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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):

To play in a World Cup national soccer team, a player must be a citizen of that country. But most World Cup players don't regularly play in the nation of their World Cup team. Some hold dual citizenship; others simply play for a league team in a foreign country where citizenship rules don't apply. 

by Ilya Kipnis

In this post, I will demonstrate how to obtain, stitch together, and clean data for backtesting using futures data from Quandl. Quandl was previously introduced in the Revolutions Blog.  Functions I will be using can be found in my IK Trading package available on my github page.