02_RProgramming/mapply/index.html

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        <h1>Introduction to the R Language</h1>
        <h2>Loop Functions - mapply</h2>
        <p>Roger Peng, Associate Professor<br/>Johns Hopkins Bloomberg School of Public Health</p>
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  <hgroup>
    <h2>mapply</h2>
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  <article>
    <p><code>mapply</code> is a multivariate apply of sorts which applies a function in parallel over a set of arguments.</p>

<pre><code class="r">&gt; str(mapply)
function (FUN, ..., MoreArgs = NULL, SIMPLIFY = TRUE,
          USE.NAMES = TRUE)
</code></pre>

<ul>
<li><code>FUN</code> is a function to apply</li>
<li>... contains arguments to apply over</li>
<li><code>MoreArgs</code> is a list of other arguments to <code>FUN</code>.</li>
<li><code>SIMPLIFY</code> indicates whether the result should be simplified</li>
</ul>

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  <hgroup>
    <h2>mapply</h2>
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  <article>
    <p>The following is tedious to type</p>

<p><code>list(rep(1, 4), rep(2, 3), rep(3, 2), rep(4, 1))</code></p>

<p>Instead we can do</p>

<pre><code class="r">&gt; mapply(rep, 1:4, 4:1)
[[1]]
[1] 1 1 1 1

[[2]]
[1] 2 2 2

[[3]] 
[1] 3 3

[[4]] 
[1] 4
</code></pre>

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  <hgroup>
    <h2>Vectorizing a Function</h2>
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  <article>
    <pre><code class="r">&gt; noise &lt;- function(n, mean, sd) {
+ rnorm(n, mean, sd)
+ }
&gt; noise(5, 1, 2)
[1]  2.4831198  2.4790100  0.4855190 -1.2117759
[5] -0.2743532

&gt; noise(1:5, 1:5, 2)
[1] -4.2128648 -0.3989266  4.2507057  1.1572738
[5]  3.7413584
</code></pre>

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    <h2>Instant Vectorization</h2>
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    <pre><code class="r">&gt; mapply(noise, 1:5, 1:5, 2)
[[1]]
[1] 1.037658

[[2]]
[1] 0.7113482 2.7555797

[[3]]
[1] 2.769527 1.643568 4.597882

[[4]]
[1] 4.476741 5.658653 3.962813 1.204284

[[5]]
[1] 4.797123 6.314616 4.969892 6.530432 6.723254
</code></pre>

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    <h2>Instant Vectorization</h2>
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  <article>
    <p>Which is the same as</p>

<pre><code class="r">list(noise(1, 1, 2), noise(2, 2, 2),
     noise(3, 3, 2), noise(4, 4, 2),
     noise(5, 5, 2))
</code></pre>

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