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R news and tutorials contributed by (552) R bloggers

On December 3, 2009 I presented a brief talk at the NYC R meetup on how to create data visualizations with R using the immensely powerful ggplot2 package. The talk is very light on motivation but heavy on examples, so it may be more useful to those with some R and/or ggplot2 experience.

Over the next few posts, I’m going to be reviewing the use of R to implement the most commonly used matrix techniques for image manipulation. The code will be surprisingly simple to understand, because the real magic behind these techniques lies in the mathematics that R provides an abstract interface to. To start, I’m going

There are many blogs on Statistics, R and other related topics scattered around the internet. The R bloggers website provides a central hub where feeds from participating blogs are collated so that they can be viewed from a single website. This resources certainly appears to be a good idea so that people can more easily identify

The "apply" family of functions in R (apply, sapply, lapply) is a very powerful suite of tools for iterating through structures of data and returning the combined results of each iteration. But with great power comes great responsibility (or something like that): these functions can sometimes be frustratingly difficult to get working exactly as you intended, especially for newcomers...

In 2006 UserR conference Jim Porzak gave a presentation on data profiling with R. He showed how to draw summary panels of the data using a combination of grid and base graphics. Unfortunately the code has not (yet) been released as a package, so when I recently needed to quickly review several datasets at the

Lately, I’ve been running a series of fMRI experiments on visual perception. In the interests of understanding the underlying properties of the images I’m using as stimuli, I’ve been trying to learn more about the matrix transformations commonly used for image compression and image manipulation. Thankfully, R provides simple-to-use implementations for all of the matrix

In scientific discovery, the first three paradigms were experimental, theoretical and (more recently) computational science. A new book of essays published by Microsoft (and available for free download -- kudos, MS!) argues that a fourth paradigm of scientific discovery is at hand: the analysis of massive data sets. The book is dedicated to the late Microsoft researcher Dr Jim...