# R

R (opens new window) is a handy language and environment for statistical computing and graphics. We have already installed the latest version on your home machines. For those of you running unmanaged machines on our security tier 2 may want to upgrade to the latest version using the code below:

Install R
Install R Studio
- Download the latest software version
- Configure X2Go
Troubleshooting

# Install R

# Install using Conda

We recommend using Conda to install R packages. Conda gives you better flexibility when choosing versions, which will not affect other researchers in your lab.

TIP

We have very easy guide on how to get Saige running in Conda environment.

If you have a conda setup based on the guide above you will need some additional channels to install the packages. You will need bioconda (opens new window) and conda-forge (opens new window) channels. If you have not set channels yet make sure to add them in the same order, but feel free to skip bioconda if you do not see a need for it:

conda config --add channels bioconda
conda config --add channels conda-forge

To install R (>=4.0.3) you have to install r-base package. In the example below we create an environment where r-base is being installed as default with devtools:

conda create --name renv --channel conda-forge  "r-base>=4.0.3" r-devtools

To be able to work with R that you just installed you have to activate your conda environment:

conda activate renv
R

Additional R packages can be found in channels or installed using R devtools. We highly recommend to use R packages from Conda channels bioconda (opens new window) and conda-forge (opens new window). Conda packages usually have r- prefix in their names. Here is an example how to install matrix R package into renv environment:

conda install -n renv r-matrix

If you still decide to install cran packages when using conda, then you should minimize the issues encounted by specifying the library path. For example to install png package:

install.packages("png", paste0(Sys.getenv("CONDA_PREFIX"), "/lib/R/library"))

# Running multiple version of R

To run multiple R versions with conda environments it may be necessary to setup custom ~/.Rprofile config. You can use example below as an inspiration for setting your .Rprofile config to link each R version to library paths within environment:

condaenv <- Sys.getenv("CONDA_PREFIX")
if (condaenv != "") {
  .libPaths(c(paste0(Sys.getenv("CONDA_PREFIX"), "/lib/R/library")))
}

# Install using system packages

# Install R
sudo apt install r-base r-base-dev -y

# Add the updated package repository to your sources list:
# https://cran.r-project.org/bin/linux/ubuntu/
echo "deb https://cloud.r-project.org/bin/linux/ubuntu $(lsb_release -cs)-cran35/" | sudo tee "/etc/apt/sources.list.d/$(lsb_release -cs)-cran35.list"

# Add keys for the CRAN repository
gpg --keyserver hkp://keyserver.ubuntu.com:80 --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9
gpg -a --export E298A3A825C0D65DFD57CBB651716619E084DAB9 | sudo apt-key add -

# Update packages
sudo apt update
sudo apt upgrade
sudo apt autoremove

# Start R
R

# Upgrade to latest R version (4.0)

The default version in Ubuntu is usually a few versions behind the newest R version available. Follow the code below if you need the newest version.

Please note

Running upgrade affects versions of all packages on your machine. This may break your or others environments. We recommend installing new R version using Conda instead.

# -- Add the new package repository to your sources list:
echo "deb https://cloud.r-project.org/bin/linux/ubuntu $(lsb_release -cs)-cran40/" | sudo tee "/etc/apt/sources.list.d/$(lsb_release -cs)-cran40.list"

# Add keys for the r-project repository
gpg --keyserver hkp://keyserver.ubuntu.com:80 --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9
gpg -a --export E298A3A825C0D65DFD57CBB651716619E084DAB9 | sudo apt-key add -

# -- Update packages
sudo apt update
sudo apt upgrade
sudo apt install r-base
sudo apt autoremove -y

# -- Start R
R

# Downgrade to a specific R version (IAAS machines)

Sometimes you may want to pin your R version to a specific release to please specific packages required for your work. Below are a guide on how to downgrade and pin your R version to a specific release (just in case your figured out the need after you upgraded R to a new version...). The guide is copied from this great forum post (opens new window).

Please note

This setup below will only work on IAAS- or BLUE-machines as they require administrator privileges. Contact us if you need to downgrade the R version on your home machine.

# Save the R-packages that is already installed in your lab

dpkg -l | grep "^ii  r-" > r_packages.txt

# Pin the software version to a specific version (in this case R 3.5)

cat <<EOF | sudo tee /etc/apt/preferences.d/pin-r35
Package: r-*
Pin: release a=$(lsb_release -cs)-cran35
Pin: version 3.5*
Pin-Priority: 800

Package: r-cran-nlme
Pin: release a=$(lsb_release -cs)-cran35
Pin: version 3.1.139-1$(lsb_release -cs)0
Pin-Priority: 800
EOF

# Purge and remove the current R version

sudo apt purge r-*
sudo apt autoremove

# Re-install your specific R version and your r-packages

sudo apt-get install $(cat r_packages.txt | awk '{print $2}')

# Check that the new correct version is running

R

R version 3.5.3 (2019-03-11) -- "Great Truth"

# Optional, remove the version pinning

Remove your pin-file if you want to revert to the newest version.

sudo rm /etc/apt/preferences.d/pin-r35
sudo apt update
sudo apt upgrade

# Install R Studio

We do provide the opportunity for a graphical interface via R Studio. Researchers may install this on unmanaged machines.

TIP

Make sure R-Studio is not already installed, if the lab was setup before command will return path.

which rstudio

Please note

Installation on home-machines requires us to run some code.

The below guide will therefore not work on home-machines.

Dependencies

Without these, it will be impossible to get RStudio to work.

R (installed as above)
A machine prepared for a graphical interface (X2Go)
An IAAS-machine or BLUE-machine with administrator access

# Download the latest software version

Search www.rstudio.com (opens new window) for the latest version in accordance with your operating system (e.g. Ubuntu 18.04 Bionic). The below text is an example code which you may cut and paste into your shell:

# download the package
wget https://download1.rstudio.org/rstudio-xenial-1.1.463-amd64.deb -O /tmp/rstudio.deb

# install essential dependencies
sudo apt update && sudo apt install -y libnss3 libasound2

# install rstudio
sudo apt install /tmp/rstudio.deb

# clean up files
rm /tmp/rstudio.deb

Latest version

If you plan to install latest version (>=1.2.0) remember to use --disable-gpu parameter when running R-Studio or setup environment variable as mentioned in Troubleshooting RStudio Rendering Errors (opens new window).

# Configure X2Go

To automatically start RStudio, revisit your Session preferences in the X2Go setup and update your Single application command path to /usr/bin/rstudioand Create a session icon on the desktop....

NOTE

There is a known bug in x2go 1.1.456 that will go away if you comment out (add # before) a line in the x2goagent.options:

sudo vim /etc/x2go/x2goagent.options
# X2GO_NXAGENT_DEFAULT_OPTIONS+=" -extension BIG-REQUESTS"

# Troubleshooting

# I cannot create R_TempDir

If your system disk is full, R will not be able to store temporary files used for computation. You may see the error message Fatal error: cannot create 'R_TempDir'. Click here to verify if the disk disk is full, and this guide to change your temp folder to a larger volume.

# Installation of devtools

You may need to install some more dependencies to get install.packages("devtools") going:

sudo apt update
sudo apt install libcurl4-openssl-dev libssh2-1-dev libssl-dev libxml2-dev

# RStudio do not recognize the latest R-version

Since R versions can be installed side-by-side on a system, you may need to select which version of R you would like RStudio to use. Click the Chaning R versions for RStudio desktop (opens new window) on the RStudio support pages for more information.

# PredictABEL, matplotlib, viridis, or plyr fails to import

If running library(PredictABEL) in rstudio server fails with:

Error: package or namespace load failed for 'PredictABEL':
 .onLoad failed in loadNamespace() for 'tcltk', details:
  call: fun(libname, pkgname)
  error: (converted from warning) couldn't connect to display ":0"

You need to install these packages related to tcl and tk:

sudo apt update
sudo apt install tcl tcl8.6 tk tk-table tk8.6

# RStudio or R becomes unresponsive when I run my code

It might be good to check the resource consumption on your lab machine. The response may be reduced if you or your lab mates consume all memory and/or CPU. A quick way to do this is via HTOP and time.

For example, if you use all your memory you might need to reduce your table sizes or upgrade your machine size. If your lab mates consume all your resources you may want to ask them to spare some for you.

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