Oct 25, 2023
Efficiently managing R library paths for community contributed packages.

How to Create a R Library Path

R has thousands of community contributed packages. Most analysts use dozens of them. For large projects it may be necessary to manage libraries on a server, using tools like renv or packrat.

The library path determines which version of a package will be utilized. It is built from a colon-separated list of directories at startup.


R packages provide different functionality through a variety of libraries. These libraries are directories in the file system containing a subdirectory for each package installed there. Upon startup, R recognizes two library paths: a site library and a user library. The site library contains base packages that are installed with R. The user library is where the users install their own packages.

R has been designed to upgrade frequently. The update process is usually transparent for the users, except for minor upgrades which may change library paths.

The LD_LIBRARY_PATH environment variable lists the directories where executables can search for Linux shared libraries. It is important to note that a trailing slash in new paths is treated differently depending on the OS.

The default library paths for r and rstudio are set in the renviron config file. To add a personal library to the list, edit this file and ensure that you have the right permissions.


One of the best tips for efficient R programming is to use.Renviron, which provides a convenient way to store a list of user-specific environment variables that are set during the R startup process. This feature is particularly helpful when you are using a cloud sync service, since it prevents you from having to synchronize your entire Documents folder every time you start R. It also allows you to store information that you do not want to share with others, such as API keys.

In addition,.Renviron can be used to specify a default local library path for packages installed by rpm or apt-get, allowing you to avoid the need to manually add a directory to the library search path with each new installation of an R package. Note, however, that if you change your default local library, any packages that were installed with the old path will not be visible in your resulting ‘Suggests’ list and will need to be reinstalled.


R users have access to thousands of community contributed packages. For large organizations there are many options for storing these libraries. Common libraries may be set up by administrators, and user-managed personal libraries can also be created. In addition, there are options for installing specialized packages such as those from the Bioconductor project for analysing genomic data.

Upon startup, the library search path is set based on the environment variables R_LIBS and R_LIBS_USER. The former should contain a colon-separated list of directories at which R library trees are rooted, and the latter will be appended to this. Only those directories which exist at the time will be included in the search path.

.install can be used to create a permanent modification of the library search path. This can be particularly useful if you need to install new packages or change the version of existing packages, especially in cases where you need to update a package to a different minor release (for example 4.1 to 4.2). In such cases, it is difficult to modify file handles and reload the new library without changing the version of a dependent package.


R will search for a package in the order in which they are listed in.libPaths() if you call library(*packagename*). If you have loaded the package, it will be added to the search path.

A function in a loaded package is always available to R. However, the function may be overridden by another package. It is best to install and load packages in the same environment, to minimize the potential for clashes between libraries.

If you use a VALET package to install and load packages, the VALET environment will be included in the library search path. This is useful if you have multiple installations of R on your system. For example, if you upgrade from a minor version of R (say 3.3.0) to a newer minor version, you should reinstall your add-on packages. This will ensure that the package is compatible with your newer version of R. Otherwise, the new minor version of R will not find your packages.

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