Why is apply() method slower than a for loop in R?

Question

As a matter of best practices, I\'m trying to determine if it\'s better to create a function and apply() it across a matrix, or if it\'s better to simply loop a

Answer 1

You can use lapply or sapply on vectors if you want. However, why not use the appropriate tool for the job, in this case ifelse()?

> ptm <- proc.time()
> ifelse_million <- ifelse(million > 0,1,-1)
> proc.time() - ptm
   user  system elapsed 
  0.077   0.007   0.093 

> all.equal(ifelse_million, loop_million)
[1] TRUE

And for comparison's sake, here are the two comparable runs using the for loop and sapply:

> ptm <- proc.time()
> apply_million <- sapply(million, squish)
> proc.time() - ptm
   user  system elapsed 
  0.469   0.004   0.474 
> ptm <- proc.time()
> loop_million <- mash(million)
> proc.time() - ptm
   user  system elapsed 
  0.408   0.001   0.417