In addition to other resources, SWIRL is installed on the Caviness cluster and is available as an interactive learning guide inside R:

$ vpkg_require r-cran
$ R -q --no-save
> library(swirl)
> swirl()

The cluster also has the majority of CRAN and Bioconductor R libraries already insalled. These are installed as point-in-time snapshots of their respective catalogs. These libraries are broken down into different valet packages based on dependencies. The current bundles are below. Together these bundles provide access to over 6,600 R modules, pre-compiled and ready for use.

$ vpkg_require r-geo
Adding dependency `r-bioc/3.5.1:20180715` to your environment
Adding dependency `gsl/1.16` to your environment
Adding dependency `gmp/6.1.2` to your environment
Adding dependency `glpk/4.65` to your environment
Adding dependency `mpfr/4.0.1` to your environment
Adding dependency `r-gnumath/3.5.1:20180715` to your environment
Adding dependency `fftw/3.3.8` to your environment
Adding dependency `r-fftw/3.5.1:20180715` to your environment
Adding dependency `szip/2.1.1` to your environment
Adding dependency `hdf4/4.2.13` to your environment
Adding dependency `hdf5/1.10.2` to your environment
Adding dependency `netcdf/4.6.1` to your environment
Adding dependency `udunits/2.2.26` to your environment
Adding dependency `r-cdf/3.5.1:20180715` to your environment
Adding dependency `geos/3.6.2` to your environment
Adding dependency `gdal/2.3.0` to your environment
Adding dependency `proj/5.1.0` to your environment
Adding package `r-geo/3.5.1:20180715` to your environment
$

Now using the library in R can be done as normal.

$ R --no-save -q
> library(CopulaRegression)
Loading required package: MASS
Loading required package: VineCopula
>

IT provides a small script called r-info which will display the internal documentation of R modules. This is helpful to get basic information on a module to decide if it requires more research. To use this tool, the library must be installed, and the module bundle must be loaded with vpkg_require. For example:

$ vpkg_require r-cran
$ r-info car
Loading required package: carData

                Information on package ‘car’

Description:

Package:            car
Version:            3.0-0
Date:               2018-03-23
Title:              Companion to Applied Regression

...

Further information is available in the following vignettes in
directory ‘/opt/shared/r/add-ons/r3.5.1/cran/20180715/car/doc’:

embedding: Using car functions inside user functions (source, pdf)     
$

You can create your own library of R modules which contains different versions than provided through VALET, or modules not available via VALET.

R looks in an environment variable called 'R_LIBS' to obtain a list of locations to search for modules. You should ensure your entry is first in the list, this will allow your library to override any conflicts which may be installed on the system. This is also important, because R installs modules into the first entry in this list by default.

Once this is done, you can use the install using install.packages. Make sure you are in your workgroup (e.g. workgroup -g «investing-entity». Here is an example:

$ workgroup -g it_css
$ vpkg_require r-cran
Adding dependency `r/3.5.1` to your environment
Adding package `r-cran/3.5.1:20180715` to your environment
$ mkdir -p $WORKDIR/sw/r/add-ons/r3.5.1/testing/default
$ echo $R_LIBS
/opt/shared/r/add-ons/r3.5.1/cran/20180715
$ R_LIBS="$WORKDIR/sw/r/add-ons/r3.5.1/testing/default:$R_LIBS"
$ R -q --no-save
> .libPaths()
[1] "/work/it_css/sw/r/add-ons/r3.5.1/testing/default"
[2] "/opt/shared/r/add-ons/r3.5.1/cran/20180715"
[3] "/opt/shared/r/3.5.1/lib64/R/library"
> chooseCRANmirror(all)
Secure CRAN mirrors

 1: 0-Cloud [https]                   2: Algeria [https]
 3: Australia (Canberra) [https]      4: Australia (Melbourne 1) [https]
 5: Australia (Melbourne 2) [https]   6: Australia (Perth) [https]
 7: Austria [https]                   8: Belgium (Ghent) [https]
 9: Brazil (PR) [https]              10: Brazil (RJ) [https]
11: Brazil (SP 1) [https]            12: Brazil (SP 2) [https]
13: Bulgaria [https]                 14: Chile [https]
15: China (Hong Kong) [https]        16: China (Lanzhou) [https]
17: China (Shanghai) [https]         18: Colombia (Cali) [https]
19: Czech Republic [https]           20: Denmark [https]
21: Ecuador (Cuenca) [https]         22: Ecuador (Quito) [https]
23: Estonia [https]                  24: France (Lyon 2) [https]
25: France (Marseille) [https]       26: France (Montpellier) [https]
27: Germany (Erlangen) [https]       28: Germany (Göttingen) [https]
29: Germany (Münster) [https]        30: Germany (Regensburg) [https]
31: Greece [https]                   32: Hungary [https]
33: Iceland [https]                  34: Indonesia (Jakarta) [https]
35: Italy (Padua) [https]            36: Japan (Tokyo) [https]
37: Japan (Yonezawa) [https]         38: Korea (Busan) [https]
39: Korea (Gyeongsan-si) [https]     40: Korea (Seoul 1) [https]
41: Korea (Ulsan) [https]            42: Malaysia [https]
43: Mexico (Mexico City) [https]     44: Norway [https]
45: Philippines [https]              46: Serbia [https]
47: Spain (Madrid) [https]           48: Sweden [https]
49: Switzerland [https]              50: Turkey (Denizli) [https]
51: Turkey (Mersin) [https]          52: UK (Bristol) [https]
53: UK (London 1) [https]            54: USA (CA 1) [https]
55: USA (IA) [https]                 56: USA (KS) [https]
57: USA (MI 1) [https]               58: USA (MI 2) [https]
59: USA (OR) [https]                 60: USA (TN) [https]
61: USA (TX 1) [https]               62: Uruguay [https]
63: (other mirrors)

Selection: 55
> install.packages("KernSmooth", dependencies=TRUE)
Installing package into ‘/work/it_css/sw/r/add-ons/r3.5.1/testing/default’
(as ‘lib’ is unspecified)

trying URL 'https://ftp.osuosl.org/pub/cran/src/contrib/KernSmooth_2.23-15.tar.gz'
Content type 'application/x-gzip' length 24572 bytes (23 KB)
==================================================
downloaded 23 KB

* installing *source* package ‘KernSmooth’ ...
** package ‘KernSmooth’ successfully unpacked and MD5 sums checked
** libs
gfortran   -fpic  -g -O2  -c blkest.f -o blkest.o
gfortran   -fpic  -g -O2  -c cp.f -o cp.o
gfortran   -fpic  -g -O2  -c dgedi.f -o dgedi.o
gfortran   -fpic  -g -O2  -c dgefa.f -o dgefa.o
gfortran   -fpic  -g -O2  -c dgesl.f -o dgesl.o
gcc -std=gnu99 -I"/opt/shared/r/3.5.1/lib64/R/include" -DNDEBUG   -I/opt/shared/gcc/4.9.4/include   -fpic  -g -O2  -c init.c -o init.o
gfortran   -fpic  -g -O2  -c linbin.f -o linbin.o
gfortran   -fpic  -g -O2  -c linbin2D.f -o linbin2D.o
gfortran   -fpic  -g -O2  -c locpoly.f -o locpoly.o
gfortran   -fpic  -g -O2  -c rlbin.f -o rlbin.o
gfortran   -fpic  -g -O2  -c sdiag.f -o sdiag.o
gfortran   -fpic  -g -O2  -c sstdiag.f -o sstdiag.o
gcc -std=gnu99 -shared -L/opt/shared/r/3.5.1/lib64/R/lib -L/opt/shared/gcc/4.9.4/lib -L/opt/shared/gcc/4.9.4/lib64 -o KernSmooth.so blkest.o cp.o dgedi.o dgefa.o dgesl.o init.o linbin.o linbin2D.o locpoly.o rlbin.o sdiag.o sstdiag.o -L/opt/shared/r/3.5.1/lib64/R/lib/atlas -lRblas -lgfortran -lm -lquadmath -lgfortran -lm -lquadmath -L/opt/shared/r/3.5.1/lib64/R/lib -lR
installing to /work/it_css/sw/r/add-ons/r3.5.1/testing/default/KernSmooth/libs
** R
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** testing if installed package can be loaded
* DONE (KernSmooth)

The downloaded source packages are in
        ‘/tmp/RtmpVq5oBb/downloaded_packages’
> library(KernSmooth)
KernSmooth 2.23 loaded
Copyright M. P. Wand 1997-2009
>

Notice that the output of .libPaths() specifies my personal library directory first?

IT developed a formalization for installing modules called udbuild which can simplify the installation of modules. Here is an example udbuild script which can be used to install a personal R library.

udbuild-testing-cuda

#!/bin/bash -l
 
PKGNAME=testing
VERSION=default
 
UDBUILD_HOME=$WORKDIR/sw
PKG_LIST='
	WideLM rpud permGPU magma gputools cudaBayesregData cudaBayesreg
	CARramps
'
 
vpkg_devrequire udbuild r/3.1.1 r-cran/20140905
init_udbuildenv r-addon cuda/6.5
 
#Sometimes R doesn't properly use CPPFLAGS which is set by VALET, fix that here:
CPATH=$CUDA_PREFIX/include:$CPATH
LIBRARY_PATH=$CUDA_PREFIX/lib64:$CUDA_PREFIX/lib64/stubs:$LIBRARY_PATH
 
#CRAN_MIRROR='http://cran.cs.wwu.edu/'
CRAN_MIRROR='http://lib.stat.cmu.edu/R/CRAN/'
 
quote() { printf '"%s", ' "$@" | sed 's/, $/\n/'; }
 
R -q --no-save <<EOT
	.libPaths()
	options(repos=structure(c(CRAN="$CRAN_MIRROR")))
	for ( pkg in c( `quote $PKG_LIST` ) ) {
		print(pkg)
		install.packages(pkg, dependencies=TRUE)
	}
 
	warnings()
EOT

This script will attempt to build the cuda capable R modules using the cuda 6.5 version into $WORKDIR/sw/r/add-ons/r3.1.1/testing/default-cuda-6.5.

Consider the simple R script file to multiply a small 3x3 matrix

matmul.R

# Calculate and print small matrix AA'
a <- matrix(1:12,3,4);
a%*%t(a)

Let's test this R script using Rscript from the command line on a compute node. Don't forget to set your workgroup to define your cluster group or investing-entity compute nodes before you use qlogin to get on a compute node. For example,

workgroup -g it_css
salloc
vpkg_require r/3.5
Rscript matmul.R

The output to the screen:

     [,1] [,2] [,3]
[1,]  166  188  210
[2,]  188  214  240
[3,]  210  240  270

To return to the head node, type

exit

To run a R script in batch instead of on the command line has nearly the same steps. Copy a template job submission script (/opt/templates/slurm/generic/threads.qs) for example and edit it to change the job name and add your commands for your job something like this:

#!/bin/bash -l
#
....
 
#SBATCH --job-name=matmultiply_R
 
...
#
# [EDIT] Execute your OpenMP/threaded program using the srun command:
#
# Add vpkg_require commands
vpkg_require r/3.5
 
# Syntax: Rscript [options] filename.R [arguments]
Rscript matmul.R 
</file>
 
Now to run the R script simply submit the job from the head node with the
''sbatch'' command.
 
<code>
sbatch matmul.qs

You should see a notification that your job was submitted. Something like this

Submitted batch job 983119

After the code completes the output of the script will appear in the file slurm-983119.out because the job number is 983119. Type

more slurm-983119.out

to display the contents of the output file on the screen. For example,

-- OpenMP job setup complete:
--  OMP_NUM_THREADS      = 2
--  OMP_PROC_BIND        = true
--  OMP_PLACES           = cores
--  MP_BLIST             = 5,17

Adding package `r/3.5.1` to your environment
     [,1] [,2] [,3]
[1,]  166  188  210
[2,]  188  214  240
[3,]  210  240  270

Consider the simple script to print a fraction from the argument list

sweep.R

args <- commandArgs(trailingOnly = TRUE)
# print fraction from argument list 
as.numeric(args[1])/as.numeric(args[2])

This is a R script with can be run from the command line on a compute node the commands

qlogin
vpkg_require r/3
Rscript sweep.R 5 200

The output to the screen:

[1] 0.025

Consider the queue script file

sweep.qs

#$ -N sweep
#$ -t 1-200
## 
## Parameter sweep array job to run the sweep.R  with
##    lambda = 0,1,2. ... 199
##
 
# Add vpkg_require commands after this line:
vpkg_require r/3
 
date "+Start %s"
echo "Host $HOSTNAME"
 
let lambda="$SGE_TASK_ID-1"
let taskCount=200
 
# Syntax: Rscript [options] filename.R [arguments]
Rscript --vanilla sweep.R $lambda $taskCount
 
date "+Finish %s"

The date and echo Host lines are just a way of keeping track of when and where the jobs are run. There will be 200 array jobs all running the same script with different parameters (arguments). The –vanilla option is used to prevent the multiple jobs from using the same disk space.

To run this in batch you must submit the job from the head node with the qsub command.

qsub sweep.qs

After the code completes the output of the script will appear in the files sweep.o535064.1 to sweep.o535064.200. The number 535064 is the job ID assigned to your job when submitted, and 1 to 200 is the Task ID (e.g. corresponds to the -t 1-200)

Adding dependency `x11/RHEL6.1` to your environment
Adding package `r/3.0.2` to your environment
[1] 0.025

You are running many jobs in the same directory. Grid engine handles the standard output by writing to separate files with "dot taskid" appended to the jobid. You need to take care of other file output in your R script.

The command-line option –vanilla implies –no-site-file, –no-init-file and –no-environ. This way you will not be reading or writing to the same files. If you need initialization command, put them in your R script instead of in in the init-file .Rprofile. If you need some environment variables, export them in your bash script instead of assigning them in your environ file .Renviron.