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copied!<p>After installing the data.table package and doing some preliminaries...</p> <pre><code>require(data.table) n0<- 1e5 n <- 1e6 DT <- data.table(A1 = sample(1:n0, n, replace = TRUE),B1 = sample(1:n0, n, replace = TRUE)) </code></pre> <p>this does the trick.</p> <pre><code>setkey(DT,A1) DT[ DT[,.N,by=A1], countC:=N ] </code></pre> <p>When you access a data.table with <code>DT[i,j]</code>, you can select rows with <code>i</code> and do something else with <code>j</code>, just like in data.frames.</p> <p><code>DT[,.N,by=A1]</code> selects all rows (since <code>i</code> is blank) and counts rows for each "A1" using the special variable <code>.N</code>.</p> <p>After setting column "A1" as key for DT, we can pass a data.table -- in this case <code>DT[,.N,by=A1]</code> -- in <code>i</code> to merge back the information in the latter data.table. In <code>j</code>, we create a new column in DT using <code>countC:=N</code>. The three vignettes on <a href="http://cran.r-project.org/web/packages/data.table/" rel="nofollow">data.table's CRAN page</a> are a good place to start learning more about how this works.</p> <p><strong>The question at hand.</strong> Oh, I think I see what the original problem was. Suppose <code>unique(x)=c(1,2,4)</code>. If you try <code>table(x)[x]</code>, you will be trying to access <code>table(x)[1]</code>, <code>table(x)[2]</code> and <code>table(x)[4]</code>. The last one is undefined since the length of the table is only 3. R always returns <code>NA</code> when we access indices greater than the length of a vector. For example, look at <code>(1:3)[4]</code>.</p> <p>In your case, if you are missing any unique values in <code>1:n0</code> that are not at the very top, you will see <code>NA</code>s.</p>

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