BwUniCluster3.0/Software/R
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The main documentation is available on the cluster via |
| Description | Content |
|---|---|
| module load | math/R |
| License | GPL |
| Citing | n/a |
| Links | Homepage | Documentation |
| Graphical Interface | No |
| Plugins | User dependent |
Description
R is a free and open-source statistical programming environment based on S, developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues in the 1970s. 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.
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 under the terms of the Free Software Foundation's GNU General Public License in source code form. It compiles and runs on a wide variety of UNIX platforms and similar systems (including FreeBSD and Linux), Windows and macOS.
Usage
The R installation also provides the standalone library libRmath. This library allows you to access R routines from your own C or C++ programs (see section 9 of the 'R Installation and Administration' manual).
Loading the R software module
It is generally recommended to load a specific version of R, e.g.,
$ module load math/R/4.4.1-mkl-2022.2.1-gnu-13.3
in data-analytical workflows instead of the default version which may change from time to time.
Installing R-Packages into your home folder
Since we cannot provide a software module for every R package, we recommend to install special R packages locally into your home folder. One option for doing this is from within an interactive R session:
> library() # List preinstalled packages
> install.packages('package_name', repos="http://cran.r-project.org") # Installing your R package and the dependencies
> library(package_name) # Loading the package into you R instance
The package is now installed permanently in your home folder and is available every time you start R.
Note:
By default R uses a version (and platform) specific path for personal libraries, such as "$HOME/R/x86_64-pc-linux-gnu-library/x.y" for R version x.y.z. This directory will be created automatically (after confirmation) when installing a personal package for the first time.
A version specific path, such as the default path, allows users to maintain multiple personal library stacks for different (major and minor) R versions and does also prevent users from mixing their stack with libraries built with different R versions.
The drawback is that, whenever switching to a new R release, the personal library stack must be rebuilt with that new R version into the corresponding (version specific) library path. This is considered good practice anyway in order to ensure a consistent personal library stack for any specific R version in use.
Pre-installed R-packages
- Rmpi
- iterators
- foreach
- doMPI
- doParallel
Installation instructions for selected R packages
The following guides provide detailed instructions for building selected optional R packages on bwUniCluster for R version 4.4.2. Please write a ticket if the instructions do not work for you or are outdated.