From ripley at stats.ox.ac.uk  Wed Mar 26 23:43:53 2014
From: ripley at stats.ox.ac.uk (Prof Brian Ripley)
Date: Wed, 26 Mar 2014 22:43:53 +0000
Subject: [Rd] R-devel Digest, Vol 133, Issue 23
In-Reply-To: <1AF47B57-FB82-49FC-8967-66EA1E0D4FD5@gmail.com>
References: <mailman.15.1395658807.28229.r-devel@r-project.org>	<20140326172419.GA20735@cs.toronto.edu>
	<1AF47B57-FB82-49FC-8967-66EA1E0D4FD5@gmail.com>
Message-ID: <53335829.2090808@stats.ox.ac.uk>

On 26/03/2014 22:00, peter dalgaard wrote:
>
> On 26 Mar 2014, at 18:24 , Radford Neal <radford at cs.toronto.edu> wrote:
>
>>> From: Richard Cotton <richierocks at gmail.com>
>>>
>>> The rep function is very versatile, but that versatility comes at a
>>> cost: it takes a bit of effort to learn (and remember) its syntax.
>>> This is a problem, since rep is one of the first functions many
>>> beginners will come across.  Of the three main uses of rep, two have
>>> simpler alternatives.
>>>
>>> rep(x, times = ) has rep.int
>>> rep(x, length.out  = ) has rep_len
>>>
>>> I think that a rep_each function would be a worthy addition for the
>>> third use case
>>>
>>> rep(x, each = )
>>>
>>> (It might also be worth having rep_times as a synonym for rep.int.)
>>
>> I think this is exactly the wrong approach.  Indeed, the aim should be
>> to get rid of functions like rep.int (or at least discourage their
>> use, even if they have to be kept for compatibility).
>>
>> Why is rep_each(x,n) better than rep(x,each=n)?  There is no saving in
>> typing (which would be trivial anyway).  There *ought* to be no
>> significant difference in speed (though that seems to have been the
>> motive for rep.int).  Are you trying to let students learn R without
>> ever learning about specifying arguments by name?
>>
>> And where would you stop?  How about seq_by(a,b,s) rather than having
>> to arduously type seq(a,b,by=s)?  Maybe we should have glm_binomial,
>> glm_poisson, etc. so we don't have to remember the "family" argument?
>> This way lies madness...
>
> Spot on.
>
> Well, maybe a slight disagreement: In a weakly typed language like R, you will always have performance losses due to type testing and dispatching, and no compiler/interpreter is intelligent enough to predict the types so that this can be avoided. Some amout of hinting is needed for reliable speedups, either by having special functions for simple cases (allowed to make assumptions on their inputs), or some sort of #pragma-like construction.
>
> Actually, rep.int seems to be a poor example of this since the speedup is pretty negligible unless you do huge amounts of short replicates. I expect that the S-PLUS compatibility was the main reason to have it. Case in point:

As the help says:

      Function ?rep.int? is a simple case handled by internal code, and
      provided as a separate function partly for S compatibility and
      partly for speed (especially when names can be dropped).

E.g.

 > a <- letters[1:10]; names(a) <- a
 > system.time(for(i in 1:1000000) rep.int(a,10))
    user  system elapsed
   1.568   0.001   1.574
 > system.time(for(i in 1:1000000) rep(a,10))
    user  system elapsed
   2.804   0.002   2.816

There are also rare occasions where it is useful to use rep.int to 
circumvent method dispatch.

Note that rep() was an interpreted function when that comment was first 
written, and the gap was much larger then.  (Nor was it byte-compiled, 
nor generic.)  For the version of rep in R 0.65.1:

 > system.time(for(i in 1:1000000) rep("a",10))
    user  system elapsed
   1.612   0.000   1.616

vs the current

 > system.time(for(i in 1:1000000) rep("a",10))
    user  system elapsed
   0.518   0.000   0.519
 > system.time(for(i in 1:1000000) rep.int("a",10))
    user  system elapsed
   0.471   0.000   0.473

>> system.time(for(i in 1:10000000) rep("a",10))
>     user  system elapsed
>   16.721   0.125  19.037
>> system.time(for(i in 1:10000000) rep.int("a",10))
>     user  system elapsed
>   14.356   0.050  14.611
>> system.time(for(i in 1:1000000) rep("a",1000))
>     user  system elapsed
>   11.655   2.157  14.263
>> system.time(for(i in 1:1000000) rep.int("a",1000))
>     user  system elapsed
>   10.957   1.708  12.917

-- 
Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (se3lf)
1 South Parks Road,                     +44 1865 272866 (PA)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595



[Rd] R-devel Digest, Vol 133, Issue 23
Prof Brian Ripley