# Rstats

## Interactive Data Visualization With Cranvas

One of the advantage of R over other popular statistical packages is that it now has “natural” support for interactive and dynamic data visualization. This is, for instance, something that is lacking with the Python ecosystem for scientific computing (Mayavi or Enthought Chaco are just too complex for what I have in mind). Some time ago, I started drafting some tutors on interactive graphics with R. The idea was merely to give an overview of existing packages for interactive and dynamic plotting, and it was supposed to be a three-part document: first part presents basic capabilities like rgl, aplpack, and iplot (aka Acinonyx)–this actually ended up as a very coarse draft; second part should present ggobi and its R interface; third and last part would be about the Qt interface, with qtpaint and cranvas.

## Back from the BoRdeaux conference

Here is a quick wrap up of the BoRdeaux conference. I won’t detail the conference program itself, but just drop some words on packages that were presented together with their applications (in various fields: epidemiology, social sciences, teaching, high dimensional data, chemometrics). Multivariate data analysis Stéphanie Bougeard talked about two new functions in the ade4 package aiming at the analysis of K+1 tables (several blocks of explanatory variables and a block of response variables).

## Easy creation of videos with R

While preparing a talk due in three days or so, I thought it would be good to show some live demonstration of regularization techniques in regression with ggplot2. It sounds like a lot of people start with splines or polynomial regression to demonstrate overfitting. I believe this has something to do with Bishop’s book on Pattern Recognition and Machine Learning, see e.g. Shane Conway’s recap’ on Stanford ML 5.2: Regularization .

## Easier literate programming with R

I have been using Sweave over the past 5 or 6 years for processing my R documents, and I have been quite happy with this program. However, with the recent release of knitr (already adopted on UCLA Stat Computing and on Vanderbilt Biostatistics Wiki) and all of its nice enhancements, I really need to get more familiar with it. In fact, there’s a lot of goodies in Yihui Xie’s knitr, including the automatic processing of graphics (no need to call print() to display a lattice object), local or global control of height/width for any figures, removal of R’s prompt (R’s output being nicely prefixed with comments), tidying and highlighting facilities, image cropping, use of framed or listings for embedding code chunk.