Mastering R Packages: Installation, Loading, and Usage Made Simple

R language, as a continuously updated open‑source analysis software, extends its core functionality through optional packages, making it convenient for users. These extension packages provide many analysis methods and functions; users can download and install them as needed.

A package is a collection of R functions, example data, and precompiled code in a well‑defined format. R comes with a set of default packages offering a variety of functions and datasets. The library directory, called a library, stores packages, and the function .libPaths() shows the library locations.

Package Installation

For the first installation of a package, use the command install.packages() . Installation often encounters issues; you can add the repos parameter to switch mirrors, or download the package locally and install it, for example install.packages("package_path", repos=NULL) . To update already installed packages, use update.packages() .

CRAN is the default R package repository; the function install.packages() can only install packages published on CRAN. Bioconductor is a specialized repository for bio‑informatics packages and requires a dedicated command. The official installation method is shown below:

<code>if (! requireNamespace('BiocManager', quietly = TRUE))
    install.packages('BiocManager')
BiocManager::install(version = "3.12")
</code>

Use the function BiocManager::install() to install packages from Bioconductor by providing the package name as an argument.

Package Loading

After installation, load the package in an R session with library("package_name") . In a single session, a package needs to be loaded only once; each time you restart R, you must reload the package. You can also use library() without arguments to view installed packages, and search() to see which packages are currently loaded in the environment.

Package Usage

Once loaded, you can use the functions and datasets defined in the package. Packages often provide small demo datasets and example code; you can view the package overview and list of functions/datasets with help(package = "package_name") . Use help() to learn more about a specific function.

Source: Liu Hongde, Sun Xiao, Xie Jianming, Bioinformatics Data Analysis and Practice