Free R Resources

A list of useful and free resources for learning to analyse data with R

There are lots of free resources that I’ve come across for learning to do various things in R. I keep seeing them and then forgetting about them. So I have made a list.

I’ll try to add things to it. Suggestions are very welcome.

It’s worth saying how great it is that there’s all this free stuff out there. Thanks to the authors and publishers for making that possible. If like me, you like physical books, I’m sure buying these books probably helps encourage people to put them out there for free.

Also, a caveat: I haven’t read all of these. So no promises of quality. Reviews very welcome.

Learning to use R

• Getting started in R and Rstudio — by Andy Field
• R for Data Science — by Garrett Grolemund & Hadley Wickham.

More advanced R programming

• Advanced R — by Hadley Wickham.

Data visualisation

• Data Visualisation: An Introduction by Keiran Healey
• The R Graphics Cookbook by Winston Chang
• Fundamentals of Data Visualisation by Claus O. Wilke (I really like the cowplot package for doing figures, which this book covers).
• ggplot2: Elegant Graphics for Data Analysis by Hadley Wickham (work-in-progress third edition).

Specific statistical topics

• Principles of econometrics with R by Constantin Colonescu. (This has been useful for a project that I’ve worked on).
• Introduction to econometrics with R by Christoph Hanck, Martin Arnold, Alexander Gerber and Martin Schmelzer
• Notes to Advanced Political Research Design and Analysis by Jeffrey B. Arnold
• Machine Learning Case Studies – by Julia Silge

Producing reproducible documents, blogs, and books

• Introduction to R markdown by Garrett Grolemund
• Rmarkdown: The Definitive Guide by Yihui Xie, J. J. Allaire, Garrett Grolemund
• Bookdown by Yihui Xie
• Blogdown by Yihui Xie

Most of these are from bookdown.org. You can browse the full list of books there using this link. Be warned: they’re not all completely finished.

Updates 23 July 2022

Causal inference

Not strictly about R but many R users will be interested in drawing causal conclusions from data and there’s a ballooning selection of free resources available. Some of which are:

• Causal Inference: What If? by Hernán MA, Robins JM
• Causal Inference for the Brave and True by Matheus Facure Alves
• The Effect: An Introduction to Research Design and Causality by Nick Huntington-Klein

Epidemiology & Public Health

• The Epidemiologist R Handbook by Batra, Neale, et al.