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Data Scientist John Myles White used R to compare life expectancy and retirement age in the USA. While male (red) and female (green) life expectancies are rising, average retirement age is going down. What this means for the future of benefit programs like Social Security is the topic of an interesting series of comments on the post.

In today's social world, it's important to be able to collaborate with others online when working with data, and to be able to easily share your outputs online. Fortunately, the R language and the broad R community provides a number of facilities for collaboration and sharing, which are summarized in Noam Ross's guide to tools for collaboration with R. Among the resources he lists:

In the sponsored article Data Science: Buyer Beware at Forbes, SAP's Ray Rivera takes a dim view of Data Science. According to Rivera, Data Science is a "management fad" in the mold of Business Process Reengineering, and casts data scentists as self-ordained "gurus" whose mission is to stand between the "ignorant masses" that need access to data and a company's valuable data stores.

This video tour of the International Space Station from NASA commander Sunny Williams (via Andrew Sullivan) is just amazing:


It's hard to overstate the importance of functions in the R language. Pretty much everything you do in R involves calling a function. From creating objects, to doing calculations, to creating plots and fitting statistical models, everything is done by calling a function. In fact, the first R command you probably ever ran:

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

The blog is.R ran an excellent series of R tips and applications in December, with posts including working with Stata files, working with graphs and networks, and text analysis.

In a recent interview with DataInformed's Ian Murphy, I discussed the history of the open-source R project and how Revolution Analytics is building on R to compete with legacy statistical software such as SAS and SPSS.

We've written before about how you can use the Rcpp package to speed up R, by converting performance-critical snippets of R code to C++.

It's been a little while since we've rounded up the new local R user groups around the world, so here are the latest ones on the scene:

451 Research analyst Matt Aslett created this Database Landscape Map: