Free Resources for Learning R Programming for Data Science

Content

• R Stats
• {ggplot2}
  • Useful Packages
• Statistics with R
• Machine Learning with R
• R Spatial
• Data Visualization
  • Colors
  • Chart Types
  • Fonts
  • Mapping
  • Other helpful resources
• R Markdown
• R Shiny Apps
• R Package Development
• R Packages Tutorials
  • Tidymodels
• Interesting Personal Blogs
• Get Help

R Stats

• “Cookbook for R” — free online book 📕 by Winston Chang
• “Technical Foundations of Informatics” — free online book 📕 by Michael Freeman & Joel Ross
• “Efficient R Programming” — free online book 📕 by Colin Gillespie & Robin Lovelace
• “YaRrr! The Pirate’s Guide to R” — free online book 📕 by Nathaniel Phillips
• “Hands-On Programming with R” — free online book 📕 by Garrett Grolemund
• “R for Data Science” — free online book 📕 by Hadley Wickham & Garrett Grolemund
• “Advanced R” — free online book 📕 by Hadley Wickham
• “Data wrangling, exploration, and analysis with R” — free online book 📕 by Jenny Bryan
• “Getting Used to R, RStudio, and R Markdown” — free online book 📕 by Chester Ismay & Patrick C. Kennedy
• “Tidy evaluation” — free online book 📕 by Lionel Henry & Hadley Wickham
• “Happy Git and GitHub for the useR” — free online book 📕 by Jenny Bryan
• “R Cookbook, 2nd Edition” — free online book 📕 by James Long & Paul Teetor
• “A ModernDive into R and the tidyverse” — free online book 📕 by Chester Ismay & Albert Y. Kim
• “Modern R with the tidyverse” — free online book 📕 by Bruno Rodrigues
• “Introduction to Data Exploration and Analysis with R” — free online book 📕 by Michael Mahoney
• “Text Mining with R: A Tidy Approach” — free online book 📕 by Julia Silge & David Robinson
• “The tidyverse style guide” — free online book 📕 by Hadley Wickham
• “Advanced R Course” — free online book 📕 by Florian Privé
• “What They Forgot to Teach You About R” — free online book 📕 by Jennifer Bryan & Jim Hester
• “R in Nutshell” — free online book 📕 by Joseph Adler
• “R for Social Scientists” — free online book 📕 by Paul C. Bauer & Rudolf Farys
• “R for Journalists” — free online book 📕 by Andrew Ba Tran
• “R for Excel” — free online book 📕 by Julie Lowndes & Allison Horst
• “Twitter for R programmers” — free online book 📕 by Oscar Baruffa & Veerle van Son
• “Mastering Spark with R” — free online book 📕 by Javier Luraschi, Kevin Kuo, Edgar Ruiz
• R Graph Gallery 📊 📈 🎨
• Five Simple Tipps to Improve Your R Code
• R-Bootcamp — free online course by Ted Laderas and Jessica Minnier about manipulating and visualizing data in R using the tidyverse suite of packages
• Advanced R for Bioinformatics Summer School
• SatRdays Neuchatel 2020 Workshop
• R Studio 2019 Workshops
• R Studio 2020 Workshops
• Going Deeper with R — free live course 📺 by R by David Keyes
• Coding Club Tutorials
• Top 100 R Tutorials: Basic to Advance
• RYouWithMe — course by R-Ladies Sydney
• R Programming: Learn the Basics of Statistical Computing — YouTube 📺 Videos
• Introduction to Text Analytics with R — YouTube 📺 Videos
• R Programming Tutorials — YouTube 📺 Videos
• “Exploratory Data Analysis & Visualization” — free online book 📕 by Zach Bogart & Joyce Robbins
• RStudio Webinars 📺 💻
• R Consortium — YouTube videos 📺
• rOpenSci Community Vimeo Channel — rOpenSci Community videos 📺
• Rstats education — a repository for discovering courses and learning materials for learning and teaching R.
• Ready for R — a free online course by Dr. Ted Laderas, an Assistant Professor at Oregon Health and Science University and an RStudio certified trainer.
• Data science with R: A robust toolkit for psychological research — a free course by Danielle Navarro
• Statistics 431: Advanced Statistical Computing with R — 9-week online course at Cal Poly taught by Dr. Kelly Bodwin and Dr. Hunter Glanz

{ggplot2}

• “ggplot2: Elegant Graphics for Data Analysis” — free online book 📕 by Hadley Wickham
• Source code of the book 📕 “Fundamentals of Data Visualization”
• Top 50 ggplot visualizations
• Gallery of ggplot2 extensions
• Extending ggplot2
• #TidyTuesday Collection
• A ggplot2 Tutorial for Beautiful Plotting in R — a blogpost by Cédric Scherer
• R Graphics: Introduction to ggplot2 — slides 📊
• An Introduction to {ggplot2} — slides 📊 by Cédric Scherer
• Data Science and Visualizations with R — free course by Jonathan Wong on the use of tidyverse packages

Useful Packages

• {cowplot} — plot arrangements, themes & annotations → wilkelab.org/cowplot
• {ggalt} — alternative coords, geoms, stats & scales → github.com/hrbrmstr/ggalt
• {gganimate} — create animations → gganimate.com
• {ggforce} — several interesting add-on features → ggforce.data-imaginist.com
• {ggmaps} — access to Google & Stamen maps → github.com/dkahle/ggmap
• {ggplotly} — create interactive plots → plot.ly/ggplot2
• {ggpubr} — publication-ready plot in one line → github.com/kassambara/ggpubr
• {ggraph} — networks, graphs & trees → github.com/thomasp85/ggraph
• {ggrepel} — prevent overlapping text labels → github.com/slowkow/ggrepel
• {ggridges} — geoms for joy plots → github.com/clauswilke/ggridges
• {ggtext} — rich-text rendering → github.com/clauswilke/ggtext
• {ggthemes} — additional themes, sclaes & geoms → github.com/jrnold/ggthemes
• {hrbrthemes} — typography-centric themes → github.com/hrbrmstr/hrbrthemes
• {lemon} — axis & legend add-ons → github.com/stefanedwards/lemon
• {patchwork} — combine ggplots → github.com/thomasp85/patchwork
• {rayshader} — hillshaded maps in 2D & 3D → github.com/tylermorganwall/rayshader
• {showtext} — use custom fonts → github.com/yixuan/showtext

Statistics with R

• “Statistical Thinking for the 21st Century” — free online book 📕 by Russell A. Poldrack
• “Statistics for Social Sciences II: Multivariate Techniques” — notes by Eduardo García Portugués
• “Doing Bayesian Data Analysis in brms and the tidyverse” — free online book 📕 by A Solomon Kurz
• “Foundations of Statistics with R” — free online book 📕 by Darrin Speegle
• Statistics and R — PhD Training Workshop by Anastasia Ushakova & Emma Waterston
• Teacups giraffes and statistics

Machine Learning with R

• Your First Machine Learning Project in R Step-By-Step — blogpost by Jason Brownlee
• Introduction to Machine Learning with the Tidyverse — two-day workshop offered at rstudio::conf 2020 by Alison Hill & Garrett Grolemund
• Hands-On Machine Learning with R — free online book 📕 by Bradley Boehmke & Brandon Greenwell
• Supervised Machine Learning: Case Studies in R — A Free, Interactive Course Using Tidy Tools
• Diploma in Machine Learning with R studio — free online machine learning course by R Studio
• Interpretable Machine Learning: A Guide for Making Black Box Models Explainable — free online book 📕 by Christoph Molnar

R Spatial

• “Geocomputation with R” — free online book 📕 by Robin Lovelace, Jakub Nowosad & Jannes Muenchow
• Spatial Data Science with R
• “Spatial Microsimulation with R” — free online book 📕 by Robin Lovelace & Morgane Dumont
• “Spatial Data Science” — free online book 📕 by Edzer Pebesma, Roger Bivand
• “30 Day Map Challenge” — free online book 📕 by Bob Rudis
• “Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA” — free online book 📕 by Elias T. Krainski, Virgilio Gómez-Rubio, Haakon Bakka, Amanda Lenzi, Daniela Castro-Camilo, Daniel Simpson, Finn Lindgren & Håvard Rue
• “Introduction to Spatial Data Programming with R” — free online book 📕 by Michael Dorman
• “Introduction to Web Mapping” — free online book 📕 by Michael Dorman
• Introduction to GIS with R — a blogpost by Jesse Sadler

Data Visualization

• “Data Visualization” — free online book 📕 by Kieran Healy
• “Interactive web-based data visualization with R, plotly, and shiny” — free online book 📕 by Carson Sievert
• “Fundamentals of Data Visualization” — free online book 📕 by Claus Wilke
• Data Visualization — free online course 📺 by Dr. Andrew Heiss (Georgia State University)
• “Visualizing Data” — homepage by Andy Kirk
• “Visual Cinnamon” — homepage by Nadieh Bremer
• Explorable Explanations
• “Nightingale” — blog by the Data Visualization Society on Medium
• “Multiple Views: Visualization Research Explained” — DataViz blog on Medium
• Graphics Principles Cheatsheet
• Data Visualization in R — workshop for the 2019 Navy and Marine Corps Public Health Conference by Brooke Anderson
• BBC Visual and Data Journalism cookbook for R graphics
• “R Graphics Cookbook, 2nd edition” — free online book 📕 by Winston Chang
• How to Use C.R.A.P. Design Principles For Better UX? — blogpost on CRAP (Contrast, Repetition, Alignment, and Proximity) principle for better data visualization
• Visualization Analysis and Design — book 📕 & lecture slides 📊 by Tamara Munzer
• Principles & Practice of Data Visualization — slides 📊 for the Data Visualization course at Oregon Health & Science University

Colors

• Viz Palette — colors in action (plus colorblind check)
• Color Space — color palette generator
• Chroma.js — color palette helper
• HCL Wizard — manipulating and assessing colors & palettes
• ColorThief — grab color palettes from any image
• Data Color Picker — Generator of equidistant sequential, monochromatic and diverging palettes
• DataWrapper’s Friendly Guide to Colors and What to Consider when Choosing Colors
• Adobe Color — creating, sharing, and exploring rule-based and custom color palettes.
• ColorBrewer — sequential, diverging, and qualitative color palettes that take accessibility into account.
• viridis — percetually uniform color scales.
• Scientific Colour-Maps — perceptually uniform color scales like viridis. Use them in R with scico.
• Colorgorical — create color palettes based on fancy mathematical rules for perceptual distance.
• Colorpicker for data — more fancy mathematical rules for color palettes (explanation).
• iWantHue — yet another perceptual distance-based color palette builder.
• ColourLovers — like Facebook for color palettes.
• Photochrome — word-based color pallettes.

Chart Types

• From Data to Viz — A decision tree for dozens of chart types with links to R and Python code.
• Data Viz Project — Descriptions and examples for 150 different types of visualizations. Also allows you to search by data shape and chart function (comparison, correlation, distribution, geographical, part to whole, trend over time, etc.).
• Visualization Universe
• Material.io
• Data Visualization 101
• Chart of Chart Suggestions
• The Data Visualisation Catalogue — Descriptions, explanations, examples, and tools for creating 60 different types of visualizations.
• The Chartmaker Directory — Examples of how to create 51 different types of visualizations in 31 different software packages, including Excel, Tableau, and R.
• R Graph Catalog — R code for 124 ggplot graphs.
• Emery’s Essentials — Descriptions and examples of 26 different chart types.

Fonts

• Google Fonts — Huge collection of free, well-made fonts.
• The Ultimate Collection of Google Font Pairings — A list of great, well-designed font pairings from all those fonts hosted by Google (for when you’re looking for good contrasting or complementary fonts).

Mapping

• Spatial.ly Blog by James Cheshire
• Bivariate Chloropleth Maps
• Value-by-Alpha Maps

Other helpful data visualization resources

• Storytelling with Data — Blog and site full of resources by Cole Nussbaumer Knaflic.
• Ann K. Emery’s blog — Blog and tutorials by Ann Emery.
• Evergreen Data — Helful resources by Stephanie Evergreen.
• PolicyViz — Regular podcast and site full of helpful resources by Jon Schwabisch.
• @HelpMeViz — Community of people who give advice on how to visualize data.
• Visualising Data — Fantastic collection of visualization resources, articles, and tutorials by Andy Kirk.
• Info We Trust — Detailed explorations of visualizations by RJ Andrews, including a beautiful visual history of the field.
• FlowingData — Blog by Nathan Yau.
• Information is Beautiful — Blog by David McCandless.
• Junk Charts — Blog by Kaiser Fung.
• WTF Visualizations — Visualizations that make you ask “What the Freak?”1
• The Data Visualization Checklist — A helpful set of criteria for grading the effectiveness of a graphic.
• Data Literacy Starter Kit — Compilation of resources to become data literate by Laura Calloway.
• Seeing Data — A series of research projects about perceptions and visualizations.

R Markdown

• “R Markdown: The Definitive Guide” — free online book 📕 by Yihui Xie, J. J. Allaire, Garrett Grolemund
• “R Markdown Cookbook” — free online book 📕 by Yihui Xie and Christophe Dervieux
• “R Markdown for Scientists” — free online book 📕 by Nicholas Tierney
• Advanced R Markdown Workshop - notes 📄 from the advanced R markdown workshop at rstudio::conf 2019 led by Alison Hill & Yihui Xie
• “blogdown: Creating Websites with R Markdown” — free online book 📕 by Yihui Xie, Amber Thomas & Alison Presmanes Hill
• “bookdown: Authoring Books and Technical Documents with R Markdown” — free online book 📕 by Yihui Xie
• Advanced R Markdown workshop — materials from the rstudio::conf 2019 Advanced R Markdown workshop
• Introduction to R Markdown for Medicine — 4 hours workshop on R Markdown for Medicine: From Data to Manuscript by Alison Hill
• Sharing on Short Notice: How to Get Your Teaching Materials Online with R Markdown — slides 📊 by Alison Hill & Desirée De Leon
• Summer of Blogdown — A week of blogdown for RStudio’s summer 2019
• Hugo - Static Site Generator — A complete course by Mike Dane

R Shiny Apps

• R Shiny Dashboard (Complete Tutorial) - YouTube 📺 Tutorials
• “Mastering Shiny” — free online book 📕 by Hadley Wickham
• “Engineering Production-Grade Shiny Apps” — free online book 📕 by Colin Fay, Sébastien Rochette, Vincent Guyader & Cervan Girard
• A Gradual Introduction to Shiny — workshop by Ted Laderas & Jessica Minnier
• Learn Shiny — tutorials 📺 by RStudio

R Package Development

• “Mastering Software Development in R” — free online book 📕 by Roger D. Peng, Sean Kross & Brooke Anderson
• “R Packages” — free online book 📕 by Hadley Wickham

R Packages Tutorials

Tidymodels

• A Gentle Introduction to tidymodels — a blogpost by Edgar Ruiz
• Exploring tidymodels With Hockey Data — a beginner-friendly guide to the tidymodels by Meghan Hall
• Predictive modeling in R with tidymodels and NFL attendance — a video tutorial 📺 on tidymodels using #TidyTuesday dataset by Julia Silge
• Modelling with Tidymodels and Parsnip — a post by Diego Usai. If you can not access the post on the above link, click here
• Modeling with parsnip and tidymodels — a post by Benjamin Chang Sorensen
• Learn tidymodels
• You can also learn tidymodels in your RStudio IDE with interactive learnr primers using learntidymodels package. Follow the instructions here.
• Tidymodels: tidy machine learning in R — a blogpost by Rebecca Barter
• Exploring Tidymodels

Interesting Personal Blogs
(Random Topics, Random Order 🤷)

• Chisato — colors & art with {ggplot2}
• Dominic Roye — mapping with {ggplot2}
• David Smale — {shiny}, {ggplot2} and more
• Andy Kirk — all about DataViz!
• Maëlle Salmon — “goofing around with R”
• David Robinson — data science using the {tidyverse}
• Charlotte Robinson — data science
• Bruno Rodrigues — data science in R and Python
• James Cheshire — beautiful mapping
• Ilya Kashnitsky — maps & demography in R
• Hugo Toscano — data science in R
• Geoff Boeing — Python & GIS
• Our World in Data — “data on the world’s largest problems”
• Cédric Scherer — data visualization

Get Help

• Getting help in R: do as I say, not as I’ve done — blogpost by Sam Tyner
• RStudio Community
• R4DS Learning Community
• #TidyTuesday Community on Twitter
• #rstats Community on Twitter
• R Cheatsheets