Getting started with R

Posted on Mar 4, 2013

I have found myself encouraging, coaxing, and eventually coaching many colleagues and friends into learning R quite a bit these days. The power of R is tremendous when it comes to data, as can be seen in this basic hour-long Google Tech Talk. But I’ll motivate more another day; this post is for the motivated, on some resources to help pick up R:

  • A very dense, but yet beginner friendly set of slides are from a one-day class given by Hadley Wickam at NICAR 2013 (a data-driven journalism conference); you can click through to the first slide below: Screen Shot 2013-03-04 at 12.13.03 AM
    Hadly Wickam has written many of the libraries (ggplot2, ddply, reshape2, lubridate, stringr) that make R both powerful and easy to use. The slides are great, and contextualized in Hadley’s transform / visualize / model cycle of data analysis, which I think really hits the nail in the head.
  • Jared Knowles’s R bootcamp are a nice set of HTML slides that take you from everything including installing R to regression and reviews of statistics and programming. These slides have much more depth in them, including real-world practice cleaning datasets. There is also a 2-hour version of the bootcamp. Until last week, my favorite set of R tutorials.
  • For those who like video-based paced learning, Coursera’s “Computing for Data Analysis” and “Data Analysis” have also been mentioned by colleagues as useful resources.

Beyond those, there are a TON of resources on the web, everything from Stack Overflow to aggregated R blogs. With any of the tutorials though, the best way to learn is to pick a resource or two, pick a data analysis question you have, and dig at it. Good luck!

UPDATE: I’ve enabled comments on this post, so other resources that are helpful for learning R can be aggregated.

1 Comment

  1. prabhasp
    March 15, 2013

    Once you get past the basics, I think the next awesome things in R, that really show its power, are the libraries plyr (functional, and POWERful data transformations) and ggplot2 (visualizations). For those without previous experience with functional programming, https://github.com/hadley/devtools/wiki/functionals is an awesome introduction.