Using RStudio on the Quest Analytics Nodes
How to use RStudio on the Quest Analytics Nodes.
The Quest Analytics Nodes allow users with active Quest allocations to use RStudio from a web browser. See Research Computing: Quest Analytics Nodes for an overview of the system.
Note: you cannot submit jobs to the Quest job scheduling system using the Quest Analytics Nodes.
Connections to the Quest Analytics Nodes are limited to the Northwestern network. If you are connecting from off-campus, you must use the Northwestern VPN.
Using any browser, connect to: https://rstudio.questanalytics.northwestern.edu
Sign in with your NetID and password.
Sessions time out after one hour of inactivity.
You can also transfer files smaller than 500MB using the Files tab in RStudio. There is an upload button that will allow you to select files from your computer to transfer.
When you connect to RStudio, you will initially be in your home directory. This is /home/<netid> on Quest.
To access a project directory, click on the button with three small dots on the right in the Files tab. Then enter the full path to your project folder (ex. /projects/<allocationID>).
To transfer files larger than 500MB to Quest, please use SFTP or another transfer method.
Users may install packages using the Packages tab in RStudio or the install.packages function in R. Any packages you install on Quest for R version 3.5.1 will also be available to you on the Quest Analytics Nodes. Some packages that require code to be compiled may need to be installed by connecting to a Quest login node and following the tips below.
R packages are installed, whether system-wide or locally in your own
directory, for specific versions of R. If you switch versions of R, you
may need to reinstall packages you use. However, packages only need to
be installed once on Quest for each version of R. Once installed, a
package is available across all log in, compute, and analytics nodes on Quest for that version of R.
A limited number of R packages have been installed centrally for each version of R. These packages can be used without downloading and installing them first. You can get a list of the currently installed packages with the command:
Users can install additional R packages from CRAN by launching RStudio and using the Packages tab in the bottom-right window, or by running the R command line console and using the install.packages command:
with a list of the packages you need. Packages should generally be installed under your home directory. They will be available for you to use across Quest nodes.
The first time you install an R package on Quest, you may see an error like:
Warning in install.packages("glmnet", repos = "http://cran.r-project.org") : 'lib = "/hpc/software/R/3.3.1/lib64/R/library"' is not writable Would you like to use a personal library instead? (y/n)
Answer "y" to use a personal library. Then it will ask you:
Would you like to create a personal library ~/R/x86_64-pc-linux-gnu-library/3.3 to install packages into? (y/n)
Answer "y" again. The installation should then proceed successfully. This will install the packages in your home directory.
Setting the Repository
If you get an error message indicating that a particular package isn't available for a certain version of R, or you get another error related to the package repository, you may need to explicitly set the mirror from which you want R to download the package. List of CRAN mirrors. Choose an http (instead of https) mirror. You can set the mirror before trying to install packages with the command:
or as part of the install.packages call with the repos argument:
install.packages(c("packagename1", "packagename2"), repos="http://cran.wustl.edu")
For Use on Login and Compute Nodes
Some packages compile underlying code as a part of the installation. To install such packages, you may need to load a newer version of the gcc compiler before starting R and installing the package. Loading a different compiler should NOT be done for packages you intend to use on the Analytics nodes, as the updated gcc modules are not available on those nodes.
For example, if while installing a package, you see an error like:
configure: WARNING: Only g++ version 4.6 or greater can be used with...
try loading the module for a newer version of gcc; For example, after logging into Quest:
module load gcc/5.1.0
module load R/3.3.3
then in the R console, issue the package installation commands again.
For Use on Analytics Nodes
R packages intended for use on the Analytics nodes should be installed from within an R session running in RStudio in the web browser on the Analytics nodes. If you encounter compilation or installation errors for a package, please contact firstname.lastname@example.org with the details for further assistance.
R packages installed via the Quest login or compute nodes with the module R/3.5.1 will also be available on the Analytics nodes.
Some compiled packages cannot be successfully installed by individual users. Please contact email@example.com if you run into trouble with a particular package.
See the R help page for install.packages for more options, such as installing local (user-created or downloaded) packages or changing the directory where packages are installed. The latter can be used to install packages in your project space instead of your home directory, which might be useful if others on the allocation also need to access the packages. To install packages from GitHub, use the install_github function in the devtools package or use the githubinstall package.
CRAN Task Views
are collections of packages used frequently in particular fields. They
allow you to install all of the packages associated with the task view
at once. See the R documentation for more details and the commands to
Please contact firstname.lastname@example.org for additional help with R package installation.
RStudio is version 1.1.447. R is version 3.5.1. The Analytics Nodes are limited to running a single version of R for all users.
The Quest Analytics Nodes are shared by many researchers. Please be aware of your memory use when analyzing large data sets. Users utilizing a large amount of memory, especially those using over 60GB of RAM, may be asked to move their analysis to other systems. Multicore and parallel processes should not be run on the Analytics nodes. Users needing to run computationally intensive jobs should schedule interactive or batch jobs on Quest instead of using the Analytics nodes. Please contact Research Computing at email@example.com with questions about R memory use or analyzing large data sets.
Issues or Problems with the Analytics Nodes
To report issues or problems with the Analytics Nodes, please email firstname.lastname@example.org with information on which service you were using (RStudio), the error you received or problem you encountered, and what you were doing prior to the problem occurring. Please note that you were using the Analytics Nodes.