Skip Navigation


Skip Side Navigation

Using R Packages on OSCER Systems

Table of Contents

What is R?

R is a programming language used for statistical computing and graphics that is supported by the R Foundation for Statistical Computing. It is highly extensible and provides a variety of statistical and graphical techniques.

Who can use R on OSCER resources?

R is available to all OSCER users.

What version of R is available on OSCER resources?

Click here to find what version of R is installed and available on OSCER systems.

You can always get the latest information by following the steps below:

  • Log into the OSCER system
  • Type module avail R
  • Scroll down the list until you see the listing for R (similar to the highlighted image below)

You can pick whichever version of R works best for your project, but should try to use the latest version available for best results. That is currently R/4.0.3-foss-2020b, but that will change.

What R packages are available on OSCER resources?

OSCER maintains several R packages that can be used without the need to install a local package. Follow the steps below to get the most up-to-date list:

  • Log into the OSCER system
  • Load the module for the version of R you need by typing (for example): module load R/4.0.3-foss-2020b
  • Then start an R session by typing: R
  • To obtain the list of R packages, type: library()

Starting R

  • Log into the OSCER system
  • Load the module for the version of R you need by typing (for example): module load R/4.0.3-foss-2020b
  • Then start an R session by typing: R

This loads R in interactive mode and will place you inside the R console. This is the place where you would install additional R packages, and run small R scripts (less than 3 minutes in duration).

NOTE: Any computational intensive work should be submitted as a job. Please refer to Running R in a Job.

Installing Additional R Packages

This section guides you through the process of installing R packages locally without root access on OSCER systems.

We highly recommend looking through the pre-installed packages to see if the package you want is already installed on OSCER systems. To do this, please refer to What R packages are available on OSCER resources?

The follow steps will guide you through the process of installing local R packages in your home directory. Please note that we do not recommend installing R packages in your scratch folder because of our autodelete policy.

In this example, we will install the package ACSWR from

  • Log into the OSCER system
  • Load the module for the version of R you need by typing(for example): module load R/4.0.3-foss-2020b
  • Start an R session by typing: R
  • To install the ACSWR package, type: install.packages(c("ACSWR"))
  • You will be prompted with the following message:
          Installing package into ‘/usr/lib64/R/library’
          (as ‘lib’ is unspecified)
          Warning in install.packages(c("ACSWR")) :
           'lib = "/usr/lib64/R/library"' is not writable
          Would you like to use a personal library instead?  (y/n)
  • Type y and press Enter. This tells R that you will be installing the package in a local folder that you have access to (your home directory).
  • Next, you will be prompted with the following message:
         Would you like to create a personal library
         to install packages into?  (y/n)
  • Type y and press Enter. This will create the folder /R/x86_64-redhat-linux-gnu-library/3.3 in your home directory.
  • Next, you'll get the following message asking you to choose an install source:
         --- Please select a CRAN mirror for use in this session ---     
    HTTPS CRAN mirror
     1: 0-Cloud [https]                   2: Algeria [https]     
     3: Australia (Canberra) [https]      4: Australia (Melbourne 1) [https]     
     5: Australia (Melbourne 2) [https]   6: Australia (Perth) [https]     
     7: Austria [https]                   8: Belgium (Ghent) [https]     
     9: Brazil (PR) [https]              10: Brazil (RJ) [https]     
    11: Brazil (SP 1) [https]            12: Brazil (SP 2) [https]     
    13: Bulgaria [https]                 14: Canada (BC) [https]     
    15: Canada (MB) [https]              16: Canada (NS) [https]     
    17: Chile 1 [https]                  18: Chile 2 [https]     
    19: China (Beijing) [https]          20: China (Hefei) [https]     
    21: China (Guangzhou) [https]        22: China (Lanzhou) [https]     
    23: China (Shanghai 1) [https]       24: China (Shanghai 2) [https]     
    25: Colombia (Cali) [https]          26: Czech Republic [https]     
    27: Denmark [https]                  28: East Asia [https]     
    29: Ecuador (Cuenca) [https]         30: Ecuador (Quito) [https]     
    31: Estonia [https]                  32: France (Lyon 1) [https]     
    33: France (Lyon 2) [https]          34: France (Marseille) [https]     
    35: France (Montpellier) [https]     36: France (Paris 2) [https]     
    37: Germany (Erlangen) [https]       38: Germany (Göttingen) [https]     
    39: Germany (Münster) [https]        40: Greece [https]     
    41: Iceland [https]                  42: India [https]     
    43: Indonesia (Jakarta) [https]      44: Iran [https]     
    45: Ireland [https]                  46: Italy (Padua) [https]     
    47: Japan (Tokyo) [https]            48: Japan (Yonezawa) [https]     
    49: Korea (Seoul 1) [https]          50: Korea (Ulsan) [https]     
    51: Malaysia [https]                 52: Mexico (Mexico City) [https]     
    53: New Zealand [https]              54: Norway [https]     
    55: Philippines [https]              56: Serbia [https]     
    57: Singapore (Singapore) [https]    58: Spain (A Coruña) [https]     
    59: Spain (Madrid) [https]           60: Sweden [https]     
    61: Switzerland [https]              62: Taiwan (Chungli) [https]     
    63: Turkey (Denizli) [https]         64: Turkey (Mersin) [https]     
    65: UK (Bristol) [https]             66: UK (London 1) [https]     
    67: USA (CA 1) [https]               68: USA (IA) [https]     
    69: USA (IN) [https]                 70: USA (KS) [https]     
    71: USA (MI 1) [https]               72: USA (NY) [https]     
    73: USA (OH) [https]                 74: USA (OR) [https]     
    75: USA (TN) [https]                 76: USA (TX 1) [https]     
    77: Vietnam [https]                  78: (HTTP mirrors)
  • Type 1 and press Enter.
  • At this point, the system will attempt to download the source package and install it in your home directory.
  • Upon completion, you have successfully installed the ACSWR package in the following location in your home directory: /R/x86_64-redhat-linux-gnu-library/3.3/ACSWR

Running R in a Job

The sample code below shows how to submit an R job via the batch script. If you are not familiar with OSCER's job scheduler or submitting batch scripts, please refer to the following tutorials first:

Steps to submit an R job:

Step 1: Create a sample R job script called helloworld.r and insert the following line of code:

print("hello world")

Step 2: Create a batch submission file called and insert the following lines of code:

#SBATCH --partition=normal
#SBATCH --ntasks=1
#SBATCH --mem=1024
#SBATCH --output=r_output_%J.txt
#SBATCH --error=r_error_%J.txt
#SBATCH --time=12:00:00
#SBATCH --job-name=jobname
#SBATCH --mail-type=ALL
#SBATCH --chdir=/home/yourusername/directory_to_run_in
module load R/4.0.3-foss-2020b
Rscript helloworld.r > output.txt

In summary, the batch script asks for 1 CPU core along with 1024MB of memory for 12 hours. If your job expeccts to run on multiple cores in parallel, please specify that in '--ntasks=' instead. If it can run on all cores in a node (currently 20 or 24 on Schooner copmute nodes), please replace '--ntasks=1' with '--exclusive', to request the entire node, and prevent other jobs from running there at the same time.

Once the job starts, it runs the commands:

module load R/4.0.3-foss-2020b
Rscript helloworld.r > output.txt

on whatever compute node the job is running on. The first command prepares to run R. And the second actually runs the R script helloworld.R and places the output in the file output.txt. You can change these to whatever values are appropriate for you.

Note: Any large output should go to your /scratch space as your home directory is modest (currently 20GB). /scratch is never backed up and is subject to file purging/cleaning of older files (files older than 2 weeks).

Step 3: Submit the batch job by typing sbatch

Step 4: Upon successful completion of the job, you will find three new files in your directory: output.txt, r_output_<SLURM_jobID>.txt and r_error_<SLURM_jobID>.txt. The '%J' will be replaced by the SLURM jobID, therefore avoiding overwriting files from older jobs.

Step 5: If the job ran successfully you should see the results in output.txt. If your R job produces other output files, they will be created in that directory as well.