This is a bare-bones introduction to ggplot2, a visualization package in R. It assumes no knowledge of R. For a better-looking version of this post, see this Github repository, which also contains some of the example datasets I use and a literate programming version of this tutorial. Preview Let’s start with a preview of what ggplot2 can do. Given Fisher’s iris data set and one simple command… 1 q
Machine Learning in R: Clustering
Clustering is a very common technique in unsupervised machine learning to discover groups of data that are "close-by" to each other. It is broadly used in customer segmentation and outlier detection. It is based on some notion of "distance" (the inverse of similarity) between data points and use that to identify data points that are close-by to each other. In the following, we discuss some very ba
ggplot An implementation of the grammar of graphics in R. Check out the documentation for ggplot2 - the next generation ggplot is an implementation of the grammar of graphics in R. It combines the advantages of both base and lattice graphics: conditioning and shared axes are handled automatically, while maintaining the ability to build up a plot step by step from multiple data sources. It also
RHIPE(phonetic spelling: hree-pay' 1) is a java package that integrates the R environment with Hadoop, the open source implementation of Google's mapreduce. Using RHIPE it is possible to code map-reduce algorithms in R e.g m <- function(key,val){ words <- strsplit(val," +")[[1]] wc <- table(words) cln <- names(wc) names(wc)<-NULL; names(cln)<-NULL; return(sapply(1:length(wc),function(r) list(key=c
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