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 function in a manner that is similar to doParallel or lapply to elements of a list.
 The computations are distributed using Spark. It is conceptually the same as the following code:
   lapply(list, func)"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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       <img src="" class="logo" alt=""><h1>Run a function over a list of elements, distributing the computations with Spark</h1>

       <div class="d-none name"><code>spark.lapply.Rd</code></div>
     </div>

     <div class="ref-description section level2">
     <p>Run a function over a list of elements, distributing the computations with Spark. Applies a
 function in a manner that is similar to doParallel or lapply to elements of a list.
 The computations are distributed using Spark. It is conceptually the same as the following code:
   lapply(list, func)</p>
     </div>

     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">spark.lapply</span><span class="op">(</span><span class="va">list</span>, <span class="va">func</span><span class="op">)</span></span></code></pre></div>
     </div>

     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
     <dl><dt>list</dt>
 <dd><p>the list of elements</p></dd>


 <dt>func</dt>
 <dd><p>a function that takes one argument.</p></dd>

 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>


 <p>a list of results (the exact type being determined by the function)</p>
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>Known limitations:</p><ul><li><p>variable scoping and capture: compared to R's rich support for variable resolutions,
    the distributed nature of SparkR limits how variables are resolved at runtime. All the
    variables that are available through lexical scoping are embedded in the closure of the
    function and available as read-only variables within the function. The environment variables
    should be stored into temporary variables outside the function, and not directly accessed
    within the function.</p></li>
 <li><p>loading external packages: In order to use a package, you need to load it inside the
   closure. For example, if you rely on the MASS module, here is how you would use it:</p><div class="sourceCode"><pre><code><span></span>
 <span>    <span class="va">train</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">hyperparam</span><span class="op">)</span> <span class="op">{</span></span>
 <span>      <span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="http://www.stats.ox.ac.uk/pub/MASS4/" class="external-link">MASS</a></span><span class="op">)</span></span>
 <span>      <span class="fu"><a href="https://rdrr.io/pkg/MASS/man/lm.ridge.html" class="external-link">lm.ridge</a></span><span class="op">(</span><span class="st">"y ~ x+z"</span>, <span class="va">data</span>, lambda<span class="op">=</span><span class="va">hyperparam</span><span class="op">)</span></span>
 <span>      <span class="va">model</span></span>
 <span>    <span class="op">}</span></span>
 <span>  </span></code></pre></div></li>
 </ul></div>
     <div class="section level2">
     <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>spark.lapply since 2.0.0</p>
     </div>

     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="sparkR.session.html">sparkR.session</a></span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">doubled</span> <span class="op">&lt;-</span> <span class="fu">spark.lapply</span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">10</span>, <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="op">{</span><span class="fl">2</span> <span class="op">*</span> <span class="va">x</span><span class="op">}</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
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	The computations are distributed using Spark. It is conceptually the same as the following code:
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	function in a manner that is similar to doParallel or lapply to elements of a list.
	The computations are distributed using Spark. It is conceptually the same as the following code:
	lapply(list, func)"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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	<main id="main" class="col-md-9"><div class="page-header">
	<img src="" class="logo" alt=""><h1>Run a function over a list of elements, distributing the computations with Spark</h1>

	<div class="d-none name"><code>spark.lapply.Rd</code></div>
	</div>

	<div class="ref-description section level2">
	<p>Run a function over a list of elements, distributing the computations with Spark. Applies a
	function in a manner that is similar to doParallel or lapply to elements of a list.
	The computations are distributed using Spark. It is conceptually the same as the following code:
	lapply(list, func)</p>
	</div>

	<div class="section level2">
	<h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
	<div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">spark.lapply</span><span class="op">(</span><span class="va">list</span>, <span class="va">func</span><span class="op">)</span></span></code></pre></div>
	</div>

	<div class="section level2">
	<h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
	<dl><dt>list</dt>
	<dd><p>the list of elements</p></dd>


	<dt>func</dt>
	<dd><p>a function that takes one argument.</p></dd>

	</dl></div>
	<div class="section level2">
	<h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>


	<p>a list of results (the exact type being determined by the function)</p>
	</div>
	<div class="section level2">
	<h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
	<p>Known limitations:</p><ul><li><p>variable scoping and capture: compared to R's rich support for variable resolutions,
	the distributed nature of SparkR limits how variables are resolved at runtime. All the
	variables that are available through lexical scoping are embedded in the closure of the
	function and available as read-only variables within the function. The environment variables
	should be stored into temporary variables outside the function, and not directly accessed
	within the function.</p></li>
	<li><p>loading external packages: In order to use a package, you need to load it inside the
	closure. For example, if you rely on the MASS module, here is how you would use it:</p><div class="sourceCode"><pre><code><span></span>
	<span> <span class="va">train</span> <span class="op"><-</span> <span class="kw">function</span><span class="op">(</span><span class="va">hyperparam</span><span class="op">)</span> <span class="op">{</span></span>
	<span> <span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="http://www.stats.ox.ac.uk/pub/MASS4/" class="external-link">MASS</a></span><span class="op">)</span></span>
	<span> <span class="fu"><a href="https://rdrr.io/pkg/MASS/man/lm.ridge.html" class="external-link">lm.ridge</a></span><span class="op">(</span><span class="st">"y ~ x+z"</span>, <span class="va">data</span>, lambda<span class="op">=</span><span class="va">hyperparam</span><span class="op">)</span></span>
	<span> <span class="va">model</span></span>
	<span> <span class="op">}</span></span>
	<span> </span></code></pre></div></li>
	</ul></div>
	<div class="section level2">
	<h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
	<p>spark.lapply since 2.0.0</p>
	</div>

	<div class="section level2">
	<h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
	<div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
	<span class="r-in"><span><span class="fu"><a href="sparkR.session.html">sparkR.session</a></span><span class="op">(</span><span class="op">)</span></span></span>
	<span class="r-in"><span><span class="va">doubled</span> <span class="op"><-</span> <span class="fu">spark.lapply</span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">10</span>, <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="op">{</span><span class="fl">2</span> <span class="op">*</span> <span class="va">x</span><span class="op">}</span><span class="op">)</span></span></span>
	<span class="r-in"><span><span class="op">}</span></span></span>
	</code></pre></div>
	</div>
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