I am writing codes to have the Euclidean distance of two matrices and then storing them in a matrix. Could anyone please review this?

a <-matrix(1:2,ncol = 2,nrow = 2)
     [,1] [,2]
[1,]    1    1
[2,]    2    2

b <-matrix(3:6,ncol = 2,nrow = 2)
     [,1] [,2]
[1,]    3    5
[2,]    4    6

n <-matrix(data = NA)
 for (i in 1:nrow(a)) {
   for (j in 1:nrow(b)) {
  • 3
    \$\begingroup\$ something in your code doesn't look correct, maybe you want something like this: stackoverflow.com/questions/35106567/… \$\endgroup\$
    – minem
    May 20, 2018 at 16:49
  • \$\begingroup\$ Also, you're missing the square root. If you install the proxy package, you can do proxy::dist(a, b, method = "Euclidean"). \$\endgroup\$
    – Alexis
    Jun 25, 2018 at 20:42

1 Answer 1


First let's take a quick look at what your code is doing. Your innermost line of code is:


Due to how you've structured your for loops, this is run for times

  1. with i=1 and j=1, setting n[1] to the squared distance between row 1 with a and row 1 of b,
  2. with i=1 and j=2, overwriting n[1] to the squared distance between row 1 of a and row 2 of b,
  3. with i=2 and j=1, setting n[2] to the squared distance between row 2 of a and row 1 of b, and
  4. with i=2 and j=2, overwriting n[2] to the squared distance between row 2 of a and row 2 of b.

So we end up with n = c(34, 20), the squared distances between each row of a and the last row of b.

It seems most likely to me that you are trying to compute the distances between each pair of points (since your n is structured as a vector). In this case, check out what we accomplish with the following, much simpler code:

#      [,1] [,2]
# [1,]    4   16
# [2,]    4   16

The resulting matrix is the squared difference of each element in the two matrices. All we need to do is to sum up the rows:

n <- rowSums((a-b)^2)
# [1] 20 20

Or if we wanted the actual distance instead of the squared distance:

n <- sqrt(rowSums((a-b)^2))
# [1] 4.472136 4.472136

Note that we dramatically simplified the calculation; a nice side benefit is that this code is much faster than using a for loop in R.


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