R package management¶
Most of the useful R packages come in the form of packages that can be installed separately. Some of those are part of the default installation 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.
Standard R package installation¶
Setting up your own package repository for R is straightforward.
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
Start R and install the package:
Some R packages depend on libraries installed on the system. In that case, you first have to load the modules for these libraries, and only then proceed to the R package installation. For instance, if you would like to install the gsl R package, you would first have to load the module for the GSL library, .e.g.,
$ module load GSL/2.5-GCC-6.4.0-2.28
Note that R packages often depend on the specific R version they were installed for, so you may need to reinstall them for other versions of R.
Installing R packages using conda¶
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 is conda.
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
$ 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:
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.
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.
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
Additional information about conda can be found on its documentation site.