Once in a while, you embark on what looks like a simple computational procedure only to encounter frustration very early on. “I can’t even read my file into R!” you cry. Step back, take a deep breath and take note of what the software is trying to tell you. Most times, you’ve just missed something 
Integrated development environments (IDEs) are software development tools, providing an interface that enables you to write, debug, run and view the output of your code. Whether you need an IDE or find them useful depends very much on your own preferences and style of working. In my own case for example, I’ve tried both Eclipse 
As so often happens these days, a brief post at FriendFeed got me thinking about data analysis. Entitled “So how many retractions are there every year, anyway?”, the post links to this article at Retraction Watch. It discusses ways to estimate the number of retractions and in particular, a recent article in the Journal of 
…was the tongue-in-cheek title of an image that I posted to Twitpic this week. It shows the usage of the word “novel” in PubMed article titles over time. As someone correctly pointed out at FriendFeed, it needs to be corrected for total publications per year. It was inspired by a couple of items that caught 
Egon writes: Can someone please plot the BioStar users on a Google Map? Sounds like a challenge. Let’s go. 1. Harvesting user IP addresses BioStar user profiles (here’s mine) include a location field. It’s free text and optional, which means that location is missing or inaccurate for many users. However, if you’re logged into BioStar 
It would be nice if there were an R package, along the lines of RMySQL, for MongoDB. For now there is not – so, how best to get data from a MongoDB database into R? One option is to retrieve JSON via the MongoDB REST interface and parse it using the rjson package. Assuming, for 
I was reading an old post that describes GEOmetadb, a downloadable database containing metadata from the GEO database. We had a brief discussion in the comments about the growth in GSE records (user-submitted) versus GDS records (curated datasets) over time. Below, some quick and dirty R code to examine the issue, using the Bioconductor GEOmetadb 
A recent question over at BioStar asked whether abstracts returned from a PubMed search could easily be visualised as “word clouds”, using Wordle. This got me thinking about ways to solve the problem using R. Here’s my first attempt, which demonstrates some functions from the RCurl and XML packages. update: corrected a couple of copy/paste 
At any R Q&A site, you’ll frequently see an exchange like this one: Q: How can I use a loop to ? A: Don’t. Use one of the apply functions. So, what are these wondrous apply functions and how do they work? I think the best way to figure out anything in 
The colossus of bioinformatics meetings, ISMB, convened in Boston this year from July 9 – 13. As in recent years, the meeting was covered online at its website, FriendFeed and Twitter. I thought it would be fun to run a quick analysis of activity at the FriendFeed room using R. 1. Fetch the data We 
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
