RStudio Server vs. RStudio Desktop OOD apps

Disclaimer: The differences presented here are specifically applicable to RStudio in the FASRC Open OnDemand environment and not for the general RStudio Desktop vs. RStudio Server.

FASRC has implemented two different Open OnDemand (OOD, formerly called VDI) applications for RStudio:

• RStudio Server through the OOD app “RStudio Server”
• RStudio Desktop through the OOD app “Remote Desktop” then launching RStudio Desktop

In this doc, we attempt to explain the major difference between the two.

RStudio Server

RStudio Server is our go-to RStudio app because it contains a wide range of precompiled R packages from bioconductor and rocker/tidyverse. This means that installing R packages in RStudio Server is pretty straightforward. Most times, it will be sufficient to simply:

> install.packages("package_name")

This simplicity was possible because RStudio Server runs inside a Singularity container, meaning that it does not use the host operating system (OS). For more information on Singularity, refer to our Singularity on the cluster docs.

Important notes:

• User-installed R libraries will be installed in ~/R/ifxrstudio/\<IMAGE_TAG\>
• This app contains many pre-compiled packages from bioconductor and rocker/tidyverse.
• FAS RC environment modules (e.g. module load) and Slurm (e.g. sbatch) are not accessible from this app.
• For the RStudio with environment module and Slurm support, go to our Open OnDemand page select Interactive Apps > Remote Desktop and refer to Open OnDemand Remote Desktop: How to open software

This app is useful for most applications, including multi-core jobs. However, it is not suitable for multi-node jobs. For multi-node jobs, the recommended app is RStudio Desktop.

Installing R packages in RStudio Server in the FASSE cluster

If you are using FASSE Open OnDemand and need to install R packages in RStudio Server, you will likely need to set the proxies as explained in our Proxy Settings documentation. Before installing packages, execute these two commands in RStudio Server:

> Sys.setenv(http_proxy="http://rcproxy.rc.fas.harvard.edu:3128")
> Sys.setenv(https_proxy="http://rcproxy.rc.fas.harvard.edu:3128")

Running as a batch or interactive job

The RStudio Server OOD app hosted on Cannon at rcood.rc.fas.harvard.edu and FASSE at fasseood.rc.fas.harvard.edu runs RStudio Server in a Singularity container (see Singularity on the cluster). The path to the Singularity image on both Cannon and FASSE clusters is the same:

/n/singularity_images/informatics/ifxrstudio/ifxrstudio:RELEASE_<VERSION>.sif

Where <VERSION> corresponds to the Bioconductor version listed in the “R version” dropdown menu. For example:

R 4.2.3 (Bioconductor 3.16, RStudio 2023.03.0)

uses the Singularity image:

/n/singularity_images/informatics/ifxrstudio/ifxrstudio:RELEASE_3_16.sif

As mentioned above, when using the RStudio Server OOD app, user-installed R packages by default go in:

~/R/ifxrstudio/RELEASE_<VERSION>

Batch job

The command-line invocation in a batch job would be, for example (this will run the R script myscript.R):

This is an example of a batch script named runscript.sh that executes R script myscript.R inside the Singularity container RELEASE_3_16:

#!/bin/bash
#SBATCH -c 1 # Number of cores (-c)
#SBATCH -t 0-01:00 # Runtime in D-HH:MM
#SBATCH -p test # Partition to submit to
#SBATCH --mem=1G # Memory pool for all cores (see also --mem-per-cpu)
#SBATCH -o myoutput_%j.out # File to which STDOUT will be written, %j inserts jobid
#SBATCH -e myerrors_%j.err # File to which STDERR will be written, %j inserts jobid

# set R packages and rstudio server singularity image locations
my_packages=${HOME}/R/ifxrstudio/RELEASE_3_16
rstudio_singularity_image="/n/singularity_images/informatics/ifxrstudio/ifxrstudio:RELEASE_3_16.sif"

# run myscript.R using RStudio Server signularity image
singularity exec --cleanenv --env R_LIBS_USER=${my_packages} ${rstudio_singularity_image} Rscript myscript.R

To submit the job, execute the command:

sbatch runscript.sh

Interactive job

Or to run R interactively (this will launch an R shell that you can interact with) — not applicable to FASSE where interactive jobs are not allowed:

singularity exec --cleanenv --env R_LIBS_USER=$HOME/R/ifxrstudio/RELEASE_3_16 /n/singularity_images/informatics/ifxrstudio/ifxrstudio:RELEASE_3_16.sif R

RStudio Desktop

RStudio Desktop is a “bare” version of RStudio. Although it has some precompiled R packages, it is a much more limited list than the RStudio Server app.

RStudio Desktop runs on the host operating system (OS), the same environment as when you ssh to Cannon or FASSE.

This app is particularly useful to run multi-node applications because the you can specify the exact modules and packages that you need to load