Tutorials & Blogs

R Programming

• swirl – An interactive R tutorial directly in your R console.
• rbootcamp – Interactive R tutorials by Bradley Boehmke.
• R Data Science Tutorials – Curated list of R tutorials and packages for Data Science, NLP and Machine Learning.
• A data.table and dplyr tour – Article presenting the most important features and the differences in syntax of both data.table and dplyr, two packages that are now essential tools for data manipulation in R.
• R for the Rest of Us – Free online courses on R for data analysis and visualization.
• Stat545 – Data wrangling, exploration, and analysis with R.
• R4DS online learning community – R for data science online learning community, which has the goal of creating a supportive and responsive online space for learners and mentors to gather and work through the R for Data Science book by Garrett Grolemund and Hadley Wickham.

Tidy Tuesday

Tidy Tuesday is a weekly social data project in R. Every week \@thomas_mock and \@R4DSCommunity post a new dataset and ask R users to explore it and share their findings on Twitter with #TidyTuesday.

• Tidy Tuesday Rocks – tidytuesday.rocks is only about 100 lines of R code and relies on your #TidyTuesday tweets, which get scraped and manually labeled once every couple weeks. It is built with Shiny and rtweet and its source code is on GitHub.

Data Visualisation

• Yan Holtz – Teaching material on data analytics and visualization by Yan Holtz. Includes intro to dataviz, about bad charts, the very basics of ggplot2, going further with ggplot2, reporting with R Markdown, intro to github.
• BBC – How the BBC Visual and Data Journalism team works with graphics in R.

Data Profiling

• Strategies for working with new data – Strategies for working with new data by Sharla Gelfand (packages visdat, skimr, assert).

R Markdown and Reproducible Research

• Pimp my RMD – Tips to improve the appearance of R Markdown output documents.
• Summer of Blogdown – A week of blogdown for RStudio’s summer 2019 interns.
• usethis – Creating your own R package for repetitive reporting.
• Beautiful Tables – How to make beautiful tables in R by David Keyes (using gt, kable, formattable, DT, reactable, flextable, huxtable, rhandsontable, pixiedust).
• RMD CSS Selector Tips – Blog post by Emily Riederer with practical advice on how to style R Markdowns with CSS code.

Shiny

• RStudio Shiny User Showcase – Examples of Shiny apps for the enterprise, industry specific apps, analytics tools, extensions of Shiny, with popular appeal, for teaching and catalogs.
• Show Me Shiny – Gallery of R Web Apps.
• Shiny Contest – Winners of the 1st Shiny Contest by Mine Çetinkaya-Rundel.
• Awesome R Shiny – A curated list of resources for R Shiny.
• shinytest – Automated testing for Shiny apps.
• Shiny examples:
  • shinyHome – Real estate market forecasting and analytics, including extensive documentation within the app.
  • Conference Tweets – A dashboard for conference tweets.
• Shiny modules:
  • Mastering Shiny – Chapter on shiny modules in Hadley Wickham’s Mastering Shiny book.
  • Modularizing Shiny app code – Article on modularizing your code using modules from shiny website.
  • Communication between modules – Article on communicating between modules from shiny website.
  • Beginner’s Guide – A beginner’s guide to Shiny modules by Emily Riederer.
  • Engineering Production-Grade Shiny Apps – Chapter on how to structure your project using modules from the book by Colin Fay and co-authors.
  • Why Shiny Modules? – 3-part blog series on shiny modules by Quentin Fazilleau (1, 2 and 3)

Package Development

• Créer un package R en quelques minutes – Illustrated step-by-step tutorial on how to create an R package (in French)
• Good Practice for R Packages – Good Practice for R Packages by Hannah Frick from Mango Solutions.
• Building R Packages – Good Practice for R Packages by Hannah Frick from Mango Solutions.
• R package primer – A minimal tutorial on package building by Karl Broman.
• Package Development List – R packages to improve package development.
• Good R Packages – Blogpost by Maëlle Salmon on how to develop good R packages (for open science).

Git

• RStudio and GitHub – Illustrated step-by-step tutorial on how to set up RStudio with Git and Github.
• Git for Humans – Git for humans presentation by Alice Bartlett.
• GitKraken – Git tutorials by GitKraken.
• Git Cheat Sheet – Git cheat sheet presented from Tower.
• Git Command Explorer – Find the right commands you need without digging through the web.
• Git Simple Guide – Simple guide to git by Roger Dudler.

Cloud Computing & Automation

• GitHub Actions – GitHub repository which stores GitHub Actions for R projects, which can be used to do a variety of CI tasks.
• GitHub Actions – Github actions with R.
• GitHub Actions – Rendering your README with GitHub Actions, blog by Gavin Simpson.
• GitHub Actions – Running R Scripts on a Schedule with GitHub Actions, blog by Simon Couch.
• GitHub Actions – Webinar by Jim Hester on Azure Pipelines and GitHub Actions at rstudio::conf2020.
• GitHub Actions – GitHub Actions Advent Calendar of 31 tips.
• GitHub Actions – Automated tweeting with GitHub Actions.
• GitHub Actions – GitHub Action to Deploy Static Assets to GitHub Pages.
• Azure Functions – Azure Functions with R and plumber.
• R at Microsoft – Overview of Microsoft products than integrate with R.
• Docker – An Introduction to Docker for R Users, blog post by Colin Fay.
• Docker – Docker for the User, github repo with workshop video, slides, code and links to resources by Noam Ross.
• Docker – An R-docker hello world example.
• Docker – Applications with R and Docker, video of conference tutorial.
• Docker – Slides from an R docker tutorial by Elizabeth Stark at the useR!2018 conference.
• Docker – Using R via rocker, a brief introduction to docker for R, slides by Dirk Eddelbuetel presented at Chicago R User Group Meeting 2019.
• Docker – Getting started with R and Docker by David Neuzerling.
• Docker – A Docker tutorial for reproducible research by rOpenSci Labs.
• Docker – Using Docker for Data Science, blog post by Robert Myles McDonnel.
• Docker – Environment management with docker by RStudio.
• Docker – The rocker project, docker containers for the R environment.

Natural Language Processing

• Basic Text Processing in R – Learn how to use R to analyze high-level patterns in texts, apply stylometric methods over time and across authors, and use summary methods to describe items in a corpus.
• Mueller Report – Text crunching and data munging, using R to analyze the redacted Mueller report.

Statistical Modelling and Machine Learning

• Dimension Reduction – RPubs workshop on dimension reduction with R, covering PCA, t-SNE, autoencoders and more.
• PCA Illustrative Example – Using PCA to Bully My Housemates by Harried Mason.
• From SNE to UMAP – Technical details behind t-SNE, LargeVis and UMAP.
• Explaining Black-Box Machine Learning Models – 3-part blog post by Shirin Glander on explaining supervised classification models built on tabular data using caret and the iml package, image classification models with keras and lime and text classification models with xgboost and lime.
• Trust in ML models – Trust in ML models. Slides from TWiML & AI EMEA Meetup + iX Articles by Shirin Glander.
• Random Forests Training – Slides of Bradley Boehmke on decision trees, bagging, & random forests with an example implementation in R.
• Auto-Keras – Tuning-free deep learning from R.
• ROC Animation – Animations with receiver operating characteristic and precision-recal curves.

RStudio Integration

• Plumber Rstudio – RStudio 1.2 Preview: Plumber Integration.
• Reticulated Python – RStudio 1.2 Preview: Reticulated Python.