R
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. In addition, we install a collection of the most common CRAN packages (homepage) called R-bundle-CRAN, as well as bioconductor bioinformatics packages (homepage) called R-bundle-Bioconductor.
Find and Load R
Find the available versions of R version 4 with:
module avail R/4
To load version 4.4.1:
module load R/4.4.1-foss-2022b
Loading the R module (e.g. R/4.4.1-foss-2022b) automatically loads the R-bundle-CRAN and the R-bundle-Bioconductor modules. With these modules, 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/5.1.0
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Seurat: Seurat/5.1.0 (E)
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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.
R-bundle-Bioconductor/3.19-foss-2022b-R-4.4.1
Names marked by a trailing (E) are extensions provided by another module.
So this version of Seurat is included in the R-bundle-Bioconductor module for R version 4.4.1. By loading the R/4.4.1-foss-2022b module, you can simply run library(Seurat) to use that tool without installing it yourself.
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 home space.
To change the location of where R installs packages, the R_LIBS_USER variable can be set in your ~/.bashrc file:
export R_LIBS_USER=$GIBBS_PROJECT/R/%v
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. 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.
To get started load the R module and start R:
salloc
module load R/4.4.1-foss-2022b
R
# in R
> install.packages("lattice", repos="http://cran.r-project.org")
This will throw a warning like:
Warning in install.packages("lattice", repos = "http://cran.r-project.org") :
'lib = "/vast/palmer/apps/avx2/software/R/4.4.1-foss-2022b/lib64/R/library"' is not writable
Would you like to use a personal library instead? (yes/No/cancel) yes
Would you like to create a personal library
‘/home/netID/R/4.4.1-foss-2022b’
to install packages into? (yes/No/cancel) yes
Note
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 -p $HOME/R/4.4.1-foss-2022b
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.
#!/bin/bash
#SBATCH -J my_r_program
#SBATCH --mem=10G
#SBATCH -t 4:00:00
module load R/4.4.1-foss-2022b
Rscript myscript.R
To actually submit the job, run sbatch my_r_job.sh where the batch script above was saved as my_r_job.sh.
RStudio
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
library("Rmpi")
n<-mpi.comm.size(0)
me<-mpi.comm.rank(0)
mpi.barrier(0)
val<-777
mpi.bcast(val, 1, 0, 0)
print(paste("me", me, "val", val))
mpi.barrier(0)
mpi.quit()
For some versions of R, the Rmpi package needs to be installed first. Then we can launch it with an sbatch script (ex1.sh):
#!/bin/bash
#SBATCH -n 4
#SBATCH -t 5:00
module purge
module load R/4.4.1-foss-2022b
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.
salloc
module load miniconda
source activate my_r_env
R
# in R
> install.packages("lattice", repos="http://cran.r-project.org")
Warning
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.