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by Sheri Gilley, Microsoft Senior Software Engineer

Over the years, we've shared several posts on using the ScaleR package to importprocess, visualize and analyze large data sets with R.

A little laughter is in order for this Friday, I think. Internet scallawag Obvious Plant snuck into the Los Angeles Zoo recently and added some of his own "Animal Fact" signs to the informational displays. Here are a couple of my favourites; you can find more at Obvious Plant's Tumblr.

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

A preview of the tutorials presented at the useR! 2016 conference.

A "advanced beginner's" guide to R published by ComputerWorld includes guides on data wrangling, visualization, and data APIs.

by Joseph Rickert

Last week, I mentioned a few of the useR tutorials that I had the opportunity to attend. Here are the links to the slides and code for all but one of the tutorials:

Before there was R, there was S. R was modeled on a language developed at AT&T Bell Labs starting in 1976 by Rick Becker and John Chambers (and, later, Alan Wilks) along with Doug Dunn, Jean McRae, and Judy Schilling.

As we were chatting with some folks after the useR! conference, the topic turned to the topic of animal intelligence, for example, how the brains of crows are more sophisticated than we thought, which may explain their tool-using skills and other nefarious behaviour. I promised I'd find the video of the monkeys upset about inequality of payment and so, Frank, this one's for you: 

We've shown a few times here how you can run R code on data in the cloud with Azure ML Studio, and even how to enable that code as a web service to be called from other applications. But what if you want to run code in a compiled language, like C++? Fortunately, you can take advantage of R's built-in support for compiled code, and call it from a package you upload to Azure ML.