Hadley Wickham, creator of ggplot2, has posted a 2 hour video on data visualization using R. You can find links to the videos and slides over at Revolutions Blog. Check back here soon. I am working with Hadley to arrange a day-long ggplot2 short course here at Vanderbilt this summer.
*** Update April 25, 2011: This code has gone through a major revision. Please see the updated code and tutorial here. *** A few months ago I showed you in this post how to use some code I wrote to produce manhattan plots in R using ggplot2. The qqman() function I described in the previous post actually calls another function, manhattan(), which has a few options you can set.
In a previous tutorial I showed you how to create plots faceted by the level of a third variable using ggplot2. A commenter asked about using faceted plots and viewports and reminded me of this function I found in the ggplot2 Google group. The arrange function below is similar to using par(mfrow=c(r,c)) in base graphics to put more than one plot in the same image window.
The first post on Getting Genetics Done was one year ago today. To celebrate, here are the top 10 most viewed posts since GGD launched last year. Incidentally, nine of the ten are tutorials on how to do something in R. Thanks to all the readers and all the commenters for sharing your thoughts!
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.
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.
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.
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.