R and R Studio Server versions
The issue of R versions is a difficult one, especially now that many important single-cell packages are only available in newer R versions, but not all "standard" R packages are.
The current "default" system version of R on compute servers is R 3.4.4. This is the version that is invoked if you type R from the command line. We have also installed other R versions "side by side" – R-3.5.3 and R 3.6.1 – which can be accessed by typing R-3.5.3 and R 3.6.1 from the command line.
We have also installed many popular add-on packages in the default R 3.4.4. environment (e.g. tidyverse, ggplot2, DESeq2), most of them also in the R-3.5.3 and R 3.6.1 environments. To see what packages are installed system-wide for a given R version, you can look at its package installation directories:
- R 3.4.4
- R 3.5.3 – /stor/system/opt/R/R-3.5.3/lib/R/library
- R 3.6.1 – /stor/system/opt/R/R-3.6.1/lib/R/library
R Studio Server, the web application, is currently configured to use R 3.4.4, the "default" system version of R. This R Studio Server R version setting can only be set to one value system-wide and cannot be specified per-user. If your POD owners agree, we can change the R Studio Server R version to, for example, R 3.6.1. If your POD has more than one compute server (as most PODs do), we can change the default R Studio Server R version one just one of the compute servers, leaving others at the default R 3.4.4, version so that both can be used as needed. Please contact us if this is an option you would like to implement.
Note also that the Enterprise version of R Studio Server can set the R version used per-user, but that is a licensed product and is quite expensive. But if your teams wish to purchase a license, we're happy to install it.
Another option, one that provides maximum per-user flexibility, is as follows. Use the R Studio Server web application for R 3.4-compatible workflows. For workflows requiring R 3.5 or 3.6, users can install the R Studio desktop application on their own desktop/laptop computers, using an underlying version(s) of R 3.5/3.6. Then, users can access files on shared storage by mounting their Work area file system via Samba (see Samba remote file system access for more information). The main drawback to this workflow is that typical personal computers do not have as much RAM as POD compute servers, and some R tasks can be memory intensive. What users can do in such cases is test the code in R Studio on their desktop computer, using smaller data sets if necessary. Then run the "full" workflow from the POD compute server command line using the appropriate R version.