| Table of Contents |
|---|
R and R Studio RStudio 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 older, but still popular R packages are. This section describes the versioning issues in both the system R and in the R Studio RStudio Server web application.
As of September 2020, the The "default" system version of R on Ubuntu 18.04 compute servers is R 3.6.1 , and the default for Ubuntu 20.04 is R 3.6.3 – this is the version that is invoked if you type R from the command line, and the version used by all R Studio Server instances.. (To see what OS version your compute server is running, type lsb_release -a on the command line.)
We also have two other versions of R installed "side by side" – R-3.4.4 and R-4.0.3 3 (and others in the future)– which can be accessed by typing R-3.4.4, the specific version from the command line (e.g. R-4.0.3 from the command line). However these multiple R versions are not available in R Studio RStudio Server because its R version setting can only be set to one value system-wide and cannot be specified per-user.
Most POD compute servers use R 3.6 as the R version in their RStudio Server web application but some use R 4.0.3 – see RStudio Server and Python JupyterHub web applications for details.
We have also installed many popular add-on packages in all the all R versions (e.g. tidyverse, ggplot2, DESeq2); however be aware that not all packages are available in all R versions.
If you need a GUI environment to access versions of R other than 3.6 or 4.10.3, an option that provides maximum per-user flexibility is as follows. Use the R Studio RStudio Server web application for R 3.6- or R 4.0.1-compatible workflows. For workflows requiring other R versions, users can install the R Studio desktop application a different version of R on their own desktop/laptop computers , using an underlying version(s) of R 3.4/4.0. Then, along with the RStudio desktop application. 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 RStudio 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.
...
In addition to the Local/Global package conflict issue described above, other issues can arise involving R Studio RStudio Server (or less commonly, command-line R). If all else fails, submit a help request to our rctf-support@utexas.edu support email.
R Studio RStudio Server becomes unresponsive
One common problem is that R Studio RStudio Server may become unresponsive, even with repeated attempts to establish a new session. To troubleshoot this sort of issue, close the R Studio RStudio Server application and make some R-associated files and directories invisible to R like this:
...
| Tip |
|---|
Large .RData files can be extremely slow to load from both R and R Studio RStudio Server. If you must save R data this way, consider renaming the .RData file to a different name so that it can be loaded explicitly only when needed, instead of always when R is invoked. |
...
Another type of problem can arise when a user's 100 GB Home directory quota has been exceeded (not applicable on the EDU pod). This can produce errors when trying to start R Studio RStudio Server or R, perform work in R, or even install additional packages. For example, you may see a "Cannot connect to service" message after logging in to R Studio RStudio Server. Or, if an R session has been established and saving a new file would exceed the Home directory quota, users will often (but not always) see an error like the following:
...
This issue can arise because R's default input/output directory is the user's home directory – but large files should not be stored or created there due to the 100 GB quota. Instead, R processing of large files should take place in the user's Work or Scratch area (e.g. /stor/work/<user's group name> or /stor/scratch/<user's group name>; users can find out which group(s) they belong to by typing the groups command on the command line). Users can navigate to Work or Scratch area directories using R's setwd function or using R Studio RStudio Server's file browser (e.g. via "Session" menu → "Set Working Directory" → "Choose Directory", or when a new R Project is created). Note that R Studio RStudio Server's file browser dialog will default to the user's Home directory, and the full path of the desired Work or Scratch area must be typed in.
...