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
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