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    <p>The secret of fast performing code in R is to keep everything in vector, or arrays, which are really just arrays in disguise.</p> <p>Here is a solution that makes use exclusively of base R arrays. Your sample of data is tiny so I use <code>replicate</code> and <code>system.time</code> combined to measure performance.</p> <p>My solution is roughly 6 times faster than your solution with <code>sapply</code> and <code>data.table</code>. (My solution takes 0.6 seconds to solve your small sample data set 1,000 times.)</p> <p>Timing your solution</p> <pre><code>system.time(replicate(1000, XX &lt;- data.frame( intervals, count = sapply(1:5, function(i) sum(events[, start &lt;= intervals[i] &amp; intervals[i] &lt;= finish]))) )) user system elapsed 4.04 0.05 4.11 </code></pre> <p>My solution. First create two helper functions to create equal sized arrays with events running down the columns and intervals running across the rows. Then do a simple vector comparison followed by <code>colSums</code>:</p> <pre><code>event.array &lt;- function(x, interval){ len &lt;- length(interval) matrix(rep(unclass(x), len), ncol=len) } intervals.array &lt;- function(x, intervals){ len &lt;- length(x) matrix(rep(unclass(intervals), len), nrow=len, byrow=TRUE) } a.start &lt;- event.array(start, intervals) a.finish &lt;- event.array(finish, intervals) a.intervals &lt;- intervals.array(start, intervals) data.frame(intervals, count=colSums(a.start &lt;= a.intervals &amp; a.finish &gt;= a.intervals)) intervals count 1 (01/02/09 09:30:00) 3 2 (01/02/09 09:45:00) 7 3 (01/02/09 10:00:00) 19 4 (01/02/09 10:15:00) 18 5 (01/02/09 10:30:00) 12 </code></pre> <p>Timing my solution</p> <pre><code>system.time(replicate(1000, YY &lt;- data.frame( intervals, count=colSums(a.start &lt;= a.intervals &amp; a.finish &gt;= a.intervals)) )) user system elapsed 0.67 0.02 0.69 all.equal(XX, YY) [1] TRUE </code></pre>
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    1. COThanks Andrie! Your solution indeed seems like a good alternative to mine. There is one glitch though: a.intervals <- intervals.array(start, intervals) seems to make R stop responding when start has length over approx. 16,000 and intervals has length of approx. 50,000 (which is 1/4 of my real interval data). Any ideas on how to overcome this apart from using smaller chuncks of intervals and combing the data at the end?
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      1. USRahul Premraj
    2. COI'm not a big data expert, so can only offer two options. 1) Cut it into chunk, as you suggest (which may or may not be time efficient in the end). 2) Use a bigger machine - have you considered Amazon EC2 or Hadoop?
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