The R programming language has endured a rough few years, at least in terms of popularity. The darling of academic-focused data analysts and researchers, R seemed increasingly eclipsed by Python, which is versatile enough for data analytics (in addition to a gazillion other things).
Yet the latest TIOBE Index rankings suggest that R might be crawling back: The language has jumped from 20th to 14th place over the past year. While it still lags Python (which stands in third place, up from fourth), it was never the kind of language that was going to burst from its niche and become a threat to super-popular languages. Nonetheless, rumors of R’s imminent demise seem a bit premature.
In order to create its rankings, TIOBE leverages data from a variety of aggregators and search engines, including Google, Wikipedia, YouTube, and Amazon. For a language to rank, it must be Turing complete, have its own Wikipedia entry, and earn more than 5,000 hits for +”<language> programming” on Google.
That means more people are searching for information related to R, which in turn suggests that people are either interested in it, or actively working with it (and in need of documentation and/or programming advice). R is also firmly entrenched in an academic context, and ends up taught to new generations of students every year; the question has always been whether it could break out of the ivory tower and enter “mainstream” business analytics shops.
Right now, R is struggling a bit for that kind of business adoption. R is cited as a required programming language in roughly a fifth of the job postings for data engineers, according to an analysis by Cloud Academy (66 percent of job postings for that position, meanwhile, mentioned Python). In practice, many data scientists and analysts end up using a mix of languages when crunching data.
“Combining R and Python is both reasonable and feasible,” Enriko Aryanto, the CTO and a co-founder of the Redwood City, Calif.-based QuanticMind, a data platform for intelligent marketing, told Dice earlier this year. “We run them both in our data science platform internally. But if I were starting my career all over again today, I might consider focusing on Python rather than R. It’s a more-general language with broader applications.”
The big question now is whether other studies will mirror TIOBE’s findings. In 2017, a KDnuggets poll of tech pros who use both R and Python showed a slow decline in R usage in favor of Python; meanwhile, a separate survey from Burtch Works revealed that Python use among analytics professionals grew from 53 percent to 69 percent (while the R user-base shrunk by nearly a third). If polls such as these begin to show a reversal in the rate of R usage, we can likely declare the language is indeed finding a larger audience. In the meantime, if you’re interested in data analytics, R is well worth a look.