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    <p>In general, you should try to use a vectorized function to begin with. Using <code>strsplit</code> will frequently require some kind of iteration afterwards (which will be slower), so try to avoid it if possible. In your example, you should use <code>nchar</code> instead:</p> <pre><code>&gt; nchar(words) [1] 1 5 5 3 </code></pre> <p>More generally, take advantage of the fact that <code>strsplit</code> returns a list and use <code>lapply</code>:</p> <pre><code>&gt; as.numeric(lapply(strsplit(words,""), length)) [1] 1 5 5 3 </code></pre> <p>Or else use an <code>l*ply</code> family function from <code>plyr</code>. For instance:</p> <pre><code>&gt; laply(strsplit(words,""), length) [1] 1 5 5 3 </code></pre> <p><em>Edit:</em></p> <p>In honor of <a href="http://en.wikipedia.org/wiki/Bloomsday" rel="noreferrer"><strong>Bloomsday</strong></a>, I decided to test the performance of these approaches using Joyce's Ulysses:</p> <pre><code>joyce &lt;- readLines("http://www.gutenberg.org/files/4300/4300-8.txt") joyce &lt;- unlist(strsplit(joyce, " ")) </code></pre> <p>Now that I have all the words, we can do our counts:</p> <pre><code>&gt; # original version &gt; system.time(print(summary(sapply(joyce, function (x) length(strsplit(x,"")[[1]]))))) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.000 3.000 4.000 4.666 6.000 69.000 user system elapsed 2.65 0.03 2.73 &gt; # vectorized function &gt; system.time(print(summary(nchar(joyce)))) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.000 3.000 4.000 4.666 6.000 69.000 user system elapsed 0.05 0.00 0.04 &gt; # with lapply &gt; system.time(print(summary(as.numeric(lapply(strsplit(joyce,""), length))))) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.000 3.000 4.000 4.666 6.000 69.000 user system elapsed 0.8 0.0 0.8 &gt; # with laply (from plyr) &gt; system.time(print(summary(laply(strsplit(joyce,""), length)))) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.000 3.000 4.000 4.666 6.000 69.000 user system elapsed 17.20 0.05 17.30 &gt; # with ldply (from plyr) &gt; system.time(print(summary(ldply(strsplit(joyce,""), length)))) V1 Min. : 0.000 1st Qu.: 3.000 Median : 4.000 Mean : 4.666 3rd Qu.: 6.000 Max. :69.000 user system elapsed 7.97 0.00 8.03 </code></pre> <p>The vectorized function and <code>lapply</code> are considerably faster than the original <code>sapply</code> version. All solutions return the same answer (as seen by the summary output). </p> <p>Apparently the latest version of <code>plyr</code> is faster (this is using a slightly older version).</p>
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    1. POHow to vectorize R strsplit?
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    1. POHow to vectorize R strsplit?
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    1. COThanks Shane, but I'm not getting the same results from what I'm doing. Its an implementation of the Verhoeff check digit scheme. I've modified my function to be compatible with the above implementations, but with an input of a 100,000 long vector, I'm getting a list of 8 elements from the first and a vector of 8 elements from the second (8 is the most likely length of the vector elements).
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    2. CO@James: Then I would imagine that there must be something else going on with your function. As you can see above, I just tested this on a vector with over 270k records and got the same results from each. You might try providing more of your code or else providing some of your data.
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    3. COIncidentally, I just installed plyr version 0.1.9 in R 2.11.1 and had similar timings as in the above.
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