The R programming language is the result of a collaborative effort with contributions from all over the world. Initially written by Robert Gentleman and Ross Ihaka of the University of Auckland in the late 90s, it is popular and in wide use. It has been featured in the New York Times. Even estimates that are several years old have put the number of users above a ¼ million. The current number is certainly much higher. One popular LinkedIn group has 30,000 members. It has been featured in the New York Times. Polls on KDNuggets.com have placed its popularity even higher than the two players that have dominated statistical computing for decades: SPSS Statistics and SAS. The open source nature, and its corresponding price, are extremely attractive to academics and students. Critically, it is also very powerful.
So what’s the catch? Even its fans admit to a learning curve. It is a programming language, so there is no Graphical User Interface to get you quickly up to speed. Software environments have been created to support working in R, and many of them are popular, but nonetheless, there is some effort to be spent on getting started. On the upside, it is universally recognized as having fine graphics capability and if measured solely in terms of sheer volume, no commercial package can compete with the number of algorithms and methods available in R.