R package management

Introduction

There exist thousands of R packages, available from online repositories like CRAN, Bioconductor or github. Depending on the R version, the more commonly used packages like ggplot2, tidyverse or readr are either already included in the centrally installed R module or can be accessed by loading the R-bundle-CRAN and R-bundle-Bioconductor modules, e.g.:

$ module load R-bundle-Bioconductor/3.16-foss-2022b-R-4.2.2

It is possible, however, that these modules do not contain all R packages you need or that the package versions do not meet your requirements. In this case you will need to locally install those packages, as will be described below. Do not hesitate to contact your local support team when encountering issues during these local installations.

Standard R package installation

Firstly, it is important to realize that R by default uses the $VSC_HOME/R path to install new packages. Since $VSC_HOME has limited quota, it is not the recommended location to install software. Instead, we recommend to use $VSC_DATA.

Secondly, it should be kept in mind that R packages often include extensions written in compiled languages (e.g. C++ or Fortran) and that the centrally installed R modules are configured to compile these extensions with optimizations for the CPU architecture at hand. This means that such R packages cannot in general be used on different partitions than the one they were created on.

Thirdly, R packages may also only work with certain versions of R and not with other versions.

With these three considerations in mind, we recommend to use a directory structure which provides a unique path for each OS version, hardware architecture and R version. The example below creates such a structure for a Rocky8 OS, Icelake CPU and R version 4.2.2:

# From within an interactive session on an icelake compute node:
$ module load R/4.2.2-foss-2022b
$ mkdir -p ${VSC_DATA}/Rlibs/${VSC_OS_LOCAL}/${VSC_ARCH_LOCAL}/R-${EBVERSIONR}

The next step is to ensure such install locations are used by default in the R package installation process. This can be done by setting the R_LIBS_USER variable to in the ~/.Renviron file as follows:

$ echo 'R_LIBS_USER=${VSC_DATA}/Rlibs/${VSC_OS_LOCAL}/${VSC_ARCH_LOCAL}/R-${EBVERSIONR}' >> ~/.Renviron

The ${VSC_OS_LOCAL} and ${VSC_ARCH_LOCAL} environment variables are predefined and match the OS version (e.g. rocky8) and CPU model (e.g. icelake) of the node. The ${EBVERSIONR} variable contains the R version (e.g. 4.2.2) of the currently loaded R module.

R will now use this path as default install path, ensuring you are always installing your packages in the appropriate R library folder.

Note

This .Renviron configuration will also work as expected in Open OnDemand apps such as RStudio Server.

The next step is to load the appropriate R module and run R.

# From within an interactive session on an icelake compute node:
$ module load R/4.2.2-foss-2022b
$ R

From here, installing packages can be as simple as:

> install.packages("DEoptim")

If you are unsure whether R will install your desired package in the correct location, you can first list the known library locations by executing .libPaths(). The first location is the default one.

You can also specify your desired library path as an extra argument in the install command. This will take precedence over any defaults.

> Rlibs <- "/path/to/my/R_library"
> install.packages("DEoptim", lib = Rlibs)

Alternatively you can download the desired package

$ wget cran.r-project.org/src/contrib/Archive/DEoptim/DEoptim_2.0-0.tar.gz

and install it from the command line with

# From within an interactive session on an icelake compute node:
$ module load R/4.2.2-foss-2022b
$ R CMD INSTALL DEoptim_2.0-0.tar.gz  -l ${VSC_DATA}/Rlibs/${VSC_OS_LOCAL}/${VSC_ARCH_LOCAL}/R-${EBVERSIONR}

If the installation of a package requires devtools, please consult the devtools documentation.

Installing R packages using conda

Note

Conda packages are incompatible with the software modules. Usage of conda is discouraged in the clusters at UAntwerpen, UGent, and VUB.

The easiest way to install and manage your own R environment(s) is conda.

Installing Miniconda

If you have Miniconda already installed, you can skip ahead to the next section, if Miniconda is not installed, we start with that. Download the Bash script that will install it from conda.io using, e.g., wget:

$ wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh

Once downloaded, run the installation script:

$ bash Miniconda3-latest-Linux-x86_64.sh -b -p $VSC_DATA/miniconda3

Optionally, you can add the path to the Miniconda installation to the PATH environment variable in your .bashrc file. This is convenient, but may lead to conflicts when working with the module system, so make sure that you know what you are doing in either case. The line to add to your .bashrc file would be:

export PATH="${VSC_DATA}/miniconda3/bin:${PATH}"

Creating an environment

First, ensure that the Miniconda installation is in your PATH environment variable. The following command should return the full path to the conda command:

$ which conda

If the result is blank, or reports that conda can not be found, modify the `PATH` environment variable appropriately by adding miniconda’s bin directory to PATH.

Creating a new conda environment is straightforward:

$ conda create -n science -c r r-essentials r-rodbc

This command creates a new conda environment called science, and installs a number of R packages that you will probably want to have handy in any case to preprocess, visualize, or postprocess your data. You can of course install more, depending on your requirements and personal taste.

A lot of bioconda and bioconductor packages are not in sync with their dependencies, therefore you may need to create a separate environment for each of those packages to avoid conflicts.

Working with the environment

To work with an environment, you have to activate it. This is done with, e.g.,

$ source activate science

Here, science is the name of the environment you want to work in.

Install an additional package

To install an additional package, e.g., `pandas`, first ensure that the environment you want to work in is activated.

$ source activate science

Next, install the package:

$ conda install -c r r-ggplot2

Note that conda will take care of all dependencies, including non-R libraries. This ensures that you work in a consistent environment.

Updating/removing

Using conda, it is easy to keep your packages up-to-date. Updating a single package (and its dependencies) can be done using:

$ conda update r-rodbc

Updating all packages in the environment is trivial:

$ conda update --all

Removing an installed package:

$ conda remove r-mass

Deactivating an environment

To deactivate a conda environment, i.e., return the shell to its original state, use the following command

$ source deactivate

More information

Additional information about conda can be found on its documentation site.

For installing R packages from github or other repositories see also R devtools: