Want to catch up on talks? While we prepare the videos for YouTube, all session recordings are available now on the event portal:

• Register / log in at conf.posit.co
• Go to “Schedule”
• Select any session and click “View recording”

Announcing the winners of the Posit Impact Awards

We were thrilled to launch the inaugural Posit Impact Awards to celebrate stories of measurable, meaningful change across our community. At posit::conf(2026), we were honored to announce our eight winners, each receiving a conference pass to join us in Houston!

A huge congratulations to all our winners, and a heartfelt thank you for the incredible work you do (and for sharing it with us)!

• Explore their stories in the 2026 Posit Impact Awards blog post and join us at the Data Science Hangout to hear from other Impact Award winners and nominees featured in the next few weeks.

Open Source updates from posit::conf(2026)

Introducing commons

commons 0.1.0 introduces a new framework for building trustworthy self-service data analysis agents in R and Python. The package implements a three-tier trust labeling system that transparently indicates whether agents invoke trusted code directly, write new code justified by vetted context, or provide untrusted answers. Built on Posit’s LLM stack (ellmer, chatlas, shinychat), commons enables teams to convert existing analytical code into AI agent capabilities while maintaining transparency about code sources.

• Learn more about commons 0.1.0.
• Join Sara & Simon’s virtual webinar on October 28th to learn more about commons. Register here.

Quarto Hub

Quarto Hub is a new hosted platform from Posit. In Quarto Hub, you can create, edit, comment on, and publish your Quarto projects (single documents, presentations, websites) collaboratively and in real time.

• Watch Carlos Scheidegger’s talk, “Quarto Hub: Collaboratively edit, create, and share documents”, on the event portal.

skiLift and skiPatrol

Posit has taken over maintenance over two R packages that provide comprehensive tools to work and collaborate within Snowflake’s data platform and ML ecosystem:

• skiLift: a DBI-compliant Snowflake connector written in R, that authenticates and communicates via the Snowflake SQL API (REST).
• skiPatrol: an R interface to Snowflake’s ML platform: model registry, feature store, experiments, model monitoring, and container services.

Learn more in the R, meet Snowflake: Introducing Posit’s skiLift and skiPatrol Packages blog post.

Even more open source updates

Beyond all the exciting conf news, September was packed with major product updates and community highlights across the board!

At the database layer

ggsql 0.5.0: Readers, Writers, and Beta status

ggsql is a tool that implements a grammar of graphics for SQL, enabling ggplot2-style data visualization directly from database queries. ggsql 0.5.0 reaches beta status with major improvements to database connectivity and rendering capabilities. The release introduces hybrid-reader mode for read-only connections with cache support, replaces the Vega-Lite renderer with a custom writer supporting PNG, JPEG, SVG, and PDF output, and adds rich text support through extended markdown. New features include caption support, minor breaks on scales, and true variable line aesthetics with gradients.

• Read more in the ggsql 0.5.0: Readers, Writers, and Beta status blog post.

orbital 0.7.0

orbital enables the running predictions of tidymodels workflows inside databases. orbital 0.7.0 and tidypredict 1.2.0 massively expand in-database prediction capabilities for tidymodels workflows. The release adds 27 new model/engine combinations including discriminant analysis, naive Bayes, neural networks, SVMs, partial least squares, and ensemble methods. tidypredict now functions as a developer toolkit with new generics for programmatic use, and improved error messaging guides users when probability predictions aren’t available.

• Read more in the orbital 0.7.0 blog post.

AI-powered R tools

ellmer 0.5.0

ellmer makes it easy to use large language models (LLM) from R. ellmer 0.5.0 brings major improvements for working with large language models in R. New file handling functions reduce token costs by efficiently managing documents. The release adds citation tracking from web search tools, token counting for cost prediction, and streaming structured output support. Developer features include tool_context() for accessing request metadata and new lifecycle hooks for building agents.

• Read more in the ellmer 0.5.0 blog post.

vitals 0.4.0

vitals is a framework for large language model evaluation in R. vitals 0.4.0 brings significant performance improvements and new agent comparison capabilities for LLM evaluation in R. The release includes claude_code() and codex() helpers for benchmarking custom ellmer-built agents against leading coding agents, plus vitals_log_read() for loading log files back into resumable Chat objects. Log files are now ~4x smaller and the log viewer is substantially faster.

• Read more in the vitals 0.4.0 blog post.

Our stuff is so Shiny

Check out all the major work done by the Shiny team in September!

Introducing shinyreact: React UI backed by a Shiny server

shinyreact enables developers to build Shiny applications with React-based UIs while keeping Shiny’s reactive computation on the server. The package provides two primary React hooks for client-server communication, gives access to the entire npm ecosystem of React components, and includes AI Agent Skills for building and converting apps.

• Read more in the Introducing shinyreact: React UI backed by a Shiny server blog post.

Shiny for Python 1.8

Shiny for Python 1.8 introduces in-memory server testing without browsers, enabling developers to test reactive logic with pytest using a mock connection. The release adds ui.page_html() for using complete HTML documents from bundlers like Vite, and session.allow_reconnect() to maintain live sessions after dropped websocket connections. Additional improvements include deprecation of ui.output_text_verbatim() in favor of clearer naming, reactive value destruction fixes, and multiple bug fixes for downloads, navsets, and data frames.

• Read more in the Shiny for Python 1.8 blog post.

Multiple tables, saved conversations, and take-home dashboards: querychat R 0.4.0 and Python 0.9.0

querychat facilitates safe and reliable natural language exploration of tabular data, powered by SQL and large language models (LLMs). querychat R 0.4.0 and Python 0.9.0 introduce multiple table support with automatic joins, persistent conversation history across sessions, and the /handoff command for exporting chats as downloadable Quarto dashboards, Shiny apps, or marimo notebooks. The release builds on shinychat’s full-page layout with editable messages and file attachments, adds YAML-based data dictionaries for context, and integrates with pins boards for chatting with pinned data.

• Read more in the Multiple tables, saved conversations, and take-home dashboards: querychat R 0.4.0 and Python 0.9.0 blog post.

Complete chat applications in shinychat: R 0.5.0 and Python 0.7.1

shinychat provides a Shiny toolkit for building generative AI applications like chatbots and streaming content. shinychat R 0.5.0 and Python 0.7.1 deliver complete chat application capabilities with conversation history, message editing and branching, greetings, file attachments, and slash commands. The new page_chat() layout provides full-window interfaces with integrated navigation, tool displays, citations, and artifact previews. Features include persistent conversation storage, search and organization tools, streaming responses with thinking panels, and toolbars for contextual actions.

• Read more in the Complete chat applications in shinychat: R 0.5.0 and Python 0.7.1 blog post.

posit::conf(2027) will be in Seattle, WA from Sept 13-15

Save the date: We’re heading back to the Pacific Northwest for posit::conf(2027)! Subscribe to “Events” to get notified when registration opens.

In the meantime, join us for our ongoing virtual events:

• Data Science Hangout for casual conversations with leaders in the data space
• Data Science Lab for live coding sessions with practitioners building cool things with code

Hope to see you there!

Welcome to our newsletter, posit::glimpse()!

If you’re currently reading this on our blog, consider subscribing to Product Updates - Open Source on our subscription page to receive this newsletter directly in your inbox.

Welcome to this month’s roundup of the latest open-source developments from Posit! Our update list is slightly shorter this time around, because our team is hard at work putting the final touches on posit::conf!

In this special feature, we are spotlighting a few presentations that will be broadcast live throughout the conference. Be sure to register today if you’d like to tune in live. And, if you enjoy being chronically online like me, I’ll be hanging out in the Discord community server throughout the event, alongside your 1000+ closest data pals 👾

Key product updates and new releases

Positron August Release Highlights

The Positron 2026.08 Release blog post highlights several key updates and improvements to Posit’s next-generation data science IDE, including expanded Data Connections (preview), more polished Quarto inline output, centralized AI provider configuration, and performance and reliability upgrades.

• Read more in the Positron August Release Highlights blog post.

Kimi K3 and GLM 5.2 are now in Posit AI

Posit has added two new open-weights models, Kimi K3 and GLM 5.2, to the Posit AI platform. Both models stream tokens nearly twice as fast as Anthropic’s models in internal testing and offer lower cost-per-token rates (partly due to more efficient tokenizers and no extra charge for cache writes).

• Read more in the Kimi K3 and GLM 5.2 are now in Posit AI blog post.
• Learn how to pick your models in the AI Newsletter: How to choose a model blog post.

cuda.ml 0.4.0

cuda.ml is an R package that brings NVIDIA GPU-accelerated machine learning directly to R workflows. This release introduces streamlined setup and developer experience and expanded tree-ensemble inference.

• Read more in the cuda.ml 0.4.0: GPU-accelerated machine learning from R blog post.

Orbital 0.6.0

Orbital converts Scikit-learn pipelines into SQL queries so that they can run in your database. Orbital 0.6.0 adds PyTorch neural network support, enabling trained torch.nn.Sequential models to compile directly to SQL for database-native inference without requiring a Python runtime, ONNX Runtime, or separate model server.

• Read more in the Neural networks in Orbital for Python 0.6.0: PyTorch straight to your database blog post.

recipes 1.4.0

recipes lets you create a pipeable sequence of feature engineering steps. Recipes 1.4.0 introduces a new way to look inside a recipe partway through, a substantial speedup for steps that are applied to many columns, and multi-column support in step_regex() and step_count().

• Read more in the recipes 1.4.0 blog post.

themis 1.1.0

themis contains extra steps for the recipes package for dealing with unbalanced data. Version 1.1.0 adds eleven new sampling steps for handling unbalanced data. It also adds setting sampling targets per class and more distance metrics.

• Read more in the themis 1.1.0 blog post.

New cheatsheets

We’ve refreshed our cheatsheets page, which now features three brand-new additions!

• Python Polars: Developed in partnership with Polars, Inc., this cheatsheet introduces Polars core concepts and key expressions.
• tidymodels: Updated cheatsheets detailing practical workflows across the entire tidymodels ecosystem.
• yardstick: Guidance on using yardstick within tidymodels to evaluate model predictive performance.

Check them below:

Python Polars: The Definitive Cheatsheet

Quick reference guide for transforming, analyzing, and visualizing data with Python Polars

Jeroen Janssens, Thijs Nieuwdorp

Great Tables plotnine

Machine learning with tidymodels

A map of the tidymodels packages, grouped by where each one fits in the machine learning workflow

Edgar Ruiz, Mine Çetinkaya-Rundel

tidymodels

Measure model performance with yardstick

Quick reference guide for measuring how well a model predicts with yardstick

Edgar Ruiz

yardstick

posit::conf(2026) talk sneak peeks

With over 100 talks, it’s impossible to feature all of them in this newsletter (but you can explore the complete lineup on the event schedule!). I wanted to give a quick preview of topics you can look forward to (whether you tune in live during the conference or catch up on demand).

1. Positron in prime time

It’s been about two years since the public release of Positron, and 🎵this IDE is on fire 🎵

The lineup of conf talks proves it. From Quarto notebooks in Positron (did you know inline output is available?!), to using Positron in education, to how it treats SQL as a first-class language, to delightful workflows in Python and R, come see what people are cooking up with Positron.

2. What can’t you do with Quarto?

The best way of discovering what Quarto can do is seeing how others are using it!

We’re starting off strong, with the first session of Virtual Day (September 14th) being “Quarto, R + Python”, where Björn Fisseler will discuss creating accessible reports and Wasim Lorgat sharing the history of notebooks. The Quarto theme continues on the 15th and 16th with talks on designing influential and scalable reports, creating learning ecosystems, teaching bilingual data science, developing wiki chatbots, and developing unreasonably effective dashboards and slides — all in Quarto.

3. I’ll definitely be joining…

   • Positron and the Three Bears: Teaching Python with Positron (presented by Marc Dotson) - Practical lessons from an educator navigating Python instruction and finding the sweet spot with Positron
   • First Impressions Matter: Styling Data Products That People Actually Want to Use (presented by Shelby Level) - Shelby creates visually striking data products, and I can’t wait to pick up some design inspiration and techniques from her session
   • A Grammar of Graphics for SQL (presented by Thomas Lin Pedersen) - We’ll hear the developer of ggsql share the story of bringing ggplot2-style visualization concepts to SQL
   • Outgrowing Spreadsheets: Rebuilding Research Data Collection with Shiny and Posit Connect (presented by Kelsey Chalmers) - Kelsey shares the before-and-after architecture and how their research team transformed a fragile, spreadsheet-based data collection process into a sustainable system using Shiny and Posit Connect

Of course, this is just the tip of the conf iceberg. One of my favorite things about conf is the diversity of talks, from real-world data science implementation, to insights from the field, to creative and whimsical experiments. I hope to see you there!

If you can’t wait til conf (or if you’re reading after conf is over!), there are other events that you can join us at:

• Data Science Lab, every Tuesday at 12pm ET
• Data Science Hangout, every Thursday at 12pm ET
• Building Repeatable AI Workflows with Custom MCP Servers, September 30
• Our next conference appearance

Have a lovely September, and reach out anytime! isabella [dot] velasquez [at] posit [dot] co

Welcome to our newsletter, posit::glimpse()!

If you’re currently reading this on our blog, consider subscribing to Product Updates - Open Source on our subscription page to receive this newsletter directly in your inbox.

Every month, we round up some of the most important open-source news for Posit’s community! We hope that you enjoy this newsletter and maybe even share it with a friend.

The table of contents on the right can help you navigate through all the updates. As you scroll, it will open up to show you subcategories →

Announcements

Register for posit::conf(2026)

Register for our annual conference, posit::conf(2026), happening September 14-16 in Houston and online! The amazing speakers and workshops will make for a wonderful time. Register for posit::conf here.

For the first time, the live streams will feature automatically translated subtitles, available in French, German, Italian, Portuguese, and Spanish.

Tidy Dev Day is on September 17, a unique opportunity to collaboratively tackle open-source issues and work directly alongside the very developers who build and maintain the tools you use every day. Learn more about Tidy Dev Day here.

Migrating to Connect Cloud

A thank-you to everyone who published on rpubs.com, quartopub.com, shinyapps.io, and bookdown.org. We’re consolidating all our publishing tools into one: Posit Connect Cloud. Connect Cloud is built to handle all of the content types in one place, with one account, and we hope to make your transition as smooth as possible.

• Learn more in the Migrating to Connect Cloud: Posit’s unified publishing solution blog post.
• Join our webinar on the migration process happening September 9th.

Key product updates and new releases

httr2 1.3.0

httr2 is a comprehensive HTTP client for R for working with web APIs. httr2 1.3.0 introduces a breaking change to OAuth token caching that requires one-time re-authentication. Recent patches added 200x faster streaming for short-line responses, OAuth server metadata discovery, OpenTelemetry tracing support, and httr2_translate() for converting requests to curl commands.

• Read more in the httr2 1.3.0 blog post.

ir 0.1.0

Introducing ir 0.1.0, a command-line tool for running self-describing R scripts and Quarto documents with embedded package dependencies and R version requirements, inspired by Python’s PEP 723 and uv run --script. The tool supports isolated environments, CRAN snapshot dates, mixed R/Python workflows, and caches resolved dependencies for efficiency, enabling one-file workflows without full project structures.

• Read more in the ir 0.1.0 blog post.

lorax

lorax introduces a unified interface for characterizing tree- and rule-based models across 12 implementations including ranger, XGBoost, LightGBM, and random forests. The package enables extracting decision rules, identifying active predictors, converting trees to partykit format for visualization, and accessing variable importance scores through consistent APIs that work across diverse tree model packages.

• Read more in the Introducing lorax: Speaking for the Tree-Based Models blog post.

mcptools 1.0.0

mcptools, an R SDK for the Model Context Protocol, is now on CRAN. It brings image support for rich content in both directions, native authenticated remote server connections eliminating the npm dependency, and streamlined Posit Connect deployment via _server.yml. This R SDK for the Model Context Protocol enables deploying R functions as MCP servers and fetching third-party MCP tools as R functions.

• Read more in the mcptools 1.0.0 blog post.

Quarto 1.10

Quarto transforms Markdown, code, and computational output into publication-ready articles, reports, presentations, websites, and books. Version 1.10 introduces offline HTML accessibility checking with bundled axe-core and WCAG conformance level targeting, plus localized string support for template authors. The release includes important fixes for quarto preview reliability, shortcode resolution in math expressions, and updated dependencies including pandoc 3.10 and typst 0.15.1.

• Read more in the Quarto 1.10 blog post.

raghilda v0.2

Raghilda is a Python package for building RAG (Retrieval-Augmented Generation) solutions. This release focuses on production-ready features for scaling RAG pipelines, with crawl and ingest API with caching and concurrency, a CloudflareCrawler for JavaScript-rendered sites, a PostgreSQL store backend, and NVIDIA NIM embeddings.

• Check out the raghilda v0.2 blog post.

roxygen2 8.1.0

roxygen2 8.1.0 improves performance with consolidated importFrom() directives that reduce package loading time from ~120ms to ~9ms for 1,000 imports, and introduces the rdtools package for faster cross-reference resolution during documentation generation. The release also adds multi-line support for @importFrom directives with hanging indents.

• Check out the roxygen2 8.1.0 blog post.

Shiny updates

Shiny is the framework for building interactive web applications in R and Python. Shiny for R 1.14 adds startApp() for non-blocking app launches and session$destroy() for proper module cleanup. Shiny for Python 1.7 includes bundled Agent Skills for coding agents and test mode with live JSON snapshots. bslib 0.12 introduces offcanvas panels that slide in from viewport edges, plus direct sidebar handle resizing.

• Read more in the Shiny updates blog post.

Positron learning resources

The Positron team is on a roll. Here are the key features from the July Release Highlights:

• The Positron Notebook Editor now serves as the default option for .ipynb files: You get a fully integrated experience for Jupyter Notebooks out of the box. Key tools like environment management, version control, and AI coding support work automatically the moment you open any .ipynb file.
• The Packages pane has officially transitioned out of preview status: Inspect, update, and explore installed libraries without leaving your IDE or relying on Terminal commands.
• Posit Assistant has reached general availability: AI assistance is enterprise-ready: enjoy full-fledged coding assistance, debugging help, and inline explanations directly in your workflow with improved stability and performance.
• Support for opening Excel workbooks has been added to Data Explorer: No need to export to CSV or switch to Microsoft Excel to check your data: preview, filter, inspect .xlsx sheets directly inside Positron before running any code.
• Enhanced language intelligence for R: Navigating large R projects is much faster and less error-prone with features like Go to Definition, Find References, and Rename Symbol that work across all packages and scripts in your workspace. Diagnostics update instantly even when files change externally.

The team has also shared several blog posts packed with tips and tricks for getting the most out of Positron:

• Discover how Positron paired with uv configures your Python environment in just one click
• Explore strategies for seamlessly managing your R and Python environments within Positron

Let’s learn about AI

You can now receive the AI newsletter via email! Paste the newsletter’s RSS feed URL into a free RSS-to-email service like https://blogtrottr.com/ to receive the newsletter by email.

• AGENTS.md, Skills, MCP servers, oh my! If you’ve heard all these terms and want to investigate the differences, check out the July 3rd edition of the AI Newsletter.
• LLMs often miss subtle visual artifacts in data visualizations, and there are ways of evaluating their oversights. Learn about bluffbench in the July 17th edition of the AI Newsletter.
• Posit Assistant in Positron now includes an EDA log feature to help you keep track of exploratory analysis done with the agent. Learn more in the July 31st edition of the AI newsletter.

Do you want event recordings? You got them!

“Will this be recorded?” If it is, you can access our recordings directly on the event pages, and we do our best to post them as quickly as possible.

Be sure to catch the presentations featured below, including Teun van den Brand’s session on ggsql and Rich Iannone’s presentation on Great Docs:

Jun 30, 2026 | 12 min | 2.7k views

DuckCon #7 – ggsql: A grammar of graphics for SQL (Teun van den Brand)

Event: DuckCon #7 (DuckDB and DuckLake community conference), https://duckdb.org/events/2026/06/24/duckcon7/ Speaker: Teun van den Brand (Posit PBC) Slides: https://blobs.duckdb.org/events/duckcon7/teun-van-den-brand-ggsql.pdf

Teun Van den Brand

ggsql

Jul 6, 2026 | 1h 5min | 174 views

Great Docs: The Future of Documentation | The Python Exchange June 2026

About the Python Exchange for the Department of Energy: https://meetup.doepy.org/ Sign up here to be notified when the next Python Exchange is scheduled: https://bit.ly/py-exchange Guest Panelist: Rich Iannone Title: Great Docs: Building the Documentation Site Generator I Always Wanted Bio: Rich Iannone Rich is a software engineer and someone who cares a great deal about documentation and summary tables. He’s been working on table presentation and documentation packages for quite a long time, and nobody has told him to stop yet, so he’s still on top of all that. He started off mostly creating open-source packages in R, but more lately, he’s been doing open-source things in Python. Rich really enjoys creating all these packages because he realizes that people can use ’em to do great things in their own work. Abstract: Documentation generators have, for me, been a thing where there are so many trade-offs. They might make beautiful sites, but be shallow feature-wise. Or full of features but really hard to configure. After some years of working across R, Python, and the broader open-source ecosystem, I started Great Docs with a simple end goal: your docs should look really great, be easy to discover, and be ready for the AI era. In this presentation, I will walk through the various places I drew inspiration for Great Docs (pkgdown, MATLAB, Elixir, and the Vue.js docs). I’ll show how Great Docs makes documentation consumable by both humans and AI agents through Skills and MCP server documentation. I’ll share some of the features I obsessively built because I wanted them in my own package docs. Finally, I’ll discuss where this project is headed beyond Python (toward documenting CLIs, TUIs, and more). At Don’t Use This Code, we want to create a unique opportunity to see Python succeed and thrive within the DOE. In collaboration with an organizing committee of Python users and advocates from various DOE sites, we host regular conversations on the topic of Python in order to create a new resource for scientists, researchers, and technical staff. Our goal is to share best practices and build a strong, lasting community for Python users within the Department of Energy National Lab system. If you, or someone you know, might make a great panelist, we’d love to get in touch! Just send us an email at DOEPy@dutc.io if you would like to participate or have any questions

Rich Iannone

Great Docs pkgdown

Additionally, we have some fantastic recordings from the Data Science Lab. Watch the Lab Managers share their expertise while live coding:

Jul 24, 2026 | 51 min | 1.1k views

Live TidyTuesday Data Viz Workflow | Nicola Rennie | Data Science Lab

The Data Science Lab is a live weekly call. Register at pos.it/dslab! Discord invites go out each week on lives calls. We’d love to have you! The Lab is an open, messy space for learning and asking questions. Think of it like pair coding with a friend or two. Learn something new, and share what you know to help others grow. On this call, Libby Heeren is joined by Nicola Rennie, who walks through her process for making a TidyTuesday data visualization in R: live coding a chart from scratch and sharing the workflow tricks she’s built up over five years of doing TidyTuesday! Nicola takes this week’s TidyTuesday “twin cities” dataset and builds a colorful sunburst (radial) chart from scratch, showing how she decides what to plot, wrangles the data with dplyr, and makes it pretty using showtext fonts, rcartocolor palettes, colored-text legends, and the ggview package for previewing plots at exactly the size they’ll save at. Along the way she shares the templates and helper packages she’s made to save time every week (can you tell she’s a pro?!). Since TidyTuesday data is public, you can grab the same dataset and follow along, too! Hosting crew from Posit: Libby Heeren, Isabella Velasquez Socials and websites: Nicola Rennie’s blog: https://nrennie.rbind.io/ Nicola Rennie’s GitHub: https://github.com/nrennie Nicola Rennie’s Bluesky: https://bsky.app/profile/nrennie.bsky.social Nicola Rennie’s LinkedIn: https://www.linkedin.com/in/nicola-rennie/ Nicola Rennie’s Mastodon: https://fosstodon.org/deck/@nrennie Resources mentioned in the video and chat: Nicola’s code from this session: https://nrennie.rbind.io/talks/posit-ds-lab/ TidyTuesday on GitHub: https://github.com/rfordatascience/tidytuesday Nicola’s templates package: https://github.com/nrennie/templates showtext package (fonts in R): https://github.com/yixuan/showtext ggview package (preview plots at save size): https://github.com/idmn/ggview rcartocolor package (accessible color palettes): https://nowosad.r-universe.dev/rcartocolor ggtext package (styled/colored text): https://wilkelab.org/ggtext/ ggiraph package (interactive ggplots): https://davidgohel.github.io/ggiraph/ marquee package (markdown text in R graphics): https://marquee.r-lib.org/ Nicola’s book, The Art of Data Visualization with ggplot2: https://nrennie.rbind.io/art-of-viz/ Bootstrap Icons: https://icons.getbootstrap.com/ okpalette (extract a palette from a photo): https://okpalette.color.pizza/ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps 00:00 Introduction 01:18 Reviewing the TidyTuesday twin cities dataset 03:41 Demoing TidyTuesday template files and R package setup 07:25 “Do showtext and systemfonts do different things?” 09:04 Loading and prepping the links and cities datasets 11:45 Exploring and filtering the dataset to a chosen city 17:25 Demoing a Shiny app to copy previous TidyTuesday chart code 23:56 Demoing sunburst chart creation with coord_radial 25:47 “Can you say why you used mutate rather than summarize?” 31:09 Adding custom colors with the rcartocolor package 33:57 Demoing colored subtitle text with the ggtext package 40:38 Attempting to make the chart interactive with the ggiraph package 43:32 Adding automated social and source captions 46:26 “So you can save a plot with the canvas call and it doesn’t mess up the ggplot object?” 49:55 “If people would like to keep up with you and see your posts, what’s the best way to do that?”

dplyr ggplot2 Shiny systemfonts

Jul 23, 2026 | 56 min | 3.8k views

Mind-Blowing Quarto Slide Extensions 💬 🖍️ 🤯 | Emil Hvitfeldt | Data Science Lab

The Data Science Lab is a live weekly call. Register at pos.it/dslab! Discord invites go out each week on lives calls. We’d love to have you! The Lab is an open, messy space for learning and asking questions. Think of it like pair coding with a friend or two. Learn something new, and share what you know to help others grow. On this call, Libby Heeren is joined by Emil Hvitfeldt, who walks through the art of “slide crafting” in Quarto and shows off the collection of Reveal JS extensions he’s been building to make slides way more fun and powerful. Emil starts with the basics of building slides in Quarto with the Reveal JS format (fragments, columns, a little CSS) and explains how Quarto extensions differ from packages. Then he shows off the good stuff: iMessage-style chat bubbles hand-drawn “rough notation” highlighting and annotations ️ the Flourish extension for animating code chunks, gorgeous reusable themes, and his in-progress “editable” extension that lets you drag, resize, and restyle slide elements right in the browser and save the changes back to your source. And, spoiler, his “editable” extension is much further along as of the publishing of this video!! If you’ve ever fought with slide layouts, there is definitely something here you can try at home! Hosting crew from Posit: Libby Heeren, Isabella Velasquez Emil’s socials and urls: website: https://emilhvitfeldt.com/ GitHub: https://github.com/emilhvitfeldt Bluesky: https://bsky.app/profile/emilhvitfeldt.bsky.social LinkedIn: https://www.linkedin.com/in/emilhvitfeldt/ Resources mentioned in the video and chat: Emil’s GitHub (home of his Quarto extensions): https://github.com/EmilHvitfeldt quarto-revealjs-editable extension: https://github.com/EmilHvitfeldt/quarto-revealjs-editable quarto-revealjs-rows extension (shared by Emil after the session): https://github.com/EmilHvitfeldt/quarto-revealjs-rows Quarto Reveal JS documentation: https://quarto.org/docs/presentations/revealjs/ Quarto Reveal JS demo slides: https://quarto.org/docs/presentations/revealjs/demo/#/title-slide News! Quarto 2 (Quarto in Rust): https://quarto.org/docs/blog/posts/2026-04-06-whats-next-quarto-2/ Dynamic Tabsets in Quarto (Josh Cowley): https://josh.quarto.pub/posts/2022-11-10-quarto-tabsets-update/2022-11-10-quarto-tabsets-update.html Embedding Reveal JS via iframe (Quarto source example): https://github.com/quarto-dev/quarto-web/blob/main/docs/presentations/revealjs/index.qmd#L12-L14 posit::conf 2026: https://conf.posit.co/2026/ R/Medicine conference: https://rconsortium.github.io/RMedicine_website/ Muntasir Masum’s slides repo: https://github.com/muntasirmasum/epi553-data-viz Example Quarto slides — Nicola Rennie (Styling Charts): https://nrennie.rbind.io/MFC-CDT-data-viz/slides/styling-charts-r.html Example Quarto slides — Colin Madland: https://cmadland.github.io/slides/ Colin Madland’s slides repo: https://github.com/cmadland/slides Example Quarto slides — Raha Dastgheyb (R/Medicine 2024): https://rdastgh1.quarto.pub/rmedicine-2024-scidatareportr/#/title-slide ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps 00:00 Introduction 01:22 Demoing a primer on Reveal JS slides in Quarto 05:11 “Does that PDF export still use Pandoc, or is there an option of exporting via Typst?” 08:17 Demoing slide crafting with a new Quarto document 11:24 Demoing how to download and use a Quarto template 14:01 “Is this UI you’re using for slide generation exclusive to Positron?” 16:16 Demoing how to install a Quarto extension from the terminal 18:45 “Is there a maximum number of columns you can have?” 19:18 Demoing adding custom CSS classes to slide columns 23:03 “Do we know if extensions will still be submitted to the OpenVSX marketplace?” 25:11 “Are these Quarto extensions global, or are they per project?” 27:42 “When you change the text fonts inside those columns, could you do it for all the columns at once, maybe by changing the top div?” 29:31 “Do you also get grid logic like you have with CSS Grid?” 31:54 Demoing Quarto VS Code extensions for autocomplete and shortcodes 34:32 Demoing the chat bubbles extension 37:38 Demoing the rough notation extension for highlighting text fragments 40:20 Demoing the Flourish extension for highlighting code chunks 43:07 Demoing the editable extension for dragging and adjusting slide elements 49:28 Demoing custom Reveal JS slide themes 52:26 “Is there a Quarto extension that can help dynamically create tabs or chunks?” (see dynamic tabs link in the resources above!) 53:58 Explaining the default code chunk highlighting feature in Quarto 55:11 “How do you embed a Reveal JS site into another Quarto document?”

Emil Hvitfeldt

Positron Quarto revealjs

We hope you learn a lot from these resources!

Showcases from the community

We love learning what you are up to. If you have a project using our tools to share, please let us know. In particular, I’d love to hear how you’re adopting and using Positron! Find me on LinkedIn and Bluesky.

Alfredo H. S. shared a small, opinionated skill for turning papers and other source material into concise Quarto RevealJS presentations with AI coding agents.

Try it out, and let Alfredo know how it goes!

• Forum discussion
• About the skill
• SKILL.md

REYL Intesa Sanpaolo’s Risk Management team built their own Incident Management Tool with Shiny and Posit Connect, cutting incident follow-up from days to 30 minutes. Now, they’re eyeing the same R-and-Connect pattern for liquidity monitoring, credit risk appetite, and stress testing.

• Check out the spotlight

Interested in testing your regression skills? Angela Heberger is looking for feedback on RelationLab, an interactive Shiny app for learning correlation and regression. Give it a shot and let Angela know what you think!

• Forum discussion
• Shiny app

We usually find these projects on social media. If you’re on LinkedIn, be sure to follow and tag Posit Open Source for us to share the amazing things you’re working on!

What’s next

Besides posit::conf (have you registered for conf yet? You should!), we have lots of goodies on the horizon.

• Emil Hvidtfelt is leading a 2-hour workshop all about making great slides in Quarto on Aug 12th. Register here!
• Join us for an upcoming Data Science Hangout or Data Science Lab.

I would love to know how to make the Glimpse newsletter better. Email me at isabella [dot] velasquez [at] posit.co.

Welcome to our newsletter, posit::glimpse()!

If you’re currently reading this on our blog, consider subscribing to Product Updates - Open Source on our subscription page to receive this newsletter directly in your inbox.

Welcome to the latest edition of the posit::glimpse() newsletter, the monthly roundup of open-source news for the Posit community. I have many updates for you from across Posit. As my wonderful colleague Kristin Bott stated, “dang this is a productive bunch of humans”.

The table of contents on the right can help you navigate through all the updates. As you scroll, it will open up to show you subcategories →

Announcements

posit::conf(2026) is happening soon!

Our annual conference, posit::conf(2026), is happening September 14-16, and we would love to see you there, whether in Houston or online! Check out the speaker lineup, workshop offerings, and register for posit::conf here.

Tidy Dev Day is happening on September 17, a unique opportunity to collaboratively tackle open-source issues and work directly alongside the very developers who build and maintain the tools you use every day. As Meghan Harris stated about last year’s event, “Tidy Dev Day (TDD) gave me the PERFECT opportunity to explore this further in a low-stress, supportive environment.” Learn more about Tidy Dev Day here.

We’ve joined the Jupyter Foundation

We are proud to announce that we are deepening our commitment to the Jupyter ecosystem by becoming an official Jupyter Foundation Member!

• Learn more in the We’ve Joined the Jupyter Foundation announcement blog post.

Announcing an expanded Posit Academy

We’ve launched a new part of Posit Academy: a free, open library of product courses, hands-on labs, and live workshops available to anyone.

• Learn more in the Announcing an expanded Posit Academy blog post.

Introducing the Posit Impact Awards

Have a story to share? We just launched the Posit Impact Awards to recognize individuals and teams who used Posit to create measurable, meaningful change. Six winners will be selected, one per category, and each will receive a conference-only pass to posit::conf(2026).

• Submit your nomination before July 20th.

Key product updates and new releases

Data visualization and reporting

ggsql 0.4.1

ggsql 0.4.1 introduces spatial plotting capabilities with database-backed geometry processing, supporting WKB format data, 21 map projections for cartographic accuracy, and a built-in Natural Earth world dataset for creating choropleth maps and geographic visualizations with backends like DuckDB spatial, PostGIS, and SpatiaLite.

• Learn more in the ggsql 0.4.1: Spatial plotting and in-layer aggregation blog post.

Ask more of your dashboard with querychat and ggsql

querychat now supports ggsql-powered visualizations, enabling natural language data exploration in dashboards through SQL-only execution (no arbitrary code), with three pre-built tools for visualizing, querying, and filtering data reactively. The package works in both Python and R, integrates with Shiny dashboards, and supports Snowflake Semantic Models for business logic definitions.

• Learn more in the Ask more of your dashboard with querychat and ggsql blog post.

Great Tables 0.22.0

Great Tables v0.22.0 significantly expands Python table presentation capabilities with footnote support, group-wise and grand summary row calculations, column merging utilities for uncertainty and ranges, text transformation methods, value substitution helpers, duration and parts-per formatters, image export functionality via gtsave(), enhanced LaTeX rendering, and makes Pandas an optional dependency for Polars-only workflows. (impressive update!)

• Learn more in the Great Tables v0.22.0 blog post.

Data access

dbplyr 2.6.0

dbplyr 2.6.0 introduces ADBC support via adbi for faster Arrow-based data transfer, JDBC support, new SQL dialect separation, and query composition functions.

• Learn more in the dbplyr 2.6.0 blog post.

webR 0.6.0

webR 0.6.0 upgrades to R 4.6.0 and adds async/await support for JavaScript Promises, curl and httr2 compatibility through WebSocket traffic proxying, modern Fortran fixes for expanded package support, and updated system libraries including OpenSSL 3.5.1 and Emscripten 5.0.7. The release powers interactive R experiences in Quarto Live and Shinylive.

• Learn more in the webR 0.6.0 blog post.

Developer tools and AI

debrief 0.1.0

The debrief package converts profvis profiling output into text-based summaries designed for AI agents, enabling AI-assisted performance optimization by providing structured reports on hotspots, call trees, and memory allocations that AI systems can read and act upon.

• Learn more in the debrief 0.1.0 blog post.

pkgsite 0.1.0

pkgsite 0.1.0 converts R package .Rd documentation files into Quarto .qmd files, enabling custom documentation sites with Quarto’s freeze feature for local example rendering, unified R/Python documentation when combined with Quartodoc, and flexible template customization. The package is available on CRAN and provides an alternative to pkgdown.

• Learn more in the pkgsite 0.1.0 blog post.

watcher 0.2.0

Watcher is a lightweight R package that watches files and directories for changes and reacts in the background. It’s quietly been the engine behind Shiny’s auto-reload for the past year. With the CRAN release of 0.2.0, we’re excited to introduce it as a general-purpose filesystem watcher for R developers.

• Learn more in the watcher 0.2.0 blog post.

Development environment

Air 0.10.0

Air 0.10.0 introduces configurable assignment style enforcement allowing teams to standardize on arrow (<-), equal (=), or preserve existing styles, along with enhanced IDE integrations for Positron and RStudio, multiple installation methods via PyPI and conda-forge, pre-commit hook support, stdin integration for editors, and shell completions.

• Learn more in the Air 0.10.0 blog post.

What’s new in Positron

Positron’s June release includes a lot of highly requested features:

• Inline output for Quarto (one of Positron’s most-requested features ever!)
• Posit Assistant, the successor to Positron Assistant
• Packages pane improvements
• A more customizable interface

For more, check out the Positron June Release Highlights post and subscribe to Positron emails.

Did you know that many of the most upvoted RStudio feature requests are already implemented in Positron? Learn about ten of them in the RStudio’s Top Feature Requests … In Positron blog post!

Machine learning and modeling

brulee 1.0.0

brulee 1.0.0 significantly expands tabular deep learning capabilities in R with five new model architectures, GPU support including Apple Silicon, 32-bit precision for improved performance, and enhanced numerical stability, all integrated with the tidymodels ecosystem.

• Learn more in the brulee 1.0.0 blog post.

CatBoost support in tidymodels

CatBoost gradient boosting support is now available in tidymodels through the boost_tree() interface, providing access to CatBoost’s strong categorical feature handling with full tidymodels integration including hyperparameter tuning, cross-validation, efficient submodel optimization, and orbital package support for SQL generation and in-database predictions.

• Learn more in the CatBoost support in tidymodels blog post.

tidyclust 0.3.0

tidyclust 0.3.0 introduces three new clustering model families and achieves full integration with tidymodels by replacing tidyclust-specific functions with native tune package support.

• Learn more in the tidyclust 0.3.0 blog post.

Event roundup

We were all over the world this month, discussing how to adopt new data tools, get better at old ones, and just loving being part of the community. If you want to learn more about where we were, or see where we’ll be next, check out our event page.

Watch the recordings from some of these events:

Jun 15, 2026 | 34 min | 348 views

Neal Richardson - MCP, or not MCP | Pydata London 26

Neal Richardson - MCP, or not MCP | Pydata London 26 \Model Context Protocol is a standard for defining tools that can be made available to LLMs and AI applications. There’s a lot of noise out there about what you should use to get the best results from AI, so in this talk, I will provide some guidance on when you should use MCP, and when you should reach for some other tool. I will describe cases where MCP is the right tool for the job, and when other things, like skills or other context files, are better. I will also devote attention to questions of security and authentication, which are important for MCP, and provide concrete examples of how MCP servers can be used to unlock agentic workflows while also strengthening data governance. This talk is intended for those who are interested in using LLMs for workflows involving data. No prior experience with MCP is required. Outline: Intro: how can I get data from this API into my Claude Code session? What is MCP? When should you use it, when should you use other tools Work through an example Sharing and deploying MCP servers, alternatives and best practices Optimizing your tools for best results www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps

Neal Richardson

Python Tutorial Education NumFOCUS PyData Opensource Learn Software Python 3 Julia Coding Learn to Code How to Program Scientific Programming

Jun 26, 2026 | 47 min | 294 views

Agents for Correct, Transparent, and Reproducible Data Analysis - Simon Couch & Sara Altman

Agents for Correct, Transparent, and Reproducible Data Analysis - Simon Couch & Sara Altman (Posit) Abstract: How do we build competent data analysis agents? Data analysis requires a willingness to pause, question conclusions, and dig into subtleties. Frontier LLMs, however, are optimized to push tasks toward completion, not to slow down when something seems off. This tendency works well for coding agents, where success is often verifiable. But for data analysis, verification is more complicated, and autonomous work by the agent can be at odds with the spirit of the discipline. Drawing on our experience building data analysis agents, we’ll share evaluations that expose where LLM-driven analysis goes wrong and design patterns that keep analyses correct, transparent, and reproducible. Resources mentioned in the session: Presentation Slides: https://simonpcouch.github.io/gen-ai-pharma-26 Presentation GitHub repository: https://github.com/simonpcouch/gen-ai-pharma-26 bluffbench: https://github.com/simonpcouch/bluffbench Posit Assistant Terminal (TUI): https://posit-dev.github.io/assistant/docs/downloads/tui/ Posit AI Newsletter: https://opensource.posit.co/tags/ai-newsletter/ Speakers: Simon Couch builds tools that make the work of data science more joyful and effective. As an engineer on the AI Core Team at Posit, his work spans coding agents, model evaluations, inference engineering, and next-edit-suggestion systems. Drawing on his background in statistics, Simon spent several years authoring and maintaining core packages in the open-source tidymodels framework—like stacks, broom, and infer — before shifting his focus to LLMs. He blogs about his work at simonpcouch.com. Simon authors the Posit AI Newsletter along with Sara Altman. Sara Altman is a Senior Developer Advocate on the AI Core team at Posit, where she focuses on how AI can be effectively and responsibly used for data science. Previously, she helped build Posit Academy and taught data science and R at Stanford. Sara authors the Posit AI Newsletter along with Simon Couch. Presented at the 2026 R/Pharma GenAI Day

Sara Altman, Simon Couch

infer tidymodels

Showcases from the community

Brilliant Earth turned their Marketing Mix Model into a Streamlit app deployed on Posit Connect via the Snowflake Native App, so their marketing team can dig into channel performance and run scenario planning on their own. One of the campaigns in the mix: their recent Ring Pop collaboration.

• Check out the spotlight.

Leo Ohyama recently shared a fantastic Lorcana Market Data Analysis & Forecasting dashboard that showcases the power of Python, R, Positron, and Quarto working together.

Leo included detailed documentation in the GitHub repository, a great resource for anyone interested in market forecasting or multi-tool workflows. Thanks for sharing your work with the community, Leo!

• Lorcana dashboard
• Lorcana GitHub repo

We were recently joined by Thomas Lin Pedersen on the Data Science Lab, where he introduced the new ggsql package.

(Almost) immediately after the DS Lab, Dylan Poulsen wrote a blog post on exploring two years of swim data with ggsql! Dylan, we’re convinced you write blogs at the speed of ggsql.

• Exploring the New ggsql Package with Two Years of Swim Data blog post

We usually find these projects on social media. If you’re on LinkedIn, be sure to follow and tag Posit Open Source for us to share the amazing things you’re working on!

What’s next

We’re taking a short break in July before returning with more community hangouts!

• On July 21, Gina Reynolds will join the Data Science Lab (a fun, chill time with live code) to show we can extend ggplot2 by creating our own custom extensions. Register here: https://pos.it/dslab

In the meantime, check out some past Data Science Lab episodes:

May 29, 2026 | 57 min | 1.4k views

Async & Parallel R with {mirai} | Charlie Gao | Data Science Lab

The Data Science Lab is a live weekly call. Register at pos.it/dslab! Discord invites go out each week on lives calls. We’d love to have you! The Lab is an open, messy space for learning and asking questions. Think of it like pair coding with a friend or two. Learn something new, and share what you know to help others grow. On this call, Libby Heeren is joined by Charlie Gao, who walks through async and parallel programming using the mirai package for R. Charlie is the author of {mirai} and {mori}! Charlie demonstrates how mirai lets you run R code in parallel across multiple cores or even distributed across remote machines. He covers the key differences between parallel and async programming, shows how to set up worker daemons (said like “demons”), scale resources dynamically, and connect to remote machines via SSH. Whether you’re running long computations, training models, or building Shiny apps, mirai helps you make the most of your computing resources without blocking your main R session:) Hosting crew from Posit: Libby Heeren, Isabella Velasquez Charlie Gao’s GitHub: https://github.com/shikokuchuo Charlie Gao’s Bluesky: https://bsky.app/profile/shikokuchuo.net Charlie Gao’s LinkedIn: https://www.linkedin.com/in/charliegao/ Charlie Gao’s Mastodon: https://fosstodon.org/@shikokuchuo Resources mentioned in the video and chat: mirai package website: https://mirai.r-lib.org/ mirai GitHub repository: https://github.com/r-lib/mirai AskDeepSeek chatbot for mirai documentation: https://mirai.r-lib.org/ (click “ask deep wiki” button) mirai - Promises (Shiny and Plumber): https://mirai.r-lib.org/articles/v02-promises.html mirai - Serialization: https://mirai.r-lib.org/articles/v03-serialization.html mirai - OpenTelemetry: https://mirai.r-lib.org/articles/v05-opentelemetry.html mirai stop_mirai function: https://mirai.r-lib.org/reference/stop_mirai.html mirai skill for Claude Code: https://github.com/r-lib/mirai/blob/main/.claude/skills/mirai/SKILL.md Plumber2 package: https://plumber2.posit.co/ Daemon (computing) on Wikipedia: https://en.wikipedia.org/wiki/Daemon_(computing) ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps of Questions / Topics: 00:00 Introduction 03:33 “What is async programming and how is it different from parallel?” 06:57 AskDeepSeek chatbot for the Mirai package 09:40 Setting up the Positron IDE with activity bar on top 15:25 “What was the motivation or need for developing Mirai?” 22:10 Mirai map function for parallel processing 25:25 “Do the contents of Mirai inherit definitions from the global environment?” 29:00 “What’s the difference between Mirai versus promises and future?” 31:50 Demonstrating sequential vs parallel processing 36:00 “Can you use Mirai with HPC?” 37:07 Dynamically scaling workers by adding and removing daemons 38:38 Setting up daemons with URLs for network connections 42:05 Launching workers over SSH to remote machines 43:09 SSH tunneling to connect workers without open ports 51:14 “Does Mirai allow R to perform the same thing as NumPy?” 51:54 “Does Mirai ship with some kind of task viewing dashboard?” 52:35 “Can we use parallel::detectCores() to see how many daemons we can use?” 53:58 “Would parallel processing be more useful than async processing in typical data science work?” 55:00 Mirai skill for Claude Code and AI agents

Charlie Gao

mirai mori plumber plumber2 Positron Shiny

Jun 23, 2026 | 58 min | 5.4k views

Data dictionaries, parquet, & Claude | Hadley Wickham | Data Science Lab

The Data Science Lab is a live weekly call. Register at pos.it/dslab! Discord invites go out each week on lives calls. We’d love to have you! The Lab is an open, messy space for learning and asking questions. Think of it like pair coding with a friend or two. Learn something new, and share what you know to help others grow. On this call, Libby Heeren is joined by Hadley Wickham, who walks through using data dictionaries with Claude Code to clean and document datasets effectively. Hadley demonstrates a workflow using three files: a data cleaning script, a data dictionary in YAML format, and the final cleaned data as a Parquet file. He shows how Claude Code and MCP REPL can help generate and maintain data dictionaries that document what you know about your data, making it easier for both humans and AI agents to work with your datasets. Using the NYC elevators dataset as an example, he walks through data cleaning tasks like normalizing whitespace, handling missing values, fixing date formats, and investigating geocoding issues - all while keeping the data dictionary, cleaning script, and Parquet file in sync through git. Hosting crew from Posit: Libby Heeren, Isabella Velasquez Hadley Wickham’s GitHub: https://github.com/hadley Hadley Wickham’s Bluesky: https://bsky.app/profile/hadley.nz Hadley Wickham’s LinkedIn: https://www.linkedin.com/in/hadleywickham/ Resources mentioned in the video and chat: MCP REPL: https://github.com/posit-dev/mcp-repl Data Dictionary YAML Format Specification: https://github.com/hadley/data-dict.yaml Parquet files in R for Data Science: https://r4ds.hadley.nz/arrow.html#sec-parquet Elevators Dataset Used in Demo: https://github.com/EmilHvitfeldt/elevators Pointblank Package for Data Validation: https://posit-dev.github.io/pointblank/ Arrow R Book: https://arrowrbook.com/ Monaspace Font Family (with ligatures): https://monaspace.githubnext.com/ YAML Multiline Strings Reference: https://yaml-multiline.info/ UBC Course on Shiny with RAG and Parquet: https://ubc-mds.github.io/DSCI_532_vis-2_book/060-03-rag.html Tom Scott Video on Timezones: https://www.youtube.com/watch?v=-5wpm-gesOY Falsehoods Programmers Believe About Names: https://www.kalzumeus.com/2010/06/17/falsehoods-programmers-believe-about-names/ UTC Is Enough for Everyone Right: https://zachholman.com/talk/utc-is-enough-for-everyone-right Daniel Chen’s Cherry Blossom Analysis: https://chendaniely.github.io/posts/2026/2026-03-30-yvr-cherry-blossoms-marathon/ Project Drawdown Climate Impact Explorer: https://drawdown.org/explorer Green Coding Bookdown Resource: https://bookdown.org/content/d1e53ac9-28ce-472f-bc2c-f499f18264a3/ IBM Green Coding Topics: https://www.ibm.com/think/topics/green-coding Secret Elevator in Central Park Article: https://undercovernyc.home.blog/2021/02/08/a-secret-elevator-hidden-in-central-park/ Artificial Cave Beneath Central Park: https://gizmodo.com/an-artificial-cave-200-beneath-central-park-with-micha-1446538828 ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps of Questions / Topics: 00:00 Introduction 03:38 “Can you talk a little bit about what MCP is?” 05:42 Introducing the elevators dataset 07:00 Creating initial data dictionary with Claude 09:03 “Are there any cases where the CSV format is actually a better choice than Parquet?” 09:50 “What font do you use?” 12:02 Adding context from the readme to the data dictionary 14:02 “What is a good way to store data dictionaries along with datasets?” 14:38 “Is this particular data dict YAML format useful for projects with only one table of data?” 17:45 “Is Claude also going to decide when it’s a good time to make a commit?” 24:02 “Does MCP REPL work on a Windows machine and can one use other AI LLM for example ChatGPT with it?” 26:14 Converting date columns to proper date types 27:16 “Do you see a meaningful distinction between data dicts and data contracts?” 29:36 “How do you view your approach to data dictionaries and the development of pointblank?” 34:16 Eliminating placeholder values and using proper missing values 36:25 “Could you show off a diff of a Parquet file?” 38:25 Investigating geocodes and creating a map of elevators 40:20 Using a leaflet map to explore Central Park elevators 42:03 “Which model is Hadley using?” 43:55 Discussion of cost consciousness and environmental impact of LLMs 46:30 “Is there a way to quantify environmental and electrical costs?” 48:50 The mystery elevator in Central Park 54:23 “How do you know this is actually faster or more productive than just writing the code?” 55:45 The importance of deep knowledge of data in qualitative work

Hadley Wickham

bookdown bookdown.org data-dict leaflet mcp-repl pointblank Shiny

Feb 19, 2026 | 55 min | 1.8k views

Using R package structure for data science projects | Kylie Ainslie | Data Science Lab

The Data Science Lab is a live weekly call. Register at pos.it/dslab! Discord invites go out each week on lives calls. We’d love to have you! The Lab is an open, messy space for learning and asking questions. Think of it like pair coding with a friend or two. Learn something new, and share what you know to help others grow. On this call, Libby Heeren is joined by Kylie Ainslie who walks through how structuring data science projects as R packages provides a consistent framework that integrates documentation for you and facilitates collaboration with others by organizing things really well. Kylie says, “I stumbled on using an R package structure to organize my projects a number of years ago and it has changed how I work in such a positive way that I want to share it with others! In a world where our attention is constantly being pulled in many directions, efficiency is crucial. Structuring projects as R packages is how I work more efficiently.” Hosting crew from Posit: Libby Heeren, Isabella Velasquez Kylie’s Bluesky: @kylieainslie.bsky.social Kylie’s LinkedIn: https://www.linkedin.com/in/kylieainslie/ Kylie’s Website: https://kylieainslie.github.io/ Kylie’s GitHub: https://github.com/kylieainslie Resources from the hosts and chat: posit::conf(2026) call for talks: https://posit.co/blog/posit-conf-2026-call-for-talks/ Kylie’s posit::conf(2025) talk: https://www.youtube.com/watch?v=YzIiWg4rySA {usethis} package: https://usethis.r-lib.org/ R Packages (2e) book: https://r-pkgs.org/ Paquetes de R (R Packages in Spanish): https://davidrsch.github.io/rpkgs-es/ {box} package: https://github.com/klmr/box extdata docs in Writing R Extensions: https://cran.r-project.org/doc/manuals/R-exts.html#Data-in-packages-1 Tan Ho’s talk on NFL data: https://tanho.ca/talks/rsconf2022-github/ {rv} package: https://a2-ai.github.io/rv-docs/ Whether to Import or Depend: https://r-pkgs.org/dependencies-mindset-background.html#sec-dependencies-imports-vs-depends {pkgdown} package: https://pkgdown.r-lib.org/ Edgar Ruiz’s {pkgsite} package: https://github.com/edgararuiz/pkgsite Attendees shared examples of data packages in the chat! Here they are: https://kjhealy.github.io/nycdogs/ https://kjhealy.github.io/gssr/ https://github.com/deepshamenghani/richmondway https://github.com/kyleGrealis/nascaR.data https://github.com/ivelasq/leaidr ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co The Lab: https://pos.it/dslab Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for learning with us! Timestamps: 00:00 Introduction 06:17 Reviewing the disorganized project example 10:01 Creating the package structure using create_package 17:50 Organizing external data and scripts in the inst folder 22:55 Adding a README and License 29:06 “What are the advantages to packaging a project?” 33:35 Writing Roxygen2 documentation 36:06 “Do you type return at the end of your functions?” 41:55 Handling dependencies with use_package 43:53 “Can you just use require(dplyr) at the top?” 47:45 Setting up a pkgdown site 50:11 Creating vignettes 52:22 “What is the role of the usethis package?” 54:18 Loading the package with devtools::load_all

Edgar Ruiz

devtools dplyr pkgdown roxygen2 usethis

I’m a real person, and I would love to know how to make the Glimpse newsletter better! Find me on LinkedIn and Bluesky, or email me at isabella [dot] velasquez [at] posit.co.

Welcome to our newsletter, posit::glimpse()!

If you’re currently reading this on our blog, consider subscribing to Product Updates - Open Source on our subscription page to receive this newsletter directly in your inbox.

posit::glimpse() is our roundup of the most important open-source news for Posit’s community! Check out the amazing updates we have in store.

New home for Posit Open Source

For over 15 years, Posit has been building wonderful open source software. To make it easier than ever for you to navigate our tools, documentation, and content, we’ve brought everything together into one central hub.

Head over to opensource.posit.co, take a look around, and let us know what you think! And if you’re on LinkedIn, give our new Posit Open Source LinkedIn showcase page a follow.

Key product updates and new releases

Bringing OpenTelemetry to R in production

Posit has now instrumented eight widely-used R packages (Shiny, plumber2, mirai, httr2, ellmer, knitr, testthat, and DBI) with OpenTelemetry support. This change brings production-grade observability to R applications, enabling traces, logs, and metrics with only environment variable configuration (no code changes required).

• Learn more in the Bringing OpenTelemetry to R in production blog post.

mirai 2.7.0 and mori 0.2.0

mirai 2.7.0 and mori 0.2.0 are now on CRAN, extending R’s async capabilities for production use. mirai adds bounded queues and try_mirai() for memory-safe async, while mori 0.2.0 graduates from experimental status, providing cross-platform shared memory for R processes without data duplication.

• Read more in the A memory model for production async R: mirai 2.7.0 and mori 0.2.0 blog post.

Quarto 2 design and features

Quarto 2 introduces a custom Markdown parser designed to provide actionable syntax errors, preserve source locations throughout the processing pipeline, and maintain syntax stability across the project’s lifetime. The Quarto team wrote a fascinating article on their thought process behind this implementation.

• Check out the Quarto 2: Parsing and source maps post here.

New Plotnine skill

A new plotnine skill is now available for AI coding agents, enabling them to generate consistent and runnable plotnine code. The skill follows the Agent Skills standard and works across any compatible agent including Claude Code and Codex.

• Learn more in the A Plotnine Skill for AI Coding Agents blog post.

Ten great things we added to Great Docs

Great Docs v0.10.0 marks the tenth release of the Python documentation tool. Learn about its transformation from a simple documentation generator into a comprehensive documentation platform via ten key features added since launch, including LLM integration, cross-reference validation, flexible theming, and enhanced API reference generation.

• Read the Ten great things we added to Great Docs blog post.

What’s new in Shiny

bslib 0.11.0 and py-shiny 1.6.0 introduce toolbar components: compact UI elements for fitting buttons, selects, and other controls into card headers/footers, input labels, and text input submit areas. These space-efficient components are particularly valuable for dashboards and AI chat interfaces where screen real estate is at a premium.

• Get a walkthrough of toolbars in R and Python in the Introducing Toolbars blog post!

What’s new in Positron

Positron’s May release includes a lot of highly requested features:

• Inline output for Quarto
• Packages pane
• Positron Notebook Editor now in beta

For more, check out the Positron May Release Highlights post and subscribe to Positron emails.

Community

May Event Roundup

We were at so many events this month, we can barely keep track! Here’s a quick wrap-up of where the team spent May:

• Charlotte, François, and Sara talked Quarto, larger than memory data, and AI at R/Medicine 2026
• Nick showcased Positron at R Exchange
• Wes joined industry leaders to discuss the future of artificial intelligence at AI Council
• Emil, Isabella, Jeroen, and Michael reconnected with the Python community at PyCon US

And we’re just getting started! See where we’ll be next, and come say hi.

Learning and discussion

In Defense of YAML

Love YAML? Hate YAML? Rich Iannone and Tomasz Kalinowski dive into the nuances of this finicky configuration format, comparing it to TOML and exploring how the YAML 1.2 specification enables a fast, fully compliant, and simple user experience. Their discussion highlights how the new py-yaml12 package delivers a modern, spec-compliant YAML experience for today’s developers.

• Read In Defense of YAML blog post.

Agentic Engineering and the Lost Art of Verification

“I almost don’t read code now,” says Wes McKinney (creator of Pandas). He talks about the new workflows that allow him to review a million lines of AI-generated code in Show Us Your Agent Skills, a new live session by Vanishing Gradients.

• Listen to the podcast and read the The Agentic Software Factory blog post.

Comparing Posit Assistant and Positron Assistant

Wondering what the difference is between Posit Assistant and Positron Assistant? Sara and Simon dive deep into this topic in their YouTube video:

Sara and Simon have more to share in the AI newsletter! Check out the recent posts that include Posit Assistant’s data cleaning mode and Gemma 4 as a budget-focused model.

Great Docs on Talk Python

Rich and Michael recently joined Michael Kennedy on the Talk Python podcast, where they discussed Great Docs:

PyCascades 2026 videos are up!

Rodrigo Silva Ferreira gave a great talk on “To Notebook or Not to Notebook: Multilingual Workflows for Data Analysis”:

Showcases from the community

Nithin M created a new website in Quarto! It has beautiful layouts, listings, and aesthetics.

See it here.

Nicolas Foss shared his {mirai} + {mori} stack for massive public health tables, resulting in a “massive 25% to 50% reduction in runtimes across our heaviest processing steps”.

When your database spans 35 billion vehicle records, manual data workflows stop being an inconvenience and start being a real ceiling on what your team can do. We’re proud to spotlight the CARFAX team, who used Posit Team to reclaim 12,000 hours and set a new bar for scalable data work across their organization.

Read their story here.

What’s next

Do you love shorts (not the type you wear, but the vertical video kind)? We’re publishing shorts over at YouTube, Instagram, TikTok, and our new Posit Open Source LinkedIn showcase page. I’d love to hear what short you’d like to see next!

We continue to have an excellent line up on the Data Science Hangout and Data Science Lab.

• On June 9, we’re doing a Community Showcase with Kylie Ainslie and Daniel Chen! Let’s learn about how others are using open-source tools in really cool ways.
• Jamie Shive, Director of Data Science at the National Hockey League, is the featured leader of the Hangout on June 11 (which could coincide with the Stanley Cup finals!). Let’s talk about everything about hockey and beyond.

I’m a real person, and I would love to know how to make the Glimpse newsletter better! Find me on LinkedIn and Bluesky, or email me at isabella [dot] velasquez [at] posit.co.

Welcome to our newsletter, posit::glimpse()!

If you’re currently reading this on our blog, consider subscribing to Product Updates - Open Source on our subscription page to receive this newsletter directly in your inbox.

posit::glimpse() is our roundup of the most important open-source news for Posit’s community! We’ve moved to monthly editions, and we still have so much to share.

Registration for posit::conf(2026) is now open!

Check out the keynote speakers, workshops, and agenda for our upcoming conference, happening in Houston and online. It’s sure to be incredible! Register here.

Key product updates and new releases

ggsql alpha release

The alpha release of ggsql brings the grammar of graphics to SQL, enabling powerful data visualization directly within SQL queries using declarative clauses. Built by the ggplot2 team with 18 years of experience, ggsql executes visualization computations as optimized SQL queries on backends, works without R or Python runtimes, and is designed for integration with Quarto, Jupyter, Positron, and AI agents.

• Learn more in the ggsql alpha release blog post.
• We love seeing folks in the community be early adopters! Harry Snart shares how he explored ggsql with Oracle Database, and Ansgar Wolsing tried it out for the #30DayChartChallenge.

RAG with raghilda

LLMs are great at reasoning and generating text, but their knowledge is frozen at training time. Retrieval-augmented generation (RAG) solves this by giving the model access to relevant information at query time, without needing to retrain it.

The new raghilda package simplifies building RAG systems in Python. It supports multiple storage backends (DuckDB, ChromaDB, OpenAI Vector Stores) with a consistent API, enabling teams to start locally and scale to hosted solutions without code rewrites.

• Read more in the RAG with raghilda blog post.

Quarto 1.9 and Quarto 2

The Quarto team has announced plans for Quarto 2, a complete rewrite in Rust with a major focus on collaborative editing! Key features include a collaborative editor for web and command-line, a visual editor that works seamlessly alongside source editing without disrupting code, and enhanced error detection across projects. The public release is expected in at least 6 months.

• Learn more in the What’s next: Quarto 2 announcement.

Meanwhile, be sure to take a look at all the exciting updates from v1.9, including:

• Chrome Headless Shell in Quarto
• Typst Books, Article Layout, and typst-gather
• PDF Accessibility and Standards

All v1.9 updates can be found in the roundup blog post.

What’s new in Positron

Our new data science IDE, Positron, has been significantly updated with numerous improvements, including Positron Server for Academic Use via JupyterHub, AI enhancements, telemetry updates and R improvements (like Addins!).

• Read more in the April 2026 Newsletter.
• For continuous updates on Positron, follow the new Positron LinkedIn page and subscribe to the Positron newsletter.

Shiny for Python 1.6

Shiny for Python 1.6 is now available. The release introduces toolbar components designed for tight spaces such as card headers, input labels, and AI chat interfaces.

The second major addition is built-in OpenTelemetry support, enabling zero-configuration observability by automatically tracing session lifecycles, reactive updates, and individual reactive expressions. Set SHINY_OTEL_COLLECT=reactivity and send traces to any OTLP-compatible backend like Pydantic Logfire, Jaeger, or Grafana Cloud.

• Read more in the Shiny for Python 1.6 brings toolbars and OpenTelemetry blog post.

Pointblank 0.24.0

Pointblank is a package for data validation. With the release of Pointblank 0.24.0, there is now OpenTelemetry integration for bridging data validation and production observability. After running checks on a table, you can push pass/fail metrics, per-validation-step trace spans, and structured threshold-breach logs to your OTel-compatible backend (Grafana, Datadog, New Relic, etc.). For those that run pipelines in Airflow, Prefect, or Dagster, validation spans slot into existing distributed traces automatically.

• Read the full OTel integration guide for setup instructions and examples.

Tidymodels

The tidymodels team is on a roll! There are new updates available for dial, parsnip, yardstick, tune, and tidymodels, as well as two new cheatsheets.

• Read the New tidymodels Releases for April 2026 blog post and tidymodels Cheatsheet blog posts.

The group has also been developing a set of skill files for machine learning with tidymodels as well as developer focused skills. You can find the current versions at skills.tidymodels.org. Give them a try; the group would love some feedback.

mori

mori is a new R package for shared memory across processes. ​​Parallel R no longer has to mean duplicating your dataset in every worker’s RAM. mori places it in OS-level shared memory once, and every worker maps the same physical pages via R’s ALTREP framework. Works with any parallel backend that uses R serialization, including mirai, parallel, and callr.

• Learn more in the mori 0.1.0 blog post.
• Tyler Morgan-Wall has already implemented a fork to the targets package to incorporate mori! Read the discussion here. It’s fantastic to see the community adopt and improve the ecosystem with open source.

tabpfn 0.1.0

The new tabpfn v0.1.0 package provides an R interface to TabPFN, a pretrained deep learning model for tabular data that delivers strong predictive performance without requiring model training. The package integrates with tidymodels syntax and future updates will add parsnip model types and additional interpretability tools.

• Learn more in the tabpfn 0.1.0 blog post.

nanoparquet 0.5.1

nanoparquet is a small, self-sufficient R package for reading and writing Parquet files. Version 0.5.1 introduces list columns, bit64::integer64 and blob::blob support, writing Parquet to the standard output.

• Read more in the nanoparquet 0.5.1 blog post.

torch Ecosystem Updates

The team has expanded torch ecosystem support to include cudatoolkit packages, torchvision datasets, and advanced model architectures and transformations for computer vision.

• Read more in the torch Ecosystem Updates blog post.

roxygen2 8.0.0

roxygen2 uses specially formatted comments in your R code to generate .Rd files. This version offers new support for S7, a raft of improvements to R6 documentation, a more natural way to configure roxygen2 in your DESCRIPTION, the changes to rendered .Rd files you’re most likely to see, and some other minor improvements, and a bunch of new vignettes.

• Learn more in the roxygen2 8.0.0 blog post.

Great Docs

Last month, we introduced Great Docs for beautiful documentation for Python packages. Author Rich Iannone shares more details in the Great Docs introductory blog post.

Learning and community

Posit website relaunch

The Posit website has a new look! Check out the refreshed pages, in particular the wonderful demo gallery with examples of workflows using Posit tools.

Showcases from the community

There are so many community examples to share, here is just a small sample():

Dianyi Yang, DPhil candidate in Politics at the University of Oxford (DPIR), shares practical guide to structuring reproducible academic research projects using Git, renv, Quarto, and GitHub, from data cleaning to manuscript preparation.

Read the blog post here!

Sophia Zilber shared a public-facing dashboard for the Cure Mito Foundation Leigh syndrome patient registry using Shiny for R.

See the dashboard here.

Tom Geens and his team used Quarto to generate HTML and LaTeX reports on occupational accidents in Belgium.

See the report here.

Gen Re uses Posit Connect to automate their underwriting workflow, ingesting broker submissions every minute and routing them through AI services that extract key information and generate an early risk assessment. What used to take 30 minutes per submission now takes just 5, saving the team roughly 600 hours of cumulative processing time each day.

Check out their story.

What’s next?

You can join us every Tuesday at the Data Science Lab and every Thursday at the Data Science Hangout!

• On May 12, Nicola Rennie will live code a TidyTuesday visualization from end-to-end and share the secrets of her craft!
• On May 21, our DSH will be a Data Career Panel with Gabriela de Queiroz, Dan Boisvert, and Makarand Malu. Bring your career questions about the field of data, hiring, asking for promotions, and more!

I’m a real person, and I would love to hear any feedback on the newsletter! Find me on LinkedIn and Bluesky, or email me at isabella [dot] velasquez [at] posit.co.