Carson Sievert

dplyr

dplyr: A grammar of data manipulation

ggplot2

An implementation of the Grammar of Graphics in R

plumber

Turn your R code into a web API

Quarto

Open-source scientific and technical publishing system built on Pandoc

rmarkdown

Dynamic Documents for R

shinychat

Chat UI component for Shiny

usethis

Set up commonly used components

ellmer

Call LLM APIs from R

querychat

Chat with your data in R and Python

chatlas

Your friendly guide to building LLM chat apps in Python with less effort and more clarity

webinars

Code and slides for RStudio webinars

dbplyr

Database (DBI) backend for dplyr

leaflet

R Interface to Leaflet Maps

rstudio-conf

Materials for rstudio::conf

crosstalk

Inter-htmlwidget communication for R (with and without Shiny)

shinylive

Run Shiny on Python and R (compiled to wasm) in the browser

bslib

Tools for theming Shiny and R Markdown via Bootstrap 3, 4, or 5

py-shiny-templates

Templates for Shiny for Python

shinytableau

Extend Tableau dashboards using Shiny

shiny-assistant

Chat assistant for Shiny

DT

R Interface to the jQuery Plug-in DataTables

shinytest2

shinydashboard

Shiny Dashboarding framework

rsconnect

Publish Shiny Applications, RMarkdown Documents, Jupyter Notebooks, Plumber APIs, and more

thematic

Theme ggplot2, lattice, and base graphics based on a few simple settings

fontawesome

Easily insert FontAwesome icons into R Markdown docs and Shiny apps

httpuv

HTTP and WebSocket server package for R

flexdashboard

Easy interactive dashboards for R

learnr

Interactive Tutorials with R Markdown

bookdown

Authoring Books and Technical Documents with R Markdown

htmltools

Tools for HTML generation and output

shinymeta

Record and expose Shiny app logic using metaprogramming

gradethis

Tools for teachers to use with learnr

shinythemes

Themes for Shiny

cheatsheets

Posit Cheat Sheets - Can also be found at https://posit.co/resources/cheatsheets/

evaluate

A version of eval for R that returns more information about what happened

ggsql

A experimental SQL extension for declarative data visualisation based on the Grammar of Graphics

later

Schedule an R function or formula to run after a specified period of time

promises

A promise library for R

py-shinylive

Python package for deploying Shinylive applications

py-shinywidgets

Render ipywidgets inside a PyShiny app

sass

Sass compiler package for R

Shiny

Easy interactive web applications with R

Shiny for Python

Easy interactive web applications with Python

shiny-examples

shinyapps-package-dependencies

Collection of bash scripts that install R package system dependencies

shinycoreci

Application-level automated tests for Shiny

shinycoreci-apps

SUPERSEDED - Apps for Shiny continuous integration testing

shinymodels

shinytest

Automated testing for shiny apps

shinyvalidate

Input validation package for the Shiny web framework

vdiffr

Visual regression testing and graphical diffing with testthat

ymlthis

write YAML for R Markdown, bookdown, blogdown, and more

Tool Calling UI in shinychat

Rich tool calling displays are now available in shinychat!

Garrick Aden-Buie, Carson Sievert, Barret Schloerke

Positron AI Session (George Stagg, Winston Chang, Tomasz Kalinowski , Carson Sievert) | posit::conf

George Stagg, Winston Chang, and Tomasz Kalinowski introduce AI capabilities in Positron, including Positron Assistant, a tool still in preview that enhances coding and data analysis. 0:00 Introduction to Posit's approach to AI 0:23 George Stagg: Positron Assistant 11:03 Winston Chang: Databot 21:30 Tomasz Kalinowski: ragnar 31:13 Carson Sievert: chatlas 41:42 Q&A posit::conf(2025) Subscribe to posit::conf updates: https://posit.co/about/subscription-management/

Carson Sievert, George Stagg, Tomasz Kalinowski, Winston Chang

Carson Sievert - Supercharge Your Shiny (for Python) App: Unleashing Interactive Jupyter Widgets

Most Python packages that provide interactive web-based visualizations (e.g., altair, plotly, bokeh, ipyleaflet, etc.) can render in Jupyter notebooks via the ipywidgets standard. The shinywidgets package brings that ipywidgets standard to Shiny, enabling the use of 100s of Jupyter Widgets as Shiny outputs. In this talk, you'll not only learn how to render Jupyter Widgets in Shiny to get interactive output, but also how to leverage user interaction with widgets to create delightful and bespoke experiences. Talk by Carson Sievert Slides: https://talks.cpsievert.me/20240814/ GitHub Repo: https://github.com/cpsievert/talks/tree/gh-pages/20240814

Carson Sievert

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

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 R updates: Extended tasks, JavaScript errors, and many bslib improvements

An overview of recent Shiny for R updates, including extended tasks, JavaScript errors, and many bslib improvements

Carson Sievert

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

Shiny for R updates: new default look, dark mode, shinylive updates, and more

An overview of recent Shiny for R updates, a new default look, dark mode, shinylive updates, and more

Carson Sievert, Garrick Aden-Buie

Shiny for R updates: tooltips, popovers, a new theme, and more

An overview of recent Shiny for R updates, including tooltips, popovers, a new theme, and more

Carson Sievert, Garrick Aden-Buie

Towards easy, delightful, and customizable dashboards in Shiny for R with {bslib}

The {bslib} R package nows makes it very easy to make delightful and customizable dashboard in Shiny for R. This post walks through a Shiny app which quickly illustrates what's possible with these components

Carson Sievert

Carson Sievert: Towards the next generation of Shiny UI

About the talk: Shiny, the R package for creating interactive web graphics, recently celebrated its 10th birthday. Since then, Shiny has grown tremendously in many areas (e.g., performance, functionality, extensions, etc); however, a "hello world" Shiny app still looks like it did 10 years ago. This is mostly because Shiny goes to great lengths to ensure backwards compatibility; and as a result, default Shiny UI will likely continue to be based on Bootstrap 3 (a CSS styling framework released in 2010). However, thanks to the new bslib R package, it is now easy to opt-into a modern Bootstrap 5 foundation that "just works" with Shiny, R Markdown, flexdashboard, pkgdown, bookdown, and more. In addition to upgrading Shiny's Bootstrap dependency, bslib also makes it much easier to do custom theming, leverage modern layout techniques, and create custom components (all from R without any CSS/HTML/JS required). At this point, bslib is still maturing, and does not yet provide what we'd consider a "complete UI toolkit", but it should eventually replace and/or improve upon all of Shiny UI. In this talk, I'll highlight bslib features that we're most excited about (e.g., expandable cards, accordions, (sidebar) layouts, input controls, etc.), discuss some best design practices for improving user experience with these tools, and present some real world examples of these tools in action. Speakers' bio: Carson is a software engineer on the Shiny team at Posit. He joined Posit in 2018, and in recent years, has focused primarily on Shiny for Python and improving Shiny UI. The Shiny UI work has manifested in the creation and development of many R packages such as bslib, thematic, htmltools, htmlwidgets, sass, shiny, rmarkdown, flexdashboard, and more. Carson also has a PhD in statistics, is a recipient of the ASA's Chambers Statistical Software Award, has maintained the R package plotly since 2015, authored the book "Interactive data visualization with R, plotly, and shiny", and ran a successful freelance consulting service for numerous years

Carson Sievert, Shiny Team

Announcing new R Shiny UI components

The {bslib} R package recently added new user interface (UI) components, including (full-screen) Cards, Value Boxes, and a responsive column wrapping layout. Start using them today in Shiny, R Markdown, and/or Quarto

Carson Sievert

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

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

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

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

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

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

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

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

plumber 1.1.0

{plumber} v1.1.0 is now on CRAN! In this post, we'll highlight some of the most exciting new features in both the v1.1.0 and v1.0.0 releases, including: parallel endpoint execution, a tidy interface, and request body parsing

Barret Schloerke, Carson Sievert

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

Shiny 1.6

Shiny 1.6 is now on CRAN! This release includes significant improvements to theming, caching, accessibility, and more

Carson Sievert, Winston Chang, Barret Schloerke

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