Looking for a more efficient way to filter an array

Question

I have two arrays I obtained from krige() , values and variances with a couple of million entries. Those two arrays are of the same length and match 1:1 with each other. I want to remove values that have a variance above a certain threshold. I don't really need to modify values in-place, generating a third array would be fine.

The following code works fine:

for (i in 1:length(values)) {
  if (variances[i] > 0.8) {
    values[i] = NA
  }
}

Unfortunately, it is very slow and use only a single processor core. Do I really need to handle the parallel calculations manually? This sounds generic enough so that it should be built-in in some way, not only by using more than one core, but maybe some vector processor instructions?

Please enlighten me.

Answer 1

As long as those arrays match, you should be able to just subset one with another:

set.seed(1)
(values <- array(1:25, c(5,5)))
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    1    6   11   16   21
#> [2,]    2    7   12   17   22
#> [3,]    3    8   13   18   23
#> [4,]    4    9   14   19   24
#> [5,]    5   10   15   20   25

(variances <- array(rnorm(25,.8,0.2),c(5,5)))
#>           [,1]      [,2]      [,3]      [,4]      [,5]
#> [1,] 0.6747092 0.6359063 1.1023562 0.7910133 0.9837955
#> [2,] 0.8367287 0.8974858 0.8779686 0.7967619 0.9564273
#> [3,] 0.6328743 0.9476649 0.6757519 0.9887672 0.8149130
#> [4,] 1.1190562 0.9151563 0.3570600 0.9642442 0.4021297
#> [5,] 0.8659016 0.7389223 1.0249862 0.9187803 0.9239651

is.na(values[variances > .8]) <- TRUE

values
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    1    6   NA   16   NA
#> [2,]   NA   NA   NA   17   NA
#> [3,]    3   NA   13   NA   NA
#> [4,]   NA   NA   14   NA   24
#> [5,]   NA   10   NA   NA   NA

For array length of 10 million it takes about a second on my laptop, data generation included:

system.time({
  values <- array(1:10e6, c(1000,10000))
  variances <- array(rnorm(10e6,.8,0.2),dim(values))
  is.na(values[variances > .8]) <- TRUE
})
#>    user  system elapsed 
#>    1.05    0.10    1.14

dim(variances)
#> [1]  1000 10000
object.size(variances)
#> 80000216 bytes
object.size(values)
#> 40000216 bytes

^{Created on 2023-01-18 with reprex v2.0.2}