[R] apply, apply speed vs. traditional looping mechanisms

[John Sorkin]

Prof Ripley,
Many thanks for your reply. I did not mean to use apply twice, I meant
to ask about apply and lapply. I understand your comment re: apply. I
assume your comment about lapply is meant to mean that lapply is
implemented in C code and therefore should be faster than a loop written
in R.

I would also like to take this opportunity to thank you for your may
contributions to R and this list.

Thank you,
John Sorkin 


John Sorkin M.D., Ph.D.
Chief, Biostatistics and Informatics
Baltimore VA Medical Center GRECC and
University of Maryland School of Medicine Claude Pepper OAIC

University of Maryland School of Medicine
Division of Gerontology
Baltimore VA Medical Center
10 North Greene Street
GRECC (BT/18/GR)
Baltimore, MD 21201-1524

410-605-7119
jsorkin at grecc.umaryland.edu

>>> Prof Brian Ripley <ripley at stats.ox.ac.uk> 05/14/06 4:26 PM >>>
On Sun, 14 May 2006, John Sorkin wrote:

> Can someone tell me why apply (and apply) are faster in performing
> repeated operations than a for (or do) loop? I am looking for a
> technical explanation.

apply() is just a wrapper for a for loop.  So it is not faster that at

least one implementation using a for loop: it may be neater and easier
to 
understand than an explicit for loop.

I don't understand why you used 'apply' twice here.

lapply() can be faster than a carefully crafted for() loop (since
C-level 
code is more efficient in memory allocation), but its main rationale is

clarity (and especially to avoid traps like

ans <- vector("list", n)
for(i in 1:n) ans[[i]] <- fun(i)

if n turns out to be zero or fun(i) to be NULL).

-- 
Brian D. Ripley,                  ripley at stats.ox.ac.uk 
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/ 
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272866 (PA)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595