加快轻按R代码

Question

I have 100 matrices which each have 604800 columns, and 101 rows. For each matrix, I need to reduce the number of columns to 60480 by computing the 10 column averages.

For example, for a vector

c(1,2,3,4,5,6,7,8,9,10,...)

The 5 column average would be:

c(3,8,13,18,...)

The code I am using to do this is:

col.av = tapply(col, rep(1:(length(col)/10), each = 10), mean)

Where col is one of my 101 x 604800 matrices. I have a for loop which iterates over the 100 matrices, however my problem is in the length of time needed to compute one run.

If I am just using one matrix, it takes 20 minutes+ to execute which is not feasible. Are there any suggestions on how I can improve the speed of computation?

Thanks

Answer 1

If you are fine with for loop, this one works for your case:

col.av <- matrix(0, nrow(col), ncol(col)/10)
for (i in 1:ncol(col.av)) {
  col.av[,i] <- rowMeans(col[,(10*(i-1)+1):(10*i)])
}

Answer 2

Or without a for-loop and a custom function for readability. You can always wrap this in your for-loop or a call to apply.

#generate data
nc=604800 
nr=101
test_m <- matrix(rnorm(nc*nr),ncol=nc)

#function to get rowmeans by 'window'-columns
get_rowmeans <- function(mm, window=10){
  indices <- seq(1,ncol(mm),by=window)
  res <- sapply(indices, function(i){
    return(rowMeans(mm[,i:(i+(window-1))]))
  })
  res
}

tt <- get_rowmeans(test_m)
#check one
> all(tt[,1]==rowMeans(test_m[,1:10]))
[1] TRUE