[Rd] proposal for adapting code of function gl()

[peter dalgaard]

On Apr 12, 2011, at 10:33 , Joris Meys wrote:

> Thanks for the explanation, I wasn't fully aware of which optimization
> I was using. I reckon your solution is more R-sound, so no reason to
> keep with my bizarre workaround. It would be nice though if gl() got
> optimized. Thank you for the example too, I'm learning every day.
> 

I have now committed a version of the below to r-devel. (A couple of demons turned out to be lurking in the details, so not exactly the same code.)

-pd

> Cheers
> Joris
> 
> On Tue, Apr 12, 2011 at 8:51 AM, peter dalgaard <pdalgd at gmail.com> wrote:
>> 
>> On Apr 11, 2011, at 23:53 , Joris Meys wrote:
>> 
>>> Based on a discussion on SO I ran some tests and found that converting
>>> to a factor is best done early in the process. Hence, I propose to
>>> rewrite the gl() function as :
>>> 
>>> gl2 <- function(n, k, length = n * k, labels = 1:n, ordered = FALSE){
>>>  rep(
>>>      rep(
>>>        factor(1:n,levels=1:n,labels=labels, ordered=ordered),rep.int(k,n)
>>>      ),length.out=length
>>>  )
>>> }
>>> 
>> 
>> That's bizarre! You are relying on an optimization in rep.factor whereby it replicates the internal codes and exploits that the result has the same structure as the input. I.e., it just tacks on class and levels attributes rather than call match() as factor() does internally.
>> 
>> However, you can do the same thing straight away:
>> 
>>> gl2
>> function (n, k, length = n * k, labels = 1:n, ordered = FALSE)
>> {
>>   y <- rep(rep.int(1:n, rep.int(k, n)), length.out = length)
>>   structure(y, levels=as.character(labels), class=c(if(ordered)"ordered","factor"))
>> }
>> 
>> I get this to be a bit faster than your version, although with a smaller speedup factor, which probably just indicates that match() is faster on this machine.
>> 
>>> Some test results  :
>>> 
>>>> system.time(X1 <- gl(5,1e7))
>>>   user  system elapsed
>>>  29.21    0.30   29.58
>>> 
>>>> system.time(X2 <- gl2(5,1e7))
>>>   user  system elapsed
>>>   1.87    0.45    2.37
>>> 
>>>> all.equal(X1,X2)
>>> [1] TRUE
>>> 
>>>> system.time(X1 <- gl(5,100,1e7))
>>>   user  system elapsed
>>>   5.98    0.05    6.05
>>> 
>>>> system.time(X2 <- gl2(5,100,1e7))
>>>   user  system elapsed
>>>   0.21    0.03    0.25
>>> 
>>>> all.equal(X1,X2)
>>> [1] TRUE
>>> 
>>>> system.time(X1 <- gl(5,100,1e7,labels=letters[1:5]))
>>>   user  system elapsed
>>>   5.88    0.02    5.98
>>> 
>>>> system.time(X2 <- gl2(5,100,1e7,labels=letters[1:5]))
>>>   user  system elapsed
>>>   0.20    0.05    0.25
>>> 
>>>> all.equal(X1,X2)
>>> [1] TRUE
>>> 
>>>> system.time(X1 <- gl(5,100,1e7,labels=letters[1:5],ordered=T))
>>>   user  system elapsed
>>>   5.82    0.03    5.89
>>> 
>>>> system.time(X2 <- gl2(5,100,1e7,labels=letters[1:5],ordered=T))
>>>   user  system elapsed
>>>   0.22    0.04    0.25
>>> 
>>>> all.equal(X1,X2)
>>> [1] TRUE
>>> 
>>> reference to SO :
>>> http://stackoverflow.com/questions/5627264/how-can-i-efficiently-construct-a-very-long-factor-with-few-levels
>>> 
>>> --
>>> Joris Meys
>>> Statistical consultant
>>> 
>>> Ghent University
>>> Faculty of Bioscience Engineering
>>> Department of Applied mathematics, biometrics and process control
>>> 
>>> tel : +32 9 264 59 87
>>> Joris.Meys at Ugent.be
>>> -------------------------------
>>> Disclaimer : http://helpdesk.ugent.be/e-maildisclaimer.php
>>> 
>>> ______________________________________________
>>> R-devel at r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-devel
>> 
>> --
>> Peter Dalgaard
>> Center for Statistics, Copenhagen Business School
>> Solbjerg Plads 3, 2000 Frederiksberg, Denmark
>> Phone: (+45)38153501
>> Email: pd.mes at cbs.dk  Priv: PDalgd at gmail.com
>> 
>> 
> 
> 
> 
> -- 
> Joris Meys
> Statistical consultant
> 
> Ghent University
> Faculty of Bioscience Engineering
> Department of Applied mathematics, biometrics and process control
> 
> tel : +32 9 264 59 87
> Joris.Meys at Ugent.be
> -------------------------------
> Disclaimer : http://helpdesk.ugent.be/e-maildisclaimer.php

-- 
Peter Dalgaard
Center for Statistics, Copenhagen Business School
Solbjerg Plads 3, 2000 Frederiksberg, Denmark
Phone: (+45)38153501
Email: pd.mes at cbs.dk  Priv: PDalgd at gmail.com