The R software is a fascinating project. It becomes a reference tool for the data mining process. With the R package system, we can extend its features potentially at the infinite. Almost all existing statistical / data mining techniques are available in R.
But if there are many packages, there are very few projects which intend to improve the R core itself. The source code is freely available. In theory anyone can modify a part or even the whole software. Revolution Analytics proposes an improved version of R. It provides Revolution R Enterprise, it seems (according to their website) that: it improves dramatically the fastness of some calculations; it can handle very large database; it provides a visual development environment with a debugger. Unfortunately, this is a commercial tool. I could not check these features . Fortunately, a community version is available. Of course, I have downloaded the tool to study its behavior.
Revolution R Community is a slightly improved version of the Base R. The enhancements are essentially related to the calculations performances: it incorporates the Intel Math Kernal libary, which is especially efficient for the matrix calculations; it can take advantage also, in some circumstances, from the power of the multi-core processors. Performance benchmarks are available on the editor's website. The results are impressive. But we note that they are based on datasets generated artificially.
In this tutorial, we extend the benchmark to other data mining methods. We analyze the behavior of the Revolution R Community 5.0 - 64 bit version in various contexts: binary logistic regression (glm); linear discriminant analysis (lda from the MASS package); induction of decision trees (rpart from the rpart package); principal component analysis based on two different principles, the first one is based on the calculations of the eigenvalues and eigenvectors from the correlation matrix (princomp), the second one is done by a singular value decomposition of the data matrix (prcomp).
Keywords: R software, revolution analytics, revolution r community, logistic regression, glm, linear discriminant analysis, lda, principal components analysis, acp, princomp, prcomp, matrix calculations, eigenvalues, eignevectors, singular value decomposition, svd, decision tree, cart, rpart
Tutorial: en_Tanagra_Revolution_R_Community.pdf
Dataset: revolution_r_community.zip
References :
Revolution Analytics, "Revolution R Community".
