Today marks the release of R 3.0.0. There will be plenty of commentary and useful information at sites such as R-bloggers (for example, Tal’s post). Version 3.0.0 is great news for bioinformaticians, due to the introduction of long vectors. What does that mean? Well, several months ago, I was using the simpleaffy package from Bioconductor 
I’m a big fan of ggplot2. Recently, I ran into a situation which called for a useful feature that I had not used previously: aes_string. Imagine that you have data consisting of observations for several variables – let’s say A, B, C – where each observation is from one of two groups – call them 
File under “I keep forgetting how to do this basic, frequently-required task, so I’m writing it down here.” Let’s create a data frame which contains five variables, vars, named A – E, each of which appears twice, along with some measurements: Now, let’s say we want only the rows that contain the maximum values of 
Updates from RStudio support: (1) “Thanks for reporting and I was able to reproduce this as well. I’ve filed a bug and we’ll take a look.” (2) Taking a further look, this is actually a bug in the Markdown package and we’ve asked the maintainer (Jeffrey Horner) to look into it. As juejung points out 
People have been telling me for a while that the latest version of RStudio, the IDE for R, is a great way to generate reports. I finally got around to trying it out and for once, the hype is justified. Start with this excellent tutorial from Jeremy Anglim. Briefly: the process is not so different 
OK, let’s do this: some statistics and visualization of the tweets for ISMB 2012. First, thanks to Stephen Turner who got things started in this post at his excellent blog, Getting Genetics Done. Subscribe to his feed if you don’t already do so. I’ve created a Github repository for this project (and future Twitter-related work). 
Finally, I get around to telling you that… …on Friday 24th February, I took a day out from my regular job to attend a meeting on Open Source Drug Discovery for Malaria. I should state straight away that whilst drug discovery and chem(o)informatics are topics that I find very interesting, I have no professional experience 
I use the biomaRt package from Bioconductor in almost every R session. So I thought I’d load the library and set up a mart instance in my ~/.Rprofile: On starting R, I was somewhat perplexed to see this error message: Twitter to the rescue. @hadleywickham told me to load utils first and @vsbuffalo explained that 
I’ve recently been working with methylation data; specifically, from the Illumina Infinium HumanMethylation450 bead chip. It’s a rather complex array which uses two types of probes to determine the methylation state of DNA at ~ 485 000 sites in the genome. The Bioconductor project has risen to the challenge with a (somewhat bewildering) variety of 
Just a brief selection of items that caught my eye this week. Note that this is a Friday as opposed to Friday, lest you mistake this for a new, regular feature. 1. R/statistics ggbio A new Bioconductor package which builds on the excellent ggplot graphics library, for the visualization of biological data. R development master 
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
