[Rd] Stability and reliability of R (comment on Re: as.vector() broken on a matrix or array of type "list")

Spencer Graves @pencer@gr@ve@ @ending from prod@y@e@com
Wed Sep 26 17:50:23 CEST 2018

On 2018-09-26 10:32, MacQueen, Don via R-devel wrote:
> With regard to Martin's  comment about the strength of (base) R:
> I have R code I wrote 15+ years ago that has been used regularly ever since with only a few minor changes needed due to changes in R. Within that code, I find particularly impressive for its stability a simple custom GUI that uses the tcltk package that has needed no updates whatsoever in all that time.
> Such stability and reliability have been extremely valuable to me.

       How much of R's stability is due to the unit tests encouraged by 
the examples in the help pages, the vast majority of which are run 
repeatedly with each new change?

       More generally, what are the lessons the computer science 
discipline can take from R's experience in this regard?

       I discussed this eight years ago in an article on "Package 
development process"  that I posted to Wikipedia eight years ago that 
has attracted 9 views per day since.  I also added a table discussing 
this to the Wikipedia article on "Software repository". That article has 
attracted over 300 views per day for at least the past 3 years.  Both 
these articles could doubtless be improved by someone more knowledgeable 
than I.

       Many thanks and kudos to Ross Ihaka, Bob Gentleman, Martin 
Maechler and the rest of the R Core team, who have managed this project 
so successfully for more than two decades now.

       Spencer Graves
> -Don
> --
> Don MacQueen
> Lawrence Livermore National Laboratory
> 7000 East Ave., L-627
> Livermore, CA 94550
> 925-423-1062
> Lab cell 925-724-7509
> On 9/26/18, 12:41 AM, "R-devel on behalf of Martin Maechler" <r-devel-bounces using r-project.org on behalf of maechler using stat.math.ethz.ch> wrote:
> [-- most of original message omitted, so as to comment on the following --]
>      -----
>      *) {Possibly such an R we would create today would be much closer to
>          julia, where every function is generic / a multi-dispach method
>          "a la S4" .... and still be blazingly fast, thanks to JIT
>          compilation, method caching and more smart things.}
>      But as you know one of the strength of (base) R is its stability
>      and reliability.  You can only use something as a "the language
>      of applied statistics and data science" and rely that published
>      code still works 10 years later if the language is not
>      changed/redesigned from scratch every few years ((as some ... are)).
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