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Revolution Analytics, founded in 2007, was the first company devoted to the R project. Since then, we've been behind several R initiatives, including the RHadoop project and the network of R user groups around the world.

The annual worldwide user conference useR! 2014 is underway at UCLA, beginning with a full day of tutorials. This year's useR! conference is a record-breaker with more than 700 attendees, so most of the tutorial sessions have been jam-packed. The tutorials cover a diverse array of R applications: data management, visualization, statistics and biostatistics, programming, and interactive applications.

The editors at DataInformed invited me to write an article about how R is used in business, and I was pleased to oblige. The article, How Companies use R to Compete in a Data-Driven World, is now live and describes how Facebook, The New York Times, X+1, ANZ Bank and many others successfully use R to analyze their data.

Hadley Wickham's been working on the next-generation update to ggplot2 for a while, and now it's available on CRAN.

by Wayne Smith, Ph.D. California State University, Northridge

Editor's note: This post was abstracted from the monthly newsletter of the Southern California Chapter of the ASA.

On May 13th and 14th, the Intel International Science and Engineering Fair (Intel ISEF) the world’s largest international pre-college competition, was held at the Los Angeles Convention Center.

A recent FastCoLabs article, "The 9 Best Languages For Crunching Data", starts its list with the R language:

It would be downright negligent to start this list with any language other than R. It has been kicking around since 1997 as a free alternative to pricey statistical software, such as Matlab or SAS.

Take a look at this spinning disk. Do you see colors?

I see two colored regions: ochre bands about 1/3 and 2/3 of the way out, each surrounded by narrow olive bands. But this image is actually monochrome black and white: it's just a rotating version of this image:

The R online training site DataCamp has created an infographic comparing R, SAS and SPSS. Provocatively titled "Statistical Language Wars", the infographic compares the history, purpose, ease of learning, popularity and marketability of skills in each of the three systems. Here's a small detail (click for the full chart):