On 21/11/2017 2:14 PM, Robert Wilkins wrote:
How difficult is it to get a good feel for the internals of R, if you want to learn the general code base, but also the CPU intensive stuff ( much of it in C or Fortran?) and the ways in which the general code and the CPU intensive stuff is connected together?
That's a pretty difficult question to answer. How hard compared to what?
R has a very large audience, but my understanding is that only a small group have a good understanding of the internals (and some of those will eventually move on to something else in their career, or retire altogether).
That's true, but the good news is that there are people who know the internals now who didn't know them 5 or 10 years ago. So there is renewal happening. And there are a number of independent implementations of the language or subsets of it; see the Wikipedia article <https://en.wikipedia.org/wiki/R_(programming_language)>.
While I'm at it, a second question: 15 years ago, nobody would ever offer a job based on R skills ( SAS, yes, SPSS, maybe, but R skills, year after year, did not imply job offers). How much has that changed, both for R and for NumPy/Pandas/SciPy ?
The web page <http://r4stats.com/articles/popularity/> is fairly up to date. It doesn't say what things were like in 2002, but in early 2017, the ranking was Python > R > SAS in the count of job ads in data science. In 2012 it was SAS > Python > R (but R and Python were very close).
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