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This week's Economist has an in-depth article on the consequences of failures reproducible research, adding more detail to the report in the New York Times in July. Errors in data analysis by researchers at Duke University led to patients in clinical trials being assigned the wrong drug:

What do you get when you combine an improv theatre artist with an AI conversation 'bot? Surrealist comedy, like this exchange:

I have an article out this week on ReadWriteHack: Unlocking Big Data with R. My thanks to the folks at ReadWriteWeb for giving us the opportunity to showcase some of the many real-world Big Data applications of R. Here are some additional links about the applications mentioned in the article:

The most recent edition of the Revolution Newsletter is out. The news section is below, and you read the full September edition (with highlights from this blog and community events) online. You can subscribe to the Revolution Newsletter to get it monthly via email.

The bitly blog has posted a really interesting analysis of the effectiveness of links shared via the social-media services Facebook, Twitter and YouTube. Here, effectiveness is measured by the "half-life" of a link: the amount of time it takes for that link to generate half the clicks it will ever attract.

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

A contest to showcase applications of R for businesses is offering $20,000 in prizes from Revolution Analytics.

At last month's useR! 2011 conference at Warwick University, there were two talks on the RevoScaleR package for big data statistics in R

The first was a keynote presentation from Revolution Analytics' Chief Scientist, Lee Edlefsen. Here is the overview of his talk, Scalable Data Analysis in R:

An article in the September 5 issue of Fortune Magazine notes that despite the economy, companies are scrambling to hire data scientists:

On Wednesday September 21, Revolution Analytics' CTO David Champagne will give a live webinar introducing three new open-source packages for R and Hadoop, which make it possible to work with Hadoop data in R, and bring in-database R analytics to Hadoop. Here are the details:

In a poll with 570 respondents conducted last month at KDNuggets, the R software was the most frequent response to the question, "What programming languages you used for data mining / data analysis in the past 12 months?". The results are tabled below (respondents could select more than one response):