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2012-09-10T02:52:02.777
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This seems a perfect fit for <code>data.table</code> and the <code>lapply(.SD,FUN)</code> and <code>.SDcols</code> arguments <ul> <li><code>.SD</code> is a data.table containing the Subset of x's Data for each group, excluding the group column(s).</li> <li><code>.SDcols</code> is a vector containing the names of the columns to which you wish to apply the function (<code>FUN</code>)</li> </ul> <h1>An example</h1> Setup the <code>data.table</code> <pre><code>library(data.table) DT <- as.data.table(df) </code></pre> The sums of <code>x</code>,<code>y</code>,<code>z</code> columns by <code>f</code> <pre><code>DT[, lapply(.SD, sum), by = f, .SDcols = c("x", "y", "z")] ## f x y z ## 1: 4 4.8041 3.9788 1.2519 ## 2: 2 1.1255 -0.8147 2.9053 ## 3: 3 0.9699 -0.1550 -8.5876 ## 4: 9 2.2685 -1.2734 1.0506 ## 5: 5 -0.1282 -2.5512 5.0668 ## 6: 10 -2.7397 0.5290 -0.3638 ## 7: 1 2.9544 -3.1139 -1.3884 ## 8: 8 -4.3488 0.6894 1.4195 ## 9: 7 2.3152 0.6474 2.7183 ## 10: 6 -0.1569 1.0142 0.9156 </code></pre> The sums of <code>x</code>, and <code>z</code> columns by <code>f</code> <pre><code>DT[, lapply(.SD, sum), by = f, .SDcols = c("x", "z")] ## f x z ## 1: 4 4.8041 1.2519 ## 2: 2 1.1255 2.9053 ## 3: 3 0.9699 -8.5876 ## 4: 9 2.2685 1.0506 ## 5: 5 -0.1282 5.0668 ## 6: 10 -2.7397 -0.3638 ## 7: 1 2.9544 -1.3884 ## 8: 8 -4.3488 1.4195 ## 9: 7 2.3152 2.7183 ## 10: 6 -0.1569 0.9156 </code></pre> Examples calculating the mean <pre><code>DT[, lapply(.SD, mean), by = f, .SDcols = c("x", "y", "z")] ## f x y z ## 1: 4 0.36955 0.30606 0.09630 ## 2: 2 0.10232 -0.07407 0.26412 ## 3: 3 0.07461 -0.01193 -0.66059 ## 4: 9 0.15123 -0.08489 0.07004 ## 5: 5 -0.01425 -0.28346 0.56298 ## 6: 10 -0.21075 0.04069 -0.02799 ## 7: 1 0.29544 -0.31139 -0.13884 ## 8: 8 -0.54360 0.08617 0.17744 ## 9: 7 0.38586 0.10790 0.45305 ## 10: 6 -0.07844 0.50710 0.45782 DT[, lapply(.SD, mean), by = f, .SDcols = c("x", "z")] ## f x z ## 1: 4 0.36955 0.09630 ## 2: 2 0.10232 0.26412 ## 3: 3 0.07461 -0.66059 ## 4: 9 0.15123 0.07004 ## 5: 5 -0.01425 0.56298 ## 6: 10 -0.21075 -0.02799 ## 7: 1 0.29544 -0.13884 ## 8: 8 -0.54360 0.17744 ## 9: 7 0.38586 0.45305 ## 10: 6 -0.07844 0.45782 </code></pre>
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