We revisit the analysis for the FTSE-100 constituent companies and find that the relation still holds four years later and across a continent.

Can we make our analysis using the R statistical computing and analysis platform run faster? Usually the answer is yes, and the best way is to improve your algorithm and variable selection.

But recently David Smith was suggesting that a big benefit of their (commercial) version of R was that it was linked to a to a better linear algebra library. So I decided to investigate.

The quick summary is that it only really makes a difference for fairly artificial benchmark tests. For “normal” work you are unlikely to see a difference most of the time.

O'Reilly's recent publication Beautiful Data has a chapter by Jeff Jonas which is enough reason in itself for me to recommend it. The chapter, Data Finds Data, is also available as a PDF download.

David posts a question about how to solve this knapsack problem using the R statistical computing and analysis platform. My reply in the comments seems to have disappeared for a while so here is my proposed solution:

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.

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.

The results from the KDD Cup 2009 challenge (which we wrote about before) are in, and the winner of the slow challenge used the R statistical computing and analysis platform for their winning submission.

I am always on the lookout for useful data sources for training in statistics, so I am excited that Data.gov has opened for business. The purpose of Data.gov is to increase public access to high value, machine readable datasets generated by the US Government.

We are interested in Social Network Analysis using the statistical analysis and computing platform R. As usual with R, the documentation is pretty bad, so this series collects our notes as we learn more about the available packages and how they work. We use here the statnet group of packages, which seems to be the most comprehensive and most actively maintained network analysis packages.

The first task which we consider in this post is to load our data into a network object, which is how all the statnet packages represent a network. Typically for R, the documentation is voluminous but not always as helpful as one could want.

When using R, the statistical analysis and computing platform, I find it really annoying that it always prompts to save the workspace when I exit. This is how I turn it off.