to take users on a 3D tour of your data, scripted entirely within R. Leaving the 3D pie charts in the pantry at home, I will discuss how to build interpretable, engaging, and informative plots using all three dimensions

rstudio Rstudio::conf(2020) Dr. Tyler Morgan-Wall Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging RStats Open Source Oss Reticulate

Katie Masiello | Professional Case Studies | RStudio (2020)

The path to becoming a world-class, data-driven organization is daunting. The challenges you will likely face along the way can be thorny, and in some cases, seem outright impossible to overcome. How do you get teams that traditionally butt heads, such as IT and data science, to complement each other and work in unison? How can you efficiently scale the scope and reach of your data products as requirements change? Your time should be spent doing truly valuable work instead of updating charts and reports. How do you prevent the support structure behind your platform from toppling like a house of cards? Despite these challenges, we think that the end result is worth it: an organization that is equipped to make important decisions, with confidence, using data analysis that comes from a sustainable environment. We see this outcome every day

rstudio Rstudio::conf(2020) Katie Masiello Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Yihui Xie | One R Markdown Document, Fourteen Demos | RStudio (2020)

R Markdown is a document format based on the R language and Markdown to intermingle computing with narratives in the same document. With this simple format, you can actually do a lot of things. For example, you can generate reports dynamically (no need to cut-and-paste any results because all results can be dynamically generated from R), write papers and books, create websites, and make presentations. In this talk, I'll use a single R Markdown document to give demos of the R packages rmarkdown, - bookdown for authoring books (https://bookdown.org), - blogdown for creating websites (https://github.com/rstudio/blogdown), - rticles for writing journal papers (https://github.com/rstudio/rticles), - xaringan for making slides (https://github.com/yihui/xaringan), - flexdashboard for generating dashboards (https://github.com/rstudio/flexdashboard), - learnr for tutorials (https://github.com/rstudio/learnr), - rolldown for storytelling (https://github.com/yihui/rolldown), And the integration between Shiny and R Markdown. To make the best use of your time during the presentation, I recommend you to take a look at the rmarkdown website in advance: https://rmarkdown.rstudio.com

blogdown bookdown bookdown.org flexdashboard learnr rmarkdown rstudio rticles Shiny Rstudio::conf(2020) Yihui Xie Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Brendan Graham | Using R Bridge the Gap in Electronic Medical Record Reporting | Posit (2020)

Electronic Medical Records (EMRs) are a treasure trove of information, but tend to fall disappointingly short when it comes to visualizing and reporting data in a user friendly and intuitive manner. Building reports in an EMR can be a frustrating experience; the developer is at the mercy of how the data is stored within the EMR and the available EMR reporting tools can be bland and uninspiring. But reporting on data in the EMR doesn't have to be this way! Combining the data-rich EMR with R's robust reporting capabilities benefits both developers and consumers of data. This talk will describe how a cross-departmental project team uses an internal R package, RMarkdown reports scheduled via R Studio Connect, and an interactive flexdashboard app to quickly implement solutions to gaps in the reporting capabilities of the EMR. The flexibility of R relative to EMR reporting tools facilitates a design thinking approach to reporting allowing for more user input, customization and quick iteration. Furthermore, the web-based app we developed is able to be embedded within the EMR itself allowing for a more streamlined workflow

flexdashboard rmarkdown Rstudio::conf(2020) Brendan Graham Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Miriah Meyer | Effective Visualizations | RStudio (2020)

Originally posted to https://rstudio.com/resources/rstudioconf-2020/effective-visualizations/

rstudio Rstudio::conf(2020) Miriah Meyer Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Daniel Falbel | What's new in TensorFlow for R | RStudio (2020)

TensorFlow is the most popular open-source platform for machine learning and it's ecosystem is evolving incredibly fast. In this talk we will explore what's new in TensorFlow 2.0 as well as how to build data pre-processing pipelines using the tfdatasets package and how to use pre-trained models with tfhub

Daniel Falbel

rstudio tensorflow tfdatasets Rstudio::conf(2020) Daniel Falbel Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Lionel Henry | Interactivity and Programming in the Tidyverse | RStudio (2020)

In Tidyverse grammars such as dplyr you can refer to the columns in your data frames as if they were objects in the workspace. This syntax is optimised for interactivity and is a great fit for data analysis, but it makes it harder to write functions and reuse code. In this talk we present some advances in the tidy eval framework that make it easier to program around Tidyverse pipelines without having to learn a lot of theory

Lionel Henry

dplyr rstudio tidyverse Rstudio::conf(2020) Lionel Henry Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Colin Fay | Production-grade Shiny Apps with golem | RStudio (2020)

Shiny is an amazing tool when it comes to creating web applications with R. Almost anybody can get a small Shiny App in a matter of minutes, provided they have a basic knowledge of R. As of today, we can safely tell that it has become the de-facto tool for web application in the R world. Building a proof-of-concept application is easy, but things change when the application becomes larger and more complex, and especially when it comes to sending that app to production—until recently there hasn't been any real framework for building and deploying production-grade Shiny Apps. This is where 'golem' comes into play: offering Shiny developers an opinionated framework for creating production-ready Shiny Applications. With 'golem', Shiny developers now have a toolkit for making a stable, easy-to-maintain, and robust for production web application with R. 'golem' has been developed to abstract away the most common engineering tasks (for example, module creation, addition of external CSS or JavaScript file, ...), so you can focus on what matters: building the application. And once your application is ready to be deployed, 'golem' guides you through testing, and brings you tools for deploying to common platforms. In this talk, Colin and Vincent will present the 'golem' package, first talking about the "why 'golem'?", then presenting the general philosophy behind this framework, and help you get started building your first Shiny App with 'golem'

rstudio Shiny Rstudio::conf(2020) Colin Fay Golem Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Jared Lander | R: Then and Now | RStudio (2020)

R has changed a lot since the meetup was founded 10 years ago. Back then we were using base graphics (or lattice) and the apply family of functions and we didn't have pipes. At the time there was an impressive 1800 packages on CRAN, now there are over 15,000 extending R's reach far beyond its traditional domain of statistics and machine learning into publishing, website building and video generation. The community has grown and changed dramatically during that time, with the New York meetup alone going from 25 to over 10,000 members. During this talk we go through a then-and-now of R code and community to palpably see how everything has changed

rstudio Rstudio::conf(2020) Jared Lander Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

William Chase | The Glamour of Graphics | RStudio (2020)

I see a lot of ugly charts. This is to be expected as I work with a lot of academics and data scientists, neither of whom have been trained in how to design attractive charts. I myself produced many ugly charts during my years as a research scientist, when the design process basically came down to random tweaking until things "looked good". If only I could go back and tell young inexperienced me that there was a better way. In this talk, I will present that better way--a series of design principles that can take any chart from drab to fab. Rather than applying these techniques willy nilly, I will show how they form a layered "Glamour of Graphics" that is structured and can be easily applied to any chart. This Glamour of Graphics has some simple implementations in ggplot, where we will replace geoms, aesthetics, and scales with typography, color, and layout. Finally, I will discuss why looks matter when it comes to charts, and how by following the Glamour of Graphics you can design charts that are more persuasive and more accurately perceived

rstudio Rstudio::conf(2020) William Chase Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Riva Quiroga | The development of "datos" package for the R4DS Spanish translation| RStudio (2020)

Originally posted at https://rstudio.com/resources/rstudioconf-2020/the-development-of-datos-package-for-the-r4ds-spanish-translation/

rstudio Rstudio::conf(2020) Riva Quiroga Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Nima Safaian | Rpanda trading simulation - from an idea to a multi-user shiny app | RStudio (2020)

The idea of rpanda commodities trading simulation was many years in the making. As energy trading professionals working in the industry, we had developed insights around how to make risk/reward market calls, and what skills make someone an exceptional commodities trader. Traders are one of the most expensive seats in terms of monetizing value from the assets. We developed rpanda as a simulated environment which replicates closely how real-life physical commodities trading works in order to assist talent development and selection, both in academics and enterprise. My co-founder and I did not know how to design production-ready software, but we always had used R/Shiny for market analysis in our corporate jobs. Rather than hiring expensive app developers, we decided to do it ourselves. We used Rstudio development stack such as Rstudio Connect and open source tools, like plumber to turn our idea into a production-ready app that is used by University of Alberta classes. In this presentation, we share our journey, technical challenges, and how we overcame them

plumber rstudio Shiny Rstudio::conf(2020) Nima Safaian Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Carl Howe & Greg Wilson | Data Science Education in 2022 | RStudio (2020)

More people are learning data science every day, and there are more ways for them to learn than ever before. To understand where we are and where we might be going, this talk looks at what data science education could look like two years from now: far enough away that we can dream, but close enough that we can only dream a little. We explore the balance between automated and collaborative learning, different ways to deliver different kinds of lessons to different kinds of people, and ways in which our tools and practices could improve

rstudio Rstudio::conf(2020) Carl Howe Greg Wilson Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Jeff Leek | Data science education as a public health intervention in E. Baltimore | RStudio (2020)

Originally posted at https://rstudio.com/resources/rstudioconf-2020/data-science-education-as-an-economic-and-public-health-intervention-in-east-baltimore/

rstudio Rstudio::conf(2020) Jeff Leek Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Lauren Chadwick | Meet You Where You R | RStudio (2020)

At RStudio, we wake up and go to bed thinking about the positive impact that open source work and data science has had and can have on the world. To maximize this impact, we find three areas of investment absolutely critical to ensure our open source community keeps up with the world’s changes and outlives us all: 1. Find ways to make R more approachable. 2. Enable teams of all types & sizes (educational, professional, etc.) to be able to leverage the work they’re doing in R, and effortlessly communicate that work to others. 3. Extend the language so our open-source community can continue to be at the forefront of innovation, no matter their preference of tool or language

rstudio Rstudio::conf(2020) Lauren Chadwick Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Jesse Sadler | vctrs: Creating custom vector classes with the vctrs package | RStudio (2020)

The base R types of vectors enable the representation of an amazingly wide array of data types. There is so much you can do with R. However, there may be times when your data does not fit into one of the base types and/or you want to add metadata to vectors. vctrs is a developer-focused package that provides a clear path for creating your own S3-vector class, while ensuring that the classes you build integrate into user expectations for how vectors work in R. This presentation will discuss the why and how of using vctrs through the example of debkeepr, a package for integrating historical non-decimal currencies such as pounds, shillings, and pence into R. The presentation will provide a step-by-step process for developing various types of vectors and thinking through the design process of how vectors of different classes should work together

rstudio vctrs Rstudio::conf(2020) Jesse Sadler Vctrs Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Rebecca Barter | Becoming an R blogger | RStudio (2020)

Blogging is an excellent way to learn, improve your communication skills, and gain exposure in the R and data science communities. In this talk, I will discuss how and why I started blogging, and why you should too. I will guide you through choosing topics, writing your blog using RStudio and blogdown, hosting it on netlify, and sharing your blog with the world. This talk is for you if you've wanted to start a blog on R, data science, or to showcase your data analyses, but don't know where to start. Materials: github https://github.com/rlbarter/rstudio-conf-2020-blogger-slides slides (pdf) https://github.com/rlbarter/rstudio-conf-2020-blogger-slides/blob/master/Becoming%20an%20R%20blogger

blogdown rstudio rstudio-conf Rstudio::conf(2020) Rebecca Barter Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Mike K Smith | Learn to teach, for goodness sake | RStudio (2020)

Even though I’ve completed 4 marathons, you certainly shouldn’t come to me for a training plan on how to achieve your goals for any race you’re about to run. So why do we often turn to “experienced R users” to help us learn R or train an organization? The RStudio certified trainers have been taught modern, evidence-based teaching practices which they use in planning training sessions in order to help delegates achieve THEIR learning goals effectively in a given time-frame. My talk will illustrate some of these teaching concepts and how, by becoming a certified trainer, you can help others learn about R more effectively. A 5 minute presentation in our Lightning Talks series

rstudio Rstudio::conf(2020) Mike K Smith Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Kelly Bodwin | Course Material Creation in the R Ecosystem | RStudio (2020)

In this talk, I will introduce a suite of three packages designed to aid course material creation in R: {demoR} for displaying code in knitted R Markdown with custom highlighting and formatting; {shindig} for shortcuts to creating simple educational Shiny apps; and {curricular} for easy creation of syllabi, homework exercises, exams, etc. Together, we will explore how these new tools - in conjunction with other existing resources - have been used to create a clean and consistent ecosystem for my R-based Introductory Statistics course. I will share some metrics on student outcomes, as well as my own experiences with the advantages and challenges in building the course. A 5-minute presentation in our Lightning Talks series

rstudio Shiny Rstudio::conf(2020) Kelly Bodwin Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Katherine Simeon | Every voice matters: An analysis of @WeAreRLadies | RStudio (2020)

As a rotating curation, @WeAreRLadies is a twitter account that has a different curator (i.e., tweeter) each week with a mission to highlight female and minority genders and their work in R. So far, curators have tweeted from 18 different countries and represent a variety of domains and levels of R expertise, ranging from R novices to those developing their own packages. With 45 R-Ladies curators to date, the account has become a popular R-related twitter resource, gaining more than 13,000 followers in the past year and hundreds of interactions each week. This talk will present a text analysis and reflection on over a year of Twitter text data from @WeAreRLadies. As the founder and maintainer of this account, I witness firsthand the bidirectional relationship between one’s learning journey and their use of R. In this talk, I will attempt to quantify this through a text analysis that explores how one’s experiences learning and using R relates to how they talk (or tweet) about it. By analyzing tweet text as well as other metrics provided by twitter (e.g., number of likes, replies, and clicks), I will showcase different ways curators have engaged with the R Twitter community and explore how account engagement has changed as the number of curators and followers continue to grow. I will also discuss how curators’ different areas of expertise have resulted in tweets and discussions that both demonstrate the variety of tools available in R, and spotlight unifying ideas and best practices in R programming. Finally, I will reflect on lessons learned and future directions for @WeAreRLadies, as well as its contribution to the R-Ladies Global initiative. Overall, this talk will discuss how diverse perspectives of @WeAreRLadies curators have enriched the conversations in the R Twitter community by validating different learning journeys and by promoting and amplifying underrepresented voices. A 5-minute presentation in our Lightning Talks series

rstudio Rstudio::conf(2020) Katherine Simeon Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Jon Harmon | Learning by Teaching: Mentoring at the R4DS Online Learning Community | RStudio (2020)

I host a weekly R Office Hour on the R4DS Online Learning Community Slack. By doing so, I have learned more about R than I ever would have thought. Here I'll present concrete examples of how R users can participate in the R community to expand their skills. R users of all skill levels can develop their skills by helping one another learn. Committing to help people with their coding challenges leads to the exploration of answers in areas you might otherwise not examine. A 5-minute presentation in our Lightning Talks series

rstudio Rstudio::conf(2020) Jon Harmon R4DS Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Javier Luraschi | Datasets in Reproducible Research with 'pins' | RStudio (2020)

Open source code is an essential piece in making science reproducible. Tools like 'rmarkdown' and GitHub facilitate running and sharing outcomes with colleagues and with the broad scientific community at large. However, it is less clear what tools should be used to retrieve, store and share datasets; while it is possible to make datasets part of your workflows today, it is usually hard and we are often left with manually sharing or downloading links to datasets. Not only that, but it's also hard to share or discover datasets. In this talk we will introduce for the first time the 'pins' package. A package designed to: pin, discover and share resources. Meaning that, you can use 'pins' to simplify your data science workflows by easily fetching resources from GitHub, Kaggle, CRAN and RStudio Connect. We will present a 'pin' as a generic resource that can contain tabular datasets like CSVs, unstructured data like JSON files, image archives as ZIP files and so on. This talk will be highly interactive showing you how to get started by installing 'pins' from CRAN, retrieve and cache resources, share and discover useful and fun data resources to improve and enhance your day-to-day workflows

rmarkdown rstudio Rstudio::conf(2020) Javier Luraschi Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Hunter Glanz | The Five Principles of Data Science Education | RStudio (2020)

In this talk, I will outline a unified philosophy of data science education, and provide tips and tools for implementing these principles in the classroom using R and RStudio. Although data science as a professional discipline is well-established, its pedagogy is still in a period of growth. Even within a single university, multiple data science courses may be offered across different departments leading to inevitable redundancy of efforts amidst rich domain-specific innovations. My experience as an instructor in many such courses has lead me to five principles that transcend domain, context, and choice of language: reproducibility, communication, version control, practical application, and data ethics. For each of these full-stack themes, I will share examples of how to leverage tools in R and RStudio to enhance learning. A 5-minute presentation in our Lightning Talks series

rstudio Rstudio::conf(2020) Hunter Glanz Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Dr. Amelia McNamara | Making a tidy dress | RStudio (2020)

After at least a year of dreaming about it, I finally produced the #rstats / #Tidyverse dress of my dreams. This involved designing fabric, getting it custom printed, making a pattern from an existing garment, and sewing the dress. I learned a lot of useful lessons during this project, including "do unit tests" (make a practice dress) and "document your work" (get your BFF to take pictures of you)

rstudio tidyverse Rstudio::conf(2020) Dr. Amelia McNamara Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Davis Vaughn | Sliding Windows and Calendars | RStudio (2020)

A number of R packages exist to make computing moving averages on a single numeric series straightforward. But generally “real” life is much messier than that! Try computing a moving average over a twenty-day sliding window when you have a time series with missing data. Oh! By the way, you should also skip over weekends when looking back twenty days. And you know that random holiday that your company celebrates that no one else does? Skip over that too. These are hard but realistic problems, and until now there has been a lack of tools necessary to solve them. In this talk, I’ll present two packages designed to tackle these issues, slide and almanac. slide is a package designed to perform arbitrary sliding window calculations. The simplest example of this would be a moving average. What makes slide unique is its support for sliding relative to an index, such as a date vector, which allows you to correctly compute the boundaries of that twenty day window. almanac is package for creating custom business calendars, and then adjusting dates relative to them. Inspired by lubridate, almanac allows you to shift dates by a set number of “business” days while respecting the weekends and holidays defined by a user-specified calendar. For example, shifting a Friday forward by 1 business day would land on a Monday, unless that Monday happened to be a holiday, in which case the next business day would actually be Tuesday. Together, slide and almanac provide the tooling necessary to solve the problem mentioned earlier. Additionally, because slide works with any arbitrary function, we can use the same procedure to compute rolling regressions, cumulative sums, and any other sliding computation. A 5-minute presentation in our Lightning Talks series

lubridate rstudio Rstudio::conf(2020) Davis Vaughn Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Colin Rundel | `livecode`: broadcast your live coding sessions from and to RStudio | RStudio (2020)

In this talk we will demonstrate livecode, a new R package for broadcasting code for live code demonstrations. This package implements a simple webserver (using httpuv) to dynamically publishes the content of a code file (i.e. .R or .Rmd) as you edit it live. This enables your students to have near realtime access to your code as you write it. The broadcast file can be viewed with any webbrowser but the package is specifically designed to be used within RStudio leveraging its builtin viewer. This gives students have direct access to the shared code within the IDE, allowing direct copying into their own source files and/or the console and thereby improving their ability to interact and experiment with your code. A 5-minute presentation in our Lightning Talks series

httpuv rstudio Rstudio::conf(2020) Colin Rundel Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Caroline Ledbetter | Rproject templates to automate and standardize your workflow | RStudio (2020)

Many teams and organizations have tasks and structures that are standard across projects. Lack of consistency and documentation can lead to lost productivity when team members join collaborations or previous work is consulted by your future self. Setting up folder structures can be particularly tedious. This talk will demonstrate using Rstudio project templates as part of an organizational package to automatically setup file structures, establish git repositories and add standardized readme files. It will also show how including report templates for Rmarkdown files can lead to more consistent and professional reports. Project info can be optionally stored so that project information can be easily added automatically to the top of reports and included in snippets for code file headers. Creating standard, easy to implement documentation and procedures can be particularly effective in encouraging skeptical collaborators to use git and Rmarkdown. Organizational packages can also be a great place to house functions that are specific and common to an organizations needs. The talk will showcase this functionality using the CIDAtools package that we developed. While the CIDAtools package was developed to address issues that sometimes arise from the less structured environment of academia, the tools presented can be equally useful in an industry setting. A 5-minute presentation in our Lightning Talks series

rmarkdown rstudio Rstudio::conf(2020) Caroline Ledbetter Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Athos Damiani | Sound annotation with Shiny and wavesurfer | RStudio (2020)

We observed a huge improvements of Machine Learning tools but the main effort were to help at post annotated dataset step. We still struggle to build a trusty pipeline to make these annotations. The package wavesurfer brings to R users the ability to annotate audio files with ease and reliability, exploring the friendly user interface of Shiny to make this hard and laborious part of the project more joyful and efficient. A 5-minute presentation in our Lightning Talks series

rstudio Shiny Rstudio::conf(2020) Athos Damiani Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Amanda Gadrow | Lessons about R I learned from my cat | RStudio (2020)

Forming good development habits for R projects is pretty straight-forward if you follow the lessons I've learned from my cat, whose advice includes "be lazy", "keep your claws sharp", and "land on your feet". Attendees of this talk will learn how to make life easier on colleagues and their future selves by using simple software engineering best practices to build their current projects. Each point will come with cat photos and code samples, the two best parts of the Internet!

rstudio Rstudio::conf(2020) Amanda Gadrow Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Jenny Bryan | Object of type ‘closure’ is not subsettable | RStudio (2020)

Your first “object of type ‘closure’ is not subsettable” error message is a big milestone for an R user. Congratulations, if there was any lingering doubt, you now know that you are officially programming! Programming involves considerably more troubleshooting and debugging than many of us expected (or signed up for). The ability to solve your own problems is an incredibly powerful stealth skill that is worth cultivating with intention. This talk will help you nurture your inner problem solver, covering both general debugging methods and specific ways to implement them in the R ecosystem

Jenny Bryan

rstudio Jenny Bryan Rstudio::conf(2020) Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Emily Riederer | RMarkdown Driven Development | RStudio (2020)

RMarkdown enables analysts to engage with code interactively, embrace literate programming, and rapidly produce a wide variety of high-quality data products such as documents, emails, dashboards, and websites. However, RMarkdown is less commonly explored and celebrated for the important role it can play in helping R users grow into developers. In this talk, I will provide an overview of RMarkdown Driven Development: a workflow for converting one-off analysis into a well-engineered and well-designed R package with deep empathy for user needs. We will explore how the methodical incorporation of good coding practices such as modularization and testing naturally evolves a single-file RMarkdown into an R project or package. Along the way, we will discuss big-picture questions like “optimal stopping” (why some data products are better left as single files or projects) and concrete details such as the {here} and {testthat} packages which can provide step-change improvements to project sustainability

rmarkdown rstudio testthat Emily Riederer Rstudio::conf(2020) Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Claus Wilke | Spruce up your ggplot2 visualizations with formatted text | RStudio (2020)

The ggtext package provides various functions to add formatted text to ggplot2 figures, both in the form of plot or axis labels and in the form of text labels or text boxes inside the plot panel. Text formatting can be achieved through a small subset of markdown, HTML, and CSS directives. Features currently supported include italics, bold, super- and sub-script, as well as changing font size, font family, and color. Basic support for adding images to formatted text is also available

ggplot2 rstudio Claus Wilke Rstudio::conf(2020) Ggtext Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Jim Hester | Azure Pipelines and GitHub Actions | RStudio (2020)

Open source R packages on GitHub often take advantage of continuous integration services to automatically check their packages for errors. This is very useful to catch things quickly, as well and increasing confidence for proposed changes, as the Pull Requests can be checked before they are merged. Travis-CI and Appveyor are the most popular current methods. However newer services, Azure Pipelines and GitHub Actions, show promise for being more powerful and simpler to configure and debug. I will discuss these services and demonstrate some of their capabilities and how to configure them for your own use in packages and reports

rstudio Jim Hester Rstudio::conf(2020) Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Larry Fenn | Journalism with RStudio, R, and the tidyverse | RStudio (2020)

The Associated Press data team primarily uses R and the Tidyverse as the main tool for doing data processing and analysis. In this talk, some of the technology behind the published stories will be showcased: - Using dbplyr to work off a hosted database containing 380 million opioid records to identify "pill mills". - Using open-sourced AP style templates for R Markdown and ggplot to quickly produce graphics and reports off breaking news. - Using R Markdown and htmlwidgets to give reporters and editors interactive reports to identify reporting leads

dbplyr rstudio tidyverse Larry Fenn Rstudio::conf(2020) Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

George Kastrinakis | Building a data science pipeline for the FT with RStudio Connect | RStudio

Talk from rstudio::conf(2020) We have recently implemented a new Data Science workflow and pipeline, using RStudio Connect and Google Cloud Services. This has vastly decreased our pipeline complexity, allowing us to bring our models and products into scheduled production more quickly. In addition, our workflow, working closely together as a team on all projects on a regular two-week sprint cycle, has increased the range of projects we have been able to take on and complete. To detail some of the key lessons we’ve learned (and some of the difficulties!), we’ll walk you through one of our recent sprints, where we productionalised the generation of a suite of behavioural and demographic features so that they can be more easily plugged in to a range of models and used across the business by the FT’s platform and product teams

rstudio George Kastrinakis Rstudio::conf(2020) Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Sharla Gelfand | Don’t repeat yourself, talk to yourself! Reporting with R | RStudio (2020)

If you’re responsible for analyses that need updating or repeating on a semi-regular basis, you might find yourself doing the same work over and over again. The principle of "don’t repeat yourself" from software engineering motivates us to use functions and packages, the core of repetition in the R universe. For analyses, it can be difficult to know how to use this principle and move beyond "copying and pasting scripts and changing the data file and the object names and updating the dates and results in RMarkdown", especially when there’s some element of human intervention required, whether it be for validating assumptions or cleaning artisanal data. This talk will focus on those next steps, showcasing opportunities to stop repeating yourself and instead anticipate the needs of and communicate effectively with your future self (or the next person with your job!) using project-oriented workflows, clever interactivity, templated analyses, functions, and yes, your own packages

rmarkdown rstudio Sharla Gelfand Rstudio::conf(2020) Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Ian Cook | Bridging the Gap between SQL and R | RStudio (2020)

Ian Cook | January 31, 2020 Like it or not, SQL is the closest thing we have to a universal language for working with structured data. Celebrating its 50th birthday in 2020, SQL today integrates with thousands of applications and has millions of users worldwide. Data analysts using SQL represent a large audience of potential R users motivated to expand their data science skills. But learning R can be frustrating for SQL users. One major frustration is the inability to directly query R data frames with SQL SELECT statements. Eager to use R for tasks that are not possible with SQL (like data visualization and machine learning), these users are dismayed to find that they must first learn an unfamiliar syntax for data manipulation. The popularity of the sqldf package (which automatically exports an R data frame into an embedded database, then runs a SQL query on it) demonstrates this frustration. But now there is a way to directly query an R data frame without moving the data out of R. In this talk, I introduce tidyquery, a new R package that runs SQL queries directly on R data frames. tidyquery is powered by dplyr and by queryparser, a new pure-R, no-dependency SQL query parser

dplyr rstudio Ian Cook Rstudio::conf(2020) Sql Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Winston Chang | Asynchronous programming in R | RStudio (2020)

Writing regular R code is straightforward: you tell R to do something, it does it, and then it returns control back to you. This is called synchronous programming. However, if you use R to coordinate threads, processes, or network communication, the regular model may be unable to do what you want, or it may only be able to do it with a significant performance penalty. In this talk I'll explain how asynchronous programming with the later package can handle these kinds of programming problems. I'll also show how to provide a synchronous interface for asynchronous code, so that users will have a simple, familiar way to use your code. Materials - github repo

Winston Chang

rstudio Winston Chang Rstudio::conf(2020) Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Nick Strayer | Stochastic Block Models with R: Statistically rigorous clustering | RStudio (2020)

Nick Strayer | January 31, 2020 Often a machine learning research project starts with brainstorming, continues to one-off scripts while an idea forms, and finally, a package is written to disseminate the product. In this talk, I will share my experience rethinking this process by spreading the package writing across the whole process. While there are cognitive overheads involved with setting up a package framework, I will argue that these overheads can serve as a scaffolding for not only good code but robust research practices. The result of this experiment is the SBMR package: a native R package written to fit and investigate the results of Bipartite Stochastic Block Models that forms the backbone of my PhD dissertation. By going over the ups and downs of this process, I hope to leave the audience with inspiration for moving the package writing process closer to the start of their projects and melding research and code more closely to improve both

Nick Strayer

rstudio Nick Strayer Rstudio::conf(2020) Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

J.J. Allaire | Open Source Software for Data Science | RStudio (2020)

Open Source Software for Data Science J.J. Allaire | February 1, 2020 Open-source software is fundamentally necessary to ensure that the tools of data science are broadly accessible, and to provide a reliable and trustworthy foundation for reproducible research. This talk will delve into why open source software is so important and discuss the role of corporations as stewards of open source software. I'll also talk about how RStudio is structured and organized to pursue its mission of creating open source software for data science. About the speaker J.J. Allaire - JJ Allaire is a software engineer and entrepreneur who has created a wide variety of products including ColdFusion Open Live Writer Lose It! and RStudio

JJ Allaire

rstudio J.J. Allaire Rstudio::conf(2020) Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Kara Woo | Boxplots: a case study in debugging and perseverance | RStudio (2019)

Come on a journey through pull request #2196. What started as a seemingly simple fix for a bug in ggplot2's box plots developed into an entirely new placement algorithm for ggplot2 geoms. This talk will cover tips and techniques for debugging, testing, and not smashing your computer when dealing with tricky bugs. VIEW MATERIALS https://github.com/karawoo/2019-01-17-rstudioconf About the Author Kara Woo Kara is a research scientist in data curation at Sage Bionetworks, where she helps other researchers document and share their data. She has previously worked as an information manager at Washington State University and at the National Center for Ecological Analysis and Synthesis (NCEAS), where she combined data management with fieldwork at a remote Siberian lake. Kara is an enthusiastic R programmer, and collects data visualizations gone beautifully wrong on a blog called accidental aRt

ggplot2 rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forecast Ggplot2 Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Kara Woo Tibble RStats Statistics Debugging Programming Education

Kelly O'Briant | Configuration management tools for the R admin | RStudio (2019)

This talk will feature an introduction to configuration management tools for the Analytic Administrator. An analytic admin is someone who is invested in continually improving analytic infrastructure, advocates for best practices in data product deployments, and acts to adopt DataOps philosophies in their organization. One of the biggest challenges for an analytic admin can be figuring out how to help IT groups develop core competencies around the management of R tooling. When IT groups are unfamiliar with R, they might lean heavily on the analytic admin for guidance or resist adoption entirely. Data science teams that rely on delivering results through integrated R based solutions can get blocked when they lack the full support of IT. I’ll present a roadmap for how analytic admins can create custom teaching tools for introducing the R toolchain. Using these strategies, a dash of creativity, and a little bit of empathy, I hope you can get the IT buy-in you’ll need to make R a fully legitimate part of your organization. About the Author Kelly O’Briant Kelly is Solutions Engineer for RStudio and also an organizer of the Washington DC chapter of R-Ladies Global. It’s an R users group for lady-folk and friends

rstudio Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Kelly O'briant

Edgar Ruiz | Databases using R The latest | RStudio (2019)

Learn about the latest packages and techniques that can help you access and analyze data found inside databases using R. Many of the techniques we will cover are based on our personal and the community’s experiences of implementing concepts introduced last year, such as offloading most of the data wrangling to the database using dplyr, and using the RStudio IDE to preview the database’s layout and data. Also, learn more about the most recent improvements to the RStudio products that are geared to aid developers in using R with databases effectively. VIEW MATERIALS https://github.com/edgararuiz/databases-w-r About the Author Edgar Ruiz Edgar is the author and administrator of the https://db.rstudio.com web site, and current administrator of the [sparklyr] web site: https://spark.rstudio.com. Author of the Data Science in Spark with sparklyr cheatsheet. Co-author of the dbplyr package and creator of the dbplot package

Edgar Ruiz

dbplyr dplyr rstudio Edgar Ruiz Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Sparklyr

Jonathan McPherson | New language features in RStudio | RStudio (2019)

RStudio 1.2 dramatically improves support for many languages frequently used alongside R in data science projects, including SQL, D3, Stan, and Python. In this talk, you'll learn how to use RStudio 1.2's new language features to work more efficiently and fluidly in multi-lingual projects. VIEW MATERIALS https://github.com/rstudio/rstudio-conf/tree/master/2019/RStudio_1.2_Language_Features--Jonathan_McPherson About the Author Jonathan McPherson Jonathan is a software engineer at RStudio working on the IDE. In the past, he’s written Web applications at a nuclear site in the desert, exploratory information visualization systems at UC Davis, and features for flagship Office products and modern web applications at Microsoft

rstudio rstudio-conf Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Jonathan McPherson

Jeffrey Arnold | Solving R for data science | RStudio (2019)

While teaching a course using "R for Data Science", I wrote a complete set of solutions to its exercises and posted them on GitHub. Then other people started finding them. And now I'm here. In this talk, I'll discuss why I did it, and what I learned from the process, both what I learned about the Tidyverse itself, and what I learned from teaching it. About Jeffrey: I am formerly an Assistant Professor of Political Science of Political Science University of Washington and Core Faculty Member in the Center for Statistics and the Social Sciences, an Instructor of Political Science and QuanTM Pre-Doctoral Fellow at Emory University. I received Ph.D. in political science at the University of Rochester. Prior to graduate studies, I was a Research Associate/Economist in the Money and Payments Studies research group at the Federal Reserve Bank of New York

rstudio tidyverse Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Jeffrey Arnold

Wes McKinney | Ursa Labs and Apache Arrow in 2019 | RStudio (2019)

Learn more about what's happening at URSA labs at https://wesmckinney.com/archives.html

rstudio Wes McKinney Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats

Amelia McNamara | Working with categorical data in R without losing your mind | RStudio (2019)

Categorical data, called “factor” data in R, presents unique challenges in data wrangling. R users often look down at tools like Excel for automatically coercing variables to incorrect datatypes, but factor data in R can produce very similar issues. The stringsAsFactors=HELLNO movement and standard tidyverse defaults have moved us away from the use of factors, but they are sometimes still necessary for analysis. This talk will outline common problems arising from categorical variable transformations in R, and show strategies to avoid them, using both base R and the tidyverse (particularly, dplyr and forcats functions). VIEW MATERIALS http://www.amelia.mn/WranglingCats.pdf (related paper from the DSS collection) http://bitly.com/WranglingCats https://peerj.com/collections/50-practicaldatascistats/ About the Author Amelia McNamara My work is focused on creating better tools for novices to use for data analysis. I have a theory about what the future of statistical programming should look like, and am working on next steps toward those tools. For more on that, see my dissertation. My research interests include statistics education, statistical computing, data visualization, and spatial statistics. At the moment, I am very interested in the effects of parameter choices on data analysis, particularly data visualizations. My collaborator Aran Lunzer and I have produced an interactive essay on histograms, and an initial foray into the effects of spatial aggregation. I talked more about spatial aggregation in my 2017 OpenVisConf talk, How Spatial Polygons Shape Our World

dplyr forcats rstudio tidyverse Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Amelia Mcnamara

Tyler Morgan-Wall | 3D mapping, plotting, and printing with rayshader | RStudio (2019)

Long form discussion: https://www.tylermw.com/3d-printing-rayshader/

rstudio Tyler Morgan-Wall Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats

Panel | Growth & change of careers, organizations & responsibility in data science | RStudio (2019)

Hosted by Eduardo Arino de la Rubia, Instagram With: Hilary Parker, Data Scientist, Stitch Fix Karthik Ram, Data Science Fellow, UC Berkeley Angela Bassa, Director of Data Science, iRobot Tracy Teal, Executive Director, Carpentries About the Author Eduardo Arino de la Rubia Technologist and Data Scientist driven to create software that people use, find useful, and pleasant. From programming through architecture, from green field to maintenance, software is interesting technologically, socially, and intellectually. I enjoy contributing to the process, either through leadership or individual effort, of creating software that is deployed joyfully and is as bug free as possible

rstudio Eduardo Arino De La Rubia Data Scientist Director of Data Science Hilary Parker Karthik Ram Angela Bassa Tracy Teal Rstudio Data Science Machine Learning Python Stats Data Visualization Data Viz Ggplot Technology Coding Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats

Jeroen Ooms | A preview of Rtools 4.0 | RStudio (2019)

Rtools is getting a major upgrade. In addition to the latest gcc, it now includes a full build system and package manager to build, install, and distribute external c/c++/fortran libraries needed by R packages. Thereby it bridges the long-standing gap between Windows and MacOS/Linux with respect to the availability of high quality, up-to-date system libraries. In this talk, we will show how to build and install system libraries with Rtools, and manage your Rtools build environment. It should be interesting both for Windows users as well as non-Windows package authors that are interested in reducing the pain of making things work on Windows. VIEW MATERIALS https://resources.rstudio.com/rstudio-conf-2019/a-preview-of-rtools-4-0 About the Author Jeroen Ooms Postdoc hacker for @ropensci at UC Berkeley

Jeroen Ooms

rstudio rstudio-conf Jeroen Ooms Rtools Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats

Emily Robinson | Building an AB testing analytics system with R and Shiny | RStudio (2019)

Online experimentation, or A/B Testing, is the gold standard for measuring the effectiveness of changes to a website. While A/B testing is used at tens of thousands of companies, it can seem difficult to parse without resorting to expensive end-to-end commercial options. Using DataCamp’s system as an example, I’ll illustrate how R is actually a great language for building powerful analytical and visualization A/B testing tools. We’ll first dive into our open-source funneljoin package, which allows you to quickly analyze sequential actions using different types of behavioral funnels. We'll then cover the importance of setting up health checks for every experiment. Finally, we'll see how Shiny dashboards can help people monitor and quickly analyze multiple A/B tests each week. VIEW MATERIALS http://bit.ly/rstudio19 About the Author Emily Robinson I work at DataCamp as a Data Scientist on the growth team. Previously, I was a Data Analyst at Etsy working with their search team to design, implement, and analyze experiments on the ranking algorithm, UI changes, and new features. In summer 2016, I completed Metis’s three-month, full-time Data Science Bootcamp, where I did several data science projects, ranging from using random forests to predict successful projects on DonorsChoose.org to building an application in R Shiny that helps data science freelancers find their best-fit jobs. Before Metis, I graduated from INSEAD with a Master’s degree in Management (specialization in Organizational Behavior). I also earned my bachelor’s degree from Rice University in Decision Sciences, an interdisciplinary major I designed that focused on understanding how people behave and make decisions

rstudio Shiny Emily Robinson AB Testing Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats

Karthik Ram | A guide to modern reproducible data science with R | RStudio (2019)

Resources: https://github.com/karthik/rstudio2019

rstudio Karthik Ram Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats

Hilary Parker | Cultivating creativity in data work | RStudio (2019)

Traditionally, statistical training has focused primarily on mathematical derivations, proofs of statistical tests, and the general correctness of what methods to use for certain applications. However, this is only one dimension of the practice of doing analysis. Other dimensions include the technical mastery of a language and tooling system, and most importantly the construction of a convincing narrative tailored to a specific audience, with the ultimate goal of them accepting the analysis. These "softer" aspects of analysis are difficult to teach, perhaps more so when the field is framed as mathematics and often housed in mathematics departments. In this talk, I discuss an alternative framework for viewing the field, borrowing upon the past work in other fields such as design. Looking forward, we as a field can borrow from these fields to cultivate and hone the creative lens so necessary to the success of applied work. VIEW MATERIALS https://www.slideshare.net/mobile/hilaryparker/rstudioconf2019l About the Author Hilary Parker I’m a Senior Data Analyst at Etsy, where I help product teams with data-driven development, via experimentation, opportunity sizing and impact analysis. I got my Ph.D. from the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health, working with Jeff Leek. I studied genomics and built tools to help researchers use genomic technologies in personalized medicine applications. I graduated from Pomona College in 2008, where I double-majored in Mathematics and Molecular Biology. True to my liberal arts upbringing, I’m a passionate teacher. Most notably, I taught an introductory Biostatistics class at the American University of Armenia (and kept a pretty cool travel blog along the way)

rstudio Hilary Parker Stitch Fix Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats

Angela Bassa | Data science as a team sport | RStudio (2019)

How do you data science as a team sport? Oftentimes a data scientific initiative starts with just a single, lonesome data scientist. But when that germ of a team is successful and starts expanding, should the team be embedded in other disciplines or should it be centralized into its own function? Where should it live in the organizational structure? Should you focus on recruiting senior data scientists or is there a benefit to attracting junior talent as well? And in terms of capabilities, should you hold out for unicorns or hire several specialists to get all jobs done? Data scientists need to work on almost every aspect of a business, so how should a team composition set the data science discipline up for success? Great data scientists have career options and won’t abide bad managers for very long: if you want to retain them, you’ll need to care about their work, connect it to the business, and design a diverse, resilient, high-performing team. Materials: https://github.com/angelabassa/rstudioconf-2019

rstudio Angela Bassa Ange Bassa IRobot Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats

James Blair | Democratizing R with Plumber APIs | RStudio (2019)

The Plumber package provides an approachable framework for exposing R functions as HTTP API endpoints. This allows R developers to create code that can be consumed by downstream frameworks, which may be R agnostic. In this talk, we’ll take an existing Shiny application that uses an R model and turn that model into an API endpoint so it can be used in applications that don’t speak R. VIEW MATERIALS https://bit.ly/2TXfFR5 About the Author James Blair James holds a master’s degree in data science from the University of the Pacific and works as a solutions engineer. He works to integrate RStudio products in enterprise environments and support the continued adoption of R in the enterprise. His past consulting work centered around helping businesses derive insight from data assets by leveraging R. Outside of R and data science, James’s interests include spending time with his wife and daughters, cooking, camping, cycling, racquetball, and exquisite food. Also, he never turns down a funnel cake

plumber rstudio Shiny James Blair Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats

Ian Fellows | Don’t let long running tasks hang users: introducing IPC for Shiny | RStudio (2019)

Long running tasks in Shiny are not cancelable and typically lock the user interface while running. This talk introduces the ipc package, which helps you build dynamic applications when non-trivial computations are involved. ipc allows your Shiny Async workers to be cancelable and communicate intermediate results/progress back to the user interface. About the Author Ian Fellows Dr. Ian Fellows is a professional statistician with research interests ranging over many sub-disciplines of statistics. His work in statistical visualization won the prestigious John Chambers Award in 2011, and in 2007-2008 his Texas Hold'em AI programs were ranked second in the world. Applied data analysis is his passion. He is accustomed to providing insightful analysis and operationalizing these analyses in enterprise systems using a variety of programming languages and tools including R, Python, Java, MapReduce and Spark

rstudio Shiny Ian Fellows Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats

Eric Nantz | Effective use of Shiny modules in application development | RStudio (2019)

As a Shiny application grows in scale, organizing code into reusable and streamlined components becomes vital to manage future enhancements and avoid unnecessary duplication. Shiny modules are customized R functions that are easily reused multiple times within an application by avoiding namespace collisions and assist with organizing the code base. Like R functions, modules can be simple utilities or elaborate pieces with multiple inputs and outputs. While the process of creating a module is uncomplicated, application developers can quickly encounter challenges including communication among modules, defining logical compositions, and avoiding hidden state modifications. In this talk, we will introduce practical principles and techniques developers can leverage to address these issues head-on such as documenting modules, passing parameters and return values effectively between modules, and how nesting modules enables dynamic user interfaces with minimal overhead. VIEW MATERIALS https://rpodcast.github.io/rsconf-2019 About the Author Eric Nantz I have a broad background in statistics, computer science, and system administration which gives me a unique set of skills for using state-of-the-art technology and techniques to accomplish important and innovative data analyses. In my professional role as a statistician, I support the design and analyses of clinical trials evaluating treatments for auto-immune disorders. I also perform statistical analyses of specialized biomarkers utilizing cutting-edge statistical software such as R and high-performance computing infrastructures. I am also the creator, producer, and host of the R-Podcast. The R-Podcast is dedicated to helping those who are new to statistical computing develop their skills and confidence in using the free and open-source statistical computing package called R to get their data analyses done

rstudio Shiny Eric Nantz Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats

Hao Zhu | Empowering a data team with RStudio addins | RStudio (2019)

RStudio addins provide a mechanism to extend RStudio in various ways. Addins can interact with the RStudio IDE through RStudio API. It can also provide users a graphical interface with the power of Shiny. In practice, we found it very useful for enhancing or streamlining interaction with data and computing infrastructure. In this talk, we will demonstrate how our team develops and uses RStudio addins to empower our work. You will see some internal tools created to help us manage database connections, and an addin which helps us access external cloud computing resources. We will also show an example of using the addins in rcrossref and citr to download and manage citation and literature databases during rmarkdown document development. VIEW MATERIALS https://github.com/hebrewseniorlife/addin_demo About the Author Hao Zhu Hao is a data analyst and software developer working at the Hinda and Arthur Marcus Institute for Aging Research. He completed his training at Boston University School of Medicine in the program on Clinical Investigation. His interests include research reproducibility, data visualization and machine learning. At the Marcus Institute, he works with different teams on various topics, ranging from smartphone motion sensors to MRI images, and helps researchers understand their data by creating analytical reports and web applications. At the same time, Hao leads the development of R packages in the Biostatistics Core. He has contributed multiple R packages to the open source R community, such as kableExtra and memor. He also has a passion for teaching and has mentored several students at the Marcus Institute

rmarkdown rstudio Shiny Hao Zhu Addins Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats

Amanda Gadrow | Getting it right: Writing reliable and maintainable R code | RStudio (2019)

How can you tell that your scripts, applications, and package functions are working as expected? Are you sure that when you make changes in one part of the code, it won't break something in another part? Have you thought deeply about how the consumers of your code (including Future You) will use it, maintain it, fix it, and improve it? Code quality is essential not only for reliable results but also for your script's maintainability and your users' satisfaction. Quality can be measured in part with targeted testing, and fortunately, there are several effective and easy-to-use code testing tools available in R. This talk will discuss some of the most useful testing packages, covering both concepts and examples. VIEW MATERIALS https://github.com/rstudio/rstudio-conf/tree/master/2019/Testing_R_Code--Amanda_Gadrow About the Author Amanda Gadrow Amanda is a software engineer with many years' experience writing automated test frameworks for enterprise software. She started learning R when she joined RStudio in 2016, and has been basking in its glory ever since. Amanda leads the QA and Support teams, and spends a significant amount of time analyzing customer data to improve the products and optimize support. She is a co-organizer of R-Ladies Columbus, and an avid musician on the side

rstudio rstudio-conf Amanda Gadrow Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats

Thomas Lin Pedersen | gganimate live cookbook | RStudio (2019)

Animation of data visualisation is becoming increasingly popular both as an attention grabber on social media and as a way to tell small data stories. gganimate is a package that extends ggplot2 for making animations and provides a grammar of animation on top of the grammar of graphics. This talk will quickly introduce gganimate, and then dive into a series of different animation and show how they were made and how they could be changed or expanded. Slides: https://data-imaginist.com/slides/rstudioconf2019 4 Resources: https://resources.rstudio.com/rstudio-conf-2019/gganimate-live-cookbook 4 Discussion https://community.rstudio.com/t/gganimate-live-cookbook-thomas-lin-pedersen-rstudio-conf-2019l-video/24852

Thomas Lin Pedersen

ggplot2 rstudio rstudio-conf Thomas Lin Pedersen Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats

Alan Dipert | Integrating React.js and Shiny | RStudio (2019)

React.js is a thriving JavaScript library that eases encapsulating and sharing sophisticated component libraries. The React.js ecosystem is filled with components for doing everything from color selection (react-color) to animation (react-spring). While it's always been technically possible to integrate React.js components with Shiny applications, it hasn't always been particularly obvious how. To make it easier, we augmented the excellent reactR package with functions specifically designed to make it easier to create new htmlwidgets, inputs, and outputs based on React.js components. In this talk, I will further motivate and demonstrate these new tools and do my best to empower the audience to try them out. About the Author Alan Dipert Alan is a software engineer on the Shiny team at RStudio. In the past, he’s helped build web applications, reporting pipelines, and many things between. When he’s not working, Alan likes to spend his time reading or being with his family

Shiny Team

rstudio Shiny Alan Dipert Reactjs React.js JavaScript Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats

Kevin Kuo | Introducing mlflow | RStudio (2019)

We introduce the R API for MLflow, which is an open source platform for managing the machine learning lifecycle. We demonstrate each component of the platform–Tracking, Projects, and Models–and describe how they can be leveraged in practical data science workflows. About the Author Kevin Kuo Kevin is a software engineer working on open source packages for big data analytics and machine learning. He has held data science positions in a variety of industries and was a credentialed actuary. He likes mixing cocktails and studying about wine

rstudio Kevin Kuo MLFlow Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Api

Rich Iannone | Introducing the gt package | RStudio (2019)

With the gt package, anyone can make great-looking display tables. Though the package is still early in development, you can do some really great things with it right now! I'll walk through a few examples that touch upon the more common table-making use cases. These will include features like adding table parts, integrating footnotes, styling/transforming table cells, using tables in R Markdown documents, and even including gt tables in email messages. VIEW MATERIALS https://github.com/rich-iannone/presentations/tree/master/2019_01-19-rstudio_conf_gt About the Author Rich Iannone My background is in programming, data analysis, and data visualization. Much of my current work involves a combination of data acquisition, statistical programming, tools development, and visualizing the results. I love creating software that helps people accomplish things. I regularly update several R package projects (all available on GitHub). One such package is called DiagrammeR and it's great for creating network graphs and performing analyses on the graphs. One of the big draws for open-source development is the collaboration that comes with the process. I encourage anyone interested to ask questions, make recommendations, or even help out if so inclined!

Rich Iannone

gt rstudio Rich Iannone Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats

Jim Hester | It depends: A dialog about dependencies | RStudio (2019)

Software dependencies can often be a double-edged sword. On one hand, they let you take advantage of others' work, giving your software marvelous new features and reducing bugs. On the other hand, they can change, causing your software to break unexpectedly and increasing your maintenance burden. These problems occur everywhere, in R scripts, R packages, Shiny applications and deployed ML pipelines. So when should you take a dependency and when should you avoid them? Well, it depends! This talk will show ways to weigh the pros and cons of a given dependency and provide tools for calculating the weights for your project. It will also provide strategies for dealing with dependency changes, and if needed, removing them. We will demonstrate these techniques with some real-life cases from packages in the tidyverse and r-lib. VIEW MATERIALS https://speakerdeck.com/jimhester/it-depends About the Author Jim Hester Jim is a software engineer at RStudio working with Hadley to build better tools for data science. He is the author of a number of R packages including lintr and covr, tools to provide code linting and test coverage for R

covr lintr rstudio Shiny tidyverse Jim Hester Software Dependencies Dependencies Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats

Jenny Bryan | Lazy evaluation | RStudio (2019)

The "tidy eval" framework is implemented in the rlang package and is rolling out in packages across the tidyverse and beyond. There is a lively conversation these days, as people come to terms with tidy eval and share their struggles and successes with the community. Why is this such a big deal? For starters, never before have so many people engaged with R's lazy evaluation model and been encouraged and/or required to manipulate it. I'll cover some background fundamentals that provide the rationale for tidy eval and that equip you to get the most from other talks. VIEW MATERIALS https://github.com/jennybc/tidy-eval-context#readme About the Author Jenny Bryan Jenny is a recovering biostatistician who takes special delight in eliminating the small agonies of data analysis. She’s part of Hadley’s team, working on R packages and integrating them into fluid workflows. She’s been working in R/S for over 20 years, serves in the leadership of rOpenSci and Forwards, and is an Ordinary Member of the R Foundation. Jenny is an Associate Professor of Statistics (on leave) at the University of British Columbia, where she created the course STAT 545

Jenny Bryan

rlang rstudio tidyverse Jenny Bryan Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats

Jesse Sadler | Learning and using the tidyverse for historical research | RStudio (2019)

My talk will discuss how R, the tidyverse, and the community around R helped me to learn to code and create my first R package. My positive experiences with the resources for learning R and the community itself led me to create a blog detailing my experiences with R as a way to pass along the knowledge that I gained. The next step was to develop my first package. The debkeepr package integrates non-decimal monetary systems of pounds, shillings, and pence into R, making it possible to accurately analyze and visualize historical account books. It is my hope that debkeepr can help bring to light crucial and interesting social interactions that are buried in economic manuscripts, making these stories accessible to a wider audience. VIEW MATERIALS https://github.com/jessesadler/rstudioconf-2019-slides About the Author Jesse Sadler I am an early modern historian interested in the social and familial basis of politics, religion, and trade. I received a Ph.D. in European History from UCLA in 2015 and have taught courses on cultural and intellectual history of early modern Europe and the Atlantic. My research investigates the familial basis of the early modern capitalism through archival research on two mercantile families from Antwerp at the end of the sixteenth and beginning of the seventeenth century. I am currently working on a manuscript that argues for the significance of sibling relationships and inheritance in the development of early modern trade. My manuscript places concepts such as patriarchy, emotion, exile, and friendship at the heart of the efficacy of long-distance trade networks and the growth of capitalism

rstudio tidyverse Jesse Sadler Learn to Code Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats

Caitlin Hudon | Learning from eight years of data science mistakes | RStudio (2019)

Over the past eight years of doing data science, I’ve made plenty of mistakes, and I’d love to share them with you -- including what I’ve learned and what I’d do differently with some hindsight. This talk will cover mistakes made during analyses (including communication when delivering results) team and infrastructure mistakes, plus some advice for incoming data scientists. About the Author Caitlin Hudon My name is Caitlin Hudon and I am lead data scientist at OnlineMedEd, a startup in Austin. I have about eight years of experience doing data science-y things in a variety of industries including IoT, marketing, higher education, non-profits, and start-ups. I am also the co-founder of R-Ladies Austin, founder of the ‘ALL the Ladies in Tech’ quarterly happy hour here in ATX, and a member of the Fall 2017 NASA Datanaut class. Outside of data, I love tacos (especially trading taco spots in Austin), traveling (I’ve been to all 50 states and am working on the continents), the Cubs (including Will Ferrell’s Harry Caray impressions), and live music

rstudio Caitlin Hudon Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats

Miles McBain | Our colour of magic The open sourcery of fantastic R packages | RStudio (2019)

What does it mean to say software is, to quote one Twitter user, ‘so f***ing magical!’? In the context of our popular community hobby of rating and sharing R packages, the term ‘magic’ seems reserved for our most powerful expressions of visceral approval. Why is this? And what does it say about how we value software? Can this magical quality be quantified? We will consider these questions in examination of magical specimens, and in the process reveal the surprising depths at which notions of magic are embedded in the R zeitgiest. VIEW MATERIALS https://github.com/MilesMcBain/rstudioconf_talk About the Author Miles McBain As an Applied Statistician Miles combines a theoretical statistical knowledge and computing expertise to help organizations understand their core business and their customers. Miles is a hacker at heart, which he channels into regular contributions to the open source and open science communities. In addition to commercial projects Miles is always interested in small data/statistics consulting jobs for start-ups and non-for-profits that enable him to expand his applied experience in areas such as as A/B testing, experimental design, and statistical power analysis. He does this mainly for the thrill of learning new domains and the opportunity to meet fascinating people

rstudio Miles McBain Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats

Max Kuhn | parsnip A tidy model interface | RStudio (2019)

parsnip is a new tidymodels package that generalizes model interfaces across packages. The idea is to have a single function interface for types of specific models (e.g. logistic regression) that lets the user choose the computational engine for training. For example, logistic regression could be fit with several R packages, Spark, Stan, and Tensorflow. parsnip also standardizes the return objects and sets up some new features for some upcoming packages. VIEW MATERIALS https://github.com/rstudio/rstudio-conf/tree/master/2019/Parsnip--Max_Kuhn About the Author Max Kuhn Dr. Max Kuhn is a Software Engineer at RStudio. He is the author or maintainer of several R packages for predictive modeling including caret, Cubist, C50 and others. He routinely teaches classes in predictive modeling at rstudio::conf, Predictive Analytics World, and UseR! and his publications include work on neuroscience biomarkers, drug discovery, molecular diagnostics and response surface methodology. He and Kjell Johnson wrote the award-winning book Applied Predictive Modeling in 2013

Max Kuhn

parsnip rstudio rstudio-conf tensorflow tidymodels Max Kuhn Parsnip Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats

Gabor Csardi | pkgman A fresh approach to package installation | RStudio (2019)

The main goals of pkgman is to make package installation fast and more reliable. This allows new, simpler and safer workflows, such as separate package libraries for projects. In this talk, we will show the features that make pkgman fast, convenient and reliable. Features that make pkgman fast: * Concurrency: pkgman performs all downloads, package builds and installations concurrently by default. * Metadata and package cache: pkgman caches all metadata and all downloaded and locally built packages in its cache. * Lazyness: pkgman only downloads and installs packages if needed. Features that make pkgman convenient: * BioC and GitHub packages are supported seamlessly. * Informative UI. pkgman can lay out the installation/update plan, that the user needs to confirm. It returns data about downloads, builds, installations, etc. Features that make pkgman reliable: * Dependency solver. pkgman makes sure that you end up in consistent, working state of dependencies. * Private library: pkgman's own dependencies do not affect your regular package library, and vice versa. pkgman does not load any packages from your regular library. About the Author Gábor Csárdi Gábor is a software engineer at RStudio, working in Hadley’s team on R infrastructure packages

Gábor Csárdi

rstudio Gabor Csardi Pkgman Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats

Brooke Watson | R at the ACLU Joining tables to to reunite families | RStudio (2019)

Last year, over 2500 immigrant children were separated from their family while in government custody. Information about their status is scattered across several government agencies, and throughout the national class-action lawsuit “Ms. L vs ICE,” the Analytics team of the ACLU has been using R to join, deduplicate, validate, and analyze it. Using specifics of this case, this talk will address common challenges arising from human-generated data in spreadsheets. With generalizable examples, I will discuss data tidying, standardization, deduplication, and validation using the tidyverse, janitor, assertthat, and other packages. Finally, I will share best practices for requesting useful data from non-quantitative subject matter experts. About the Author Brooke Watson I am a Data Scientist at the ACLU, where I use code and statistics to support civil rights litigation and advocacy. Previously, I worked in public health and disease research, most recently as a Research Scientist with the EcoHealth Alliance. I completed my Master’s degree in epidemiology from the London School of Hygiene and Tropical Medicine and swam for Tennessee’s Lady Vols as an undergrad

rstudio tidyverse Brooke Watson ACLU Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats

Joe Rickert | R Consortium initiatives in medicine | Posit (2019)

R / Pharma and R / Medicine, two new R Consortium sponsored conferences that appeared in 2018, are both grass roots, community-level manifestations of fundamental trends driving the growth of R. R / Pharma organized as an R Consortium working group by R users at several prominent pharmaceutical companies demonstrated that R has reached sufficient critical mass in Pharma to be recognized as a technology important enough to explore inter-company cooperation, while the addition of Genetech/Roche to R Consortium shows the commitment of upper level management to migrate from SAS to R. - At the other, non-corporate end of the spectrum, the R / Medicine conference was well attended by practicing clinicians coming to R, and in some cases statistics and data analysis, for the first time in order to move forward with the practice of data driven medicine. - In this talk, I will present some highlights from these conferences, and other related R Consortium initiatives, describe some of the R-based projects that illustrate the kind of work being done, and indicate what to expect this year. About the Author Joseph Rickert Joseph is Posit's “Ambassador at Large” for all things R, editor of the R Views blog, and Posit's representative on the R Consortium’s board of directors. Joseph came to Posit via Revolution Analytics and Microsoft where he was a data scientist, blogger and community manager. Before joining Revolution Analytics Joseph worked as a statistician for a small healthcare economics consulting firm, and before that he held a variety of technical, marketing and sales positions while working for startups that spanned multiple industries including government contracting, local area networks, disk drives and test equipment. Joseph studied Classics and Mathematics as an undergraduate at Franklin & Marshall College and earned an M.A. in Humanities and an M.S. in Statistics from the California State University

Joe Rickert Pharma Medicine Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats

Mark Sellors | R in production | RStudio (2019)

With the increase in people using R for data science comes an associated increase in the number of people and organisations wanting to put models or other analytic code into "production". We often hear it said that R isn't suitable for production workloads, but is that true? In this talk, Mark will look at some of the misinformation around the idea of what "putting something into production" actually means, as well as provide tips on overcoming the obstacles put in your path. VIEW MATERIALS https://rinprod.com/ About the Author Mark Sellors Mark is the Head of Data Engineering at Mango Solutions as well as the author of the 'Field Guide to the R Ecosystem'. He has more than a decade’s experience working with analytical computing environments, DevOps and Unix/Linux. He uses his experience to help Mango’s customers transform their analytic capabilities to ensure they can make the most of their data. Mark and his team are at the forefront of the data engineering field, deploying high performance analytical environments using a wide range of tools, such as R, Python, Spark, and cloud computing. He is experienced in the complete product life-cycle from initial ideas and proofs of concept through to development, test, release and production

rstudio Mark Sellors Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats

Garrett Grolemund | R Markdown The bigger picture | RStudio (2019)

Statistics has made science resemble math, so much so that we've begun to conflate p-values with mathematical proofs. We need to return to evaluating a scientific discovery by its reproducibility, which will require a change in how we report scientific results. This change will be a windfall to commercial data scientists because reproducible means repeatable, automatable, parameterizable, and schedulable. VIEW MATERIALS https://github.com/garrettgman/rmarkdown-the-bigger-picture About the Author Garrett Grolemund Garrett is a data scientist and master instructor for RStudio. He excels at teaching, statistics, and teaching statistics. He wrote the popular lubridate package and is the author of Hands On Programming with R and the upcoming book, Data Science with R, from O’Reilly Media. He holds a PhD in Statistics and specializes in Data Visualization

lubridate rmarkdown rstudio Garrett Grolemund Pvalues Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats

Karl Broman | R qtl2 Rewrite of a very old R package | RStudio (2019)

For nearly 20 years, I've been developing, maintaining, and supporting an R package, R/qtl, for mapping quantitative trait loci (genetic loci that contribute to variation in quantitative traits, such as blood pressure) in experimental crosses (such as in mice). It's a rather large package, with 39k lines of R code, 24k lines of C code, and nearly 300 user-accessible functions. In the past several years, I've been working on rewriting the package, to better handle high-dimensional data and more complex experimental crosses. This has been a good opportunity to take advantage of many new tools, including Rcpp, Roxygen2, and testthat. I'll describe my efforts to avoid repeating the mistakes I made the first time around. VIEW MATERIALS https://bit.ly/rstudio2019 About the Author Karl Broman Karl Broman is Professor in the Department of Biostatistics & Medical Informatics at the University of Wisconsin–Madison; research in statistical genetics; developer of R/qtl (for R). Karl received a BS in mathematics in 1991, from the University of Wisconsin–Milwaukee, and a PhD in statistics in 1997, from the University of California, Berkeley; his PhD advisor was Terry Speed. He was a postdoctoral fellow with James Weber at the Marshfield Clinic Research Foundation, 1997–1999. He was a faculty member in the Department of Biostatistics at Johns Hopkins University, 1999–2007. In 2007, he moved to the University of Wisconsin–Madison, where he is now Professor. Karl is a Senior Editor for Genetics, Academic Editor for PeerJ, and a member of the BMC Biology Editorial Board. Karl is an applied statistician focusing on problems in genetics and genomics – particularly the analysis of meiotic recombination and the genetic dissection of complex traits in experimental organisms. The latter is often called “QTL mapping.” A QTL is a quantitative trait locus – a genetic locus that influences a quantitative trait. Recently he has been focusing on the development of interactive data visualizations for high-dimensional genetic data; see his R/qtlcharts package and his D3 examples

roxygen2 rstudio testthat Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Qtl2 Karl Broman

Barret Schloerke | Reactlog 2.0 Debugging the state of Shiny | RStudio (2019)

The revamped reactlog provides an updated visual display to traverse through the reactive behavior within your shiny application. Using live shiny applications, we will use reactlog’s directed dependency graph to find missing reactive dependencies in “working” applications and address suboptimal reactive coding patterns. Correcting these coding patterns will reduce the amount of calculations done by shiny and keep reactive objects from being created unnecessarily. VIEW MATERIALS http://github.com/schloerke/presentation-2019-01-18-reactlog About the Author Barret Schloerke I specialize in Large Data Visualization where I utilize the interactivity of a web browser, the fast iterations of the R programming language, and large data storage capacity of Hadoop

Barret Schloerke

reactlog rstudio Shiny Barret Schloerke Reactlog Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats

Mel Gregory | RStudio Cloud for education | RStudio (2019)

RStudio Cloud aims to take the friction out of doing data science with R, allowing students (and instructors) to skip over installation, setup, and IT challenges and go straight to the good stuff. In this talk, you will learn about RStudio Cloud, how it can streamline the learning process, and how to use it to facilitate teaching classes, workshops, and other learning situations. Additionally, we will highlight some best practices for using RStudio Cloud in an educational setting, and talk about other learning resources available. About the Author Mel Gregory Mel is a software engineer and has been for a while. She likes simplicity

rstudio Mel Gregory Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats

Darby Hadley | RStudio Job Launcher Changing where we run R stuff | RStudio (2019)

RStudio Job Launcher provides the ability to start processes within batch processing systems and container orchestration platforms. In this talk, we will explore what is possible when you have the ability to launch containerized R sessions including scaling, isolating, and customizing environments. We will review examples of launching ad-hoc jobs as well as dockerized R sessions in Kubernetes using the Job Launcher. About the Author Darby Hadley Darby is a QA engineer for multiple teams at RStudio. He has a passion for improving products, creating efficient processes, and helping people. Before joining RStudio he worked primarily in the video game industry

rstudio Darby Hadley Job Launcher Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats

Javier Luraschi | Scaling R with Spark | RStudio (2019)

This talk introduces new features in sparklyr that enable real-time data processing, brand new modeling extensions and significant performance improvements. The sparklyr package provides an interface to Apache Spark to enable data analysis and modeling in large datsets through familiar packages like dplyr and broom. VIEW MATERIALS https://github.com/rstudio/rstudio-conf/tree/master/2019/Scaling%20R%20with%20Spark%20-%20Javier%20Luraschi About the Author Javier Luraschi Javier is a Software Engineer with experience in technologies ranging from desktop, web, mobile and backend; to augmented reality and deep learning applications. He previously worked for Microsoft Research and SAP and holds a double degree in Mathematics and Software Engineering

dplyr rstudio rstudio-conf Javier Luraschi Sparklyr Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats

Alex Hayes | Solving the model representation problem with broom | RStudio (2019)

The R objects used to represent model fits are notoriously inconsistent, making data analysis inconvenient and frustrating. The broom package resolves this issue by defining a consistent way to represent model fits. By summarizing essential information about fits in tidy tibbles, broom makes it easy to programmatically work with model objects. Combining broom with list-columns results in an especially powerful way to work with many model fits at once. This talk will feature several case studies demonstrating how broom resolves common problems in data analysis VIEW MATERIALS https://buff.ly/2FGKFkj About the Author Alex Hayes Alex is interested in how statistics can help people make better decisions. He's active in the R and data science communities, particularly interested in improving interfaces to modeling sofware. In his free time, he tries to get outside to climb and bike

rstudio Alex Hayes Broom Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats

Edzer Pebesma | Spatial data science in the Tidyverse | RStudio (2019)

Package sf (simple feature) and ggplot2::geom_sf have caused a fast uptake of tidy spatial data analysis by data scientists. Important spatial data science challenges are not handled by them, including raster and vector data cubes (e.g. socio-economic time series, satellite imagery, weather forecast or climate predictions data), and out-of-memory datasets. Powerful methods to analyse such datasets have been developed in packages stars (spatiotemporal tidy arrays) and tidync (tidy analysis of NetCDF files). This talk discusses how the simple feature and tidy data frameworks are extended to handle these challenging data types, and shows how R can be used for out-of-memory spatial and spatiotemporal datasets using tidy concepts. VIEW MATERIALS https://edzer.github.io/rstudio_conf/2019/index.html About the Author Edzer Pebesma I lead the spatio-temporal modelling laboratory at the institute for geoinformatics. I hold a PhD in geosciences, and am interested in spatial statistics, environmental modelling, geoinformatics and GI Science, semantic technology for spatial analysis, optimizing environmental monitoring, but also in e-Science and reproducible research. I am an ordinary member of the R foundation. I am one of the authors of Applied Spatial Data Analysis with R (second edition), am Co-Editor-in-Chief for the Journal of Statistical Software, and associate editor for Spatial Statistics. I believe that research is useful in particular when it helps solving real-world problems

ggplot2 rstudio tidyverse Edzer Pebesma Spatial Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats

Irene Steves | Teaching data science with puzzles | RStudio (2019)

Of the many coding puzzles on the web, few focus on the programming skills needed for handling untidy data. During my summer internship at RStudio, I worked with Jenny Bryan to develop a series of data science puzzles known as the "Tidies of March." These puzzles isolate data wrangling tasks into bite-sized pieces to nurture core data science skills such as importing, reshaping, and summarizing data. We also provide access to puzzles and puzzle data directly in R through an accompanying Tidies of March package. I will show how this package models best practices for both data wrangling and project management. VIEW MATERIALS https://github.com/isteves/ds-puzzles About the Author Irene Steves This summer I was an intern at RStudio, where I worked with Jenny Bryan to develop a series of coding challenges to cultivate and reward the mastery of R and the tidyverse. I was previously a Data Science Fellow at the National Center for Ecological Analysis and Synthesis (NCEAS), where I reviewed data submissions to a national repository for completion, clarity, and data management best practices. As a fellow, I also collaborated on a number of open science projects to improve access to Ecological Metadata Language (EML) and datasets in the DataONE network (see metajam, dataspice)

Jenny Bryan

rstudio tidyverse Irene Steves Puzzles Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats

Nic Crane | The future's Shiny: Pioneering genomic medicine in R | Posit (2019)

Shiny’s expanding capabilities are rapidly transforming how it is used in an enterprise. This talk details the creation of a large-scale application, supporting hundreds of concurrent users, making use of the future and promises packages. The 100,000 genomes project is an ambitious exercise that follows on from the Human Genome Project - aiming to put the UK at the forefront of genomic medicine, with the NHS as the first health service in the world to offer precision medicine to patients with rare diseases and cancer. Data is at the heart of this project; not only the outputs of the genomic sequencing, but vast amounts of metadata used to track progress against the 100,000 genome target and the status and path of each case through the sample tracking pipeline. In order to make this data readily available to stakeholders, Shiny was used to create an application containing multiple interactive dashboards. A scaled-up version of the app is being rolled out in early 2019 to a much larger audience to support the National Genomics Informatics Service, with the challenge of creating a complex app capable of supporting so many users without grinding to a halt. In this talk, I will explain why Shiny was the obvious technology choice for this task, and discuss the design decisions which enabled this project's success. VIEW MATERIALS https://github.com/thisisnic/rstudio-conf-2019 About the Author Nic Crane Nic Crane is a Data Scientist at Elucidata, and has formerly worked for Mango Solutions and IBM. She is passionate about learning and teaching all things data science

rstudio rstudio-conf Shiny Nic Crane Human Genome Project Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats

Carl Howe | The next million R users | RStudio (2019)

Many students believe that R is obscure, complex, and difficult to write. However, data from a new large-scale survey of R users conducted by RStudio shows that new R users are taking dramatically different learning paths from those who learned R as recently as 2 years ago, and these new learning paths are changing its perception. In this talk, we'll present this new survey data, describe how new tools and techniques for teaching R can satisfy the demands of today's R learners, and outline a vision for adding millions of new R users to our community. VIEW MATERIALS https://github.com/rstudio/learning-r-survey/blob/master/slides/Next-Million-R-Users.pdf

rstudio Carl Howe Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats

Tonya Filz | The resilient R champion | RStudio (2019)

Merriam-Webster defines resilience as the ability to recover from or adjust easily to misfortune or change. As a Customer Success Representative who works alongside data scientists using RStudio’s toolchain, I’ve had a front row seat to the challenges faced by data scientists as they aim to promote the use of RStudio’s toolchain in their organization. This talk will focus on effective strategies that have been used to overcome some of the most difficult organizational barriers that are faced by data scientists using R. Specific topics will include funding barriers, IT support, server space, the “open source mentality”, and political pressures within organizations. About the Author Tonya Filz Helping data scientists and data analysts succeed by leveraging RStudio to clean, visualize, analyze, and communicate conclusions to key business leaders

rstudio Tonya Filz Customer Success Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats

David Robinson | The unreasonable effectiveness of public work | RStudio (2019)

In this talk, I'll lay out the reasons that blogging, open source contribution, and other forms of public work are a critical part of a data science career. For beginners, a blog is a great accompaniment to data science coursework and tutorials, since it gives you experience applying practical data science skills to real problems. For data scientists at any stage of their careers, open source development offers practice in collaboration, documentation, and interface design that complement other kinds of software development. And for data scientists more advanced in their careers, writing a book is a great way to crystallize your expertise and ensure others can build on it. All of these practices build skills in communication and collaboration that form an essential component of data science work. Each also lets you build a public portfolio of your skills, get feedback from your peers, and network with the larger data science community. VIEW MATERIALS https://bit.ly/drob-rstudio-2019 About the Author David Robinson David is the Chief Data Scientist at DataCamp, an education company for teaching data science through interactive online courses. His interests include statistics, data analysis, education, and programming in R. David is co-author with Julia Silge of the tidytext package and the O’Reilly book Text Mining with R. He also the author of the broom, gganimate, and fuzzyjoin packages, and of the e-book Introduction to Empirical Bayes. David previously worked as a data scientist at Stack Overflow, and received a PhD in Quantitative and Computational Biology from Princeton University

Julia Silge

rstudio David Robinson Open Source Oss Blogging Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats

Earo Wang | Melt the clock Tidy time series analysis | RStudio (2019)

Time series can be frustrating to work with, particularly when processing raw data into model-ready data. This work presents two new packages that address a gap in existing methodology for time series analysis (raised in rstudio::conf 2018). The tsibble package supports organizing and manipulating modern time series, leveraging tidy data principles along with contextual semantics: index and key. The tsibble data structure seamlessly flows into forecasting routines. The fable package is a tidy renovation of the forecast package. It promotes transparent forecasting practices and concise model representations, to empower analysts tackling a broad domain of forecasting problems. This collection of packages form the tidyverts, which facilitates a fluent and fluid workflow for analyzing time series. VIEW MATERIALS https://slides.earo.me/rstudioconf19 About the Author Earo Wang I’m currently doing my Ph.D. on statistical visualisation of temporal-context data at Monash University, supervised by Professor Di Cook and Professor Rob J Hyndman. I enjoy developing open-source tools with R, and is the (co)author of some widely-used R packages including anomalous, hts, sugrrants, rwalkr and tsibble. My research areas invovle data visualisation, time series analysis, and computational statistics

rstudio Earo Wang Time Series Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss

Felienne | Explicit Direct Instruction in Programming Education | RStudio (2019)

In education, there is and has always been debate about how to teach. One of these debates centers around the role of the teacher: should their role be minimal, allowing students to find and classify knowledge independently, or should the teacher be in charge of what happens in the classroom, explaining students all they need to know? These forms of teaching have many names, but the most common ones are exploratory learning and direct instruction respectively. While the debate is not settled, more and more evidence is presented by researchers that explicit direct instruction is more effective than exploratory learning in teaching language and mathematics and science. These findings raise the question whether that might be true for programming education too. This is especially of interest since programming education is deeply rooted in the constructionist philosophy, leading many programmers to follow exploratory learning methods, often without being aware of it. This talk outlines this history of programming education and additional beliefs in programming that lead to the prevalence of exploratory forms of teaching. We also explain the didactic principles of direct instruction, explore them in the context of programming, and hypothesize how it might look like for programming

rstudio Felienne Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss

Yihui Xie | pagedown Creating beautiful PDFs with R Markdown and CSS | RStudio (2019)

The traditional way to beautiful PDFs is often through LaTeX or Word, but have you ever thought of printing a web page to PDF? Web technologies (HTML/CSS/JavaScript) are becoming more and more amazing. It is entirely possible to create high-quality PDFs through Google Chrome or Chromium now. Web pages are usually single-page documents, but they can be paginated thanks to the JavaScript library Paged.js, so that you can have elements like headers, footers, and page margins for the printing purpose. In this talk, we introduce a new R package, pagedown (https://github.com/rstudio/pagedown), to create PDF documents based on R Markdown and Paged.js. Applications of pagedown includes, but not limited to, books, articles, posters, resumes, letters, and business cards. With the power of CSS and JavaScript, you can typeset your documents with amazing elegance (e.g., a single line of CSS, "tr:nth-child(even) { background: #eee; }", will give you a striped table, and "border-radius: 50%;" gives you a circular element) and power (e.g., HTML Widgets). VIEW MATERIALS https://bit.ly/pagedown

pagedown rstudio Yihui Xie Pagedown Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss

Matt Dancho | Using R, the Tidyverse, H2O, and Shiny to reduce employee attrition | RStudio (2019)

An organization that loses 200 high-performing employees per year has a lost productivity cost of about $15M/year. This cost is massive, yet many organizations don’t know it exists. It doesn’t show up on a financial statement. Therefore, it goes unnoticed. This presentation showcases how several open source tools integrate to form a solution to the employee attrition problem. Specifically: (1) How the tidyverse enables problem identification through visualization. (2) How recipes + H2O can be combined to explain key relationships to attrition and predict employee attrition. (3) How Shiny can be used to create a powerful dashboard that empowers business leaders to make data-driven decisions across the organization

rstudio Shiny tidyverse Matt Dancho Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss

Hadley Wickham | vctrs Tools for making size and type consistent functions | RStudio (2019)

vctrs is a new package that provides tools (cognitive and computational) to ensure that functions behave consistently with respect to inputs of varying length and type. The end goal of vctrs is to be invisible to the end user of the tidyverse (simply enabling their predictions about function outputs to be more correct), but will help developers write functions that "just work"

Hadley Wickham

rstudio tidyverse vctrs Hadley Wickham Vctrs Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss

Claus Wilke | Visualizing uncertainty with hypothetical outcomes plots | RStudio (2019)

Uncertainty is a key component of statistical inference. However, uncertainty is not easy to convey effectively in data visualizations. For example, viewers have a tendency to interpret visualizations of the most likely outcome as the only possible one. Viewers may also misjudge the likelihood of different possible outcomes or the extent to which moderately rare outcomes may deviate from the expectation. One way in which we can help the viewer grasp the amount of uncertainty present in a dataset is by showing a variety of different possible modeling outcomes at once. For example, in a linear regression, we could plot a number of different regression lines with slopes and intercepts drawn from the range of likely values, as determined by the variation in the data. Such visualizations are called Hypothetical Outcomes Plots (HOPs). HOPs can be made in static form, showing the various hypothetical outcomes all at once, or preferably in an animated form, where the display cycles between the different hypothetical outcomes. With recent progress in ggplot2-based animation, via gganimate, as well as packages such as tidybayes that make it easy to generate hypothetical outcomes, we can easily produce animated HOPs in a few lines of R code. This presentation will cover the key concepts, packages, and techniques to generate such visualizations. VIEW MATERIALS: https://docs.google.com/presentation/d/1zMuBSADaxdFnosOPWJNA10DaxGEheW6gDxqEPYAuado/edit?usp=sharing

ggplot2 rstudio Claus Wilke Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss

Sigrid Keydana | Why TensorFlow eager execution matters | RStudio (2019)

In current deep learning with Keras and TensorFlow, when you've mastered the basics and are ready to dive into more involved applications (such as generative networks, sequence-to-sequence or attention mechanisms), you may find that surprisingly, the learning curve doesn't get much flatter. This is largely due to restrictions imposed by TensorFlow's traditional static graph paradigm. With TensorFlow Eager Execution, available since summer and announced to be the default mode in the upcoming major release, model architectures become more flexible, readable, composable, and last not least, debuggable. In this session, we'll see how with Eager, we can code sophisticated architectures like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) in a straightforward way. VIEW MATERIALS https://github.com/skeydan/rstudio_conf_2019_eager_execution

rstudio tensorflow Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate Sigrid Keydana GAN VAE Deep Learning

Lionel Henry | Working with names and expressions in your tidy eval code | RStudio (2019)

n practice there are two main flavors of tidy eval functions: functions that select columns, such as `dplyr::select()`, and functions that operate on columns, such as `dplyr::mutate()`. While sharing a common tidy eval foundation, these functions have distinct properties, good practices, and available tooling. In this talk, you'll learn your way around selecting and doing tidy eval style. Materials: https://speakerdeck.com/lionelhenry/selecting-and-doing-with-tidy-eval

Lionel Henry

dplyr rstudio Lionel Henry Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Joe Cheng | Shiny in production: Principles, practices, and tools | RStudio (2019)

Shiny is a web framework for R, a language not traditionally known for web frameworks, to say the least. As such, Shiny has always faced questions about whether it can or should be used “in production”. In this talk we’ll explore what “production” even means, review some of the historical obstacles and objections to using Shiny for production purposes, and discuss practices and tools that can help your Shiny apps flourish. About the Author: Joe Cheng is the Chief Technology Officer at RStudio. Joe was the original creator of Shiny, and leads the team responsible for Shiny and Shiny Server. GitHub: https://github.com/jcheng5 Materials: https://speakerdeck.com/jcheng5/shiny-in-production

Joe Cheng

rstudio Shiny Joe Cheng Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Tareef Kawaf | Welcome and the Posit Vision | Posit (2019)

From Posit President, TAREEF KAWAF About Tareef: Tareef Kawaf is a software startup executive and current president of RStudio, Inc., a Massachusetts-based company that develops both open-source and commercial software for the R statistical programming language. Prior to joining RStudio, Mr. Kawaf served as senior vice president of engineering and operations at Brightcove, Inc.. Over 8 years he helped Brightcove build and operate the second-largest online video platform, helping it grow from 0 to 92M in revenue and complete its initial public offering (IPO). Mr. Kawaf jointly holds a patent for the “Method and System for Dynamic Pricing,” issued in 2001 which is a core component of Oracle’s ATG Commerce solutions and helps retailers define sophisticated rules for couponing, discounting, and personalized commerce. Mr. Kawaf received his B.S. degree in Computer Science with a minor in Mathematics from the University of Massachusetts Amherst in 1994. He and his family currently reside outside of Boston, MA

rstudio Tareef Kawaf Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Jeff Allen | RStudio Connect Past, present, and future | RStudio (2019)

RStudio Connect is a publishing platform that helps to operationalize the data science work you're doing. We'll review the current state of RStudio including its ability to host Shiny applications and Plumber APIs, schedule and render R Markdown documents, and manage access. Then we'll unveil some exciting new features that we've been working on, and give you a sneak peek at what's coming up next. Materials: http://rstd.io/rsc170

plumber rstudio Shiny Jeff Allen Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Shiny in Production: Data Products at Scale Workshop - rstudio::conf(2019L)

What is the 2-day Shiny in Production Workshop? That's a great question, I'm glad you asked. Shiny in Production | Data Products at Scale Workshop - 2 Days This two-day workshop will teach you the best practices that make production-quality Shiny application performance possible. Does 30,000 simultaneous users for a Shiny application sound challenging? Shiny is used all over the world to deliver interactive, visual data products from data science teams to internal and external audiences at scale. The course will not spend time on writing applications, but will focus on the open source and professional ecosystem around shiny that includes performance optimization, testing, and production deployment. This workshop relies on RStudio professional products for deployment. You should take this workshop if you are comfortable writing Shiny apps but are afraid for what will happen when people start looking at them. The workshop is taught by Sean Lopp, RStudio Solutions Engineer. Sean works with RStudio product teams and helps enterprise customers to realize the value of R and Shiny. Speakers: Sean Lopp and a who's-who of RStudio Engineers, R-Admins, and Shiny Developers

Reproducible Examples with the reprex package

Reproducible Examples and the `reprex` package. https://speakerdeck.com/jennybc/reprex-reproducible-examples-with-r Jump to: 0:08 Intro 0:40 Basic usage of reprex 3:35 Motivation, why use reprex? "Help me help you" 4:08 Define `reprex`? Three commons ways to use the term. 1. noun, a reproducible example 2. the reprex package. a tool to build R `reprex`s 3. reprex::reprex(), a function in `reprex` to make a reprex. 5:26 When should you use a reprex? 6:14 reprex installation and setup. How do you actually get repex on your machine? 7:59 Advanced setup and discussion. 9:45 Please use advanced features responsibly. 11:02 Why does the reprex package exist? Anyone who has helped teach R or dealt with github issues, twitter, stack overflow & RStudio community questions knows that helping people diagnose their coding problems can be hard. This tool comes from hard-won experience. It’s aim to is help people ask well formed questions and increase the chances of getting well formed answers quickly. 12:52 philosophy behind reprex 1. code that I can run 2. code that I don't need to run 3. code that I can easily run 13:52 code that I can run. 17:25 Tips on writing good `reprex`s. Dos and don'ts. 18:52 How do I get my data into my reprex? Getting small data and CSV type data into your reprex is easy. “I have a big hairy data object and I can only show their problem by using it”, but that's not always the case. 21:02 code that I don't need to run reprex gives your reader the code and reveals the output being produced by that code. For experienced coders, that might be enough to help you. 22:44 code that I can easily run Don't copy and paste from the R console. This is usually annoying for your reader. Worse than console copy-pasta is the screenshot. (Many people think screenshots of code are downright offensive.) 25:03 reprex_clean If you copy someone else's reprex into your consolve, it may include their output, making your new reprex a untidy. Here are tips for taking someone else's reprex code and output, and create a clean reprex reply. 25:54 shock and awe More interesting features of the reprex package. - 26:29 What about figures and plots in your reprex? So happy you asked about that. reprex will automatically upload your images to imgur.com. - 28:23 Create a reprex by explicitly providing your code in the reprex call. - 29:00 when you need your reprex to work in the current working directory. - 30:45 Differently flavored markdown. Optimize your reprex markdown output for github, stack overflow, or the RStudio community. - 30:31 Make your reprex create an R script, with your reprex outputs as comments. This is handy for pasting into an email or slack-type-app. - 32:25 Rich text format, rtf output. (currently experimental feature as of this video) - 33:06 supress the reprex add at the bottom of your reprex - 33:19 Include session info. - 33:54 Auto styling of your code. Good if you're dealing with poorly formatting code. - 34:25 Change your comments string. - 34:32 Silence Tidyverse startup messages. - 35:00 Capture a reprex that sends messages to standard output and standard input (e.g. package installation compilation messages). 36:13 Set up personal defaults for your reprex usage. 36:54 reprex RStudio addins; render reprex and reprex selection. These accelerate your use of reprex. 39:01 The human side of reproducible examples. How to ask questions in ways that are most likely to get answered. Sorry for the tough love, but this is important. Why are you always asked to give a reprex? - Experts try to use reproducible examples to ensure their advice works. - Making a good reprex is hard. But, you are asking them to solve a problem for you, so meet them halfway. - Creating reprexes is good coding practice. - Making a good reprex is often a good way to debug your issue in the embarrassment-free privacy of your own home. - reprexes lead to discussions more likely to help people in the future. 44:34 Behind the scenes of reprex 44:44 Thanks for those that helped make reprex possible. Questions and Answers - 46:05 can reprex capture variables and objects in the current environment? (not yet, maybe in development) - 47:25 does reprex actually check that the code is self contained? (self contained) - 48:08 does readr::read_csv support the text argument? (yep, just read the help manual for readr)

Working with Two Datasets: Binds, Set Operations, and Joins -- Pt 4 Intro to Data Manipulation

Data wrangling is too often the most time-consuming part of data science and applied statistics. Two tidyverse packages, tidyr and dplyr, help make data manipulation tasks easier. Keep your R code clean and clear and reduce the cognitive load required for common but often complex data science tasks. `dplyr` docs: dplyr.tidyverse.org/reference/ - http://dplyr.tidyverse.org/reference/setops.html - http://dplyr.tidyverse.org/reference/join.html ---------------- Pt. 1: What is data wrangling? Intro, Motivation, Outline, Setup https://youtu.be/jOd65mR1zfw - /01:44 Intro and what’s covered Ground Rules: - /02:40 What’s a tibble - /04:50 Use View - /05:25 The Pipe operator: - /07:20 What do I mean by data wrangling? Pt. 2: Tidy Data and tidyr https://youtu.be/1ELALQlO-yM - /00:48 Goal 1 Making your data suitable for R - /01:40 `tidyr` “Tidy” Data introduced and motivated - /08:10 `tidyr::gather` - /12:30 `tidyr::spread` - /15:23 `tidyr::unite` - /15:23 `tidyr::separate` Pt. 3: Data manipulation tools: `dplyr` https://youtu.be/Zc_ufg4uW4U - /00.40 setup - /02:00 `dplyr::select` - /03:40 `dplyr::filter` - /05:05 `dplyr::mutate` - /07:05 `dplyr::summarise` - /08:30 `dplyr::arrange` - /09:55 Combining these tools with the pipe (Setup for the Grammar of Data Manipulation) - /11:45 `dplyr::group_by` Pt. 4: Working with Two Datasets: Binds, Set Operations, and Joins https://youtu.be/AuBgYDCg1Cg Combining two datasets together - 00.42 `dplyr::bind_cols` - 01:27 `dplyr::bind_rows` - 01:42 Set operations `dplyr::union`, `dplyr::intersect`, `dplyr::set_diff` - 02:15 joining data - `dplyr::left_join`, `dplyr::inner_join`, - `dplyr::right_join`, `dplyr::full_join`, ______________________________________________________________ Cheatsheets: https://www.rstudio.com/resources/cheatsheets/ Documentation: `tidyr` docs: tidyr.tidyverse.org/reference/ - `tidyr` vignette: https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html `dplyr` docs: http://dplyr.tidyverse.org/reference/ - `dplyr` one-table vignette: https://cran.r-project.org/web/packages/dplyr/vignettes/dplyr.html - `dplyr` two-table (join operations) vignette: https://cran.r-project.org/web/packages/dplyr/vignettes/two-table.html ______________________________________________________________

What is data wrangling? Intro, Motivation, Outline, Setup -- Pt. 1 Data Wrangling Introduction

Data wrangling is too often the most time-consuming part of data science and applied statistics. Two tidyverse packages, tidyr and dplyr, help make data manipulation tasks easier. These videos introduce you to these tools. Keep your R code clean and clear and reduce the cognitive load required for common but often complex data science tasks. Pt. 1: What is data wrangling? Intro, Motivation, Outline, Setup https://youtu.be/jOd65mR1zfw - 01:44 Intro and what’s covered Ground Rules - 02:40 What’s a tibble - 04:50 Use View - 05:25 The Pipe operator: - 07:20 What do I mean by data wrangling? Pt. 2: Tidy Data and tidyr https://youtu.be/1ELALQlO-yM - /00:48 Goal 1 Making your data suitable for R - /01:40 `tidyr` “Tidy” Data introduced and motivated - /08:15 `tidyr::gather` - /12:38 `tidyr::spread` - /15:30 `tidyr::unite` - /15:30 `tidyr::separate` Pt. 3: Data manipulation tools: `dplyr` https://youtu.be/Zc_ufg4uW4U - 00.40 setup - /02:00 `dplyr::select` - /03:40 `dplyr::filter` - /05:05 `dplyr::mutate` - /07:05 `dplyr::summarise` - /08:30 `dplyr::arrange` - /09:55 Combining these tools with the pipe (Setup for the Grammar of Data Manipulation) - /11:45 `dplyr::group_by` - /15:00 `dplyr::group_by` Pt. 4: Working with Two Datasets: Binds, Set Operations, and Joins https://youtu.be/AuBgYDCg1Cg Combining two datasets together - /00.42 `dplyr::bind_cols` - /01:27 `dplyr::bind_rows` - /01:42 Set operations `dplyr::union`, `dplyr::intersect`, `dplyr::set_diff` - /02:15 joining data `dplyr::left_join`, `dplyr::inner_join`, `dplyr::right_join`, `dplyr::full_join`, ______________________________________________________________ Cheatsheets: https://www.rstudio.com/resources/cheatsheets/ Documentation: `tidyr` docs: tidyr.tidyverse.org/reference/ - `tidyr` vignette: https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html `dplyr` docs: http://dplyr.tidyverse.org/reference/ - `dplyr` one-table vignette: https://cran.r-project.org/web/packages/dplyr/vignettes/dplyr.html - `dplyr` two-table (join operations) vignette: https://cran.r-project.org/web/packages/dplyr/vignettes/two-table.html ______________________________________________________________ New York Times “For Big-Data Scientists, ‘Janitor Work’ Is Key Hurdle to Insights”, By STEVE LOHRAUG. 17, 2014 https://www.nytimes.com/2014/08/18/technology/for-big-data-scientists-hurdle-to-insights-is-janitor-work.html ______________________________________________________________

Tidy Data and tidyr -- Pt 2 Intro to Data Wrangling with R and the Tidyverse

Data wrangling is too often the most time-consuming part of data science and applied statistics. Two tidyverse packages, tidyr and dplyr, help make data manipulation tasks easier. Keep your code clean and clear and reduce the cognitive load required for common but often complex data science tasks. http://tidyr.tidyverse.org/reference/ - http://tidyr.tidyverse.org/reference/gather - http://tidyr.tidyverse.org/reference/spread - http://tidyr.tidyverse.org/reference/unite - http://tidyr.tidyverse.org/reference/separate ---------------- Pt. 1: What is data wrangling? Intro, Motivation, Outline, Setup https://youtu.be/jOd65mR1zfw - /01:44 Intro and what’s covered Ground Rules - /02:40 What’s a tibble - /04:50 Use View - /05:25 The Pipe operator: - /07:20 What do I mean by data wrangling? Pt. 2: Tidy Data and tidyr https://youtu.be/1ELALQlO-yM - 00:48 Goal 1 Making your data suitable for R - 01:40 `tidyr` “Tidy” Data introduced and motivated - 08:10 `tidyr::gather` - 12:30 `tidyr::spread` - 15:23 `tidyr::unite` - 15:23 `tidyr::separate` Pt. 3: Data manipulation tools: `dplyr` https://youtu.be/Zc_ufg4uW4U - 00.40 setup - /02:00 `dplyr::select` - /03:40 `dplyr::filter` - /05:05 `dplyr::mutate` - /07:05 `dplyr::summarise` - /08:30 `dplyr::arrange` - /09:55 Combining these tools with the pipe (Setup for the Grammar of Data Manipulation) - /11:45 `dplyr::group_by` - /15:00 `dplyr::group_by` Pt. 4: Working with Two Datasets: Binds, Set Operations, and Joins https://youtu.be/AuBgYDCg1Cg Combining two datasets together - /00.42 `dplyr::bind_cols` - /01:27 `dplyr::bind_rows` - /01:42 Set operations `dplyr::union`, `dplyr::intersect`, `dplyr::set_diff` - /02:15 joining data `dplyr::left_join`, `dplyr::inner_join`, `dplyr::right_join`, `dplyr::full_join`, ______________________________________________________________ Cheatsheets: https://www.rstudio.com/resources/cheatsheets/ Documentation: `tidyr` docs: tidyr.tidyverse.org/reference/ - `tidyr` vignette: https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html `dplyr` docs: http://dplyr.tidyverse.org/reference/ - `dplyr` one-table vignette: https://cran.r-project.org/web/packages/dplyr/vignettes/dplyr.html - `dplyr` two-table (join operations) vignette: https://cran.r-project.org/web/packages/dplyr/vignettes/two-table.html ______________________________________________________________

Data Manipulation Tools: dplyr -- Pt 3 Intro to the Grammar of Data Manipulation with R

Data wrangling is too often the most time-consuming part of data science and applied statistics. Two tidyverse packages, tidyr and dplyr, help make data manipulation tasks easier. Keep your code clean and clear and reduce the cognitive load required for common but often complex data science tasks. `dplyr` docs: dplyr.tidyverse.org/reference/ - http://dplyr.tidyverse.org/reference/union.html - http://dplyr.tidyverse.org/reference/intersect.html - http://dplyr.tidyverse.org/reference/set_diff.htm ---------------- Pt. 1: What is data wrangling? Intro, Motivation, Outline, Setup https://youtu.be/jOd65mR1zfw - /01:44 Intro and what’s covered Ground Rules - /02:40 What’s a tibble - /04:50 Use View - /05:25 The Pipe operator: - /07:20 What do I mean by data wrangling? Pt. 2: Tidy Data and tidyr https://youtu.be/1ELALQlO-yM - /00:48 Goal 1 Making your data suitable for R - /01:40 `tidyr` “Tidy” Data introduced and motivated - /08:10 `tidyr::gather` - /12:30 `tidyr::spread` - /15:23 `tidyr::unite` - /15:23 `tidyr::separate` Pt. 3: Data manipulation tools: `dplyr` https://youtu.be/Zc_ufg4uW4U - 00.40 setup - 02:00 `dplyr::select` - 03:40 `dplyr::filter` - 05:05 `dplyr::mutate` - 07:05 `dplyr::summarise` - 08:30 `dplyr::arrange` - 09:55 Combining these tools with the pipe (Setup for the Grammar of Data Manipulation) - 11:45 `dplyr::group_by` Pt. 4: Working with Two Datasets: Binds, Set Operations, and Joins https://youtu.be/AuBgYDCg1Cg Combining two datasets together - /00.42 `dplyr::bind_cols` - /01:27 `dplyr::bind_rows` - /01:42 Set operations `dplyr::union`, `dplyr::intersect`, `dplyr::set_diff` - /02:15 joining data `dplyr::left_join`, `dplyr::inner_join`, `dplyr::right_join`, `dplyr::full_join`, ______________________________________________________________ Cheatsheets: https://www.rstudio.com/resources/cheatsheets/ Documentation: `tidyr` docs: tidyr.tidyverse.org/reference/ - `tidyr` vignette: https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html `dplyr` docs: http://dplyr.tidyverse.org/reference/ - `dplyr` one-table vignette: https://cran.r-project.org/web/packages/dplyr/vignettes/dplyr.html - `dplyr` two-table (join operations) vignette: https://cran.r-project.org/web/packages/dplyr/vignettes/two-table.html ______________________________________________________________

Data Science

Blog Posts tagged Data Science#

ASA DataFest 2021

Custom Google Analytics Dashboards with R: Building The Dashboard

Introducing torch for R

TensorFlow for R

sparklyr 0.7: Spark Pipelines and Machine Learning

Resources tagged Data Science#

Seriously, have you tried this AI stuff? I've never written code like this in my life.

AI-Powered Data Engineering Workflows: Positron for Databricks Users

querychat in R: Query Your Data with Natural Language | Shiny + LLMs | Veerle van Leemput

querychat in Python: Query Your Data with Natural Language | Shiny + LLMs | Veerle van Leemput

How Open Source, Python and AI Are Shaping the Data Future with Wes McKinney

Take Positron to Work with Positron Pro

Zero Tolerance for Dirty Data: A Pointblank Prescription for Data Hygiene (Arnav Patel, Synovus)

Who are 'the ggplot2 extenders' and how to become one (Gina Reynolds) | posit::conf(2025)

When R Met Python: A Meet Cute on Posit Connect (Blake Abbenante, Suffolk Construction)

What we're doing to make Quarto fast(er) (Carlos Scheidegger, Posit) | posit::conf(2025)

What R We Counting? (Ben Arancibia, GSK) | posit::conf(2025)

Web applications with Shiny and React and AI (Winston Chang, Posit) | posit::conf(2025)

Virtual Day Q & A Hangout with Virtual Speakers | posit::conf(2025)

Virtual Day AMA with Rich Iannone | posit::conf(2025)

Using Quarto to Improve Formatting/Automate the Generation of Hundreds of Reports (Keaton Wilson)

Using paired programming to have fun & sell your solutions (Kris Fabick & Kristin Carr)

Use Your Data Skills for Good (Sharon Machlis) | posit::conf(2025)

Uncharted Nuuk: Data Exploration in Search of the Unexpected (Emil Malta, Statistics Greenland)

Trustworthy Data Visualization (Kieran Healy, Duke University) | posit::conf(2025)

Trust, but Verify: Lessons from Deploying LLMs in a Large Health System (Timothy Keyes)

Translating R for Data Science into Portuguese: A Community-Led Initiative (Beatriz Milz, UFABC)

Tips & tricks from the maintainers of Positron (Sharon Wang & Melissa Barca) | posit::conf(2025)

There's a new Plumber in town (Thomas Lin Pedersen, Posit) | posit::conf(2025)

Theming Made Easy: Introducing brand.yml (Garrick Aden-Buie, Posit) | posit::conf(2025)

The Psychology of Technologists (Cat Hicks, Catharsis Consulting) | posit::conf(2025)

The Power of Snowflake and Posit Workbench (Jonathan Regenstein, Snowflake) | posit::conf(2025)

The Curse of Documentation (Michael Chow, Posit) | posit::conf(2025)

Teaching data visualization with R entirely in Quarto (Claus Wilke, UT Austin) | posit::conf(2025)

Teaching Data Sharing through R Data Packages (Kelly McConville, Bucknell) | posit::conf(2025)

Talk data to me: How to present youR data to any audience (Freda Warner, CIHR) | posit::conf(2025)

surveydown (John Paul Helveston, GWU) | posit::conf(2025)

Shinystate (Eric Nantz, Eli Lilly) | posit::conf(2025)

shinyGovStyle- accessible gov design in RShiny (Cameron Race & Sarah Wong-Brown) | posit::conf(2025)

Semantic Search for the Rest of Us with DuckDB (Marcos Huerta, Carmax) | posit::conf(2025)

See if talking to the doctor is right for you (Max Hockstein, Georgetown) | posit::conf(2025)

Same Data, Different Tools: Visualizing with R and Python (Olivia Hebner, Summit)

Running a polyglot data science community (Melissa Van Bussel, StatCan) | posit::conf(2025)

rOpenSci Champions: Building Communities of Open-Source Leaders (Noam Ross, rOpenSci)

Ripples of Change with R- It's more than just coding (Yvonne Kienast, CIHI) | posit::conf(2025)

R-multiverse: a new way to publish R packages (Will Landau, Eli Lilly) | posit::conf(2025)

R & Python playing nice, in Production (Claudia Penaloza, Continental Tires) | posit::conf(2025)

Quarto for Business Collaboration & Technical Documentation in Word docx format (Bill Pikounis, J&J)

Putting an {ellmer} AI in production with the blessing of IT (Andrie de Vries) | posit::conf(2025)

Purrrfectly parallel, purrrfectly distributed (Charlie Gao, Posit) | posit::conf(2025)

Precision Medicine for All: Using Tidymodels to Validate PRS in Brazil (Flávia Rius) | posit::conf

Practical {renv} (Shannon Pileggi, The PCCTC) | posit::conf(2025)

Positron: The First Five Minutes (Isabella Velásquez, Posit) | posit::conf(2025)

Positron for Babies: Communicating Technical Concepts to all Audiences (Ryan Johnson)

Positron AI Session (George Stagg, Winston Chang, Tomasz Kalinowski , Carson Sievert) | posit::conf

Posit and the modern data stack: Using Posit, Snowflake, and Databricks (James Blair)

Polyglot Data Science: Why and How to Combine R and Python (Jeroen Janssens) | posit::conf(2025)

Plotgardener Genomic Data Visualization Made Easy (Rishabh Sharma Vemuri & Abiye Berhanu)

Outgrowing Your Laptop with Positron (Austin Dickey, Posit) | posit::conf(2025)

Oops! I accidentally made a production dashboard (Jonathan Keane, Posit) | posit::conf(2025)

On Teaching Adults to Code (Kristin Bott, Posit) | posit::conf(2025)

Old Apps, New Tricks: How AI can write Automated Tests for your Shiny Apps (Karan Gathani, Posit)

Observability at scale (Barret Schloerke, Posit) | posit::conf(2025)

Multiple Console Sessions in Positron (Dhruvi Sompura, Posit) | posit::conf(2025)

Modular, layout-as-code approach for customizable Shiny dashboards (Kim Schouten, De NZa)

Making Things Nice in Python (Rich Iannone, Posit) | posit::conf(2025)

Making the most of artificial and human intelligence for data science (Hadley Wickham, Joe Cheng)

Making Ecological Modelling Accesible with EcoCommons (Jenna Wraith, QCIF) | posit::conf(2025)

Make Big Geospatial Data Accessible with Arrow (Cari Gostic, Sonoma Technology) | posit::conf(2025)

maidr: Empowering Accessible, Multimodal Data Visualizations (JooYoung Seo) | posit::conf(2025)

Lift Off! Building REST APIs that Fly (Joe Kirincic, RESTORE-Skills) | posit::conf(2025)

Leveraging LLMs for student feedback in introductory data science courses (Mine Çetinkaya-Rundel)

Keeping Data Alive: Persistent Storage Options for Dynamic Cloud Applications (Alex Chisholm, Posit)

It's all fun and games til your analysis code is finished (Alex Rossell Hayes) | posit::conf(2025)

Is that LLM feature any good? (Simon P. Couch, Posit) | posit::conf(2025)

Integrating AI, Data Science Platforms, and Psychological Safety (Chris Engelhardt, Gen Re)

Instant Impact: Developing {docorator} to Simplify R Adoption for Teams (Becca Krouse, GSK)

IDE-ntity Crisis: Choosing the Right Tool for Me (Isabel Zimmerman, Posit) | posit::conf(2025)