Question

I have two vector e and g. I want to know for each element in e the percentage of elements in g that are smaller. One way to implement this in R is:

set.seed(21)
e <- rnorm(1e4)
g <- rnorm(1e4)
mf <- function(p,v) {100*length(which(v<=p))/length(v)}
mf.out <- sapply(X=e, FUN=mf, v=g)

With large e or g, this takes a lot of time to run. How can I change or adapt this code to make this run faster?

Note: The mf function above is based on code from the mess function in the dismo package.

Answer 1

The reason this is so slow is because you’re calling your function length(e) times. It doesn’t make a large difference for small vectors, but the overhead from R function calls really starts to add up with larger vectors.

Normally, you would need to move this to compiled code, but luckily you can use findInterval:

set.seed(21)
e <- rnorm(1e4)
g <- rnorm(1e4)
O <- findInterval(e,sort(g))/length(g)

# Now for some timings:
f <- function(p,v) mean(v<=p)
system.time(o <- sapply(e, f, g))
#   user  system elapsed 
#   0.95    0.03    0.98
system.time(O <- findInterval(e,sort(g))/length(g))
#   user  system elapsed 
#      0       0       0 
identical(o,O)  # may be FALSE
all.equal(o,O)  # should be TRUE

# How fast is this on large vectors?
set.seed(21)
e <- rnorm(1e7)
g <- rnorm(1e7)
system.time(O <- findInterval(e,sort(g))/length(g))
#   user  system elapsed 
#  22.08    0.08   22.31