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by Seth Mottaghinejad, Analytic Consultant for Revolution Analytics

You may have heard before that R is a vectorized language, but what do we mean by that? One way to read that is to say that many functions in R can operate efficiently on vectors (in addition to singletons). Here are some examples:

> log(1) # input and output are singletons
[1] 0

Francis Smart offers five excellent reasons to use R, in a well-researched post ideal for sharing with anyone thinking about making the switch to R. (You might also share this YouTube video for a quick 90-second introduction to R.)

Revolution Analytics has just introduced a 10-module series of R courses in Singapore. If you'd like to learn how to do data analysis in R, already know data analysis in another language like SAS and want to transition to R, or just want to enhance your R skills in a specific area, one of these hands-on courses may be of interest. The available modules (which you mix-and-match) are:

by Joseph Rickert

Worldwide R user group activity for the first Quarter of 2014 appears to be way up compared to previous years as the following plot shows.

By Mike Bowles

Ensemble methods are the backbone of machine learning techniques. However, it can be a daunting subject for someone approaching it for the first time, so we asked Mike Bowles, machine learning expert and serial entrepreneur to provide some context.

The fine folks behind the Big Data Journal have just published a new e-book Big Data: Harnessing the Power of Big Data Through Education and Data-Driven Decision Making. (Note: Adobe Flash is required to view the e-book.) In the eBook, you'll find the following technical papers on the topics of Big Data, Data Science, and R:

Europe's borders are changing again. This is big news, but you might be surprised to find out just how malleable the borders in Europe have been over the past 1000 years, as this timelapse map shows:


Most people know R as a statistics/analytics language for analysis of quantitative data, and don't think of it as a tool for processing raw text. But R actually has some quite powerful facilities for processing character data. And as Gaston Sanchez learned, text manipulation is an important part of a modern data scientist's repertoire: