I joined the Ritchie Lab back in 2007, and even though it's only been three years away from the bench, I've forgotten much of what I learned back in biochem classes.  I'm giving a talk on lipid genetics next week, and I found the Wiley Essential Biochemistry website very helpful for brushing up on some basic lipoprotein biology. There are 27 chapters covering a broad range of topics from enzyme kinetics to phosphofructokinase regulation.

**** UPDATE, May 15 2014 ***** The functions described here have now been wrapped into an R package. View the updated blog post or see the online package vignette for how to install and use. If you'd still like to use the old code described here, you can access this at version 0.0.0 on GitHub.

Head over to Revolutions Blog for a list of PDF and powerpoint resources for making the transition to R from other programming or stats languages.  All of these notes come from the New York R meetup. I enjoyed browsing the meetup's files - lots of powerpoints, PDFs, and example R data files for various topics, including several slideshows on ggplot2.

It took several months after learning about ggplot2 before I gave it a try myself.  I was apprehensive about learning a new graphics system with a new set of commands.  Thing is, if you've ever used plot() in R, you already know how to use much of the functionality in ggplot2!  In this tutorial I want to show you just how easy it is to start producing high-quality graphics using ggplot2.

Way back will wrote on this topic.  See his previous post for Stata code for doing this.  Unfortunately the R package that was used to create QQ-plots here has been removed from CRAN, so I wrote my own using ggplot2 and some code I received from Daniel Shriner at NHGRI. Of course you can use R's built-in qqplot() function, but I could never figure out a way to add the diagonal using base graphics.

While flirting around with previously mentioned ggplot2 I came across an incredibly useful set of functions in the plyr package, made by Hadley Wickham, the same guy behind ggplot2.  If you've ever used MySQL before, think of "GROUP BY", but here you can arbitrarily apply any R function to splits of the data, or write one yourself.

Strict quality control procedures are extremely important for any genome-wide association study.  One of the first steps you should take when running QC on your GWAS is to look for related samples in your dataset.  This does two things for you.

This handy introduction comes from UCLA's Statistical Computing webbook on logistic regression. If you're familiar with linear regression but you've never used logistic regression before, this is a good place to start.

There's no doubt that by now you've noticed that we're big fans of R around here.

For readers at Vanderbilt: At yesterday's R course I found out that Theresa Scott in the Biostatistics department holds a weekly R clinic and encourages new R users who want to learn more to bring any questions about R, or even your own code and data. The R clinic is held weekly on Thursday from 2:00-3:00 in MCN.