Is the R programming language in serious trouble?
According to the latest update of the TIOBE Index, the answer seems to be “yes.” After managing to keep a place amongst the Index’s top 20 languages for the past three years, R has finally tumbled out.
R is a very niche language, used primarily in data analytics (and even then, primarily in a university and research context). So its fall “is quite surprising because the field of statistical programming is still booming, especially thanks to the popularity of data mining and artificial intelligence,” read the note accompanying this month’s list. “It seems that there is a consolidation going on in the statistical programming market. Python has become the big winner.”
Why is Python (which ranked fourth on this month’s list) winning big in data science? That’s a very good question, and TIOBE has a theory: “Statistical programming is finding its way from university to industry nowadays and Python is more accepted by the industry.”
That certainly makes sense: Python is widely taught in schools (ensuring a steady flood of Python-ready tech pros into the broader economy), and powers a hefty portion of most companies’ tech stacks, and so it seems inevitable that firms large and small would come to rely on it for the all-important work for data analytics.
(In order to generate its monthly 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. This methodology, as you can imagine, has sparked its own share of controversy over the years.)
This isn’t the first time an organization has reported that Python is swallowing R whole. Way back in ye olden days of February 2018, a KDnuggets poll showed a slow decline in R usage in favor of Python among tech pros who utilized both languages; at the same time, a separate survey from Burtch Works revealed that Python use among analytics professionals grew from 53 percent to 69 percent over that same time two-year period, while the R user-base shrank by nearly a third.
“R has issues with scalability,” Enriko Aryanto, the CTO and a co-founder of the Redwood City, Calif.-based QuanticMind, a data platform for intelligent marketing, told Dice. “It’s a single-threaded language that runs in RAM, so it’s memory-constrained, while Python has full support for multi-threading and doesn’t have memory issues. When choosing a language, it all comes down to choosing what’s best to solve your problem.” And companies, wrestling with ever-larger datasets, really need something that can scale.
Of course, many researchers still prefer R for data-analytics work, and so the language seems unlikely to completely fade away anytime soon. Nonetheless, Python’s broad base seems to be giving it a sizable advantage when it comes to the language people choose to crunch their datasets.
