R: first steps

Definitely use R if your data is in tabular format and you want to learn from it. R is a fantastic tool to munge, summarize, extract, plot, statistically analyze, and otherwise learn from your data. Another great feature of R is CRAN, the Comprehensive R Archive Network. CRAN is sort of like Galaxy’s tool sheds, and is one of the first places to look if you want to see if a well-developed solution to your problem already exists. Here’s a poignant anecdote I like from Martin Morgan, about one of his first experiences with R:

I’d initially written a C program that was literally thousands of lines of code. After a while, I decided to try programming in R; since the facilities for data input and optimization were already there, I was able to do it in just six lines of R code.

If you haven’t used R before and are new to programming, a good place to start is the Codeschool R starter course. If you are an experienced programmer but want an overview of the language, you can’t do better than the R intro that comes with R itself. Also, Bioconductor is developed by Martin Morgan and his core of programmers here at the FHCRC, so there are lots of knowledgeable folks around.

Other handy R sites include the Quick-R site and the list of resources compiled by RStudio. Hadley Wickham is putting together a great-looking Advanced R book, including a list of what he views as essential R vocabulary. Check out RStudio, even if you are fanatical about your text editor.

Edit this page on GitHub

brought to you by fredhutch.io