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R is a free software environment for statistical computing and graphics. On the Yale Clusters there are a couple ways in which you can set up your R environment. There is no R executable provided by default; you have to choose one of the following methods to be able to run R.

The R Module

We provide several versions of R as software modules. These modules provide a broad selection of commonly used packages pre-installed. Notably, this includes a number of geospatial packages like sf, sp, raster, and terra.

In addition, we install a collection of the most common bioconductor bioinformatics packages (homepage) called R-bundle-Bioconductor. This can be loaded in addition to the matching R module to provide simple access to these tools.

Find and Load R

Find the available versions of R version 4 with:

module avail R/4

To load version 4.2.0:

module load R/4.2.0-foss-2020b

To show installed R packages and their versions for the R/4.2.0 module:

module help R/4.2.0-foss-2020b

Between the base R module and the R-bundle-Bioconductor module, there are over 1000 R packages installed and ready to use.

To find if your desired package is available in these modules, you can run module spider $PACKAGE/$VERSION:

module spider Seurat/4.1.1

  Seurat: Seurat/4.1.1 (E)
    This extension is provided by the following modules. To access the extension you must load one of the following modules. Note that any module names in parentheses show the module location in the software hierarchy.


Names marked by a trailing (E) are extensions provided by another module.

So to get this version of Seurat, you can load the R-bundle-Bioconductor module. Then you simple library(Seurat) to use that tool.

Install Packages

The software modules include many commonly used packages, but you can install additional packages specifically for your account. As part of the R software modules we define an environment variable which directs R to install packages to your project space. This helps prevent issues where R cannot install packages due to home-space quotas. To change the location of where R installs packages, the R_LIBS_USER variable can be set in your ~/.bashrc file:


where %v is a placeholder for the R major and minor version number (e.g. 4.2). R will replace this variable with the correct value automatically to segregate packages installed with different versions of R.

We recommend you install packages in an interactive job with the slurm option -C oldest. This will ensure the compiled portions of your R library are compatible with all compute nodes on the cluster. If there is a missing library your package of interest needs you should be able to load its module. If you cannot find a dependency or have trouble installing an R package, please get in touch with us.


Grace's login nodes have newer architecture than the oldest nodes on the cluster. Always install packages in an interactive job submitted with the -C oldest Slurm flag if you want to ensure your code will work on all generations of the compute nodes.

To get started load the R module and start R:

module load R/4.2.0-foss-2020b
# in R
> install.packages("lattice", repos="")

This will throw a warning like:

Warning in install.packages("lattice") :
'lib = "/ysm-gpfs/apps/software/R/4.2.0-foss-2020b/lib64/R/library"' is not writable
Would you like to create a personal library
to install packages into?  (y/n)


If you encounter a permission error because the installation does not prompt you to create a personal library, create the directory in the default location in your home directory for the version of R you are using; e.g.,

mkdir /path/to/your/project/space/R/4.2
You only need the general minor version such as 4.2 instead of 4.2.2.

You can customize where packages are installed and accessed for a particular R session using the .libPaths function in R:

# List current package locations
> .libPaths()

# Add new default location to the standard defaults, e.g. project/my_R_libs
> .libPaths(c("/home/netID/project/my_R_libs/", .libPaths()))

Run R

We will kill R jobs on the login nodes that are using excessive resources. To be a good cluster citizen, launch your R computation in jobs. See our Slurm documentation for more detailed information on submitting jobs.

Interactive Job

To run R interactively, first launch an interactive job on a compute node. If your R sessions will need up to 10 GiB of RAM and up to 4 hours, you would submit you job with:

salloc --mem=10G -t 4:00:00

Once your interactive session starts, you can load the appropriate module or Conda environment (see above) and start R by entering R on your command prompt. If you are happy with your R commands, save them to a file which can then be submitted and run as a batch job.

Batch Mode

To run R in batch mode, create a plain-text batch script to submit. In that script, you can run your R script. In this case myscript.R is in the same directory as the batch script, batch script contents shown below.

#SBATCH -J my_r_program
#SBATCH --mem=10G
#SBATCH -t 4:00:00

module load R/4.1.0-foss-2020b
Rscript myscript.R

To actually submit the job, run sbatch where the batch script above was saved as


You can run RStudio app via Open Ondemand. Here you can select the desired version of R and RStudio and launch an interactive compute session.

Parallel R

On a cluster you may want to use R in parallel across multiple nodes. While there are a few different ways this can be achieved, we recommend using the R software module which already includes Rmpi, parallel, and doMC.

To test it, we can create a simple R script named ex1.R



mpi.bcast(val, 1, 0, 0)
print(paste("me", me, "val", val))


Then we can launch it with an sbatch script (


#SBATCH -n 4 
#SBATCH -t 5:00

module purge
module load R/4.1.0-foss-2020b

srun Rscript ex1.R

This script should execute a simple broadcast and complete in a few seconds.

Virtual Display Session

It is common for R to require a display session to save certain types of figures. You may see a warning like "unable to start device PNG" or "unable to open connection to X11 display". There is a tool, xvfb, which can help avoid these issues.

The wrapper xvfb-run creates a virtual display session which allows R to create these figures without an X11 session. See the guide for xvfb for more details.

Conda-based R Environments

If there isn't a module available for the version of R you want, you can set up your own R installation using Conda. With Conda you can manage your own packages and dependencies, for R, Python, etc.

Most of the time the best way to install R packages for your Conda R environment is via conda.

# load miniconda
module load miniconda
# create the conda environment including r-base and r-essentials package collections
conda create --name my_r_env r-base r-essentials
# activate the environment
conda activate my_r_env

# Install the lattice package (r-lattice)
conda install r-lattice

If there are packages that conda does not provide, you can install using the install.packages function, but this may occasionally not work as well. When you install packages with install.packages make sure to activate your Conda environment first.

module load miniconda
source activate my_r_env
# in R
> install.packages("lattice", repos="")


Conda-based R may not work properly with parallel packages like Rmpi when running across multiple compute nodes. In general, it's best to use the module installation of R for anything which requires MPI.

Last update: February 27, 2023