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POR grouping by condition in data.table
 

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    <p>In R, I have a large data.table. For every row, I want to count rows with a similar value of x1 (+/- some tolerance, tol). I can get this to work using adply, but it's too slow. It seems like the sort of thing data.table would be good for - in fact, I'm already using data.table for part of the computation.</p> <p>Is there a way to do this entirely with data.table? Here is an example:</p> <pre><code>library(data.table) library(plyr) my.df = data.table(x1 = 1:1000, x2 = 4:1003) tol = 3 adply(my.df, 1, function(df) my.df[x1 &gt; (df$x1 - tol) &amp; x1 &lt; (df$x1 + tol), .N]) </code></pre> <p>Results:</p> <pre><code> x1 x2 V1 1: 1 4 3 2: 2 5 4 3: 3 6 5 4: 4 7 5 5: 5 8 5 --- 996: 996 999 5 997: 997 1000 5 998: 998 1001 5 999: 999 1002 4 1000: 1000 1003 3 </code></pre> <h2>Update:</h2> <p>Here's a sample dataset that is a little closer to my real data:</p> <pre><code>set.seed(10) x = seq(1,100000000,100000) x = x + sample(1:50000, length(x), replace=T) x2 = x + sample(1:50000, length(x), replace=T) my.df = data.table(x1 = x, x2 = x2) setkey(my.df,x1) tol = 100000 og = function(my.df) { adply(my.df, 1, function(df) my.df[x1 &gt; (df$x1 - tol) &amp; x1 &lt; (df$x1 + tol), .N]) } microbenchmark(r_ed &lt;- ed(copy(my.df)), r_ar &lt;- ar(copy(my.df)), r_og &lt;- og(copy(my.df)), times = 1) Unit: milliseconds expr min lq median uq max neval r_ed &lt;- ed(copy(my.df)) 8.553137 8.553137 8.553137 8.553137 8.553137 1 r_ar &lt;- ar(copy(my.df)) 10.229438 10.229438 10.229438 10.229438 10.229438 1 r_og &lt;- og(copy(my.df)) 1424.472844 1424.472844 1424.472844 1424.472844 1424.472844 1 </code></pre> <p>Obviously, solutions from both @eddi and @Arun are much faster than mine. Now I just have to try to understand rolls.</p>
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