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You know Hadley Wickham as the inventor of the ggplot2 visualization phenomenon, the creator of time-saving R packages like plyr and lubridate, and the Chief Scientist at RStudio.

I'm in New York City for the Strata + Hadoop World conference, and last night I got the chance to stop by theCUBE for an live interview about Revolution R Enterprise 7. You can watch the full interview below, or click the links on the topics to skip ahead.

We're very excited to formally announce that Revolution R Enterprise 7 is here! This release includes the latest release of Open Source R (R 3.0.2).

Kids these days have it easy. In my day, you had to walk in the snow uphill both ways just to see a grainy VHS copy of E.T. the Extra Terrestrial, but now it's just a tap o' the iPad away. But seriously, this video is sweet and sentimental and brings back some good memories of a time before streaming:


Have a great weekend! We'll be back on Monday!

To take a spreadsheet beyond what it's designed for — data presentation, summarization and simple calculations — into the world of complex data analysis can be an alluring prospect.

The .Rprofile file is a great way to customize your R session every time you start it up. You can use it to change R's defaults, define handy command-line functions, automatically load your favourite packages — anything you like! The Getting Genetics Blog has a nice example .Rprofile file to give you some inspiration on what to do. One popular setting is options(stringsAsFactors=FALSE), which prevents R from converting character data into factor objects when you import data frames.

If your econometrics is a bit rusty and you're also looking to learn the R language, you can kill two birds with one stone with Introductory Econometrics using Quandl and R.  The first three parts of this seven-part tutorial introduces the basics of regression analysis, while the remaining sections provide R code you can try yourself to reproduce econometric analyses using data provided by the

If you're trying to predict when an event will occur (for example, a consumer buying a product) or trying to infer why events occur (what were the factors that led to a component failing?), time-to-event models are a useful framework. These models are closely related to survival analysis in life sciences, except that the outcome of interest isn't "time to death" but time to some other event (e.g. in marketing, "time to purchase").

As longtime readers of this blog will know, I love optical illusions, and the checkerboard shadow illusion is one of my all-time favourites.