R is hot. Whether measured by more than 6,100 add-on packages, the 41,000+ members of LinkedIn’s R group or the 170+ R Meetup groups currently in existence, there can be little doubt that interest in the R statistics language, especially for data analysis, is soaring. Why R? It’s free, open source, powerful and highly extensible. “You have a lot of prepackaged stuff that’s already available, so you’re standing on the shoulders of giants,” Google’s chief economist told The New York Times back in 2009. Because it’s a programmable environment that uses command-line scripting, you can store a series of complex data-analysis steps in R. That lets you re-use your analysis work on similar data more easily than if you were using a point-and-click interface, notes Hadley Wickham, author of several popular R packages and chief scientist with RStudio. That also makes it easier for others to validate research results and check your work for errors — an issue that cropped up in the news recently after an Excel coding error was among several flaws found in an influential economics analysis report known as Reinhart/Rogoff. The error itself wasn’t a surprise, blogs Christopher Gandrud, who earned a doctorate in quantitative research methodology from the London School of Economics. “Despite our best efforts we always will” make errors, he notes. “The problem is that we often use tools and practices that make it difficult to find and correct our mistakes.” Sure, you can easily examine complex formulas on a spreadsheet. But it’s not nearly as easy to run multiple data sets through spreadsheet formulas to check results as it is to put several data sets through a script, he explains. Indeed, the mantra of “Make sure your work is reproducible!” is a common theme among R enthusiasts. Learn to use R – Your Hands-on Guide