I am a sucker for good quality data. I wrote about data.gov, the US Government data site before, and now I find OECD Statistics which has some 300 data sets, many of which seems to be readily accessible (though some may require subscription)

I like the "multicore" library for a particular task. I can easily write a combination of if(require("multicore",...)) that means that my function will automatically use the parallel mclapply() instead of lapply() where it is available. Which is grand 99% of the time, except when my function is called from mclapply() (or one of the lower level functions) in which case much CPU trashing and grinding of teeth will result.

So, I needed a function to determine if my function was called from any function in the "multicore" library. Here it is.

Somebody on the R-help mailing list asked how to get Rmpi working on his Fedora Linux machine so he could do high-performance computing on a cluster of machines (or a single multicore machine) using the R statistical computing and analysis platform. Since it is unusually painful to get working, I might as well copy the instructions here.

O’Reilly has published Data Mashups in R as a $4.99 PDF download in their Short Cut series. In 27 pages it takes you through an example of how to combine foreclosure information with maps and geographical information to produce plots like the one here. This is all done with the R statistical computing and analysis platform.

Hugh Miller, the team leader of the winner of the KDD Cup 2009 Slow Challenge (which we wrote about recently) kindly provides more information about how to win this public challenge using the R statistical computing and analysis platform on a laptop (!).