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copied!<p>This answer is an expanded version of my earlier comments which I have now deleted.</p> <p>zoo's <code>rollapply</code> already supports plain vectors and matrices. Furthermore its <code>rollapply</code> routine extracts the plain vectors or matrices from a zoo object before operating on it so there is no reason for a zoo object to take materially longer than a non-zoo object. The slowness you observed was a bug in <code>rollapply</code> (the extraction was not taking place properly) that was fixed in early November in the development version. This version is on R-Forge and installed like this:</p> <pre><code>install.packages("zoo", repo = "http://r-forge.r-project.org") </code></pre> <p>On the other hand, the generality of <code>rollapply</code> means its going to be much slower than special purpose routines or vectorized operations. </p> <p>zoo does have some specialized versions of <code>rollapply</code> (<code>rollmean</code>, <code>rollmedian</code>, <code>rollmax</code>) that are optimized for particular operations and will be much faster. If you can manufacture something out of those, e.g. a rolling sum of k terms is the same as <code>k</code> times a rolling mean, then you can get substantial speedups. Faster still will be manufacturing the rolling result from plain operations such as <code>+</code> .</p> <p>The post indicated that the function in question was just an example but the particular function could make a big difference in terms of speed since it will affect whether the sorts of speedups discussed are available.</p> <p>For example, running 3 replications of each of <code>rollapply</code>, <code>2 * rollmean</code> and a simple vectorized addition shows this:</p> <pre><code>> library(zoo) > library(rbenchmark) > n <- 10^4 > set.seed(123) > a <- rnorm(n) > library(rbenchmark) > benchmark(rollapply = a1 <- rollapplyr(a, 2, sum, fill = 0), + rollmean = a2 <- 2 * rollmeanr(a, 2, fill = 0), + add = a3 <- c(0, a[-1] + a[-n]), replications = 3, order = "relative") test replications elapsed relative user.self sys.self user.child sys.child 3 add 3 0.00 0.00000 0.00 0 NA NA 2 rollmean 3 0.07 1.00000 0.08 0 NA NA 1 rollapply 3 1.85 26.42857 1.84 0 NA NA > > all.equal(a1, a2) [1] TRUE > all.equal(a1, a3) [1] TRUE </code></pre>

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