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It was a great honour to be able to present to the China R Users Conference in Beijing last month, and share various applications of R that I've encountered recently. I was really invigorated by the enthusiasm of the many R users at the conference, and everyone was very warm and friendly. Here's a photo I took from the stage, so you can see how many people were there (and you can't even see the balcony in this photo):

Check out this tweet:

Spreadsheets remain an important way for people to share and work with data. Among other providers, Google has provided the ability to create online spreadsheets and other documents.

Back in 2009, David Smith posted a blog entry on how to use R, and specifically the XML package to import data from a Google Spreadsheet. Once you marked your Google sheet as exported, it took about two lines of code to import your data into a data frame.

The useR! 2014 conference promises to be the biggest yet, with more than 500 registrations already. But there's room for plenty more R users! Check out the update from the organizing committer below, and if you haven't already done so, register for the conference at the useR! 2014 website.

This robot from the Korea Advanced Institute of Science and Technology sports Oscar Pistorius style foot blades and a spinning "tail" that provides the stability of a speeding velociraptor. It can run at a record-breaking 46 kpm, and foam blocks thrown in its path don't even slow it down (Geek.com via Graeme Noseworthy).

 

If you learned R in its early days (say, the early 2000's or even the late 1990's) you may still be using some — ahem — old-fashioned ways to accomplish some tasks better served by newer functions and packages. To help those of us who have may missed some of R's more recent innovations, Karl Broman created hipsteR, a guide for "re-educating people who learned R before it was cool". Some of the suggestions Karl offers:

Many companies are considering switching from SAS to R for statistical data analysis, and may be wondering how R compares in performance and data size scalability to the legacy SAS systems (base SAS and SAS/Stat) they are currently using. Performance and scalability for R is exactly what Revolution R Enterprise (RRE) was designed for.

by Matt Sundquist, Plotly co-founder

In a recent post we showed how to use ggplot2 and Plotly’s R API to make shareable, interactive graphs. This week, in response to questions and requests, we’ll highlight how to use Plotly for your workflow. We assume that you are working with Plotly from within R. Let’s get started.