Update Friday, May 14, 2010: See this newer post on LocusZoom. If you caught Cristen Willer's seminar here a few weeks ago you saw several beautiful figures in the style of a manhattan plot, but zoomed in around a region of interest, with several other useful information overlays.
Frank Harrell is teaching his 3-session short course on regression modeling strategies using R here at Vanderbilt next month. Frank is a professor and chair of the Vanderbilt Biostatistics Department, and the author of several massively popular R libraries, including Design, rms, and the indispensable Hmisc.
Found this nice way to visually summarize contingency tables using ggplot2 in R on Hadley Wickham's ggplot2 cheat sheet. Using the same data in my previous post on making scatterplots in small multiples, I'll demonstrate how to use ggfluctuation() to make a fluctuation plot.
**** 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.
Learning R blog details some of the new features in the latest update to ggplot2. The latest version includes functions to make it easier to change axis and legend labels, as well as a function to easily set the limits of the plot display outside the range of the data. Be sure to check back next week - I'm putting together a short introductory ggplot2 tutorial.
I've covered plyr once before, showing you how to get means and variances for two quantitative traits across multilocus genotypes. JD Long over at Cerebral Mastication recently posted a nice screencast illustrating how plyr "just works" as an alternative to R's family of apply commands.
Just discovered this very handy R command to capture the output from a system command as an R object. I wanted to use R to read in the output from another program (PLINK) and do some processing on each output file. Of course if the files are named sequentially (plink1.out, plink2.out, plink3.out, etc.) this would be simple with a for loop.
One of R's biggest strengths is its unparalleled graphing capabilities. Just see any of our previous posts on ggplot2, visualization, or other posts tagged with R. R has several fundamentally different systems for plotting, including base graphics, lattice, and ggplot2. Furthermore, many add-on packages come with their own functions for producing problem-domain specific graphics.