Try aggregate. It takes only 8 seconds for the 800000 rows in the
example below.
m <- as.data.frame(matrix(rnorm(16000000), ncol = 20))
m$ID <- rbinom(nrow(m), 10, prob = 0.5)
system.time(
aggregate(m[, 5:20], list(ID = m$ID), sd)
)
user system elapsed
6.14 1.37 7.55
>
HTH,
Thierry
------------------------------------------------------------------------
----
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature
and Forest
Cel biometrie, methodologie en kwaliteitszorg / Section biometrics,
methodology and quality assurance
Gaverstraat 4
9500 Geraardsbergen
Belgium
tel. + 32 54/436 185
[EMAIL PROTECTED]
www.inbo.be
To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to
say what the experiment died of.
~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data.
~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of
data.
~ John Tukey
-----Oorspronkelijk bericht-----
Van: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]
Namens Daren Tan
Verzonden: maandag 7 juli 2008 11:18
Aan: [EMAIL PROTECTED]
Onderwerp: [R] How can I optimize this piece of code ?
Currently it needs 50+ mins to run on a 800000 rows. I need to run it
hundreds of times :P
t(apply(unique_ids, 1, function(x) { sd(subset(m[, 5:20], m[,"ID"] ==
x)) } ))
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