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PORe-writing a complicated ddply with aggregate
 

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  1. PORe-writing a complicated ddply with aggregate
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    <p>I want to re-write the following somewhat complicated plyr command so that it is faster and uses either aggregate, tapply, or data.table.</p> <p>This function allows you to input multiple ID variables and measure variables and then returns several calculations. However, on much larger datasets it may not be the most efficient. </p> <hr> <p>Here is the code...</p> <pre><code>require(ggplot2) # to get the diamonds data set require(plyr) mean_sd_for_several_variables &lt;- function(df, lvls, measures) { res &lt;- ddply(df, lvls, function(x) { ret &lt;- vector() for(measure in measures) { mean_sd &lt;- c(mean(x[,measure]), sd(x[,measure])) names(mean_sd) &lt;- c(paste0("mean_", measure), paste0("sd_", measure)) ret &lt;- c(ret, mean_sd) } return(ret) } ) print(res) } </code></pre> <p>... which returns:</p> <pre><code>mean_sd_for_several_variables(diamonds, c("color", "cut"), c("price","depth")) color cut mean_price sd_price mean_depth sd_depth 1 D Fair 4291.1 3286.1 64.048 3.29220 2 D Good 3405.4 3175.1 62.366 2.22240 3 D Very Good 3470.5 3523.8 61.750 1.46223 4 D Premium 3631.3 3711.6 61.169 1.15806 5 D Ideal 2629.1 3001.1 61.678 0.71201 6 E Fair 3682.3 2976.7 63.320 4.42103 7 E Good 3423.6 3330.7 62.204 2.23059 8 E Very Good 3214.7 3408.0 61.730 1.42377 9 E Premium 3538.9 3795.0 61.176 1.16454 10 E Ideal 2597.6 2956.0 61.687 0.70718 11 F Fair 3827.0 3223.3 63.508 3.70209 12 F Good 3495.8 3202.4 62.202 2.23976 13 F Very Good 3778.8 3786.1 61.722 1.38939 14 F Premium 4324.9 4012.0 61.260 1.16775 15 F Ideal 3374.9 3766.6 61.676 0.69398 16 G Fair 4239.3 3609.6 64.340 3.57340 17 G Good 4123.5 3702.5 62.527 2.03893 18 G Very Good 3872.8 3861.4 61.841 1.33169 19 G Premium 4500.7 4356.6 61.279 1.15341 20 G Ideal 3720.7 4006.3 61.700 0.68714 21 H Fair 5135.7 3886.5 64.585 3.14173 22 H Good 4276.3 4020.7 62.500 2.09212 23 H Very Good 4535.4 4185.8 61.968 1.31895 24 H Premium 5216.7 4466.2 61.322 1.15164 25 H Ideal 3889.3 4013.4 61.733 0.72939 26 I Fair 4685.4 3730.3 64.221 3.68771 27 I Good 5078.5 4631.7 62.475 2.17958 28 I Very Good 5255.9 4687.1 61.935 1.32890 29 I Premium 5946.2 5053.7 61.329 1.15338 30 I Ideal 4452.0 4505.2 61.794 0.72334 31 J Fair 4975.7 4050.5 64.357 3.31595 32 J Good 4574.2 3707.8 62.396 2.12091 33 J Very Good 5103.5 4135.7 61.902 1.33679 34 J Premium 6294.6 4788.9 61.390 1.13989 35 J Ideal 4918.2 4476.2 61.822 0.94669 </code></pre>
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    1. COhmm, don't have much time right know, but feel that I did something similar in my gateveys package. The function `calcShares` here: https://github.com/mbannert/gateveys/blob/master/R/gateveys.R uses data.table in similar way. Not sure if this is more intuitive to you, but at least is data.table.
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