On 05.01.2011 22:49, Young Cho wrote:
When introduced to R, I learned how to use *apply whenever I could to avoid
for-loops and all. And, getting the habit, I think I somehow got the
mis-conception that it is a magic source, always an optimal way of coding in
R.
That is right, but your apply emulates a loop over all rows. And
vectorized solutions are almost always preferable.
If you try to run the apply() way in the other dimension of the matrix
you will find that it is as fast the vectorizes solution (since only 5
iterations are required then).
Uwe Ligges
Thanks a lot for all of your helpful advice and comment!
Young
On Wed, Jan 5, 2011 at 3:09 PM, David Winsemius<dwinsem...@comcast.net>wrote:
On Jan 5, 2011, at 2:40 PM, Douglas Bates wrote:
On Wed, Jan 5, 2011 at 1:22 PM, David Winsemius<dwinsem...@comcast.net>
wrote:
On Jan 5, 2011, at 10:03 AM, Young Cho wrote:
Hi,
I am doing some simulations and found a bottle neck in my R script. I
made
an example:
a = matrix(rnorm(5000000),1000000,5)
tt = Sys.time(); sum(a[,1]*a[,2]*a[,3]*a[,4]*a[,5]); Sys.time() - tt
[1] -1291.026
Time difference of 0.2354031 secs
tt = Sys.time(); sum(apply(a,1,prod)); Sys.time() - tt
[1] -1291.026
Time difference of 20.23150 secs
Is there a faster way of calculating sum of products (of columns, or of
rows)?
You should look at crossprod and tcrossprod.
Hmm. Not sure that would help, David. You could use a matrix
multiplication of a %*% rep(1, ncol(a)) if you wanted the row sums but
of course you could also use rowSums to get those.
Thanks for pointing that out. I misread the OP's code.
And is this an expected behavior?
Yes. For loops and *apply strategies are slower than the proper use of
vectorized functions.
To expand a bit on David's point, the apply function isn't magic. It
essentially loops over the rows, in this case. By multiplying columns
together you are performing the looping over the rows in compiled
code, which is much, much faster. If you want to do this kind of
operation effectively in R for a general matrix (i.e. not knowing in
advance that it has exactly 5 columns) you could use Reduce
a<- matrix(rnorm(5000000),1000000,5)
system.time(pr1<- a[,1]*a[,2]*a[,3]*a[,4]*a[,5])
user system elapsed
0.15 0.09 0.37
system.time(pr2<- apply(a, 1, prod))
user system elapsed
22.090 0.140 22.902
all.equal(pr1, pr2)
[1] TRUE
system.time(pr3<- Reduce(get("*"), as.data.frame(a), rep(1, nrow(a))))
Slightly faster would be:
system.time(pr3<- Reduce("*", as.data.frame(a)))
And thanks for the nice example. Using a data.frame to feed Reduce
materially enhances its value to me.
user system elapsed
0.410 0.010 0.575
all.equal(pr3, pr2)
[1] TRUE
--
David Winsemius, MD
West Hartford, CT
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