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POquick/elegant way to construct mean/variance summary table
 

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    <p>I can achieve this task, but I feel like there must be a "best" (slickest, most compact, clearest-code, fastest?) way of doing it and have not figured it out so far ...</p> <p>For a specified set of categorical factors I want to construct a table of means and variances by group.</p> <p><strong>generate data</strong>:</p> <pre><code>set.seed(1001) d &lt;- expand.grid(f1=LETTERS[1:3],f2=letters[1:3], f3=factor(as.character(as.roman(1:3))),rep=1:4) d$y &lt;- runif(nrow(d)) d$z &lt;- rnorm(nrow(d)) </code></pre> <p><strong>desired output</strong>:</p> <pre><code> f1 f2 f3 y.mean y.var 1 A a I 0.6502307 0.09537958 2 A a II 0.4876630 0.11079670 3 A a III 0.3102926 0.20280568 4 A b I 0.3914084 0.05869310 5 A b II 0.5257355 0.21863126 6 A b III 0.3356860 0.07943314 ... etc. ... </code></pre> <p><strong>using <code>aggregate</code>/<code>merge</code>:</strong></p> <pre><code>library(reshape) m1 &lt;- aggregate(y~f1*f2*f3,data=d,FUN=mean) m2 &lt;- aggregate(y~f1*f2*f3,data=d,FUN=var) mvtab &lt;- merge(rename(m1,c(y="y.mean")), rename(m2,c(y="y.var"))) </code></pre> <p><strong>using <code>ddply</code>/<code>summarise</code></strong> (possibly best but haven't been able to make it work):</p> <pre><code>mvtab2 &lt;- ddply(subset(d,select=-c(z,rep)), .(f1,f2,f3), summarise,numcolwise(mean),numcolwise(var)) </code></pre> <p>results in</p> <pre><code>Error in output[[var]][rng] &lt;- df[[var]] : incompatible types (from closure to logical) in subassignment type fix </code></pre> <p><strong>using <code>melt</code>/<code>cast</code></strong> (maybe best?)</p> <pre><code>mvtab3 &lt;- cast(melt(subset(d,select=-c(z,rep)), id.vars=1:3), ...~.,fun.aggregate=c(mean,var)) ## now have to drop "variable" mvtab3 &lt;- subset(mvtab3,select=-variable) ## also should rename response variables </code></pre> <p>Won't (?) work in <code>reshape2</code>. Explaining <code>...~.</code> to someone could be tricky!</p>
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