In a presentation to the Chicago R User Group last night, Drew Conway used his new Infochimps package in R to assess the relative popularity of programming languages. Drew used the word.stats function in the Infochimps package to count the frequency of common computer languages mentioned in Twitter messages, and displayed the results in this bar chart:
In a presentation to the Chicago R User Group last night, Drew Conway used his new Infochimps package in R to assess the relative popularity of programming languages. Drew used the word.stats function in the Infochimps package to count the frequency of common computer languages mentioned in Twitter messages, and displayed the results in this bar chart:
It’s not perfect: languages like C and C++ are excluded because they’re impossible to search for, “ada” is excluded because it’s ambiguous (and otherwise that niche language would be ranked most popular), and R is measured by the frequency of its community twitter hashtag #rstats and not the letter R. But it’s interesting nonetheless. There’s lots more info about Infochimps in general, and how this chart in particular was created, in the slides downloadable from Drew’s blog.
Another way to look at programming language popularity is the frequency of mentions on two popular programmer’s resource sites. In a post at the Dataists blog, Drew Conway (again) and John Myles White used R and the XML package to extract the number of questions on stackoverflow.com and number of projects on github.com for about 50 programming languages, and plotted the results in this scatterplot:
As you can see, R tanks higher than the median for github projects and quite a lot higher for stackoverflow questions.
So R is doing quite well amongst programming languages in general. As a specialized statistics language, a more relevant comparison may come from looking at tags at the statistical question-and-answer site stats.stackexchange.com, where R currently has 260 questions compared to 6 for SAS and 22 for SPSS.