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POImprove performance function
 

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    <p>I learn methods of improve performance a code. For study that I wrote function wich return basic descriptive statistic like a <code>psych::describe</code>. I've tried different versions of the loops and at the moment it's all I could do. </p> <p><strong>Code</strong>:</p> <pre><code>x &lt;- matrix(rnorm(10*100), nrow=100) # sample data for tests descStats &lt;- function(x, na.rm = TRUE, trim = NULL, skew = FALSE, byrow = FALSE, digits = getOption("digits")) { if (!is.matrix(x)) x &lt;- as.matrix(x) if(byrow) x &lt;- t(x) stats &lt;- c("n", "mean", "se", "sd", "median", "min", "max", "range") # descriptive statistics if (skew) { library(moments) stats &lt;- c(stats, "skewness", "kurtosis") } if (!is.null(trim)) { stats &lt;- append(stats, "trimmed", which(stats == "mean")) trimmed &lt;- function(x) base::mean(x, trim=trim) } n &lt;- function(x) length(x) range &lt;- function(x) max(x) - min(x) mean &lt;- function(x) .Internal(mean(x)) # redefined mean function sd &lt;- function(x) sqrt(sum((x - mean(x))^2)/(length(x)-1)) # redefined sd function se &lt;- function(x) sqrt(sd(x)/length(x)) median &lt;- function(x) { # redefined median function n &lt;- length(x) half &lt;- (n + 1L)%/%2L if (n%%2L == 1L) result &lt;- .Internal(sort(x, partial = half))[half] else { result &lt;- mean(.Internal(sort(x, partial = half + 0L:1L))[half + 0L:1L]) } } describe &lt;- function(x, na.rm=FALSE) { if (na.rm) x &lt;- x[!is.na(x)] result &lt;- vapply(stats, function(fun) eval(call(fun, x)), FUN.VALUE=numeric(1)) return(result) } out &lt;- t(vapply(seq_len(ncol(x)), function(i) describe(x[,i], na.rm=na.rm), FUN.VALUE=numeric(length(stats)))) out &lt;- round(out, digits=digits) return(out) } print(descStats(x)) ## n mean trimmed se sd median min max range ## [1,] 100 0.2524298 0.2763559 0.1024722 1.0500560 0.2842625 -2.905826 3.362598 6.268424 ## [2,] 100 -0.1201740 -0.0627668 0.1027268 1.0552801 -0.0614541 -3.071836 2.247063 5.318899 ## [3,] 100 0.2074781 0.1946393 0.1006384 1.0128089 0.1928790 -2.312749 2.564297 4.877047 ## [4,] 100 0.1088077 0.1127540 0.0935370 0.8749172 0.0864728 -2.757226 2.883687 5.640913 ## [5,] 100 -0.2163515 -0.2147170 0.1064167 1.1324524 -0.2836884 -3.431254 2.950466 6.381720 ## [6,] 100 -0.0324696 -0.0229878 0.0968330 0.9376630 0.0919468 -2.474992 1.860961 4.335953 ## [7,] 100 -0.1497724 -0.1665687 0.1047835 1.0979579 -0.1753578 -2.908781 2.885645 5.794425 ## [8,] 100 -0.0197306 0.0101194 0.1030385 1.0616927 0.0615438 -2.711356 2.506423 5.217779 ## [9,] 100 -0.0346922 -0.0290022 0.1018726 1.0378033 0.0231049 -2.467852 2.528595 4.996447 ## [10,] 100 0.1251403 0.1222156 0.1012441 1.0250359 0.1606492 -2.566209 2.854519 5.420728 </code></pre> <p>In each case I compared the elapsed time with <code>microbenchmaark</code>. For instance:</p> <pre><code>library(microbenchmark) bench &lt;- microbenchmark(descStats(x), descStats2(x), times=1000) print(bench) boxplot(bench, outline=FALSE) </code></pre> <p>Can anyone be able to offer a more efficient or compact version of the code?</p> <p><strong>Update:</strong></p> <p>The final version of this function you can see <a href="https://raw.github.com/unikum/r-scripts/master/descStats.R" rel="nofollow">here</a>.</p>
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    1. COI tried `Rprof`. I changed `eval(call)` to `get` but it led to an increase the elapsed time. `sort.int` used by `mean.default` and `median.default`.
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      1. USArtem Klevtsov
    2. COAdd `sd <- function(x) sqrt(sum((x - mean(x))^2)/(length(x)-1))` led to reduction the elapsed time of ~ 25%.
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    3. COAlso improved `median`. I copy part of the original `median` code and replace call `mean` and `sort` with `.Internal`.
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      1. USArtem Klevtsov
 

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