R

Using R on the Midway Cluster

R is a powerful tool for quantitative research and data analysis across various fields.

For detailed, step-by-step instructions on setting up and using R on Midway2, refer to this tutorial. These instructions are also applicable to Midway3 due to similar configurations.

Checking Available R Modules

To see the list of currently available R modules, run:

module avail R

Using RStudio IDE

The RStudio Integrated Development Environment (IDE) is available as rstudio modules. To check available versions, run:

module avail rstudio

When using RStudio, it is recommended to connect to the RCC cluster via ThinLinc for a smoother experience.

Spatial Packages

The sf package in R provides tools for handling spatial data using simple features. For more information, visit the sf Package on CRAN.

Midway2 Environment

Dependencies: GDAL 2.4.1, udunits 2.2
R Version: 4.2.0

Midway3 Environment

Dependencies: GDAL 3.3.3, udunits 2.2, GEOS 3.9.1, GCC 10.2.0, SQLite 3.36.0
R Versions: 4.2.1 and 4.3.1

Module Loading

Before using or installing sf on Midway3, load the necessary modules:

Midway2Midway3

module load gdal/2.4.1 udunits/2.2 R/4.2.0

module load gdal/3.3.3 udunits/2.2 geos/3.9.1 gcc/10.2.0 sqlite/3.36.0 R/4.3.1

Additional Packages

The terra and raster packages are also installed.

Installing R Packages: A Comprehensive Guide

R packages can be distributed as source code or compiled binaries. Source packages must be compiled before installation, typically requiring the GNU Compiler Collection (GCC). Here’s how to get started with R package installation on our HPC clusters.

Check GCC Version

To ensure compatibility, verify the version of GCC (g++) on your system:

$ g++ --version
g++ (GCC) 8.5.0 20210514 (Red Hat 8.5.0-10)
Copyright (C) 2018 Free Software Foundation, Inc.
This is free software; see the source for copying conditions.  There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

Before You Install

Check Disk Space: Ensure you have enough disk space using the quota command:
```
$ quota
```
Load Necessary Modules: For packages like ncdf4 and hdf5r, load the required modules before starting R:
```
$ module load hdf5 netcdf
```
For more information on working with netCDF files in R, you can check out this resource: NetCDF with R.

Installing Packages

To install R packages, use the install.packages function within an R session:

install.packages("packageName")

For multiple packages:

install.packages(c("package1", "package2"))

FAQ

Delete All R Packages and Start Over: If you need to reset your R environment, delete all R packages and related files:
```
$ rm -rf ~/R ~/.R ~/.rstudio* ~/.Rhistory ~/.Rprofile ~/.RData
```
Update ~/.bashrc: Remove any added or modified environment variables if necessary.
See Where R Packages Are Installed:
```
.libPaths()
Sys.getenv("R_LIBS_USER")
```
List Installed Packages:
```
installed.packages()
```
List Default Packages:
```
getOption("defaultPackages")
```
Remove a Package:
```
remove.packages("packageName")
```

Using the `renv` R Package to Create a Personal R Project Environment on the Midway Cluster

The renv package in R helps manage project-specific libraries, ensuring consistent package versions across projects. Here’s a step-by-step guide to set up and use renv for your project located at /project/pi-cnetid/rproject/ on the Midway cluster.

Step 1: Load Necessary Modules

Before starting, ensure you have the necessary modules loaded. You can load the R module appropriate for your project:

module load R/4.3.1

Step 2: Set Up the Project Directory

Navigate to your project directory:

cd /project/pi-cnetid/rproject/

Step 3: Initialize `renv` in Your Project

Start R and initialize renv in your project directory:

Inside the R session, run:

# Install renv if not already installed
install.packages("renv")

# Initialize renv in the project directory
renv::init()

This command will set up a new project-specific library in the renv subdirectory of your project directory and create an renv.lock file to record the state of your library.

Step 4: Install Packages within the `renv` Environment

With renv initialized, install any necessary packages:

# Install required packages
renv::install("dplyr")
renv::install("ggplot2")
# Add more packages as needed

Step 5: Snapshot the Current State of Your Library

After installing the necessary packages, snapshot the current state of your library to renv.lock:

renv::snapshot()

This records the exact versions of the packages you have installed, allowing you to recreate the environment later.

Step 6: Restore Library from `renv.lock` (Optional)

If you need to recreate the environment on another system or after a system change, you can restore the library:

renv::restore()

This command reads the renv.lock file and installs the specified package versions.

Step 7: Using the Project

Whenever you start working on your project, load the renv environment:

# Activate the renv environment
renv::activate()

This ensures that all package installations and library paths are managed by renv.

Example Workflow

Here’s a complete example workflow from the terminal to R session:

Load R Module:

module load R/4.3.1
cd /project/pi-cnetid/rproject/

Start R and Initialize renv:
```
R
```

Within R Session:

install.packages("renv")
renv::init()
renv::install("dplyr")
renv::install("ggplot2")
renv::snapshot()
q()  # Quit R session

Future Sessions:
```
renv::activate()
```

By following these steps, you can set up and manage a project-specific R environment on the Midway cluster, ensuring consistency and reproducibility for your R projects.

R