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In case you missed them, here are some articles from December of particular interest to R users:

A ComputerWorld tutorial on basic data processing with R.

Prediction: R will replace legacy SAS solutions and go mainstream.

O'Reilly has just published the results of the Data Scientist Salary Survey, based on data collected from attendees of the O'Reilly Strata conferences in 2012 and 2013. There were some interesting results from the salary portion of the survey:

Our crack-shot R trainer Luba Gloukhov generated a spirited (pun intended!) discussion from her post K-means Clustering 86 Single Malt Scotch Whiskies, with mentions of her analysis at FlowingData and

David Smith's picture
January 13, 2014

We had a marvellous series of guest posts here on the blog over the past few weeks. I'd like to give a special thanks to all of our guest bloggers for contributing, with special thanks to Joe Rickert for stepping in as our acting editor for the past 3 weeks. If you were celebrating or vacationing over the holidays, here's what you missed:

I tweeted out the image below earlier this month, and it quickly went viral:

By Jay Emerson and Mike Kane

We’re very happy to announce our recent publication with Steve Weston in the Journal of Statistical Software (JSS), “Scalable Strategies for Computing with Massive Data”, JSS Volume 55 Issue 14. In a nutshell:

by Daniel Hanson 
QA Data Scientist, Revolution Analytics

Some Applications of the xts Time Series Package

by Derek McCrae Norton - Senior Sales Engineer

Optimization is something that I hear clients ask for on a fairly regular basis. There are many problems that a few functions to carry out optimization can solve. R has much of this functionality in the base product, such as nlm(), and optim().

by Uday Tennety:  Director, Advanced Analytics Services at Revolution Analytics

The R ecosystem has become widely popular lately with large players such as Pivotal, Tibco, Oracle, IBM, Teradata and SAP integrating R into their product suites. All these big players are using value chain integration and platform envelopment strategies to build a network effect in order to gain maximum leverage against their competitors in the Big Data and Analytics space.