R Resource Page

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

Here are direct links to external pagedownloading Rcall_made and to instructions on how to install R under external pageWindowscall_made, external pageMaccall_made or external pageLinuxcall_made.

R is used most conveniently with an IDE (Integrated Development Environment). We recommend external pageRStudiocall_made 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 pageAn Introduction to Rcall_made. 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 DownloadR tutorial (PDF, 566 KB)vertical_align_bottom that accompanies the Downloadsample session (R, 9 KB)vertical_align_bottom of this course.

You can download a concise Downloadreference card (PDF, 72 KB)vertical_align_bottom 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 pageS Programmingcall_made. 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 page2015 paper in Naturecall_made explored the increasing popularity of R among scientists.