A Beautiful WWW put together a great set of resources for getting started with machine learning in R.  First, they recommend the previously mentioned free book, The Elements of Statistical Learning.  Then there's a link to a list of dozens of machine learning and statistical learning packages for R.  Next, you'll need data.  Hundreds of free real datasets are available at the UCI machine learning repository.

The New York Times had an interesting piece yesterday about how SAS is facing several business threats from companies like the recently IBM-acquired SPSS, and from burgeoning interest in open-source software like R.

Theresa Scott, instructor of the previously mentioned R workshop and weekly R clinic, is giving a lecture entitled "Reproducible Research with R, LaTeX, & Sweave" in MRB III, room 1220, this Wednesday 11/18 at 1:30.  You can see more details about the lecture here. Looks like her slides as well as much more introductory material on R, Latex, and Sweave are on her website. Reproducible Research with R, LaTeX, &

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.

R is a great tool with lots of resources for genetics, genome-wide association studies, and many other biological applications.  We've covered several places to find help in R in the past, but if you're still apprehensive about diving into R's command-line interface, fear not.  The R commander is a graphical user interface (GUI) for R that works under Windows, Linux, and Mac.

One of the nicer things about many statistics packages is the extremely granular control you get over your graphical output.  But I lack the patience to set dozens of command line flags in R, and I'd rather not power the computer by pumping the mouse trying to set all the clicky-box options in Stata's graphics editor.  I want something that just looks nice, using the out-of-the-box defaults.

At this week's R clinic Frank Harrell will unveil the new rms (Regression Modeling Strategies) package that is a replacement for the R Design package.  He will demonstrate the differences with Design, especially related to enhanced graphics for displaying effects in regression models.  Frank will also discuss the implementation of quantile regression in rms.

Any R user no matter what level of experience has had trouble finding the package or the function to do what you want to do and then figuring out how to use it.  The sos package in R just made that a lot easier. First, fire up R, then install the sos package (don't omit the quotes): install.packages("sos") It'll ask you to choose a mirror.  Choose the closest one.