> -----Original Message-----
> From: r-devel-boun...@r-project.org [mailto:r-devel-bounces@r-
> project.org] On Behalf Of Gabriel Becker
> Sent: July 22, 2011 11:38 AM
> To: Spencer Graves
> Cc: Alireza Mahani; r-devel@r-project.org
> Subject: Re: [Rd] Best practices for writing R functions
>
> On Fri, Jul 22, 2011 at 8:14 AM, Spencer Graves
> <spencer.gra...@prodsyse.com
> > wrote:
>
> > From my personal experience and following this list some for a
> few
> > years, the best practice is initially to ignore the compute time
> question,
> > because the cost of your time getting it to do what you want is far
> greater,
> > at least initially. Don't worry about compute time until it becomes
> an
> > issue. When it does, the standard advice I've seen on this list is
> to
> > experiment with different ways of writing the same thing in R, guided
> by
> > "profiling R code", as described in the "Writing R Extensions"
> manual.
> > (Googling for "profiling R code" identified examples.)
> >
> >
> > Hope this helps.
> > Spencer Graves
> >
> >
> >
> > On 7/22/2011 6:26 AM, Alireza Mahani wrote:
> >
> >> I am developing an R package for internal use, and eventually for
> public
> >> release. My understanding is that there is no easy way to avoid
> copying
> >> function arguments in R (i.e. we don't have the concept of pointers
> in R),
> >> which makes me wary of freely creating chains of function calls
> since each
> >> function call implies data copy overhead.
> >>
> >
> AFAIK R does not automatically copy function arguments. R actually
> tries
> very hard to avoid copying while maintaining "pass by value"
> functionality.
But beware that a function makes a copy of the argument as soon as you try to
modify something in that argument. So, for example, if you have a big list
object as an argument and are going to modify one element in the list, you will
save memory by making a local copy of the single element and modifying that.
However, as others have said, I would not worry until you find there is a
problem.
Paul
> Consider the following functions and their output:
>
>
> nomod = function(dat)
> {
> TRUE
> }
>
> mod = function(dat, i)
> {
> dat[5] = 5
> TRUE
> }
>
>
> > vec = rep(0, times = 10)
> > tracemem(vec)
> [1] "<0x8c85978>"
> > nomod(vec)
> [1] TRUE
> > mod(vec)
> tracemem[0x8c85978 -> 0x8c85c70]: mod
> [1] TRUE
>
> So in the nomod function, the argument never actually gets copied (that
> is
> what tracemem tracks). R only copies data when you modify an object,
> not
> when you simply pass it to a function
>
> HTH,
> ~G
>
>
>
> >
> >> Is the above assessment fair? Are there any good write-ups on best
> >> practices
> >> for writing efficient R libraries that take into consideration the
> >> above-mentioned limitations, and any others that might exist?
> >>
> >> Thank you,
> >> Alireza
> >>
> >>
> >> --
> >> View this message in context: http://r.789695.n4.nabble.com/**
> >> Best-practices-for-writing-R-**functions-
> tp3686674p3686674.**html<http://r.789695.n4.nabble.com/Best-practices-
> for-writing-R-functions-tp3686674p3686674.html>
> >> Sent from the R devel mailing list archive at Nabble.com.
> >>
> >> ______________________________**________________
> >> R-devel@r-project.org mailing list
> >> https://stat.ethz.ch/mailman/**listinfo/r-
> devel<https://stat.ethz.ch/mailman/listinfo/r-devel>
> >>
> >>
> > ______________________________**________________
> > R-devel@r-project.org mailing list
> > https://stat.ethz.ch/mailman/**listinfo/r-
> devel<https://stat.ethz.ch/mailman/listinfo/r-devel>
> >
>
>
>
> --
> Gabriel Becker
> Graduate Student
> Statistics Department
> University of California, Davis
>
> [[alternative HTML version deleted]]
>
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