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see if there was a blog post somewhere describing how to customize one’s .rprofile but was surprised to find just one outdated post.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).

   library(ggplot2)
   library(plyr)
   

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"] <- "http://cran.us.r-project.org"
   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)
   attach(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.