Why are loops slow in R compared to apply?

It's not always the case that loops are slow and apply is fast. There's a nice discussion of this in the May, 2008, issue of R News:

Uwe Ligges and John Fox. R Help Desk: How can I avoid this loop or make it faster? R News, 8(1):46-50, May 2008.

In the section "Loops!" (starting on pg 48), they say:

Many comments about R state that using loops is a particularly bad idea. This is not necessarily true. In certain cases, it is difficult to write vectorized code, or vectorized code may consume a huge amount of memory.

They further suggest:

• Initialize new objects to full length before the loop, rather than increasing their size within the loop.
• Do not do things in a loop that can be done outside the loop.
• Do not avoid loops simply for the sake of avoiding loops.

They have a simple example where a for loop takes 1.3 sec but apply runs out of memory.

Just as a comparison (don't read too much into it!): I ran a (very) simple for loop in R and in JavaScript in Chrome and IE 8. Note that Chrome does compilation to native code, and R with the compiler package compiles to bytecode.

# In R 2.13.1, this took 500 ms
f <- function() { sum<-0.5; for(i in 1:1000000) sum<-sum+i; sum }
system.time( f() )

# And the compiled version took 130 ms
library(compiler)
g <- cmpfun(f)
system.time( g() )

@Gavin Simpson: Btw, it took 1162 ms in S-Plus...

And the "same" code as JavaScript:

// In IE8, this took 282 ms
// In Chrome 14.0, this took 4 ms
function f() {
    var sum = 0.5;
    for(i=1; i<=1000000; ++i) sum = sum + i;
    return sum;
}

var start = new Date().getTime();
f();
time = new Date().getTime() - start;