R - statistical computing and graphics


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 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". ...

Read more on the R project home page

VersionBuild DateInstallation Pathmodulefilecompiler
R 3.5.1 (gcc)06-oct-2018/sw/viz/R/3.5.1R/3.5.1gcc/8.2.0.hlrn
R 3.6.2 (gcc)05-feb-2020/sw/viz/R/3.6.2R/3.6.2gcc/7.5.0
R 4.0.2 (gcc)18-aug-2020/sw/viz/R/4.0.2R/4.0.2gcc/8.3.0
rstudio 0.98.110201-Aug-2014/sw/viz/R/rstudio_1.1.453

For a manual consult the R home page.


For the installation of R-packages by users with the help of rstudio or Rscript, the appropriate compiler module must be loaded in addition to the R-module.



Before starting R, load a modulefile

module load R/version

This provides access to the script R that sets up an environment and starts the R-binary. The corresponding man - and info pages become available.

Info pages: R-admin, R-data, R-exts, R-intro, R-lang, R-admin, R-FAQ, R-ints

As programming environment, rstudio Version 1.1.453 is installed and available, when a module file for R is loaded. rstudio starts the version of R specified with the module file.

Running R on the frontends

This is possible, but resources and runtime are  limited. Be friendly to other users and work on the shared compute nodes!

Running R on the compute nodes

Allocate capacity in the batch system, and log onto the related node:

$ salloc -N 1 -p large96:shared
$ squeue --job <jobID>

The output of salloc shows your job ID. With squeue you see the node you are going to use. Login with X11-forwarding:

$ ssh -X <nodename>

Load a module file and work interactively as usual. When ready, free the resources:

$ scancel <jobID>

You may also use srun:

$ srun -v -p large96:shared --pty --interactive bash

Do not forget to free the resources when ready.

R packages

List of installed R packages

The following packages are installed by default, when a new version of R is build. Please contact support to extend this list.

Users may request package installation via support or install in their HOME - directory.

Building R-packages  - users approach

Users may install their own packages in the HOME-directory from the rstudio gui or using Rscript. R-packages must be build with the same compiler as R itself was build, see the table above. This happens, when Rscript is used and the appropriate compiler module is loaded.

Building R-packages - administrators approach

R administrators may use rstudio or Rscript for installation. For installing packages in /sw/viz/R it is suggested, to use Rscript like

$ Rscript -e 'install.packages("'$package'",repos="'$REPOSITORY'",INSTALL_opts="--html")'

Using INSTALL_opts="--html" keeps documentation of installed packages up to date!

This becomes rapidly work intensive, when installing a huge bundle of packages or even the package set for a new R release. For convenience, we maintain a list of default packages and scripts to install them all. These are located in the installation directory:

  • install_packages,
  • install_cran
  • install_github
  • install_bioc
  • remove_package,
  • sync_wiki

Here also the workarounds are collected needed to install stiff packages, whose developers do not care and do not support all Rscript options.

Read more