You are here
R is Hot
Much in the same way that social networking, reality TV and craft beer were considered marginal fads before gaining widespread acceptance from the mainstream culture, the fast-growing popularity of R strongly suggests that it is heading toward a similar level of acceptance by the analytic community.
R has already won praise and plaudits from established media outlets such as the New York Times, Forbes, Intelligent Enterprise, InfoWorld and The Register. When you consider that R is a high-level computer programming language designed mostly for quants (the nickname for a subspecies of geeks who focus on quantitative analysis), the adoring media attention seems nothing short of astounding.
So it’s entirely fair to ask: Why all the hoopla? Why is an esoteric programming language created in the early 1990s by two academics in New Zealand suddenly all the rage? Why is R so hot?
Let’s examine some of the reasons behind the rising popularity of R. As is the case with almost every new trend, there are underlying economic and social factors – nothing just “happens,” there are always root causes. For example, it’s no secret that our digital information systems generate new data at an unimaginably fast pace – sometimes it seems as though we’re drowning in data.
Despite this apparently inexhaustible supply of new data, the perceived value of data is rising, which has led to the development of quicker, better and more powerful methods for analyzing complex sets of numbers. The current generation of analytic solutions are cumbersome and costly, however, which has opened the door to newer and less expensive techniques for crunching big numbers. Many of these newer and less costly techniques are written in R, which has rapidly become the “common language” of people whose careers or livelihoods are driven by data.
“R is the most powerful and flexible statistical programming language in the world,” says Norman Nie, a nationally recognized scholar in the fields of survey research, quantitative social science and political behavior. A co-founder of SPSS in the late 1960s, Nie is currently CEO and president of Revolution Analytics, a company based in Palo Alto that provides commercialized versions of R programs.
“What was once a secret of drug-development statisticians at pharmaceutical companies, quants on Wall Street, and PhD-level statistical researchers around the globe (not to mention pioneers at Web 2.0 companies like Google and Facebook) is suddenly becoming mainstream,” says Nie.
Robert A. Muenchen, the author of R for SAS and SPSS Users, writes that R has already had a profound impact on research in a variety of fields that rely on quantitative analysis to generate usable information.
Since its release in 1996, R has dramatically changed the landscape of research software. There are very few things that SAS or SPSS will do that R cannot, while R can do a wide range of things that the others cannot. Given that R is free and the others quite expensive, R is definitely worth investigating.
More Than a Programming Language
Unlike traditional analytic software products, R is a fully-fledged programming language. But R has already evolved into more than just a language – R represents a radically different approach to the challenges posed by increasingly large and complex sets of data. In that respect, it is something of a cultural phenomenon.
R is an open source project, which means that it depends on a worldwide community of active developers to grow and evolve. Like Linux, the most famous open source project, R isn’t “owned” by any single person or entity. R is maintained and supported by thousands of individuals who use it and who contribute to its ongoing development.
The members of this global community serve as R’s parents and custodians – and they take their responsibilities seriously. Like doting parents, they take pride in the achievements of their offspring – and they are quick to leap in when they perceive a problem.
“I can’t think of any programming language that has such an incredible community of users,” says Mike King, a quantitative analyst at Bank of America. King uses R to write programs for capital adequacy modeling, decision systems design and predictive analytics. “If you have a question, you can get it answered quickly by leaders in the field. That means very little downtime.”
Click below to download the full white paper.