From spencer.graves at prodsyse.com  Fri Jul 22 17:14:22 2011
From: spencer.graves at prodsyse.com (Spencer Graves)
Date: Fri, 22 Jul 2011 08:14:22 -0700
Subject: [Rd] Best practices for writing R functions
In-Reply-To: <1311341191873-3686674.post@n4.nabble.com>
References: <1311341191873-3686674.post@n4.nabble.com>
Message-ID: <4E2993CE.4020903@prodsyse.com>

       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.
>
> 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
> Sent from the R devel mailing list archive at Nabble.com.
>
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[Rd] Best practices for writing R functions
Spencer Graves