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Use External Packages in your R Jobs

This tutorial describes how to create custom R libraries for use in jobs on OSG Connect.


The material in this tutorial builds upon the Run R Scripts on OSG tutorial. If you are not already familiar with how to run R jobs on OSG Connect, please see that tutorial first.

Use custom R libraries on OSG

Often we may need to add R external libraries that are not part of the base R installation. As a user, we could add the libraries in our home (or stash) directory and then compress the library to make them available on remote machines for job executions.

Setup Workflow Files

First we'll need to create a working directory, you can either run $ tutorial R-addlib or type the following:

$ mkdir tutorial-R-addlib
$ cd tutorial-R-addlib

In the previous tutorial, recall that we created the script hello_world.R that contained the following:

print("Hello World!")

We also created a wrapper script called to execute our R script. The contents of that file is shown below:


# create a tmp directory
mkdir rtmp

Rscript hello_world.R

Finally, we had the R.submit submit script which we used to submit the job to OSG Connect:

universe = vanilla
log = R.log.$(Cluster).$(Process)
error = R.err.$(Cluster).$(Process)
output = R.out.$(Cluster).$(Process)

+SingularityImage = "/cvmfs/" 
executable =
transfer_input_files = hello_world.R

request_cpus = 1
request_memory = 1GB
request_disk = 1GB

queue 1

Create a Directory for Packages

It is helpful to create a dedicated directory to install the package into. This will facilitate zipping the library so it can be transported with the job. Say, you decided to build the library in R-packages in the current folder. If it does not already exist, make the necessary directory by typing the following in your shell prompt:

$ mkdir -p R-packages

Start an R Container and Install Packages

Start an R container by running:

$ singularity shell /cvmfs/

Other Supported R Versions

To see a list of all Singularity containers containing R, look at the list of OSPool Supported Containers

Before starting to run R, set the R_LIBS environment variable so R knows where to find our custom library directory:

$ export R_LIBS=$PWD/R-packages

We also need to set the TMPDIR variable so that R has a place to download any intermediate or temporary package files.

$ export TMPDIR=$PWD

Now we can run R and check that our library location is being used (here the > is the R-prompt):

Singularity osgvo-r:3.5.0:~> R
> .libPaths()
[1] "/home/alice/tutorial-R-addlib/R-packages"
[2] "/usr/lib64/R/library"                     
[3] "/usr/share/R/library"

We should be able to see our R-packages path in [1]. We can also check for available libraries within R.

> library()

Press q to close that display.

To install packages within R, we use the command (where “XYZ” is the name of the target package):

> install.packages("XYZ", repos = "", dependencies = TRUE)

For this tutorial, we are going to use the lubridate package. To install lubridate, enter this command:

> install.packages("cowsay", repos="")

Turn Package Directory Into a tar.gz File

Proceeding with the cowsay package, the next step is create a tarball of the package so we can send the tarball along with the job.

Exit from the R prompt by typing:

> quit()



In either case, be sure to say n when prompted to Save workspace image? [y/n/c]:. And then exit out of the container by typing "exit":

Singularity osgvo-r:3.5.0:~> exit

To tar the package directory, type the following at the shell prompt:

$ tar -czf R-packages.tar.gz R-packages/

Use Packages in an R Script

Now, let's change the hello_world job to use the new package. First, modify the hello_world.R R script by adding and changing the following lines:


say("Hello World!", "cow")

Define Packages in the Executable

R library locations are set upon launch and can be modified using the R_LIBS environmental variable. To set this correctly, we need to modify the wrapper script. Change the file so it matches the following:


# Uncompress the tarball
tar -xzf R-packages.tar.gz

# Set the library location
export R_LIBS="$PWD/R-packages"
# set TMPDIR variable

# run the R program
Rscript hello_world.R

Include Packages in the Submit File

Next, we need to modify the submit script so that the package tarball is transferred correctly with the job. Change the submit script R.submit so that transfer_input_files and arguments are set correctly. The completed file, which can bee seen in R.submit should look like below:

universe = vanilla
log = R.log.$(Cluster).$(Process)
error = R.err.$(Cluster).$(Process)
output = R.out.$(Cluster).$(Process)

+SingularityImage = "/cvmfs/" 
executable =
transfer_input_files = R-packages.tar.gz, hello_world.R

request_cpus = 1
request_memory = 1GB
request_disk = 1GB

queue 1

Submit Jobs and Review Output

Now we are ready to submit the job:

$ condor_submit R.submit

and check the job status:

$ condor_q

Once the job finished running, check the output files as before. They should now look like this:

$ cat R.out.0000.0
Hello World! 
    \   ^__^ 
     \  (oo)\ ________ 
        (__)\         )\ /\ 
             ||      ||

Variations on This Process

Install multiple packages at once

If you have multiple packages to be added, it may be better to list each of the install.packages() commands within a separate R script and source the file to R. For example, if we needed to install ggplot2, dplyr, and tidyr, we can list them to be installed in a script called setup_packages.R which would contain the following:

install.packages("ggplot2", repos="", dependencies = TRUE)
install.packages("dplyr", repos="", dependencies = TRUE)
install.packages("tidyr", repos="", dependencies = TRUE)

Then, install all of the packages by running the setup file within R:

> source(`setup_packages.R`)

Getting Help

For assistance or questions, please email the OSG User Support team at