Earlier this month, IBM made a very public point of investing in the R programming language.
The R Consortium, the open-source foundation built to shepherd the language’s interaction with the broader tech community, suggested in a press release that Big Blue, as its newest Platinum member, would encourage the language’s use in data science.
As of June 2016, R stood in 16th place on the TIOBE Index, down three slots from a year earlier. That places it in roughly the same tier as another data-science language, MATLAB, which currently stands in 17th place (down from 15th place at this point in 2015). R occupies a similar place on the most recent RedMonk Programming Language Rankings, which are compiled via a different methodology than the TIOBE Index.
R owes its popularity to a number of factors. For starters, it’s open-source and multi-platform; whether you’re more comfortable with a Linux, Max, or Windows environment, the language is usable on all of them. R also pulls double-duty as a language and an Integrated Developer Environment (IDE), with extensibility via thousands of packages.
If you’re interested in using R as part of a data-science initiative (as many do), check out this Dice article that breaks down its idiosyncrasies, as well as its basic functions such as fetching packages and performing arithmetic. Once you’ve gotten a handle on the language, you can explore how its various add-ons enable the creation of data visualizations and statistical models.
Although it remains to be seen whether R will eventually surpass Python as the programming language of choice for many data scientists, IBM’s decision to support projects that help the language’s evolution could help it sustain as a valuable data-analytics tool for quite some time to come. Which means, if you’re interested in crunching data, that studying R—or at least becoming familiar with it—should be on your priority list.