![]() Check the now-named ~/R.bak/x86_64-pc-linux-gnu-library/x directory, where x is the R version being used to see the packages that were locally installed previously. 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. The resulting error messages can be rather obscure, but typically show up after system maintenance has been performed. DESeq2), some of which have been updated system-wide, but others of which have been locally installed and are not at a compatible level. This can sometimes cause problems when users invoke R tools with many dependencies (e.g. Globally-installed R add-on packages may be updated during system maintenance. Local/Global package installation conflicts To see what packages are installed system-wide for a given R version, users can look at the version's package installation directories: Users can list the contents of these directories to see what packages they have installed locally. If a user has installed packages under multiple versions of R, there will be sub-directories for the different versions (e.g. User local package installations directories are typically under the user's ~/R directory (e.g. This local directory is searched first, followed by one or more system directories where we have installed add-on packages system-wide. Typically, each user has a local package installation directory with packages they have installed. The libraries/add-on packages available in any given R version depend on the configured package installation directories, which can be listed in the R environment via the. Then run the "full" workflow from the POD compute server command line using the appropriate R version. What users can do in such cases is test the code in RStudio on their desktop computer, using smaller data sets if necessary. 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. 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). ![]() For workflows requiring other R versions, users can install a different version of R on their own desktop/laptop computers along with the RStudio desktop application. ![]() Use the RStudio Server web application for R 3.6- or R 4.0.1-compatible workflows. If you need a GUI environment to access versions of R other than 3.6 or 4.0.3, an option that provides maximum per-user flexibility is as follows. tidyverse, ggplot2, DESeq2) however be aware that not all packages are available in all R versions. We have also installed many popular add-on packages in all the R versions (e.g. 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. 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. We also have other versions of R installed "side by side" – R-4.0.3 (and others in the future)– which can be accessed by typing the specific version from the command line (e.g. (To see what OS version your compute server is running, type lsb_release -a on the command line.) 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. This section describes the versioning issues in both the system R and in the RStudio Server web application. 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.
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