R’s remarkable growth

By David Smith

(This article was first published on Revolutions, and kindly contributed to R-bloggers)

Python has been getting some attention recently for its impressive growth in usage. Since both R and Python are used for data science, I sometimes get asked if R is falling by the wayside, or if R developers should switch course and learn Python. My answer to both questions is no.

First, while Python is an excellent general-purpose data science tool, for applications where comparative inference and robust predictions are the main goal, R will continue to be the prime repository of validated statistical functions and cutting-edge research for a long time to come. Secondly, R and Python are both top-10 programming languages, and while Python has a larger userbase, R and Python are both growing rapidly — and at similar rates.

The Stack Overflow blog runs the numbers in a post today, The Impressive Growth of R. Analysis of activity on the Q&A site suggests “R is growing at a similar rate to Python in terms of a year-over-year percentage”. Python is the growth leader in the first tier of languages (including JavasScript, C# and PHP), in the second tier R is similarly the growth leader.

On an industry-by-industry basis, R is also growing in every category. Academia and the healthcare industries are both the biggest users of R (according to StackOverflow traffic), and are also growing the fastest year-over-year.

There’s lots more analysis in the complete Stack Overflow blog post, linked below. Of particular interest to R users is an analysis of the R package ecosystem and the most-mentioned packages in Stack Overflow Q&A’s. A recent RedMonk post also analyzes the top packages in the R ecosystem, with similar results.

Stack Overflow Blog: The Impressive Growth of R

To leave a comment for the author, please follow the link and comment on their blog: Revolutions.

R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more…

Source:: R News