Which function inside a loop is more efficient (ncol/nrow() or dim())

Question

In an exercise attempt I am trying to create a multiplication table using a for loop. I am new to programming and R is my first language that I learn, so I would like to know which functions inside loops are faster and more efficient. For now, I am not using methods of the apply family because I think that understanding of basic functions like the loops is important.

Here are two ways that I use to create a multiplicaton table:

Using dim() function:

mtx <- matrix(nrow=10, ncol=10)

for(i in 1:dim(mtx)[1]){
  for(j in 1:dim(mtx)[2]){
    mtx[i,j] <- i*j
  }
}

Using ncol/nrow() function:

mtx <- matrix(nrow=10, ncol=10)

for(i in 1:ncol(mtx)){
  for(j in 1:nrow(mtx)){
    mtx[i,j] <- i*j
  }
}

Which way is more efficient and generaly better to use?

Thank you

Answer 1

If you use the functions like you do in your example, the difference is really neglectable. This is because the functions get called only once per loop definition (and not every loop iteration!)

I would definitely prefer ncol/nrow because its much easier too read than dim(x)[1].

That being said, if you just go for the timings, the dim function is faster than ncol/nrow. If you look at the source code, you can see that ncol is implemented as

function (x) 
dim(x)[2L]

which means that ncol calls dim and is therefore marginally slower.

If you really want to save some speed with big matrices I would suggest to create the loop vectors beforehand like this:

rows <- 1:nrow(mtx)
cols <- 1:ncols(mtx)
for (i in rows) {
    for (j in cols) {
        mtx[i, j] <- i * j    
    }
}