Some people familiar with R describe it as a supercharged version of Microsoft’s Excel spreadsheet software that can help illuminate data trends more clearly than is possible by entering information into rows and columns.
On second thoughts, though, I imagine no tea was spilled. It would take rather more than that. There is the required bit of stuffy huffiness from a spokesperson for the SAS Institute, too:
SAS says it has noticed R’s rising popularity at universities, despite educational discounts on its own software, but it dismisses the technology as being of interest to a limited set of people working on very hard tasks. “I think it addresses a niche market for high-end data analysts that want free, readily available code,” said Anne H. Milley, director of technology product marketing at SAS. She adds, “We have customers who build engines for aircraft. I am happy they are not using freeware when I get on a jet.”
R also gets some stick (though not in the article) from the computer science side of things for being fairly slow in comparison to some potential competitors. But it’s an exemplary open-source project and is now the lingua franca of academic statistics, for good reason. In day-to-day use for its designed purpose it’s hard to beat. The commitment of many of the core project contributors is really remarkable. In the social sciences R’s main competitor is Stata, which also has many virtues (including a strong user community) but costs money to own. I like R because it helps keep your data analysis honest, it has very strong graphical capabilities, it’s a gateway to understanding new work in statistics, and it’s free. Just take my advice and be sure to read the Posting Guide before you start asking any questions on r-help.