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<title>xtable vs pixiedust: Speed Comparison</title>
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<h1 class="title toc-ignore"><code>xtable</code> vs <code>pixiedust</code>: Speed Comparison</h1>
<h4 class="author"><em>Benjamin Nutter</em></h4>
<h4 class="date"><em>2016-04-19</em></h4>
<p>The process for comparing the speed of <code>xtable</code> and <code>pixiedust</code> will be a random sample of 10,000 rows from the <code>mtcars</code> dataset, with replacement. A table with this many rows is certainly at the fringe of the size of tables people my try to produce using either package and is large enough to give us some idea of how the two packages differ in terms of speed.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(dplyr)
<span class="kw">library</span>(ggplot2)
<span class="kw">library</span>(microbenchmark)
<span class="kw">library</span>(stargazer)
<span class="kw">library</span>(xtable)
<span class="kw">set.seed</span>(<span class="dv">100</span>)
LargeTable <-<span class="st"> </span>mtcars[<span class="kw">sample</span>(<span class="dv">1</span>:<span class="kw">nrow</span>(mtcars), <span class="dv">1000</span>, <span class="dt">replace =</span> <span class="ot">TRUE</span>), ]</code></pre></div>
<p>The <code>xtable</code> times are calculated as follows:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">Xtable <-<span class="st"> </span><span class="kw">microbenchmark</span>(<span class="dt">xtable =</span> <span class="kw">print.xtable</span>(<span class="kw">xtable</span>(LargeTable, <span class="dt">type =</span> <span class="st">"html"</span>), <span class="dt">type =</span> <span class="st">"html"</span>,
<span class="dt">print.results =</span> <span class="ot">FALSE</span>),
<span class="dt">times =</span> <span class="dv">10</span>, <span class="dt">unit =</span> <span class="st">"ms"</span>)</code></pre></div>
<p>The <code>stargazer</code> times are calculated in a similar manner.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">Stargazer <-<span class="st"> </span><span class="kw">microbenchmark</span>(<span class="dt">stargazer =</span> {x <-<span class="st"> </span><span class="kw">capture.output</span>(<span class="kw">stargazer</span>(LargeTable,
<span class="dt">type =</span> <span class="st">"html"</span>, <span class="dt">summary =</span> <span class="ot">FALSE</span>))},
<span class="dt">times =</span> <span class="dv">10</span>, <span class="dt">unit =</span> <span class="st">"ms"</span>)</code></pre></div>
<p>The <code>pixiedust</code> times are calculated below. We apply the default background pattern just to add a little more complexity to the table. This should elaborate if adding more sprinkles adds to the processing time. Further investigation will be needed to determine if the time is added in the <code>sprinkle</code> function, or in the printing.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">libs <-<span class="st"> </span><span class="kw">list.files</span>(<span class="st">"F:/pixiedust_library"</span>, <span class="dt">full.names =</span> <span class="ot">TRUE</span>)
lib_versions <-<span class="st"> </span><span class="kw">gsub</span>(<span class="st">"(pixiedust-|[.]tar[.]gz)"</span>, <span class="st">""</span>, <span class="kw">basename</span>(libs))
for (i in <span class="kw">seq_along</span>(libs)){
<span class="kw">library</span>(pixiedust,
<span class="dt">lib.loc=</span><span class="kw">file.path</span>(<span class="st">"F:/pixiedust_library"</span>, lib_versions[i]))
Pixie <-<span class="st"> </span><span class="kw">microbenchmark</span>({<span class="kw">dust</span>(LargeTable) %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">sprinkle_print_method</span>(<span class="st">"html"</span>) %>%
<span class="st"> </span><span class="kw">sprinkle</span>(<span class="dt">bg_pattern_by =</span> <span class="st">"rows"</span>)},
<span class="dt">times =</span> <span class="dv">10</span>, <span class="dt">unit =</span> <span class="st">"ms"</span>)
Pixie$expr <-<span class="st"> </span><span class="kw">paste0</span>(<span class="st">"pixiedust "</span>, lib_versions[i])
<span class="kw">assign</span>(<span class="kw">paste0</span>(<span class="st">"Pixie_"</span>, lib_versions[i]), Pixie)
<span class="kw">detach</span>(<span class="st">"package:pixiedust"</span>, <span class="dt">unload=</span><span class="ot">TRUE</span>)
}
<span class="kw">rm</span>(<span class="dt">list =</span> <span class="kw">c</span>(<span class="st">"Pixie"</span>, <span class="st">"lib_versions"</span>, <span class="st">"libs"</span>, <span class="st">"LargeTable"</span>, <span class="st">"i"</span>, <span class="st">"x"</span>))</code></pre></div>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">Compare <-<span class="st"> </span><span class="kw">bind_rows</span>(<span class="kw">mget</span>(<span class="dt">x =</span> <span class="kw">ls</span>()))
<span class="kw">ggplot</span>(Compare,
<span class="kw">aes</span>(<span class="dt">x =</span> expr, <span class="dt">y =</span> time)) +<span class="st"> </span>
<span class="st"> </span><span class="kw">geom_boxplot</span>() +<span class="st"> </span>
<span class="st"> </span><span class="kw">theme</span>(<span class="dt">axis.text.x =</span> <span class="kw">element_text</span>(<span class="dt">angle =</span> <span class="dv">90</span>, <span class="dt">hjust =</span> <span class="dv">1</span>))</code></pre></div>
<p><img src="data:image/png;base64,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" alt /></p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">Median <-<span class="st"> </span>Compare %>%
<span class="st"> </span><span class="kw">group_by</span>(expr) %>%
<span class="st"> </span><span class="kw">summarise</span>(<span class="dt">median_time =</span> <span class="kw">median</span>(time)) %>%
<span class="st"> </span><span class="kw">mutate</span>(<span class="dt">relative_time =</span> median_time /<span class="st"> </span><span class="kw">min</span>(median_time)) %>%
<span class="st"> </span><span class="kw">print</span>()</code></pre></div>
<pre><code>## Source: local data frame [8 x 3]
##
## expr median_time relative_time
## <chr> <dbl> <dbl>
## 1 pixiedust 0.2.0 4833278822 71.204772
## 2 pixiedust 0.3.0 4836604173 71.253761
## 3 pixiedust 0.4.0 4851566693 71.474192
## 4 pixiedust 0.5.0 4860370532 71.603892
## 5 pixiedust 0.6.1 4865850720 71.684627
## 6 pixiedust 0.7.0 149273778 2.199129
## 7 stargazer 27556753396 405.971268
## 8 xtable 67878581 1.000000</code></pre>
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