R moves up to 5th place in IEEE language rankings
This article was posted by David Smith on revolution analytics.
IEEE Spectrum has just published its third annual ranking with its 2016 Top Programming Languages, and the R Language is once again near the top of the list, moving up one place to fifth position.
As David said last year (when R moved up to take sixth place), this is an extraordinary result for a domain-specific language. The other four languages in the top 5 (C, Java, Python amd C++) are all general-purpose languages, suitable for just about any programming task. R by contrast is a language specifically for data science, and its high ranking here reflects both the critical importance of data science as a discipline today, and of R as the language of choice for data scientists.
IEEE Spectrum ranks languages according to a large number of factors, including search rankings and trends, social media mentions, and job posting. (You can adjust the weighting of these factors to generate your own rankings using this interactive tool.) It also includes scholarly citations of the languages, a factur that influenced R's rise in this ranking
To read the full article, click here. For more articles about R, click here.
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Comment
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Comment by Jane Roughan on January 2, 2017 at 11:53pm
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For me, I tend to use R + Python + D3 and Javascript for visualisations and SQL for quick analysis of operational data.
When I want to perform a quick analysis of files, I tend to use R. Previously for visualizations I have used D3 and Python as preferred, but have noticed an increase in vis packages for R of late.
When I want to multi-thread running of complex, time-consuming algorithms, I prefer Python with Spark.
But I am relatively new comer to data science in the last 1 and 1/2 years.
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Comment by Vincent Granville on December 28, 2016 at 11:35pm
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Even more interesting, what are the most popular language combinations? For data scientists, R + Python + SQL is probably one of the most popular. Usually though, multi-lingual programmers master and use one programming language more so than the other ones (in my case, Perl over R, SQL, SAS, Javascript, C, Shell command language and LaTeX -- even though my "native" language was C; and I use R for dataviz only not for machine learning as I have developed my own simple, scalable algorithms over time for pretty much any statistical procedure such as regression or clustering)
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It would be interesting to compare this language ranking table with the one published here (displayed below). The one below does not include R, possibly because it does not consider R (or SQL or SAS or Matlab for that matter) as a programming language. Of course the methodologies to produce the two tables are very different.
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