R is a free open-source programming language and software environment for statistical computing and data science.

Data Lab Resources

  • Installing R and RStudio – Low-effort installation guide to set up R and RStudio on your Windows, macOS, or Linux computer.
  • A Gentle Introduction to R – Interactive introductory workshop to get you started with the statistical programming language R.
  • R and RStudio Basics – Detailed written tutorial that covers all the basics of the R programming language and the RStudio IDE.

Tufts University Courses

The following courses have been known to either teach or use the R statistical programming language.
Please see departmental listings for details and SIS for current or future offerings.

  • BIO-0132: Biostatistics
  • BIO-0133: Ecological Statistics and Data
  • CEE-0152: Environmental Health Data Lab
  • CEE-0156: Biostatistics
  • CS-0152-SBI: Statistical Bioinformatics
  • DATA-0200 Foundations of Data Analytics
  • ENV-0170: Environmental Data Analysis and Visualization
  • ES-0056: Probability and Statistics
  • UEP-0236: Spatial Statistics

Udemy Courses

Tufts provides all affiliates with free access to Udemy Business, an online learning platform with curated on-demand courses.
To access the platform, navigate to tufts.udemy.com, click on Continue with SSO, and then log in with your Tufts credentials.
The platform is also accessible via the Udemy Business mobile application available on the Apple App Store and Google Play.

External Resources

  • R for Data Science – Renowned free online book that covers the best data science practices and workflows using R.
  • Hands-On Programming with R – Free online book aimed at beginners and containing numerous hands-on exercises.
  • RStudio Cheatsheets – Excellent references on using various popular R packages for specific tasks and workflows.
  • RStudio Education – Curated R learning paths for both beginners and advanced users along with resources for instructors.
  • RStudio Primers – Interactive tutorials covering all aspects of using R for data cleaning, analysis, and visualization.
  • RShiny Tutorial – Comprehensive video-based tutorial on creating interactive web applications using RShiny.
  • Quick-R – Useful references for experienced Stata, SAS, and SPSS users looking to transition into R programming.
  • R Graph Gallery – Collection of various types of data visualizations created using R along with the corresponding code.
  • Beautiful Plotting in R – Thorough cheatsheet outlining how to create and style various plots using the ggplot2 package.
  • UCLA R Resources – Comprehensive collection of various R resources by the Statistical Consulting Group at UCLA.
  • CRAN Task Views – Subject-specific collections of useful R packages.