The apply functions in R don t provide improved performance over other looping functions (e.g. for). One exception to this is lapply which can be a little faster because it does more work in C code than in R (see this question for an example of this).

But in general, the rule is that you should use an apply function for clarity, not for performance.

I would add to this that apply functions have no side effects, which is an important distinction when it comes to functional programming with R. This can be overridden by using assign or <<-, but that can be very dangerous. Side effects also make a program harder to understand since a variable s state depends on the history.

Edit:

Just to emphasize this with a trivial example that recursively calculates the Fibonacci sequence; this could be run multiple times to get an accurate measure, but the point is that none of the methods have significantly different performance:

fibo <- function(n) {
  if ( n < 2 ) n
  else fibo(n-1) + fibo(n-2)
}
system.time(for(i in 0:26) fibo(i))
# user  system elapsed 
# 7.48    0.00    7.52 
system.time(sapply(0:26, fibo))
# user  system elapsed 
# 7.50    0.00    7.54 
system.time(lapply(0:26, fibo))
# user  system elapsed 
# 7.48    0.04    7.54 
library(plyr)
system.time(ldply(0:26, fibo))
# user  system elapsed 
# 7.52    0.00    7.58 

Edit 2:

Regarding the usage of parallel packages for R (e.g. rpvm, rmpi, snow), these do generally provide apply family functions (even the foreach package is essentially equivalent, despite the name). Here s a simple example of the sapply function in snow:

library(snow)
cl <- makeSOCKcluster(c("localhost","localhost"))
parSapply(cl, 1:20, get("+"), 3)

This example uses a socket cluster, for which no additional software needs to be installed; otherwise you will need something like PVM or MPI (see Tierney s clustering page). snow has the following apply functions:

parLapply(cl, x, fun, ...)
parSapply(cl, X, FUN, ..., simplify = TRUE, USE.NAMES = TRUE)
parApply(cl, X, MARGIN, FUN, ...)
parRapply(cl, x, fun, ...)
parCapply(cl, x, fun, ...)

It makes sense that apply functions should be used for parallel execution since they have no side effects. When you change a variable value within a for loop, it is globally set. On the other hand, all apply functions can safely be used in parallel because changes are local to the function call (unless you try to use assign or <<-, in which case you can introduce side effects). Needless to say, it s critical to be careful about local vs. global variables, especially when dealing with parallel execution.

Edit:

Here s a trivial example to demonstrate the difference between for and *apply so far as side effects are concerned:

df <- 1:10
# *apply example
lapply(2:3, function(i) df <- df * i)
df
# [1]  1  2  3  4  5  6  7  8  9 10
# for loop example
for(i in 2:3) df <- df * i
df
# [1]  6 12 18 24 30 36 42 48 54 60

Note how the df in the parent environment is altered by for but not *apply.

Sometimes speedup can be substantial, like when you have to nest for-loops to get the average based on a grouping of more than one factor. Here you have two approaches that give you the exact same result :

set.seed(1)  #for reproducability of the results

# The data
X <- rnorm(100000)
Y <- as.factor(sample(letters[1:5],100000,replace=T))
Z <- as.factor(sample(letters[1:10],100000,replace=T))

# the function forloop that averages X over every combination of Y and Z
forloop <- function(x,y,z){
# These ones are for optimization, so the functions 
#levels() and length() don t have to be called more than once.
  ylev <- levels(y)
  zlev <- levels(z)
  n <- length(ylev)
  p <- length(zlev)

  out <- matrix(NA,ncol=p,nrow=n)
  for(i in 1:n){
      for(j in 1:p){
          out[i,j] <- (mean(x[y==ylev[i] & z==zlev[j]]))
      }
  }
  rownames(out) <- ylev
  colnames(out) <- zlev
  return(out)
}

# Used on the generated data
forloop(X,Y,Z)

# The same using tapply
tapply(X,list(Y,Z),mean)

Both give exactly the same result, being a 5 x 10 matrix with the averages and named rows and columns. But :

> system.time(forloop(X,Y,Z))
   user  system elapsed 
   0.94    0.02    0.95 

> system.time(tapply(X,list(Y,Z),mean))
   user  system elapsed 
   0.06    0.00    0.06 

There you go. What did I win? ;-)

...and as I just wrote elsewhere, vapply is your friend! ...it s like sapply, but you also specify the return value type which makes it much faster.

foo <- function(x) x+1
y <- numeric(1e6)

system.time({z <- numeric(1e6); for(i in y) z[i] <- foo(i)})
#   user  system elapsed 
#   3.54    0.00    3.53 
system.time(z <- lapply(y, foo))
#   user  system elapsed 
#   2.89    0.00    2.91 
system.time(z <- vapply(y, foo, numeric(1)))
#   user  system elapsed 
#   1.35    0.00    1.36 

Jan. 1, 2020 update:

system.time({z1 <- numeric(1e6); for(i in seq_along(y)) z1[i] <- foo(y[i])})
#   user  system elapsed 
#   0.52    0.00    0.53 
system.time(z <- lapply(y, foo))
#   user  system elapsed 
#   0.72    0.00    0.72 
system.time(z3 <- vapply(y, foo, numeric(1)))
#   user  system elapsed 
#    0.7     0.0     0.7 
identical(z1, z3)
# [1] TRUE

I ve written elsewhere that an example like Shane s doesn t really stress the difference in performance among the various kinds of looping syntax because the time is all spent within the function rather than actually stressing the loop. Furthermore, the code unfairly compares a for loop with no memory with apply family functions that return a value. Here s a slightly different example that emphasizes the point.

foo <- function(x) {
   x <- x+1
 }
y <- numeric(1e6)
system.time({z <- numeric(1e6); for(i in y) z[i] <- foo(i)})
#   user  system elapsed 
#  4.967   0.049   7.293 
system.time(z <- sapply(y, foo))
#   user  system elapsed 
#  5.256   0.134   7.965 
system.time(z <- lapply(y, foo))
#   user  system elapsed 
#  2.179   0.126   3.301 

If you plan to save the result then apply family functions can be much more than syntactic sugar.

(the simple unlist of z is only 0.2s so the lapply is much faster. Initializing the z in the for loop is quite fast because I m giving the average of the last 5 of 6 runs so moving that outside the system.time would hardly affect things)

One more thing to note though is that there is another reason to use apply family functions independent of their performance, clarity, or lack of side effects. A for loop typically promotes putting as much as possible within the loop. This is because each loop requires setup of variables to store information (among other possible operations). Apply statements tend to be biased the other way. Often times you want to perform multiple operations on your data, several of which can be vectorized but some might not be able to be. In R, unlike other languages, it is best to separate those operations out and run the ones that are not vectorized in an apply statement (or vectorized version of the function) and the ones that are vectorized as true vector operations. This often speeds up performance tremendously.

Taking Joris Meys example where he replaces a traditional for loop with a handy R function we can use it to show the efficiency of writing code in a more R friendly manner for a similar speedup without the specialized function.

set.seed(1)  #for reproducability of the results

# The data - copied from Joris Meys answer
X <- rnorm(100000)
Y <- as.factor(sample(letters[1:5],100000,replace=T))
Z <- as.factor(sample(letters[1:10],100000,replace=T))

# an R way to generate tapply functionality that is fast and 
# shows more general principles about fast R coding
YZ <- interaction(Y, Z)
XS <- split(X, YZ)
m <- vapply(XS, mean, numeric(1))
m <- matrix(m, nrow = length(levels(Y)))
rownames(m) <- levels(Y)
colnames(m) <- levels(Z)
m

This winds up being much faster than the for loop and just a little slower than the built in optimized tapply function. It s not because vapply is so much faster than for but because it is only performing one operation in each iteration of the loop. In this code everything else is vectorized. In Joris Meys traditional for loop many (7?) operations are occurring in each iteration and there s quite a bit of setup just for it to execute. Note also how much more compact this is than the for version.

When applying functions over subsets of a vector, tapply can be pretty faster than a for loop. Example:

df <- data.frame(id = rep(letters[1:10], 100000),
                 value = rnorm(1000000))

f1 <- function(x)
  tapply(x$value, x$id, sum)

f2 <- function(x){
  res <- 0
  for(i in seq_along(l <- unique(x$id)))
    res[i] <- sum(x$value[x$id == l[i]])
  names(res) <- l
  res
}            

library(microbenchmark)

> microbenchmark(f1(df), f2(df), times=100)
Unit: milliseconds
   expr      min       lq   median       uq      max neval
 f1(df) 28.02612 28.28589 28.46822 29.20458 32.54656   100
 f2(df) 38.02241 41.42277 41.80008 42.05954 45.94273   100

apply, however, in most situation doesn t provide any speed increase, and in some cases can be even lot slower:

mat <- matrix(rnorm(1000000), nrow=1000)

f3 <- function(x)
  apply(x, 2, sum)

f4 <- function(x){
  res <- 0
  for(i in 1:ncol(x))
    res[i] <- sum(x[,i])
  res
}

> microbenchmark(f3(mat), f4(mat), times=100)
Unit: milliseconds
    expr      min       lq   median       uq      max neval
 f3(mat) 14.87594 15.44183 15.87897 17.93040 19.14975   100
 f4(mat) 12.01614 12.19718 12.40003 15.00919 40.59100   100

But for these situations we ve got colSums and rowSums:

f5 <- function(x)
  colSums(x) 

> microbenchmark(f5(mat), times=100)
Unit: milliseconds
    expr      min       lq   median       uq      max neval
 f5(mat) 1.362388 1.405203 1.413702 1.434388 1.992909   100