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General Information

Short Description: R, developed by GNU, is a freely available language and environment for statistical computing and graphics which provides a wide variety of statistical and graphical techniques: linear and nonlinear modelling, statistical tests, time series analysis, classification, clustering, and many other modern statistical methods

Long Description:  R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R.

R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering and more) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity.

One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control.

R is available as Free Software. It compiles and runs on a wide variety of UNIX platforms and similar systems (including FreeBSD and Linux), Windows and MacOS.

Installation Information

Version:  3.4.0

Version Date:  April 21, 2017 (updated quarterly).

Technical Support:  Please contact research services (

R can be easily installed for MAC, PC, or Linux by following the appropriate link on this page:  For new users we recommend downloading the base package.  You will download an executable file.  Opening this file will launch an installation wizard which will take you through the installation process.  This procedure should take approximately 5 minutes.  Please contact Boston College Research Services if you have difficulty.  

For large R projects, we recommend using the Linux Cluster.