[Rd] Most efficient way to check the length of a variable mentioned in a formula.

[Gabriel Becker]

Joris,

For me

length(environment(form)[["x"]])

Was about twice as fast as

length(get("x",environment(form))))

In the year-old version of R (3.0.2) that I have on the virtual machine i'm
currently using.

As for you, the eval method was much slower (though my factor was much
larger than 20)

> system.time({thing <- replicate(10000,length(environment(form)[["x"]]))})
   user  system elapsed
  0.018   0.000   0.018
> system.time({thing <-
replicate(10000,length(get("x",environment(form))))})   user  system
elapsed
  0.031   0.000   0.033
> system.time({thing <- replicate(10000,eval(parse(text = "length(x)"),
envir=environment(form)))})
   user  system elapsed
  4.528   0.003   4.656

I can't speak this second to whether this pattern will hold in the more
modern versions of R I typically use.

~G

> sessionInfo()
R version 3.0.2 (2013-09-25)
Platform: x86_64-pc-linux-gnu (64-bit)

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C
 [9] LC_ADDRESS=C               LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base






On Fri, Oct 17, 2014 at 11:04 AM, Joris Meys <jorismeys at gmail.com> wrote:

> Dear R gurus,
>
> I need to know the length of a variable (let's call that X) that is
> mentioned in a formula. So obviously I look for the environment from which
> the formula is called and then I have two options:
>
> - using eval(parse(text='length(X)'),
>                     envir=environment(formula) )
>
> - using length(get('X'),
>             envir=environment(formula) )
>
> a bit of benchmarking showed that the first option is about 20 times
> slower, to that extent that if I repeat it 10,000 times I save more than
> half a second. So speed is not really an issue here.
>
> Personally I'd go for option 2 as that one is easier to read and does the
> job nicely, but with these functions I'm always a bit afraid that I'm
> overseeing important details or side effects here (possibly memory issues
> when working with larger data).
>
> Anybody an idea what the dangers are of these methods, and which one is the
> most robust method?
>
> Thank you
> Joris
>
> --
> Joris Meys
> Statistical consultant
>
> Ghent University
> Faculty of Bioscience Engineering
> Department of Mathematical Modelling, Statistics and Bio-Informatics
>
> tel : +32 9 264 59 87
> Joris.Meys at Ugent.be
> -------------------------------
> Disclaimer : http://helpdesk.ugent.be/e-maildisclaimer.php
>
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>



-- 
Gabriel Becker
Graduate Student
Statistics Department
University of California, Davis

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