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:
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.