It was one of those corridor conversations a couple of months ago, that led to an introductory session on R, the statistical platform. I was chatting with Foonin Ho, professor in the Marketing department at SF State, when he said that some others would be interested as well. I've known that Chris Bettinger has been working with a few others on the use of R on campus, and we also have Stat CORR at SFSU, a group whose aim is to support faculty in their statistical analysis needs. Stat CORR is organized by Richard Harvey, and they use many titles of statistical analysis packages, including R.
My own interest in using R stems from a few different threads. My PhD work was in Decision Sciences at Georgia State University, and that love for statistics never went away! Additionally, students have to use software that the instructor uses, and so instead of using packages like SAS or SPSS, many usually end up settling for Excel. If we used R in our courses, both students and faculty would get access to a powerful package that they can use in class and beyond! R is distributed under a set of Free and Open Source licenses (see http://www.rproject.org/Licenses/).
We finally decided to meet up on November 22, 2013, and given the short notice, it was a small gathering, but nevertheless, the workshop got off the ground. It was led by Chris Bettinger, and attended by faculty and students. We went through a handful of steps:
 We looked at the central repository for R: CRAN
 Install R GUI
 Nuances of Unix vs Windows: How the path to a file can be tricky, so change \ to /
 Chris went on to show us R Studio http://www.rstudio.com/

We also looked as some examples from the site for QuickR http://www.statmethods.net/index.html
At the end of the session, we got enough to get started. Chris suggested a handful of books. Here are his recommendations:
Introductory Statistics with R, Peter Dalgaard
This is a good basic introduction to stats using R. You learn how R operates and can comfortably move to advanced texts and topics after getting through this. The first few chapters will seem bewildering because they are about data handling. Get through them and then loop back to them after you finish the book.
Basic Statistics with R, Raykov
If you are teaching a class to people who haven't learned any statistical software previously, then this is a way to get through it. Students will pick up a lot of R along the way, but it won't gel for them the way it will if they go through the Dalgaard text described above.
A Handbook of Statistical Analyses Using R, Brian Everitt
Great reference work. You get an easy to use handbook of statistical processes with easy to follow R code (both technical code and examples).
R Graphics Cookbook, Winston Chang
A lot of people use R to do very fancy graphical things. This is the book for that. It assumes you know a bit of R already, especially how to manipulate data (e.g. recode, sort, etc.)
A Beginner’s Guide to R, Alain Zuur
This is the best intro guide for learning about how R handles data. If you know you are going to be using MySQL or PostgreSQL as your data handler and want to use R as your analysis engine, this is a really great, easy to wrap you head around guide to the basics.