Hi,
I have this sample-code (see above) and I was wondering wether it is possible
to speed things up.
What this code does is the following:
x is 4D array (you can imagine it as x, y, z-coordinates and a time-coordinate).
So x contains 50x50x50 data-arrays for 91 time-points.
Now I want to reduce the 91 time-points.
I want to merge three consecutive time points to one time-points by calculating
the mean of this three time-points for every x,y,z coordinate.
The reduce-sequence defines which time-points should get merged.
And the apply-function in the for-loop calculates the mean of the three
3D-Arrays and puts them into a new 4D array (data_reduced).
The problem is that even in this example it takes really long.
I thought apply would already vectorize, rather than loop over every coordinate.
But for my actual data-set it takes a really long time … So I would be really
grateful for any suggestions how to speed this up.
x <- array(rnorm(50 * 50 * 50 * 90, 0, 2), dim=c(50, 50, 50, 91))
data_reduced <- array(0, dim=c(50, 50, 50, 90/3))
reduce <- seq(1,90, 3)
for( i in 1:length(reduce) ) {
data_reduced[ , , , i] <- apply(x[ , , , reduce[i] :
(reduce[i]+3) ], 1:3, mean)
}
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