BwUniCluster2.0/Software/R

Description	Content
module load	math/R
License	GPL
Citing	n/a
Links	Homepage | Documentation
Graphical Interface	No
Plugins	User dependent

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 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.

Versions and Availability

A list of versions currently available on all bwHPC-C5-Clusters can be obtained from the

Cluster Information System CIS

{{#widget:Iframe |url=https://cis-hpc.uni-konstanz.de/prod.cis/bwUniCluster/math/R |width=99% |height=280 }}
On the command line interface of any bwHPC cluster, a list of the available R versions using

$ module avail math/R

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 module

You can load the default version of R with the following command:

$ module load math/R

The module will try to load all modules it needs to function (e.g. compiler/intel). If loading the module fails, check if you have already loaded one of those modules, but not in the version required by R.

If you wish to load another (older) version of R, you can do so using

$ module load math/R/<version>

with <version> specifying the desired version.

Program Binaries

Standard usage:

Usage: R [options] [< infile] [> outfile]
    R CMD command [arguments]
  
Example: R CMD BATCH script.R

Executing R in batch mode:

R CMD BATCH --no-save --no-restore <INPUT_FILE>.R

For help run

R --help

For command help run

R CMD command --help

Further information and help

Man pages:          man R               man Rscript
Info pages. e.g.:   info R-intro        info R-FAQ
Manuals:            $R_DOC_DIR/manual

Multithreading in R

An easy way to use multiple cores on a single node in R is to use the doParallel package in combination with foreach.

Examples

As with all processes that require more than a few minutes to run, non-trivial compute jobs must be submitted to the cluster queuing system.

Example scripts are available in the directory $R_EXA_DIR:

$ module show math/R                      # show environment variables, which will be available after 'module load'
$ module load math/R                      # load module
$ ls $R_EXA_DIR                           # show content of directory $R_EXA_DIR
$ cat $R_EXA_DIR/README                   # show examples README

Run a first simple example job

$ module load math/R                                       # load module
$ mkdir Rtest                                              # create test directory
$ cp $R_EXA_DIR/bwhpc-r.moab $R_EXA_DIR/fit.R Rtest/       # copy example files to test directory
$ cd Rtest/                                                # change to directory
$ nano bwhpc-r.moab                                        # change job options, quit with 'CTRL+X'
$ msub bwhpc-r.moab                                        # submit job
$ checkjob -v <JOBID>                                      # check state of job
$ ls                                                       # when job finishes the results will be visible in this directory

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 possibility doing this is from within an interactive R session:

> 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.

Users can customize a common location of their personal library packages, e.g. ~/R_libs, rather than the default location. A customized directory must exist before installing a personal package for the first time, i.e.

$ mkdir -p ~/R_libs

The location must also be defined in a configuration file ~/.Renviron within the home directory containing the following line:

R_LIBS_USER="~/R_libs"

By setting up a (fixed) custom location for personal library packages, any personal package installed into that directory will be visible across different R versions. This may be advantageous if the packages are to be used with different (future) R versions.

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 rebuild with that new R version into the corresponding (version specific) library path. However, this is considered good practice anyway in order to ensure a consistent personal library stack for any specific R version in use. Mixing libraries built with different major and minor R versions is discouraged, as this may (or may not) result in unpredictable and subtle errors. Packages that are built and installed with one version of R may be incompatible with a newer version of R, at least when the major or minor version changes. The same is true if several versions are used simultaneously, e.g. a newer R version for a more recently started project and and older version for another project (but eventually picking up libraries built with the newer R version).

Special care has also to be taken by users who always load the default version, i.e.

$ module load math/R

as the default version number may change any time. Is is therefore highly recommended to always load a specific version, e.g.

$ module load math/R/3.5.3

Example builds for R-Packages

These examples(s) are for building R modules. Please write us a ticket if some instructions do not work for you or are outdated.

• Rgdal
• Rjags

Version-Specific Information

For information specific to a single version, see the information available via the module system with the command

$ module help math/R

Installed R plugins

• Bioinformatics: cummeRbund

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