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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.

The current "default" system version of R on Ubuntu 18.04 compute servers is R 3.4.4. This .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.    We have also installed other R versions (To see what OS version your compute server is running, type lsb_release -a on the command line.)

We also have other versions of R installed "side by side" – R-34.50.3 and R 3.6.1(and others in the future)– which can be accessed by typing R-3.5.3 and R 3.6.1 the specific version 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-34.50.3 and R 3.6.1 environments.
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 ). However multiple R versions are not available in RStudio Server because its 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.

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 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 4.0.3, an option Another option, one that provides maximum per-user flexibility (especially for single-compute-server PODs) 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 R 3.5 or 3.6other 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 3.5/3.6. 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.


User local package installations directories are typically under the user's ~/R directory (e.g. /stor/home/<user_name>/R). If a user has installed packages under multiple versions of R, there will be sub-directories for the different versions (e.g. ~/R/x86_64-pc-linux-gnu-library/3.46, ~/R/x86_64-pc-linux-gnu-library/34.60). Users can list the contents of these directories to see what packages they have installed locally.

To see what packages are installed system-wide for a given R version, users can look at the version's package installation directories:

  • R 3.4.4 (Ubuntu 18.04 only)
    • /usr/lib/R/library
    • /usr/lib/R/site-library
  • R (Ubuntu 18.04 only) /stor/system/opt/R/R-3.56.31/lib/R/library
  • R (Ubuntu 20.04 only)
    • /usr/lib/R/library
    • /usr/lib/R/site-librar
  • R 4.0.3 /stor/system/opt/R/R-34.60.13/lib/R/library

Local/Global package installation conflicts


Code Block
mv ~/R ~/R.bak

If this resolves the issueproduces a different error indicating that one or more locally installed packages are missing, the user may later find that they need to re-install other packages that were previously installed locally (check can re-install them then see if the problem is resolved. Check the now-named  ~/R.bak/x86_64-pc-linux-gnu-library/3.x directory, where x is the R version being used to see the packages that were locally installed packages).If this produces a different error indicating that one or more locally installed packages are missing, the user can re-install them then see if the problem is resolved.previously. Even if this resolves the immediate issue, the user may later find that they need to re-install other packages that were previously installed locally. 

Finally, if renaming the local R installation directory does not resolve the issue, it may be an issue with the globally installed packages, so Contact Us.

Troubleshooting other R/RStudio server issues

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 support email.

RStudio R Studio 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:

Code Block
mv ~/.rstudio ~/.rstudio.bak
mv ~/.RData ~/.RData.bak  
mv ~/.local/share/rstudio ~/.local.rstudio.bak

Note that .RData files may be in different directories. For example, if you a working in an R Project you have set up, there may be an .RData file in the project directory.


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.

Browser issues

A number of other issues can occur when attempting to use RStudio server in a browser. Most commonly these occur when first connecting to the server, but can also occur at later stages of use.

One common error is the "This site can't be reached" browser error (Windows), which may indicate a browser-related security issue.

For this and other browser related problems:

  • Close all browser windows and restart your computer.
    • Fragmented RAM (computer memory) can sometimes cause problems.
  • Make sure the browser software being used is the latest version.
    • Version checks are usually found in the browser's "Help" → "About" dialog, accessible from the options menu in the upper right of a browser's taskbar.
  • Try different browsers (e.g. Firefox instead of Chrome, Microsoft Edge, or Safari). 
    • If it is a security issue, a different browser may indicate that there are security risks accessing the website, but give the option to accept the risk and go ahead.
  • Try accessing the site from an "Incognito" or "Private" browser window (usually found in an options menu in the upper right of a browser's taskbar.
    • This can bypass browser cache or cookie issues that can interfere with connections.
  • Clear the browser's Cookie cache

If problems persist, please email our support email, including a description of what error or issue you are experiencing.

EDU pod connection issue

An issue on the EDU pod may arise when students use the virtual host to access RStudio server. The virtual host acts as a front end load balancer for the request, and forwards it to one of the back-end compute servers. If this occurs, try accessing individual servers specifically:

If accessing a specific server works when using the virtual host does not, please let us know by emailing our support email.

Disk quota exceeded

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. Users For example, you may see a "Cannot connect to service" message after logging in to 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:

Code Block
cannot create file'/stor/home/abattenh/output.tsv', reason 'Disk quota exceeded

To determine the status of your Home directory quota, just use SSH to login to one of your POD's compute servers. A message such as the one below will be displayed:

Code Block
Quota Report for abattenh
Mount Point          Used            Total      Last Checked
stor/home/abattenh   52G (51%)       100G       Mon 21 Sep 2020 11:32:02 AM CDT

If this issue arises, you should Contact Us to help relocate some of your Home directory contents to your Work or Scratch area. Just moving them yourself does not resolve the problem because Home directories have frequent snapshots taken that preserve copies of deleted files, and it requires a systems administrator to remove these snapshots (see Home directories for more information).

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.