R / RStudio

Setting up R & RStudio:

If you are using a Mac, it would probably be to your benefit to first download XCode from the App Store. This will take you somewhere between 20 minutes to an hour, depending on your internet speed. Depending on the packages you'll be using, you may also need to install XQuartz.

You can download the most recent version of R here. Note that this only installs the "command line" version of R!

Most R users will want to use RStudio instead, which provides you with an easy-to-use graphic interface for R. You can download the most recent version of RStudio here. Note that RStudio requires an existing installation of R!

Programming using R/RStudio:

There are many useful (and free!) tutorials on R on the internet. Here are some tutorials that you may find especially helpful:

• For absolute beginners
  • The R tutorial from Code School is a very gentle (and often funny!) introduction for people who are absolute beginners to R, and who consider themselves coding novices.
  • This R tutorial written by Kelly Black is also designed for people who are beginners to R, but who may want a more straightforward/serious introduction to R.
• Beginner / Intermediate
  • Harvard's Institute for Quantitative Social Science has a series of excellent tutorials on essential R tasks.
  • This R tutorial written by Garrett Grolemund and Hadley Wickham is an excellent resource for people who want to dive right into data science. Although it is written for beginners, it moves at a brisk pace. You can also purchase a physical copy of this tutorial here.
• Misc
  • Gaston Sanchez's entire website is a treasure trove of stats knowledge.

Once you become familiar with basic R syntax, the absolute best way to learn R is to inherit somebody else's code and play around with it. Try asking your labmates for old datasets and sample code! As with everything, the more you make mistakes, the more you learn.

A partial list of very useful R packages:

• Organizing data
  • dplyr – data aggregation tools
  • tidyr – data restructuring
• Data visualization
  • ggplot2 – data visualization
  • scales – formatting data for visualization
• Statistics
  • ez – useful for running repeated measures ANOVAs
  • lme4 – useful for constructing GLMs
    • lmerTest – provides p-values on GLMs run on lme4
    • MuMIn – provides r-squared values on GLMs run on lme4