This repository is a clearing house for resources for individual R workshops from Research Computing and Data Services.
R Fundamentals Bootcamp Sep 3-6, 2024 from 9:30am-3:30pm in Wieboldt Hall, Room 421 (Chicago)
R Fundamentals Bootcamp Sep 9-12, 2024 from 9:30am-3:30pm in Wieboldt Hall, Room 421 (Chicago)
R Fundamentals Bootcamp June 11-14, 2024 from 9:30am-3:30pm in Mudd Library, North Study Lounge (Evanston)
R Intro to Tidyverse June 24, 2024 from 9:30am-3:30pm in Mudd Library, North Study Lounge (Evanston)
R Intermediate Tidyverse June 25, 2024 from 9:30am-3:30pm in Mudd Library, North Study Lounge (Evanston)
R Statistical Modeling June 26, 2024 from 9:30am-12:00pm in Mudd Library, North Study Lounge (Evanston)
R Fundamentals Series: the most recently taught materials
Databases: Information on how to connect to databases from R is part of the databases workshop materials, which also covers the basics of SQL. The example code there may be a useful reference, but you’ll need a database connection to run it. See that repository for more details.
R Markdown: this one is a little older than the others, but the material should still be relevant
Intro to R (in-person workshop)
For workshops, it’s best to install R and RStudio on your own laptop (both are free). If you can’t install these programs or run into issues installing packages, Posit Cloud is a good option.
RStudio Cheat Sheets are short pdfs that summarize key R functions on specific topics. Many people print them out for reference while working in R. The ggplot cheat sheet, in particular, in indispensable.
R Reference Card: lists many commonly used functions
See our guide to free, online resources for learning R on the Research Computing Services blog for suggestions of resources to get started or get better with R.
Resources for specific topics and R are below.
Happy Git with R: another resource from UBC Stat 545 and Jenny Bryan’s team
Github Quickstart for Scientists: aims just at teaching the workflow that many scientists use
If you’re coming to R from Stata, SPSS, SAS, Matlab, or Python, the following resources might be useful to you. Some of them may be a little outdated, but each contains some tables of equivalent commands across programs that might help you get familiar with R more quickly.
R/Stata Comparison from Princeton’s Data & Statistical Services
R for SAS and SPSS Users is a book by Bob Muenchen of r4stats.com
The Tidynomicon: R for Python Programmers by Greg Wilson; may be helpful for those coming to R from other C-derived programming languages as well.
Matlab/R Reference: from David Hiebeler of the University of Maine.
Matlab/NumPy (Python)/R Commands Chart: from Vidar Bronken Gundersen; this one is about 10 years old, but it mostly covers basic commands, which haven’t changed
haven Package: for importing Stata, SAS, and SPSS data into R.
UCLA’s Statistics Consulting Group has a great set of tutorials showing how to conduct many types of ANOVA and regression analysis in various statistical packages, including R. Highly recommended; check here first.
An Introduction to Statistical Learning with Applications in R: book, available online, by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
The Elements of Statistical Learning: Data Mining, Inference, and Prediction: book, available online, by Trevor Hastie, Robert Tibshirani, Jerome Friedman
Deep Learning with R: book, by François Chollet with J. J. Allaire
Cookbook for R, Statistical Analysis section provides examples of many basic statistical methods.
A Little Book of R for Bioinformatics covers basic analysis topics in the field.
Linear Algebra in R by Søren Højsgaard
Understanding Vectorization in R: Vectorization in R: Why? by Noam Ross or Let’s talk about vectorization by Alyssa Frazee
Writing Good R Code and Writing Well by Joseph Rickert points to lots of other good resources
Writing Better R Code by Laurent Gatto
Tidyverse Style Guide: style guide used by authors of some of R’s most popular packages
Efficient R Programming by Colin Gillespie and Robin Lovelace