Carson Sievert

Quarto

Open-source scientific and technical publishing system built on Pandoc

dbplyr

Database (DBI) backend for dplyr

chatlas

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

ellmer

Call LLM APIs from R

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

rmarkdown

Dynamic Documents for R

shinychat

Chat UI component for Shiny

usethis

Set up commonly used components

querychat

Chat with your data in R and Python

Shiny

Easy interactive web applications with R

Shiny for Python

Easy interactive web applications with Python

webinars

Code and slides for RStudio webinars

promises

A promise library for R

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

shinymodels

shinymeta

Record and expose Shiny app logic using metaprogramming

gradethis

Tools for teachers to use with learnr

shinythemes

Themes for Shiny

ymlthis

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

vdiffr

Visual regression testing and graphical diffing with testthat

evaluate

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

ggsql

A 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

py-shinylive

Python package for deploying Shinylive applications

py-shinywidgets

Render ipywidgets inside a PyShiny app

sass

Sass compiler package for R

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

shinytest

Automated testing for shiny apps

shinyvalidate

Input validation package for the Shiny web framework

Tool Calling UI in shinychat

Rich tool calling displays are now available in shinychat!

Garrick Aden-Buie, Carson Sievert, Barret Schloerke

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

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

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