A while back I showed you how to make volcano plots in base R for visualizing gene expression results. This is just one of many genome-scale plots where you might want to show all individual results but highlight or call out important results by labeling them, for example, with a gene name. But if you want to annotate lots of points, the annotations usually get so crowded that they overlap one another and become illegible.

Joanna Zhao’s and Jenny Bryan’s R graph catalog is meant to be a complement to the physical book, Creating More Effective Graphs, but it’s a really nice gallery in its own right. The catalog shows a series of different data visualizations, all made with R and ggplot2. Click on any of the plots and you get the R code necessary to generate the data and produce the plot.

The 20th annual ISMB meeting was held over the last week in Long Beach, CA. It was an incredible meeting with lots of interesting and relevant talks, and lots of folks were tweeting the conference, usually with at least a few people in each concurrent session. I wrote the code below that uses the twitteR package to pull all the tweets about the meeting under the #ISMB hashtag. You can download that raw data here.

Title: A Backstage Tour of ggplot2 with Hadley Wickham Date: Wednesday, February 8, 2012 Time: 11:00AM - 12:00PM Pacific Presenter: Hadley Wickham, Professor of Statistics, Rice University Register here.

I just discovered a handy function in R to produce a scatterplot matrix of selected variables in a dataset. The base graphics function is pairs(). Producing these plots can be helpful in exploring your data, especially using the second method below. Try it out on the built in iris dataset.

**** 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.

Abhijit over at Stat Bandit posted some nice code for making forest plots using ggplot2 in R. You see these lots of times in meta-analyses, or as seen in the BioVU demonstration paper. The idea is simple - on the x-axis you have the odds ratio (or whatever stat you want to show), and each line is a different study, gene, SNP, phenotype, etc.

Last Year I introduced you to R Commander, a nice graphical user interface (GUI) for R for those of you who are still hesitant to leave the clicky-box style research a la SPSS for the far more superior reproducible research using R. As most of you know I'm a huge fan of ggplot2. Many of you came to the short course Hadley Wickham gave here a few weeks ago on ggplot2 and plyr.

Hadley Wickham, creator of ggplot2, an immensely popular framework for Tufte-friendly data visualization using R, is teaching two short courses at Vanderbilt this week. Once we opened registration to Vanderbilt students and staff we instantly filled all the available seats, so unfortunately I wasn't able to announce the course here. But the good news is that Hadley's made all the data, code, and slides from the course available online here.

Update Tuesday, September 14, 2010: Fixed the ylim issue, now it sets the y axis limit based on the smallest observed p-value. A while back Will showed you how to create QQ plots of p-values in Stata and in R using the now-deprecated sma package. A bit later on I showed you how to do the same thing in R using ggplot2.