How do I install an R package from source in R?

Installing an R package from source can be handy if you’re dealing with an unreleased version or a custom fork. Below are common methods to get you started quickly.

1. Local Source Tarball (.tar.gz file)
If you have a local .tar.gz file for the package, you can use:

install.packages("path/to/package_source.tar.gz", repos = NULL, type = "source")

• repos = NULL tells R not to look for the package in a CRAN or other repository.
• type = "source" instructs R to compile and install from the source package.

Make sure you have the required development tools (e.g., Rtools on Windows, Xcode on macOS, or essential build libraries on Linux) to compile source packages.

2. Installing from GitHub or Other Repositories
If your source package is on GitHub, you can use the devtools or remotes package:

# First install 'devtools' or 'remotes' if not already installed
install.packages("devtools")
library(devtools)

# Then install from GitHub
install_github("username/package_name")

Adjust username and package_name to match the repo details. This approach clones the repository locally, then builds and installs it.

3. Consider Dependencies
When installing from source, ensure all dependencies (both R packages and any system libraries) are installed. If a dependency isn’t available, the install might fail. In some cases, you may need to install system-level libraries or tools—for example, to compile C/C++ code.

4. Debugging Installation Issues

• Check error logs: If installation fails, read the output carefully to see which dependency or compiler flag caused the error.
• Update R: Sometimes, updating to a newer R version helps if the package requires a more recent environment.
• Set environment variables: For specialized compiled code, you might need to configure certain paths or flags (e.g., PKG_CONFIG_PATH).

Where to Go from Here
Mastering package installations from source is one aspect of being proficient in R—especially if you plan to work with cutting-edge or unreleased libraries. For a deeper dive into coding and algorithmic skills, check out Grokking the Coding Interview: Patterns for Coding Questions or Grokking Data Structures & Algorithms for Coding Interviews. If system scalability is on your radar, explore Grokking System Design Fundamentals. For personalized guidance and mock sessions, visit Coding Mock Interviews offered by ex-FAANG engineers. You can also find concise tutorials on the DesignGurus.io YouTube channel.