Customizing your .rprofile

[This article was first published on Inundata » R, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

I searched around to see if there was a blog post somewhere describing how to customize one’s .rprofile but was surprised to find just one outdated post. So here is quick intro on the topic. If you are a power R user, you already know about what it does. For those of you that don’t, it is just a text file called .rprofile that sits in your R home directory (not sure where it is? Instructions to find it on a pc or a mac) and all of the commands in there are executed at startup.

  1. Load frequently used packages
    These days I never run R without having to use ggplot2 or plyr so I just include that here (although I hope that someday both packages will become absorbed into the R core).

  2. Create aliases for frequently used functions
    # Shorten S3 methods so s(obj) instead of summary(obj)
    s <- base::summary;
    h <- utils::head;
    n <- base::names;
  3. Set your preferred repository

    Hate the menu that asks you to choose a repository when installing a package? Just hardcode it.

    # Get your current repo name
    current_repo <- getOption("repos") 
    # change this to your closest one
    current_repo["CRAN"] <- ""
    options(repos = current_repo)
  4. Create a new environment so you don’t lose your custom startup functions

    I always start a new script with rm(list=ls()) to clear out everything. The unfortunate consequence of this is that it also takes out all the cool new functions from your .rprofile. Get around that by creating a new environment and putting your functions there.

    custom_env <- new.env()
    # If you don't want to clutter this file, leave functions elsewhere.
    sys.source(".my_custom_functions.r", envir = custom_env)

You can also set a range of other options but these are a good start.

Update: As Jason Priem astutely points out, these tricks can impede reproducibility of your work (especially if you fail to load the appropriate libraries & functions in your final script). While these are valuable time savers during the development phase, you certainly want to be more thorough before sharing your code.

To leave a comment for the author, please follow the link and comment on their blog: Inundata » R. offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)