Introduction

Most of the useful R packages come in the form of packages that can be installed separatly. Some of those are part of the default installtion on VSC infrastructure. Given the astounding number of packages, it is not sustainable to install each and everyone system wide. Since it is very easy for a user to install them just for himself, or for his research group, that is not a problem though. Do not hesitate to contact support whenever you encounter trouble doing so.

Installing your own packages using conda

The easiest way to install and manage your own R environment 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 iniconda'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.

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 independencies, 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 environement 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.

Alternatives to conda

Setting up your own package repository for R is straightforward.

  1. Load the appropriate R module, i.e., the one you want the R package to be available for:
    $ module load R/3.2.1-foss-2014a-x11-tcl
  2. Start R and install the package :
    > install.packages("DEoptim")
  3. Alternatively you can download the desired package:
    $ wget cran.r-project.org/src/contrib/Archive/DEoptim/DEoptim_2.0-0.tar.gz
  4. And install the package from the command line: $ R CMD INSTALL DEoptim_2.2-3.tar.gz -l /$VSC_HOME/R/
  5. These packages might depend on the specific R version, so you may need to reinstall them for the other version.

Systems