...
We
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encourage
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users
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to
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maintain
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their
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own
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R
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packages.
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This
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ensures
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that
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the
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user
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has
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no
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discontinuity
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in
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their
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workflow
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waiting
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for
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packages
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to
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be
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installed
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and
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packages
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work
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with
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the
...
user’s chosen,
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loaded
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R
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module.
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R
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packages
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are
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built
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specifically
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to
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a
...
version
...
of
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R
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and
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may
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not
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work
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properly
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if
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a
...
different
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version
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of
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R
...
is
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loaded.
...
O2’s LMOD “module” feature allows users to quickly and easily switch between R versions.
Table of Contents
| Table of Contents | ||||
|---|---|---|---|---|
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General Commands
Command | Meaning |
|---|---|
| shows the version of R installed on O2 |
| shows if other module(s) needs to be loaded to use R (e.g., |
| loads an individual module (i.e., |
| unloads an individual module (i.e., |
| shows the current version of R loaded |
| From R, shows |
Setting up a Personal R Library
This sets up an environment to store R packages locally. From R, all packages will be installed and saved to this R Personal Library by default. It is recommended to use a separate R library for each version of R selected (e.g., 3.6.1, 4.1.1, etc.). Multiple members of a group can share an R library saved in a group-accessible location by following the echo ... and export ... commands.
In O2 bash (hardcoded):
This approach is ideal for researchers using mainly a single R version and sharing the R Personal Library with other lab members.
| Code Block | ||
|---|---|---|
| ||
mkdir -p ~/R-VersionSelected<version_selected>/library mfk8@login02:~$ echo 'R_LIBS_USER="~/R-VersionSelected<version_selected>/library"' > $HOME/.Renviron mfk8@login02:~$ export export R_LIBS_USER="~/R-<version_selected>" |
In O2 bash (non-hardcoded alternative):
This alternative approach is ideal for researchers working concurrently with more than one R version or switching often among R versions. In this approach, the R Personal Library is automatically set based on the R module loaded.
| Code Block |
|---|
echo 'R_LIBS_USER="~/R/%p-VersionSelectedlibrary/library%V"' ``` ## Starting R Interactively R should not be executed from the login servers (login0X). Instead, launch an interactive session, specifying the memory requirements. R does not implicitly use multiple cores unless the code is parallelized, so 1 core is sufficient. Here's a model R workflow: ``` mfk8@login02> $HOME/.Renviron |
| Info |
|---|
Note: |
Starting R Interactively
R must be run in a compute node rather than a login node. To start an interactive session in a compute node, you need to specify the resources (i.e., walltime, cpus, and memory) via the $ srun command and specify interactive as the partition. For example:
| Code Block |
|---|
# 1. Start an interactive job (resources: 1 hour, 5GB of memory, and 1 cpu) mfk8@login01:~$ srun --pty -p interactive -t 0-121:00 --mem 8G5G -c 1 bash # 2. Load R/4.1.2 mfk8@compute-a-0116-01163:~$ module load gcc/69.2.0 R/34.1.2 # 3.5 #orStart versionR chosen mfk8@compute-a-0116-01163:~$ R > #notice how the prompt changes. ``` ## Installing Packages Using Bioconductor The majority of bio-related packages can be downloaded through Bioconductor, example installation given below. Please reference the [installation page on the Bioconductor website](https://www.bioconductor.org/install/) in case this method changes in the future. ```R > if (!requireNamespace. |
R does not implicitly use multiple core unless the code is parallelized. For researchers using the R package doParallel, please check https://harvardmed.atlassian.net/wiki/spaces/O2/pages/1616511993/Job+Efficiency#R-package-doParallel
Installing Packages Using Bioconductor
The Bioconductor repository host a wide range of bio-related packages. To install Bioconductor, please check the installation page on the Bioconductor website in case the method described below changes in the future.
| Code Block |
|---|
if (!require("BiocManager", quietly = TRUE)) install.packages("BiocManager") > BiocManager::install(version = "3.1214") ``` User example: ```R > if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") > BiocManager::install("affy") ``` Bioconductor packages are listed at <http://www.bioconductor.org/packages/release/BiocViews.html#___Software> ## Installing Packages Using CRAN The command **`install.packages()`** searches CRAN by default and asks you to select a mirror, but a repos can be specified using this command: ```R > install.packages("nameofpackage |
| Panel | ||||||
|---|---|---|---|---|---|---|
| ||||||
The latest Bioconductor release is 3.15 but requires >=R/4.2.0. Unfortunately, the patched version of R is not available yet to be installed as a module on O2. Nonetheless, HMS-RC will install R/4.2.1 once become available. |
Installing Packages Using CRAN
The command install.packages() searches CRAN by default and ask you to select a mirror, but a repository can be specified using this command:
| Code Block | ||
|---|---|---|
| ||
install.packages("PkgName", repos="http://cran.r-project.org") ``` |
User
...
example:
| Code Block | ||
|---|---|---|
| ||
install.packages("foreignggplot2", repos="http://cran.r-project.org")
> library(foreign)
```
A list of CRAN available packages is available at <httpggplot2) |
A list of CRAN available packages is available at http://cran.r-project.org/web/packages/
...
Installing
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Packages
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from
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GitHub using Devtools
The R package devtools allow researchers to install software from GitHub repositories. First, you need to install devtools from the CRAN repo. Second, you need to load the R package using the library command. Lastly, you use the install_github() command to install R packages from GitHub. For example:
| Code Block | ||
|---|---|---|
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# 1. Install devtools
install.packages("devtools")
# 2. Load R pkg
library(devtools)
# 3. Install a pkg from GitHub
# install_github("username/repo")
install_github("tidyverse/ggplot2") |
Installing Packages from a Local file
To install a package manually, place the compressed R package (usually is compressed as a tarball or zip file) inside your R Personal Library (e.g., ~/R-<version_selected>/library) and run the following command:
| Code Block | ||
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install.packages("pkgName", lib="~/R-VersionSelected<version_selected>/library") ``` Conversely, if all dependencies are already existing for the package, R CMD INSTALL can be used from the command line (on an interactive compute node): ``` mfk8@login02:~$ |
Conversely, you can install a package via R CMD INSTALL. However, this assumes all dependencies are already installed in your R Personal Library. For example:
| Code Block | ||
|---|---|---|
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# 1. O2 shell - start interactive job srun --pty -p interactive -t 0-121:00 --mem 80005G /bin/bash mfk8@compute-a-01-01:~$bash # 2. Load R modules module load gcc/69.2.0 R/34.1.2 # 3.5 #orInstall versionpkg selectedfrom mfk8@compute-a-01-01:~$a local file R CMD INSTALL downloadedRPackage.tar.gz ``` ## Installing Other R packages For installing other packages |
Installing Packages with Special Requirements
We’ve created a Confluence page with step-by-step instructions to install some commonly used R packages (e.g.,
...
SF, monocle3, etc.) requiring a few [non-common] extra steps - Installing Other R packages on O2. Often, the extra steps involve installing a system library. To simplify the process, we have installed those system libraries as modules on O2.
Loading R packages
Once the package is installed, the library(<pkg_name>) can be used to load a package; for example:
| Code Block | ||
|---|---|---|
| ||
library("ggplot2") |