speed up in R apply

On Wed, Jan 5, 2011 at 1:22 PM, David Winsemius <dwinsemius at 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.

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))))

  user  system elapsed
 0.410   0.010   0.575

all.equal(pr3, pr2)

[1] TRUE