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copied!<p>The <code>apply</code> functions in R don't provide improved performance over other looping functions (e.g. <code>for</code>). One exception to this is <code>lapply</code> which can be a little faster because it does more work in C code than in R (see <a href="https://stackoverflow.com/questions/1692336/applying-a-function-to-a-distance-matrix-in-r">this question for an example of this</a>). </p> <p>But in general, the rule is that <strong><em>you should use an apply function for clarity, not for performance</em></strong>. </p> <p>I would add to this that <strong><em>apply functions have <a href="http://en.wikipedia.org/wiki/Side_effect_(computer_science)" rel="noreferrer">no side effects</a></em></strong>, which is an important distinction when it comes to functional programming with R. This can be overridden by using <code>assign</code> or <code><<-</code>, but that can be very dangerous. Side effects also make a program harder to understand since a variable's state depends on the history.</p> <p><em>Edit:</em></p> <p>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:</p> <pre><code>> 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 </code></pre> <p><em>Edit 2:</em></p> <p>Regarding the usage of parallel packages for R (e.g. rpvm, rmpi, snow), these do generally provide <code>apply</code> family functions (even the <code>foreach</code> package is essentially equivalent, despite the name). Here's a simple example of the <code>sapply</code> function in <code>snow</code>:</p> <pre><code>library(snow) cl <- makeSOCKcluster(c("localhost","localhost")) parSapply(cl, 1:20, get("+"), 3) </code></pre> <p>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 <a href="http://www.stat.uiowa.edu/~luke/R/cluster/cluster.html" rel="noreferrer">Tierney's clustering page</a>). <code>snow</code> has the following apply functions:</p> <pre><code>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, ...) </code></pre> <p>It makes sense that <code>apply</code> functions should be used for parallel execution since they <em>have no <strong><a href="http://en.wikipedia.org/wiki/Side_effect_(computer_science)" rel="noreferrer">side effects</a></em></strong>. When you change a variable value within a <code>for</code> loop, it is globally set. On the other hand, all <code>apply</code> functions can safely be used in parallel because changes are local to the function call (unless you try to use <code>assign</code> or <code><<-</code>, 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.</p> <p><em>Edit:</em></p> <p>Here's a trivial example to demonstrate the difference between <code>for</code> and <code>*apply</code> so far as side effects are concerned:</p> <pre><code>> 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 </code></pre> <p>Note how the <code>df</code> in the parent environment is altered by <code>for</code> but not <code>*apply</code>.</p>

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