R Resource Page

R is a language and environment for statistical computing and graphics. For comprehensive information visit the home page of the external page R Project for Statistical Computing.

Here are direct links to external page downloading R and to instructions on how to install R under external page Windows, external page Mac or external page Linux.

R is used most conveniently with an IDE (Integrated Development Environment). We recommend external page RStudio for this purpose, which works with all platforms (Windows, Mac, Linux) and has a number of rather useful features. If you plan to bring your own laptop, please, try and install R and RStudio in advance. Should you encounter problems, we will help you at the end of the first day. (If you already have a way of using R, e.g., Tinn-R, RKWard, etc, you are, of course, free to use that).

To get acquainted with R, we recommend reading external page An Introduction to R. Some sections are not relevant for the current course. We recommend reading the following sections: Sections 1-3, 5.1-5.4, 5.7.1, 5.8-5.9, 6, 7.1-7.2, 7.4, 9, 10.1, 10.3, 12.1-12.2, 12.4-12.6, 13. ~50 pages total. There is also an Download R tutorial (PDF, 567 KB) that accompanies the Download sample session (R, 9 KB) of this course.

You can download a concise Download reference card (PDF, 72 KB) that is useful to have around when writing R code.

A large number of books have been written on R (and its elder sister, S). For an older, but still relevant overview we recommend: William N. Venables and Brian D. Ripley. external page S Programming. Springer, New York, 2000.

A number of R tutorials are also available on the Web; unfortunately, in recent years these have mostly been transitioned to a fee-based model.

A external page 2015 paper in Nature explored the increasing popularity of R among scientists.