"Life is growth. If we stop growing, technically and spiritually, we are as good as dead." -Morihei Ueshiba

A curated list of web-books, conference slides/recordings, links, and references for anything and everything around R and R Shiny.

• Mastering Shiny by Hadley Wickham {book} {source}
• Engineering Production-Grade Shiny Apps by Colin Fay, Sébastien Rochette, Vincent Guyader and Cervan Girard {book} {source}
• Outstanding User Interfaces with Shiny b Kenton Russel {book} {source}
• Building Web Apps with R Shiny by Lisa DeBruine {book} {pdf} {source}

• Advanced R, 2 edition by Hadley Wickham {book}
  • Advanced R Solutions, 2 edition by Malte Grosser, Henning Bumann and Hadley Wickham {solutions book}
  • Advanced R Exercise, 2 edition (solutions, unofficial) by Indrajeet Patil {solutions book} {github}
  • 1 version {book} {solutions book}
• R for Data Science, 1 edition by Hadley Wickham and Garrett Grolemund {book}
  • 2e version (still in development) {book}
• R packages (2e) by Hadley Wickham and Jenny Bryan {book}

• R Markdown: The Definitive Guide by Yihui Xie, J. J. Allaire, Garrett Grolemund (2022-04-11) {book} {github}
• Dynamic Documents with R and knitr by Yihui Xie {book} {github}
• blogdown: Creating Websites with R Markdown by Yihui Xie, Amber Thomas, Alison Presmanes Hill (2023-03-17) {book}
• bookdown: Authoring Books and Technical Documents with R Markdown by Yihui Xie (2023-03-17) {book} {github}

• R Graphics Cookbook, 2nd edition by Winston Chang (2023-03-20) {book}
• ggplot2: Elegant Graphics for Data Analysis, 3rd edition by Hadley Wickham, Danielle Navarro, and Thomas Lin Pedersen {book} {github}

• An Introduction to Statistical Programming Methods with R by Matthew Beckman, Stéphane Guerrier, Justin Lee, Roberto Molinari, Samuel Orso & Iegor Rudnytskyi (2020-10-20) {book}
• Tidy Modeling with R by Max Kuhn and Julia Silge {book}
• Feature Engineering and Selection: A Practical Approach for Predictive Models by Max Kuhn and Kjell Johnson (2019-06-21) {book}

• HTTP testing in R by Alex Whan, Aurèle, Christophe Dervieux, Daniel Possenriede, Hugo Gruson, Jon Harmon, Lluís Revilla Sancho, Xavier A {book} {pdf book} {github}
• R Programming for Data Science by Roger D. Peng (2022-05-31) {book}
• R (BGU course) by Jonathan D. Rosenblatt (2019-10-10) {book}

• Github actions with R by Chris Brown, Murray Cadzow, Paula A Martinez, Rhydwyn McGuire, David Neuzerling, David Wilkinson, Saras Windecker (2021-04-09) {book}
• JavaScript for R by John Coene (2021-04-19) {book}

• Text Mining with R by Julia Silge and David Robinson {book} {source}
• Supervised Machine Learning for Text Analysis in R by Emil Hvitfeldt and Julia Silge (2022-05-11) {book}

• Efficient R programming by Colin Gillespie, Robin Lovelace (2021-03-18) {book}
• R Cookbook, 2nd Edition by James (JD) Long and Paul Teetor (2019-09-26) {book}
• R for Dummies by Andrie de Vries {book}
• R in a nutshell by Joseph Adler {book} {alternate link}
• Learning Base R {code links}
• Learning R: A Step-by-Step Function Guide to Data Analysis by Richard Cotton {book} {book alternate link}
• Hands-On Programming with R by Garrett Grolemund {book}
• R for Everyone (Advanced Analytics and Graphics) and LaTeX - Adapted from Jared P. Lander's R for Everyone - by Shaoshuang Wen, University of South Carolina (2020-12-14) {book}
• The Art of R programming by Norman Matloff {book}
• R in Action by Chuchu Wang {academia book}
• Data Science in Education Using R by Ryan A. Estrellado, Emily A. Bovee, Jesse Mostipak, Joshua M. Rosenberg, and Isabella C. Velásquez {book}
• Learning R (A step by step function guide to Data Analysis) by Richard Cotton {book}
• The R Book (second edition) by Michael J. Crawley {book}
• The Book of R (Programming and Statistics) by Tilman M. Davies {book}

• Big Book of R - Comphrehensive book of R books. last-ever bookmark. {book}
• Happy Git and GitHub for the useR by Jennifer (Jenny) Bryan {book} {github}
• Introduction to Snapshot Testing in R - implementing testthat, shinytest2 and vdiffr by Indrajeet Patil {slides} {source} {presentaion showcase}
• R Function A Day by Indrajeet Patil {book} {github}

• Dealing with the Second Hardest Thing in Computer Science - Naming variables standards by Indrajeet Patil {slides} {source} {presentaion showcase}
• A debugging manifesto by Julia Evans {website}
• New zine: The Pocket Guide to Debugging by Julia Evans {website}

• How to Quickly Build a Production-Ready Real World Data Dashboard? by Kamil Wais {Roche} {slides}
• Taking Flight with Shiny: A Modules-First Learning Approach by Emily Riederer {slides}
• Sharing app state between modules by Marcin Dubel {slides}
• How to build user-centric applications? by Anna Skrzydlo {slides}
• Finding #RStats resources with Shiny and GitHub Actions by Nicola Rennie {portfolio} {slides}
• Debugging Shiny Apps by Tan Ho {slides} {github repo}
• Towards the next generation of Shiny UI a.k.a. How to make a dashboard with {bslib} by Carson Sievert {slides}
• Connecting Slack Teams to Shiny Apps by Jon Harmon {slides}{slides alternate link}
• Say goodbye to unnecessary waiting: mastering asynchronous programming in Shiny by Veerle van Leemput {slides}
• Baking JavaScript into a Shiny Package by Jon Harmon {slides} {alternate link slides}
• Shiny Semantic meets Shiny for Python by Pavel Demin {slides}

Currently organized by Rachael Dempsey {2022-2023}

• shinymeta : Record and expose Shiny app logic using metaprogramming {website} {github}
• histoslider : histogram slider input for Shiny {github}
• bslib : Tools for theming Shiny and R Markdown via Bootstrap 3, 4, or 5 {github}
• shinyDatetimePicker : A datetime picker for Shiny {github}

• r-lib/actions : GitHub Actions for the R community {github}
• shinycoreci: Application-level tools to perfrom manual and automated tests for Shiny apps {website} {github}

• tidyverse/reprex : Render bits of R code for sharing, e.g., on GitHub or StackOverflow. {website} {github}

• chatgpt : Interface to ChatGPT from R {github} {cran}

• Futureverse {website} - future {github} {website} and promises {github} {website}
  • A future is an abstraction for a value that may be available at some point in the future. The state of a future can either be unresolved or resolved
  • A future always returns a promise. It stores the state of a future to be evaluated later in the r shiny workflow.
• callr {github} - Perform a computation in a separate R process, without affecting the current R process at all.
• Parallelization:
  • parallelly: Enhancing the 'parallel' Package {github}
  • ParallelLogger: Support for parallel computation with progress bar, and option to stop or proceed on errors {github}
  • Reference reads
    • R Programming for Data Science - Parallel Computation {book}
    • R (BGU course) - Parallel Computing {book}
    • Quick Intro to Parallel Computing in R by Matt Jones {blog}
    • Parallelized loops with R {blog}
• coro {github} - Implements coroutines for R, i.e. functions that can be suspended and resumed later on

• Risk Assessment Application - {app}

• Making Project Management Seamless using Automated Gantt Charts in R Shiny - {app} {github}

• Markdown cheatsheet {github}
• Bootstrap : Powerful, extensible, and feature-packed frontend toolkit. Build and customize with Sass, utilize prebuilt grid system and components, and bring projects to life with powerful JavaScript plugins. {website}
• Bootswatch : free themes for Bootstrap {website} {github}
  • Lux {website}
• App wireframe
  • draw.io {website} {alternate website}
  • Excalidraw {website}
• {crontab guru} : cron scheduler - The quick and simple editor for cron schedule expressions

• GitHub Actions (R)

  • r-lib/actions/examples : GitHub Actions for R projects, which can be used to do a variety of CI tasks {github}
  • Deploy to shinyapps.io : GitHub action to automate deployment of shiny applications on https://shinyapps.io {website} {github}

• GitHub Actions (general)

  • GitHub Profile Summary Cards : A tool to generate your github summary card for profile README and schedule it using GitHub action. {website}

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