Indeed, the mantra of 'Make sure your work is reproducible!' is a common theme among R enthusiasts. 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. Sure, you can easily examine complex formulas on a spreadsheet. 'The problem is that we often use tools and practices that make it difficult to find and correct our mistakes.' 'Despite our best efforts we always will' make errors, he notes. The error itself wasn't a surprise, blogs Christopher Gandrud, who earned a doctorate in quantitative research methodology from the London School of Economics. 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. 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. Complete guide as PDF download (2013 version free registration required)īecause it's a programmable environment that uses command-line scripting, you can store a series of complex data-analysis steps in R.Part 5: Syntax quirks you'll want to know.Part 3: Easy ways to do basic data analysis.'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. Why R? It's free, open source, powerful and highly extensible. Whether measured by more than 10,000 add-on packages, the 95,000+ members of LinkedIn's R group or the more than 400 R Meetup groups currently in existence, there can be little doubt that interest in the R statistics language, especially for data analysis, is soaring.

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