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R and RStudio#

R is a free software environment for statistical computing and graphics. RStudio is an IDE for R.

Availability / Target HPC systems#

For standalone-simulations, the following HPC systems are best suited:

  • JupyterHub: best suited for interactive use of RStudio (only for Tier3 HPC accounts)
  • Woody: best suited for smaller calculations
  • TinyFat: for calculations with large memory requirements

Different versions of R are available via environment modules. They may also vary between the clusters. All available versions can be listed via module avail r.


  • As some compute nodes cannot access the internet directly, you might have to configure a proxy server, e.g. for package installation. Use the commands Sys.setenv(http_proxy="http://proxy:80") and Sys.setenv(https_proxy="http://proxy:80") in your R command line.

  • We used to provide the Microsoft R Open distribution (modules r/xxx-mro) as this distribution internally uses Intel MKL for some compute intensive routines to improve performance. As Microsoft stopped their R distribution, we switched to Conda (modules r/xxx-conda).

RStudio on JupyterHub#

Interactive usage of RStudio is available as a custom kernel in JupyterHub. The following steps are necessary:

  1. Access JupyterHub according to this guide.
  2. Select Rocker/RStudio from the job profiles. RStudio can either run locally on JupyterHub or as a Slurm job on a compute node.


  3. Choose RStudio from the available notebook types.


  4. You can now use RStudio.


  5. After you finished your work, remember to stop your instance manually by going back to the hub control panel (File > Hub Control Panel) and selecting Stop My Server. Closing the browser or logging out from JupyterHub will NOT free the resources!


RStudio on woody#

The Open Source Edition of RStudio Server is not able to handle multiple users; thus, we cannot provide a central RStudio Server.

However, a single-user instance of RStudio Server can be run through Apptainer on Memoryhog, a Woody, TinyFat or TinyGPU compute node. The setup is based on the Rocker Project. It can be used in the following way:

  • Start an interactive job on a Woody, TinyFat or TinyGPU compute node or connect directly to Memoryhog.
  • Execute the script under /apps/rstudio/ to start RStudio Server.
  • The script will tell you how to forward the required port to your local machine and the access credentials.
  • Use RStudio Server interactively.
  • Once you are finished, don't forget to kill the server and job!