R Programming Language: Predictions of Its Death Premature?

A few years ago, some analysts predicted that R, a specialty programming language used mainly in data science and statistics, would eventually fade into obscurity. The cause of death? Ultra-popular Python, which was enjoying a spike in adoption by data scientists, analysts, and engineers.

The data seemed to back up this assertion. In 2018, a KDnuggets poll of technologists who used both R and Python showed a slow decline in R usage in favor of Python. At roughly the same time, a separate survey from Burtch Works revealed that Python use among analytics professionals grew from 53 percent to 69 percent over a two-year period, while the R user-base shrunk by nearly a third.

But a funny thing happened along the way to R’s supposed obsolescence: the language regained an audience. According to the TIOBE Index, which attempts to rank the world’s programming languages by popularity, R has jumped from 16th place to 11th over the past year, placing it ahead of technologist darlings such as Swift and Ruby. 

To determine 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. It’s not the most scientific way to determine usage, but it gives us a pretty good monthly snapshot of a particular programming language’s “buzz.”

For academics, data scientists, and others, R continues to have a lot to offer. It’s multi-platform, open-source, and pretty lightweight. While it has a relatively narrow use-case, and some aspects of it are a little idiosyncratic, technologists swear by it when it comes to data-related workflows (including finance). If you’re exploring anything related to data, make sure to explore R—you can start with R’s dedicated website, which breaks down the language’s use-cases and latest features, and go from there.

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