The .Rprofile file is used to customize the R session every time you start it up. The R profile script (.Rprofile) can be created in the home directory. This script gets executed whenever you start a new R session. One can use it to pre-load libraries, set global options, or define custom functions. In this article, we will discuss customizing the R sessions.
Customizing R Session
The R profile script (.Rprofile) file can be used to
- Change R’s default,
- Define handy command-line functions,
- Automatically load your favorite packages
On start-up R will look for the Rprofile in the following places:
1) R Home Directory: R.home() is used to find the directory path in which R is installed.
2) User’s Home Directory: path.expand("~") is used to find the user’s home directory.
3) R Current Working Directory: getwd() is used to find the R’s current working directory.
One can employ a few minor modifications on R default settings. For example, The default prompt is > and the output printed in the console is seven numbers after decimals. The following setting will:
- Replace the default standard R prompt
- Update (reduce) the number of digits from 7 to 4. Note: it does not reduce the precision with which these numbers are internally processed and stored.
- The
show.signif.stars=FALSEwill not show stars to indicate the significance of p-values at the conventional level.
options(prompt = "Imdad> ", digits = 4, show.signif.stars = F)
Edit Profile using usethis Package
- Use the usethis::edit_r_profile() function (from the usethis package) to edit your easily .Rprofile.
- Remember to include sensitive information (like API keys) directly in the script. Consider using a separate
- The .Renviron file for such cases.
- If you have both a project-specific .Rprofile and a user-level one, source the user profile at the beginning of your project’s .Rprofile.
In summary .Rprofile script file allows the user to customize the R environment by setting options, loading libraries, and defining functions that you want available in every session.
Learn about R Workspace, Objects, and .RData File