R help - Nov 2017 - Best way to study internals of R ( mix of C, C++, Fortran, and R itself)?

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?

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).

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 ?

thanks in advance

Robert

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1) What is easy for one person may be very hard for another, so your question is
really unanswerable. You do need to know C and Fortran to get through the source
code. Get started soon reading the R Internals document if it sounds interesting
to you... you are bound to learn something even if you don't stick with it.
If you have questions about the internals though, you should read the Posting
Guide to find out where to ask them (hint: not here).

2) There are lots of blogs and surveys out there about how R's popularity
has increased over time, though Python seems to have higher billing in job
descriptions I have seen. Generally if you know multiple tools and the
underlying theory you are working with then you are more likely to succeed, so
don't limit yourself by dismissing R for reasons of comparative popularity.
-- 
Sent from my phone. Please excuse my brevity.

On November 21, 2017 11:14:45 AM PST, Robert Wilkins <iwritecode2 at
gmail.com> 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?
>
>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).
>
>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 ?
>
>thanks in advance
>
>Robert
>
>	[[alternative HTML version deleted]]
>
>______________________________________________
>R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
>https://stat.ethz.ch/mailman/listinfo/r-help
>PLEASE do read the posting guide
>http://www.R-project.org/posting-guide.html
>and provide commented, minimal, self-contained, reproducible code.

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).

Duncan Murdoch

The R Community made a call for one person to be in charge of R Contributed
Documentation, and I have done a request for being in charge of such duty.

If assigned, my plan is to implement Atlassian's Confluence along the R
Community (Accessed though R Project.org), in order to generate a Wiki and
Document Store for R, at all levels (R Internals, User Tutorials, etc.)

In a similar way the Apache Software Foundation does:
https://cwiki.apache.org/confluence/dashboard.action

So contribution and user guide for internals could be documented in such
platform for future users.


El 21/11/2017 10:26 p. m., "Jeff Newmiller" <jdnewmil at
dcn.davis.ca.us>
escribi?:
> 1) What is easy for one person may be very hard for another, so your
> question is really unanswerable. You do need to know C and Fortran to get
> through the source code. Get started soon reading the R Internals document
> if it sounds interesting to you... you are bound to learn something even if
> you don't stick with it. If you have questions about the internals
though,
> you should read the Posting Guide to find out where to ask them (hint: not
> here).
>
> 2) There are lots of blogs and surveys out there about how R's
popularity
> has increased over time, though Python seems to have higher billing in job
> descriptions I have seen. Generally if you know multiple tools and the
> underlying theory you are working with then you are more likely to succeed,
> so don't limit yourself by dismissing R for reasons of comparative
> popularity.
> --
> Sent from my phone. Please excuse my brevity.
>
> On November 21, 2017 11:14:45 AM PST, Robert Wilkins <
> iwritecode2 at gmail.com> 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?
> >
> >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).
> >
> >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 ?
> >
> >thanks in advance
> >
> >Robert
> >
> >       [[alternative HTML version deleted]]
> >
> >______________________________________________
> >R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> >https://stat.ethz.ch/mailman/listinfo/r-help
> >PLEASE do read the posting guide
> >http://www.R-project.org/posting-guide.html
> >and provide commented, minimal, self-contained, reproducible code.
>
> ______________________________________________
> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/
> posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
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