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Comment by xte | Hacker News Reader

Just as a reminder https://panicz.github.io/pamphlet/ about how R is not such an innovative solution for statistic...

Could you point out where in that pamphlet there are criticisms of R? I see a Scheme tutorial prefaced by a petulant diatribe against programming languages that are not Scheme, and nothing specific about R. I quote, “On the other hand, being a rather seasoned Schemer, I knew that most of these languages were only inferior incarnations of Lisp, and I could find little value in learning a new syntactical disguise for those well-established concepts. I was very soon to find out that my skepticism was right, and that learning R would require internalizing a lot of unfortunate idiosyncrasies that the authors of the language had chosen for their own reasons, and that I would be constantly stumbling over those incomprehensible limitations and discrep- ancies between common practices and the language developers’ decisions.”

And, “However, the case of the R programming language is particularly interesting, because initially it was just a harmless implementation of Scheme; but then as a result of the irresponsible experiments of mad scientists it mutated into a monster.”

I have a hard time taking this seriously, and it probably isn’t meant to be taken as such. It has practically nothing to do with R so I don’t know why one would bring it up.

Maybe adding Greenspun's tenth rule (ref. Wikipedia) or adding the recent "A Programmable Programming Language" [1] or even "Why Lisp" [2]...

Saying "gasoline car sucks" IMO automatically imply that any car using gasoline sucks, or if you prefer "$type car outshine any other car type".

[1] https://cacm.acm.org/magazines/2018/3/225475-a-programmable-...

[2] http://www.gigamonkeys.com/book/introduction-why-lisp.html

The beauty of R (and Python) is the ecosystem, not necessarily the language. Most statisticians want libraries that they can use to do the modeling so that they can focus their time on the data, not the programming.

On python "hy" (programming language) is a lispy-python so can be used with any cython-compatible module, it's not as clear as classic CL/scheme but IMVHO good enough.

Yeah, let’s not reinvent everything that is available in the R ecosystem again for some reason. R might not win a beauty price for best programming language but it does the trick just fine.

I'm curious to understand why is born in the first place since we already have many lisp/scheme then... R itself seem a reinvention of the wheel...

According to an article published in 1996 by Ihaka and Gentleman:

“The work has been heavily influenced by two existing languages-Becker, Chambers, and Wilks' S (1985) and Steel and Sussman's Scheme (1975). We felt that there were strong points in each of these languages and that it would be interesting to see if the strengths could be combined. The resulting language is very similar in appearance to S, but the underlying implementation and semantics are derived from Scheme. In fact, we implemented the language by first writing an interpreter for a Scheme subset and then progressively mutating it to resemble S.”

(https://www.stat.auckland.ac.nz/~ihaka/downloads/R-paper.pdf)

Unfortunately R’s gain was LispStat’s loss: https://www.jstatsoft.org/issue/view/v013

Thanks :-)

Hear tell it was begat of an unholy tryst between PHP and Excel, but I am not judging.

A much less flippant answer it was a work-a-like intended as a successor to a (licensed) stats language 'S'.

[0] https://en.wikipedia.org/wiki/S_(programming_language)

Thanks :-)

An incrementalist approach that I’ve half wondered about is to build an implementation of R on top of Racket. My understanding is that R was originally built on a Scheme runtime. Since that time there’s been a lot of C/C++/Fortran code added in, but maybe too that could be leveraged via Racket’s FFI.

If I have a choice and need to do something reasonably simple or that is well-supported by Racket’s libraries, I like to reach for Racket. If I have a statistical or data munging task, and deployment story isn’t too important, it’s R all the way.

Potentially an easier path I've been thinking about is to take the already well polished Rcpp<->R bridge and extend it one more layer into a guile<->Rcpp<->R bridge. Part of the motivation here is to get better integration for statistical programming into GNU/TeXmacs: an scientific "office suite" built using guile scheme.

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