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Posted to commits@spark.apache.org by we...@apache.org on 2025/05/23 03:34:43 UTC
(spark-website) branch asf-site updated: Add docs for Apache Spark 4.0.0
This is an automated email from the ASF dual-hosted git repository.
wenchen pushed a commit to branch asf-site
in repository https://gitbox.apache.org/repos/asf/spark-website.git
The following commit(s) were added to refs/heads/asf-site by this push:
new 88fd09c5af Add docs for Apache Spark 4.0.0
88fd09c5af is described below
commit 88fd09c5af330846a74e907a49beaa84584df9ef
Author: Wenchen Fan <we...@apache.org>
AuthorDate: Fri May 23 03:32:26 2025 +0000
Add docs for Apache Spark 4.0.0
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+
+ <h1>SparkR - Practical Guide</h1>
+
+
+
+ <div class="d-none name"><code>sparkr-vignettes.Rmd</code></div>
+ </div>
+
+
+
+<!--
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
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+ the License. You may obtain a copy of the License at
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+ http://www.apache.org/licenses/LICENSE-2.0
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+<p>SparkR is deprecated from Apache Spark 4.0.0 and will be removed in a future version.</p>
+<div class="section level2">
+<h2 id="overview">Overview<a class="anchor" aria-label="anchor" href="#overview"></a>
+</h2>
+<p>SparkR is an R package that provides a light-weight frontend to use Apache Spark from R. With Spark 4.0.0, SparkR provides a distributed data frame implementation that supports data processing operations like selection, filtering, aggregation etc. and distributed machine learning using <a href="https://spark.apache.org/mllib/" class="external-link">MLlib</a>.</p>
+</div>
+<div class="section level2">
+<h2 id="getting-started">Getting Started<a class="anchor" aria-label="anchor" href="#getting-started"></a>
+</h2>
+<p>We begin with an example running on the local machine and provide an overview of the use of SparkR: data ingestion, data processing and machine learning.</p>
+<p>First, let’s load and attach the package.</p>
+<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><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="https://www.apache.org" class="external-link">SparkR</a></span><span class="op">)</span></span></code></pre></div>
+<p><code>SparkSession</code> is the entry point into SparkR which connects your R program to a Spark cluster. You can create a <code>SparkSession</code> using <code>sparkR.session</code> and pass in options such as the application name, any Spark packages depended on, etc.</p>
+<p>We use default settings in which it runs in local mode. It auto downloads Spark package in the background if no previous installation is found. For more details about setup, see <a href="#SetupSparkSession">Spark Session</a>.</p>
+<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/sparkR.session.html">sparkR.session</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## Java ref type org.apache.spark.sql.classic.SparkSession id 1</span></span></code></pre>
+<p>The operations in SparkR are centered around an R class called <code>SparkDataFrame</code>. It is a distributed collection of data organized into named columns, which is conceptually equivalent to a table in a relational database or a data frame in R, but with richer optimizations under the hood.</p>
+<p><code>SparkDataFrame</code> can be constructed from a wide array of sources such as: structured data files, tables in Hive, external databases, or existing local R data frames. For example, we create a <code>SparkDataFrame</code> from a local R data frame,</p>
+<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">cars</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">cbind</a></span><span class="op">(</span>model <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/colnames.html" class="external-link">rownames</a></span><span class="op">(</span><span class="va">mtcars</span><span class="op">)</span>, <span class="va">mtcars</span><span class="op">)</span></span>
+<span><span class="va">carsDF</span> <span class="op"><-</span> <span class="fu"><a href="../reference/createDataFrame.html">createDataFrame</a></span><span class="op">(</span><span class="va">cars</span><span class="op">)</span></span></code></pre></div>
+<p>We can view the first few rows of the <code>SparkDataFrame</code> by <code>head</code> or <code>showDF</code> function.</p>
+<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/head.html">head</a></span><span class="op">(</span><span class="va">carsDF</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## model mpg cyl disp hp drat wt qsec vs am gear carb</span></span>
+<span><span class="co">## 1 Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4</span></span>
+<span><span class="co">## 2 Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4</span></span>
+<span><span class="co">## 3 Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1</span></span>
+<span><span class="co">## 4 Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1</span></span>
+<span><span class="co">## 5 Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2</span></span>
+<span><span class="co">## 6 Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1</span></span></code></pre>
+<p>Common data processing operations such as <code>filter</code> and <code>select</code> are supported on the <code>SparkDataFrame</code>.</p>
+<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">carsSubDF</span> <span class="op"><-</span> <span class="fu"><a href="../reference/select.html">select</a></span><span class="op">(</span><span class="va">carsDF</span>, <span class="st">"model"</span>, <span class="st">"mpg"</span>, <span class="st">"hp"</span><span class="op">)</span></span>
+<span><span class="va">carsSubDF</span> <span class="op"><-</span> <span class="fu"><a href="../reference/filter.html">filter</a></span><span class="op">(</span><span class="va">carsSubDF</span>, <span class="va">carsSubDF</span><span class="op">$</span><span class="va">hp</span> <span class="op">>=</span> <span class="fl">200</span><span class="op">)</span></span>
+<span><span class="fu"><a href="../reference/head.html">head</a></span><span class="op">(</span><span class="va">carsSubDF</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## model mpg hp</span></span>
+<span><span class="co">## 1 Duster 360 14.3 245</span></span>
+<span><span class="co">## 2 Cadillac Fleetwood 10.4 205</span></span>
+<span><span class="co">## 3 Lincoln Continental 10.4 215</span></span>
+<span><span class="co">## 4 Chrysler Imperial 14.7 230</span></span>
+<span><span class="co">## 5 Camaro Z28 13.3 245</span></span>
+<span><span class="co">## 6 Ford Pantera L 15.8 264</span></span></code></pre>
+<p>SparkR can use many common aggregation functions after grouping.</p>
+<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">carsGPDF</span> <span class="op"><-</span> <span class="fu"><a href="../reference/summarize.html">summarize</a></span><span class="op">(</span><span class="fu"><a href="../reference/groupBy.html">groupBy</a></span><span class="op">(</span><span class="va">carsDF</span>, <span class="va">carsDF</span><span class="op">$</span><span class="va">gear</span><span class="op">)</span>, count <span class="op">=</span> <span class="fu"><a href=" [...]
+<span><span class="fu"><a href="../reference/head.html">head</a></span><span class="op">(</span><span class="va">carsGPDF</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## gear count</span></span>
+<span><span class="co">## 1 4 12</span></span>
+<span><span class="co">## 2 3 15</span></span>
+<span><span class="co">## 3 5 5</span></span></code></pre>
+<p>The results <code>carsDF</code> and <code>carsSubDF</code> are <code>SparkDataFrame</code> objects. To convert back to R <code>data.frame</code>, we can use <code>collect</code>. <strong>Caution</strong>: This can cause your interactive environment to run out of memory, though, because <code><a href="../reference/collect.html">collect()</a></code> fetches the entire distributed <code>DataFrame</code> to your client, which is acting as a Spark driver.</p>
+<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">carsGP</span> <span class="op"><-</span> <span class="fu"><a href="../reference/collect.html">collect</a></span><span class="op">(</span><span class="va">carsGPDF</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/class.html" class="external-link">class</a></span><span class="op">(</span><span class="va">carsGP</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## [1] "data.frame"</span></span></code></pre>
+<p>SparkR supports a number of commonly used machine learning algorithms. Under the hood, SparkR uses MLlib to train the model. Users can call <code>summary</code> to print a summary of the fitted model, <code>predict</code> to make predictions on new data, and <code>write.ml</code>/<code>read.ml</code> to save/load fitted models.</p>
+<p>SparkR supports a subset of R formula operators for model fitting, including ‘~’, ‘.’, ‘:’, ‘+’, and ‘-‘. We use linear regression as an example.</p>
+<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">model</span> <span class="op"><-</span> <span class="fu"><a href="../reference/spark.glm.html">spark.glm</a></span><span class="op">(</span><span class="va">carsDF</span>, <span class="va">mpg</span> <span class="op">~</span> <span class="va">wt</span> <span class="op">+</span> <span class="va">cyl</span><span class="op">)</span></span></code></pre></div>
+<p>The result matches that returned by R <code>glm</code> function applied to the corresponding <code>data.frame</code> <code>mtcars</code> of <code>carsDF</code>. In fact, for Generalized Linear Model, we specifically expose <code>glm</code> for <code>SparkDataFrame</code> as well so that the above is equivalent to <code>model <- glm(mpg ~ wt + cyl, data = carsDF)</code>.</p>
+<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/summary.html">summary</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## </span></span>
+<span><span class="co">## Deviance Residuals: </span></span>
+<span><span class="co">## (Note: These are approximate quantiles with relative error <= 0.01)</span></span>
+<span><span class="co">## Min 1Q Median 3Q Max </span></span>
+<span><span class="co">## -4.2893 -1.7085 -0.4713 1.5729 6.1004 </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Coefficients:</span></span>
+<span><span class="co">## Estimate Std. Error t value Pr(>|t|)</span></span>
+<span><span class="co">## (Intercept) 39.6863 1.71498 23.1409 0.00000000</span></span>
+<span><span class="co">## wt -3.1910 0.75691 -4.2158 0.00022202</span></span>
+<span><span class="co">## cyl -1.5078 0.41469 -3.6360 0.00106428</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## (Dispersion parameter for gaussian family taken to be 6.592137)</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Null deviance: 1126.05 on 31 degrees of freedom</span></span>
+<span><span class="co">## Residual deviance: 191.17 on 29 degrees of freedom</span></span>
+<span><span class="co">## AIC: 156</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Number of Fisher Scoring iterations: 1</span></span></code></pre>
+<p>The model can be saved by <code>write.ml</code> and loaded back using <code>read.ml</code>.</p>
+<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/write.ml.html">write.ml</a></span><span class="op">(</span><span class="va">model</span>, path <span class="op">=</span> <span class="st">"/HOME/tmp/mlModel/glmModel"</span><span class="op">)</span></span></code></pre></div>
+<p>In the end, we can stop Spark Session by running</p>
+<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/sparkR.session.stop.html">sparkR.session.stop</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
+</div>
+<div class="section level2">
+<h2 id="setup">Setup<a class="anchor" aria-label="anchor" href="#setup"></a>
+</h2>
+<div class="section level3">
+<h3 id="installation">Installation<a class="anchor" aria-label="anchor" href="#installation"></a>
+</h3>
+<p>Different from many other R packages, to use SparkR, you need an additional installation of Apache Spark. The Spark installation will be used to run a backend process that will compile and execute SparkR programs.</p>
+<p>After installing the SparkR package, you can call <code>sparkR.session</code> as explained in the previous section to start and it will check for the Spark installation. If you are working with SparkR from an interactive shell (e.g. R, RStudio) then Spark is downloaded and cached automatically if it is not found. Alternatively, we provide an easy-to-use function <code>install.spark</code> for running this manually. If you don’t have Spark installed on the computer, you may download it [...]
+<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/install.spark.html">install.spark</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
+<p>If you already have Spark installed, you don’t have to install again and can pass the <code>sparkHome</code> argument to <code>sparkR.session</code> to let SparkR know where the existing Spark installation is.</p>
+<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/sparkR.session.html">sparkR.session</a></span><span class="op">(</span>sparkHome <span class="op">=</span> <span class="st">"/HOME/spark"</span><span class="op">)</span></span></code></pre></div>
+</div>
+<div class="section level3">
+<h3 id="SetupSparkSession">Spark Session<a class="anchor" aria-label="anchor" href="#SetupSparkSession"></a>
+</h3>
+<p>In addition to <code>sparkHome</code>, many other options can be specified in <code>sparkR.session</code>. For a complete list, see <a href="https://spark.apache.org/docs/latest/sparkr.html#starting-up-sparksession" class="external-link">Starting up: SparkSession</a> and <a href="https://spark.apache.org/docs/latest/api/R/reference/sparkR.session.html" class="external-link">SparkR API doc</a>.</p>
+<p>In particular, the following Spark driver properties can be set in <code>sparkConfig</code>.</p>
+<table class="table">
+<thead><tr class="header">
+<th>Property Name</th>
+<th>Property group</th>
+<th>spark-submit equivalent</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td><code>spark.driver.memory</code></td>
+<td>Application Properties</td>
+<td><code>--driver-memory</code></td>
+</tr>
+<tr class="even">
+<td><code>spark.driver.extraClassPath</code></td>
+<td>Runtime Environment</td>
+<td><code>--driver-class-path</code></td>
+</tr>
+<tr class="odd">
+<td><code>spark.driver.extraJavaOptions</code></td>
+<td>Runtime Environment</td>
+<td><code>--driver-java-options</code></td>
+</tr>
+<tr class="even">
+<td><code>spark.driver.extraLibraryPath</code></td>
+<td>Runtime Environment</td>
+<td><code>--driver-library-path</code></td>
+</tr>
+<tr class="odd">
+<td><code>spark.kerberos.keytab</code></td>
+<td>Application Properties</td>
+<td><code>--keytab</code></td>
+</tr>
+<tr class="even">
+<td><code>spark.kerberos.principal</code></td>
+<td>Application Properties</td>
+<td><code>--principal</code></td>
+</tr>
+</tbody>
+</table>
+<p><strong>For Windows users</strong>: Due to different file prefixes across operating systems, to avoid the issue of potential wrong prefix, a current workaround is to specify <code>spark.sql.warehouse.dir</code> when starting the <code>SparkSession</code>.</p>
+<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">spark_warehouse_path</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/file.path.html" class="external-link">file.path</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/path.expand.html" class="external-link">path.expand</a></span><span class="op">(</span><span class="st">'~'</span><span class="op">)</span>, <span class="st">"spark-warehouse"</span><span class="op">)</s [...]
+<span><span class="fu"><a href="../reference/sparkR.session.html">sparkR.session</a></span><span class="op">(</span>spark.sql.warehouse.dir <span class="op">=</span> <span class="va">spark_warehouse_path</span><span class="op">)</span></span></code></pre></div>
+<div class="section level4">
+<h4 id="cluster-mode">Cluster Mode<a class="anchor" aria-label="anchor" href="#cluster-mode"></a>
+</h4>
+<p>SparkR can connect to remote Spark clusters. <a href="https://spark.apache.org/docs/latest/cluster-overview.html" class="external-link">Cluster Mode Overview</a> is a good introduction to different Spark cluster modes.</p>
+<p>When connecting SparkR to a remote Spark cluster, make sure that the Spark version and Hadoop version on the machine match the corresponding versions on the cluster. Current SparkR package is compatible with</p>
+<pre><code><span><span class="co">## [1] "Spark 4.0.0"</span></span></code></pre>
+<p>It should be used both on the local computer and on the remote cluster.</p>
+<p>To connect, pass the URL of the master node to <code>sparkR.session</code>. A complete list can be seen in <a href="https://spark.apache.org/docs/latest/submitting-applications.html#master-urls" class="external-link">Spark Master URLs</a>. For example, to connect to a local standalone Spark master, we can call</p>
+<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/sparkR.session.html">sparkR.session</a></span><span class="op">(</span>master <span class="op">=</span> <span class="st">"spark://local:7077"</span><span class="op">)</span></span></code></pre></div>
+<p>For YARN cluster, SparkR supports the client mode with the master set as “yarn”.</p>
+<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/sparkR.session.html">sparkR.session</a></span><span class="op">(</span>master <span class="op">=</span> <span class="st">"yarn"</span><span class="op">)</span></span></code></pre></div>
+<p>Yarn cluster mode is not supported in the current version.</p>
+</div>
+</div>
+</div>
+<div class="section level2">
+<h2 id="data-import">Data Import<a class="anchor" aria-label="anchor" href="#data-import"></a>
+</h2>
+<div class="section level3">
+<h3 id="local-data-frame">Local Data Frame<a class="anchor" aria-label="anchor" href="#local-data-frame"></a>
+</h3>
+<p>The simplest way is to convert a local R data frame into a <code>SparkDataFrame</code>. Specifically we can use <code>as.DataFrame</code> or <code>createDataFrame</code> and pass in the local R data frame to create a <code>SparkDataFrame</code>. As an example, the following creates a <code>SparkDataFrame</code> based using the <code>faithful</code> dataset from R.</p>
+<div class="sourceCode" id="cb24"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">df</span> <span class="op"><-</span> <span class="fu"><a href="../reference/createDataFrame.html">as.DataFrame</a></span><span class="op">(</span><span class="va">faithful</span><span class="op">)</span></span>
+<span><span class="fu"><a href="../reference/head.html">head</a></span><span class="op">(</span><span class="va">df</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## eruptions waiting</span></span>
+<span><span class="co">## 1 3.600 79</span></span>
+<span><span class="co">## 2 1.800 54</span></span>
+<span><span class="co">## 3 3.333 74</span></span>
+<span><span class="co">## 4 2.283 62</span></span>
+<span><span class="co">## 5 4.533 85</span></span>
+<span><span class="co">## 6 2.883 55</span></span></code></pre>
+</div>
+<div class="section level3">
+<h3 id="data-sources">Data Sources<a class="anchor" aria-label="anchor" href="#data-sources"></a>
+</h3>
+<p>SparkR supports operating on a variety of data sources through the <code>SparkDataFrame</code> interface. You can check the Spark SQL Programming Guide for more <a href="https://spark.apache.org/docs/latest/sql-programming-guide.html#manually-specifying-options" class="external-link">specific options</a> that are available for the built-in data sources.</p>
+<p>The general method for creating <code>SparkDataFrame</code> from data sources is <code>read.df</code>. This method takes in the path for the file to load and the type of data source, and the currently active Spark Session will be used automatically. SparkR supports reading CSV, JSON and Parquet files natively and through Spark Packages you can find data source connectors for popular file formats like Avro. These packages can be added with <code>sparkPackages</code> parameter when init [...]
+<div class="sourceCode" id="cb26"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/sparkR.session.html">sparkR.session</a></span><span class="op">(</span>sparkPackages <span class="op">=</span> <span class="st">"com.databricks:spark-avro_2.12:3.0.0"</span><span class="op">)</span></span></code></pre></div>
+<p>We can see how to use data sources using an example CSV input file. For more information please refer to SparkR <a href="https://spark.apache.org/docs/latest/api/R/reference/read.df.html" class="external-link">read.df</a> API documentation.</p>
+<div class="sourceCode" id="cb27"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">df</span> <span class="op"><-</span> <span class="fu"><a href="../reference/read.df.html">read.df</a></span><span class="op">(</span><span class="va">csvPath</span>, <span class="st">"csv"</span>, header <span class="op">=</span> <span class="st">"true"</span>, inferSchema <span class="op">=</span> <span class="st">"true"</span>, na.strings <span class="op">=</span> <span class="st">"NA"</span><span class="op">)</span></span></code></p [...]
+<p>The data sources API natively supports JSON formatted input files. Note that the file that is used here is not a typical JSON file. Each line in the file must contain a separate, self-contained valid JSON object. As a consequence, a regular multi-line JSON file will most often fail.</p>
+<p>Let’s take a look at the first two lines of the raw JSON file used here.</p>
+<div class="sourceCode" id="cb28"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">filePath</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="fu"><a href="../reference/sparkR.conf.html">sparkR.conf</a></span><span class="op">(</span><span class="st">"spark.home"</span><span class="op">)</span>,</span>
+<span> <span class="st">"/examples/src/main/resources/people.json"</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/readLines.html" class="external-link">readLines</a></span><span class="op">(</span><span class="va">filePath</span>, n <span class="op">=</span> <span class="fl">2L</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## [1] "{\"name\":\"Michael\"}" "{\"name\":\"Andy\", \"age\":30}"</span></span></code></pre>
+<p>We use <code>read.df</code> to read that into a <code>SparkDataFrame</code>.</p>
+<div class="sourceCode" id="cb30"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">people</span> <span class="op"><-</span> <span class="fu"><a href="../reference/read.df.html">read.df</a></span><span class="op">(</span><span class="va">filePath</span>, <span class="st">"json"</span><span class="op">)</span></span>
+<span><span class="fu"><a href="../reference/count.html">count</a></span><span class="op">(</span><span class="va">people</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## [1] 3</span></span></code></pre>
+<div class="sourceCode" id="cb32"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/head.html">head</a></span><span class="op">(</span><span class="va">people</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## age name</span></span>
+<span><span class="co">## 1 NA Michael</span></span>
+<span><span class="co">## 2 30 Andy</span></span>
+<span><span class="co">## 3 19 Justin</span></span></code></pre>
+<p>SparkR automatically infers the schema from the JSON file.</p>
+<div class="sourceCode" id="cb34"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/printSchema.html">printSchema</a></span><span class="op">(</span><span class="va">people</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## root</span></span>
+<span><span class="co">## |-- age: long (nullable = true)</span></span>
+<span><span class="co">## |-- name: string (nullable = true)</span></span></code></pre>
+<p>If we want to read multiple JSON files, <code>read.json</code> can be used.</p>
+<div class="sourceCode" id="cb36"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">people</span> <span class="op"><-</span> <span class="fu"><a href="../reference/read.json.html">read.json</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Sys.getenv.html" class="external-link">Sys.getenv</a></span><span class="op">(</span><span class="st">"SPARK_HOME"</span><sp [...]
+<span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"/examples/src/main/resources/people.json"</span>,</span>
+<span> <span class="st">"/examples/src/main/resources/people.json"</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="fu"><a href="../reference/count.html">count</a></span><span class="op">(</span><span class="va">people</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## [1] 6</span></span></code></pre>
+<p>The data sources API can also be used to save out <code>SparkDataFrames</code> into multiple file formats. For example we can save the <code>SparkDataFrame</code> from the previous example to a Parquet file using <code>write.df</code>.</p>
+<div class="sourceCode" id="cb38"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/write.df.html">write.df</a></span><span class="op">(</span><span class="va">people</span>, path <span class="op">=</span> <span class="st">"people.parquet"</span>, source <span class="op">=</span> <span class="st">"parquet"</span>, mode <span class="op">=</span> <span class="st">"overwrite"</span><span class="op">)</span></span></code></pre></div>
+</div>
+<div class="section level3">
+<h3 id="hive-tables">Hive Tables<a class="anchor" aria-label="anchor" href="#hive-tables"></a>
+</h3>
+<p>You can also create SparkDataFrames from Hive tables. To do this we will need to create a SparkSession with Hive support which can access tables in the Hive MetaStore. Note that Spark should have been built with Hive support and more details can be found in the <a href="https://spark.apache.org/docs/latest/sql-programming-guide.html" class="external-link">SQL Programming Guide</a>. In SparkR, by default it will attempt to create a SparkSession with Hive support enabled (<code>enableHi [...]
+<div class="sourceCode" id="cb39"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/sql.html">sql</a></span><span class="op">(</span><span class="st">"CREATE TABLE IF NOT EXISTS src (key INT, value STRING)"</span><span class="op">)</span></span>
+<span></span>
+<span><span class="va">txtPath</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="fu"><a href="../reference/sparkR.conf.html">sparkR.conf</a></span><span class="op">(</span><span class="st">"spark.home"</span><span class="op">)</span>, <span class="st">"/examples/src/main/resources/kv1.txt"</span><span class="op">)</span></span>
+<span><span class="va">sqlCMD</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/sprintf.html" class="external-link">sprintf</a></span><span class="op">(</span><span class="st">"LOAD DATA LOCAL INPATH '%s' INTO TABLE src"</span>, <span class="va">txtPath</span><span class="op">)</span></span>
+<span><span class="fu"><a href="../reference/sql.html">sql</a></span><span class="op">(</span><span class="va">sqlCMD</span><span class="op">)</span></span>
+<span></span>
+<span><span class="va">results</span> <span class="op"><-</span> <span class="fu"><a href="../reference/sql.html">sql</a></span><span class="op">(</span><span class="st">"FROM src SELECT key, value"</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># results is now a SparkDataFrame</span></span>
+<span><span class="fu"><a href="../reference/head.html">head</a></span><span class="op">(</span><span class="va">results</span><span class="op">)</span></span></code></pre></div>
+</div>
+</div>
+<div class="section level2">
+<h2 id="data-processing">Data Processing<a class="anchor" aria-label="anchor" href="#data-processing"></a>
+</h2>
+<p><strong>To dplyr users</strong>: SparkR has similar interface as dplyr in data processing. However, some noticeable differences are worth mentioning in the first place. We use <code>df</code> to represent a <code>SparkDataFrame</code> and <code>col</code> to represent the name of column here.</p>
+<ol style="list-style-type: decimal">
+<li><p>indicate columns. SparkR uses either a character string of the column name or a Column object constructed with <code>$</code> to indicate a column. For example, to select <code>col</code> in <code>df</code>, we can write <code>select(df, "col")</code> or <code>select(df, df$col)</code>.</p></li>
+<li><p>describe conditions. In SparkR, the Column object representation can be inserted into the condition directly, or we can use a character string to describe the condition, without referring to the <code>SparkDataFrame</code> used. For example, to select rows with value > 1, we can write <code>filter(df, df$col > 1)</code> or <code>filter(df, "col > 1")</code>.</p></li>
+</ol>
+<p>Here are more concrete examples.</p>
+<table class="table">
+<colgroup>
+<col width="47%">
+<col width="52%">
+</colgroup>
+<thead><tr class="header">
+<th>dplyr</th>
+<th>SparkR</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td><code>select(mtcars, mpg, hp)</code></td>
+<td><code>select(carsDF, "mpg", "hp")</code></td>
+</tr>
+<tr class="even">
+<td><code>filter(mtcars, mpg > 20, hp > 100)</code></td>
+<td><code>filter(carsDF, carsDF$mpg > 20, carsDF$hp > 100)</code></td>
+</tr>
+</tbody>
+</table>
+<p>Other differences will be mentioned in the specific methods.</p>
+<p>We use the <code>SparkDataFrame</code> <code>carsDF</code> created above. We can get basic information about the <code>SparkDataFrame</code>.</p>
+<div class="sourceCode" id="cb40"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">carsDF</span></span></code></pre></div>
+<pre><code><span><span class="co">## SparkDataFrame[model:string, mpg:double, cyl:double, disp:double, hp:double, drat:double, wt:double, qsec:double, vs:double, am:double, gear:double, carb:double]</span></span></code></pre>
+<p>Print out the schema in tree format.</p>
+<div class="sourceCode" id="cb42"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/printSchema.html">printSchema</a></span><span class="op">(</span><span class="va">carsDF</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## root</span></span>
+<span><span class="co">## |-- model: string (nullable = true)</span></span>
+<span><span class="co">## |-- mpg: double (nullable = true)</span></span>
+<span><span class="co">## |-- cyl: double (nullable = true)</span></span>
+<span><span class="co">## |-- disp: double (nullable = true)</span></span>
+<span><span class="co">## |-- hp: double (nullable = true)</span></span>
+<span><span class="co">## |-- drat: double (nullable = true)</span></span>
+<span><span class="co">## |-- wt: double (nullable = true)</span></span>
+<span><span class="co">## |-- qsec: double (nullable = true)</span></span>
+<span><span class="co">## |-- vs: double (nullable = true)</span></span>
+<span><span class="co">## |-- am: double (nullable = true)</span></span>
+<span><span class="co">## |-- gear: double (nullable = true)</span></span>
+<span><span class="co">## |-- carb: double (nullable = true)</span></span></code></pre>
+<div class="section level3">
+<h3 id="sparkdataframe-operations">SparkDataFrame Operations<a class="anchor" aria-label="anchor" href="#sparkdataframe-operations"></a>
+</h3>
+<div class="section level4">
+<h4 id="selecting-rows-columns">Selecting rows, columns<a class="anchor" aria-label="anchor" href="#selecting-rows-columns"></a>
+</h4>
+<p>SparkDataFrames support a number of functions to do structured data processing. Here we include some basic examples and a complete list can be found in the <a href="https://spark.apache.org/docs/latest/api/R/index.html" class="external-link">API</a> docs:</p>
+<p>You can also pass in column name as strings.</p>
+<div class="sourceCode" id="cb44"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/head.html">head</a></span><span class="op">(</span><span class="fu"><a href="../reference/select.html">select</a></span><span class="op">(</span><span class="va">carsDF</span>, <span class="st">"mpg"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## mpg</span></span>
+<span><span class="co">## 1 21.0</span></span>
+<span><span class="co">## 2 21.0</span></span>
+<span><span class="co">## 3 22.8</span></span>
+<span><span class="co">## 4 21.4</span></span>
+<span><span class="co">## 5 18.7</span></span>
+<span><span class="co">## 6 18.1</span></span></code></pre>
+<p>Filter the SparkDataFrame to only retain rows with mpg less than 20 miles/gallon.</p>
+<div class="sourceCode" id="cb46"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/head.html">head</a></span><span class="op">(</span><span class="fu"><a href="../reference/filter.html">filter</a></span><span class="op">(</span><span class="va">carsDF</span>, <span class="va">carsDF</span><span class="op">$</span><span class="va">mpg</span> <span class="op"><</span> <span class="fl">20</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## model mpg cyl disp hp drat wt qsec vs am gear carb</span></span>
+<span><span class="co">## 1 Hornet Sportabout 18.7 8 360.0 175 3.15 3.44 17.02 0 0 3 2</span></span>
+<span><span class="co">## 2 Valiant 18.1 6 225.0 105 2.76 3.46 20.22 1 0 3 1</span></span>
+<span><span class="co">## 3 Duster 360 14.3 8 360.0 245 3.21 3.57 15.84 0 0 3 4</span></span>
+<span><span class="co">## 4 Merc 280 19.2 6 167.6 123 3.92 3.44 18.30 1 0 4 4</span></span>
+<span><span class="co">## 5 Merc 280C 17.8 6 167.6 123 3.92 3.44 18.90 1 0 4 4</span></span>
+<span><span class="co">## 6 Merc 450SE 16.4 8 275.8 180 3.07 4.07 17.40 0 0 3 3</span></span></code></pre>
+</div>
+<div class="section level4">
+<h4 id="grouping-aggregation">Grouping, Aggregation<a class="anchor" aria-label="anchor" href="#grouping-aggregation"></a>
+</h4>
+<p>A common flow of grouping and aggregation is</p>
+<ol style="list-style-type: decimal">
+<li><p>Use <code>groupBy</code> or <code>group_by</code> with respect to some grouping variables to create a <code>GroupedData</code> object</p></li>
+<li><p>Feed the <code>GroupedData</code> object to <code>agg</code> or <code>summarize</code> functions, with some provided aggregation functions to compute a number within each group.</p></li>
+</ol>
+<p>A number of widely used functions are supported to aggregate data after grouping, including <code>avg</code>, <code>count_distinct</code>, <code>count</code>, <code>first</code>, <code>kurtosis</code>, <code>last</code>, <code>max</code>, <code>mean</code>, <code>min</code>, <code>sd</code>, <code>skewness</code>, <code>stddev_pop</code>, <code>stddev_samp</code>, <code>sum_distinct</code>, <code>sum</code>, <code>var_pop</code>, <code>var_samp</code>, <code>var</code>. See the <a hre [...]
+<p>For example we can compute a histogram of the number of cylinders in the <code>mtcars</code> dataset as shown below.</p>
+<div class="sourceCode" id="cb48"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">numCyl</span> <span class="op"><-</span> <span class="fu"><a href="../reference/summarize.html">summarize</a></span><span class="op">(</span><span class="fu"><a href="../reference/groupBy.html">groupBy</a></span><span class="op">(</span><span class="va">carsDF</span>, <span class="va">carsDF</span><span class="op">$</span><span class="va">cyl</span><span class="op">)</span>, count <span class="op">=</span> <span class="fu"><a href="../ [...]
+<span><span class="fu"><a href="../reference/head.html">head</a></span><span class="op">(</span><span class="va">numCyl</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## cyl count</span></span>
+<span><span class="co">## 1 8 14</span></span>
+<span><span class="co">## 2 4 11</span></span>
+<span><span class="co">## 3 6 7</span></span></code></pre>
+<p>Use <code>cube</code> or <code>rollup</code> to compute subtotals across multiple dimensions.</p>
+<div class="sourceCode" id="cb50"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/column_aggregate_functions.html">mean</a></span><span class="op">(</span><span class="fu"><a href="../reference/cube.html">cube</a></span><span class="op">(</span><span class="va">carsDF</span>, <span class="st">"cyl"</span>, <span class="st">"gear"</span>, <span class="st">"am"</span><span class="op">)</span>, <span class="st">"mpg"</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## SparkDataFrame[cyl:double, gear:double, am:double, avg(mpg):double]</span></span></code></pre>
+<p>generates groupings for {(<code>cyl</code>, <code>gear</code>, <code>am</code>), (<code>cyl</code>, <code>gear</code>), (<code>cyl</code>), ()}, while</p>
+<div class="sourceCode" id="cb52"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/column_aggregate_functions.html">mean</a></span><span class="op">(</span><span class="fu"><a href="../reference/rollup.html">rollup</a></span><span class="op">(</span><span class="va">carsDF</span>, <span class="st">"cyl"</span>, <span class="st">"gear"</span>, <span class="st">"am"</span><span class="op">)</span>, <span class="st">"mpg"</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## SparkDataFrame[cyl:double, gear:double, am:double, avg(mpg):double]</span></span></code></pre>
+<p>generates groupings for all possible combinations of grouping columns.</p>
+</div>
+<div class="section level4">
+<h4 id="operating-on-columns">Operating on Columns<a class="anchor" aria-label="anchor" href="#operating-on-columns"></a>
+</h4>
+<p>SparkR also provides a number of functions that can directly applied to columns for data processing and during aggregation. The example below shows the use of basic arithmetic functions.</p>
+<div class="sourceCode" id="cb54"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">carsDF_km</span> <span class="op"><-</span> <span class="va">carsDF</span></span>
+<span><span class="va">carsDF_km</span><span class="op">$</span><span class="va">kmpg</span> <span class="op"><-</span> <span class="va">carsDF_km</span><span class="op">$</span><span class="va">mpg</span> <span class="op">*</span> <span class="fl">1.61</span></span>
+<span><span class="fu"><a href="../reference/head.html">head</a></span><span class="op">(</span><span class="fu"><a href="../reference/select.html">select</a></span><span class="op">(</span><span class="va">carsDF_km</span>, <span class="st">"model"</span>, <span class="st">"mpg"</span>, <span class="st">"kmpg"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## model mpg kmpg</span></span>
+<span><span class="co">## 1 Mazda RX4 21.0 33.810</span></span>
+<span><span class="co">## 2 Mazda RX4 Wag 21.0 33.810</span></span>
+<span><span class="co">## 3 Datsun 710 22.8 36.708</span></span>
+<span><span class="co">## 4 Hornet 4 Drive 21.4 34.454</span></span>
+<span><span class="co">## 5 Hornet Sportabout 18.7 30.107</span></span>
+<span><span class="co">## 6 Valiant 18.1 29.141</span></span></code></pre>
+</div>
+</div>
+<div class="section level3">
+<h3 id="window-functions">Window Functions<a class="anchor" aria-label="anchor" href="#window-functions"></a>
+</h3>
+<p>A window function is a variation of aggregation function. In simple words,</p>
+<ul>
+<li><p>aggregation function: <code>n</code> to <code>1</code> mapping - returns a single value for a group of entries. Examples include <code>sum</code>, <code>count</code>, <code>max</code>.</p></li>
+<li><p>window function: <code>n</code> to <code>n</code> mapping - returns one value for each entry in the group, but the value may depend on all the entries of the <em>group</em>. Examples include <code>rank</code>, <code>lead</code>, <code>lag</code>.</p></li>
+</ul>
+<p>Formally, the <em>group</em> mentioned above is called the <em>frame</em>. Every input row can have a unique frame associated with it and the output of the window function on that row is based on the rows confined in that frame.</p>
+<p>Window functions are often used in conjunction with the following functions: <code>windowPartitionBy</code>, <code>windowOrderBy</code>, <code>partitionBy</code>, <code>orderBy</code>, <code>over</code>. To illustrate this we next look at an example.</p>
+<p>We still use the <code>mtcars</code> dataset. The corresponding <code>SparkDataFrame</code> is <code>carsDF</code>. Suppose for each number of cylinders, we want to calculate the rank of each car in <code>mpg</code> within the group.</p>
+<div class="sourceCode" id="cb56"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">carsSubDF</span> <span class="op"><-</span> <span class="fu"><a href="../reference/select.html">select</a></span><span class="op">(</span><span class="va">carsDF</span>, <span class="st">"model"</span>, <span class="st">"mpg"</span>, <span class="st">"cyl"</span><span class="op">)</span></span>
+<span><span class="va">ws</span> <span class="op"><-</span> <span class="fu"><a href="../reference/orderBy.html">orderBy</a></span><span class="op">(</span><span class="fu"><a href="../reference/windowPartitionBy.html">windowPartitionBy</a></span><span class="op">(</span><span class="st">"cyl"</span><span class="op">)</span>, <span class="st">"mpg"</span><span class="op">)</span></span>
+<span><span class="va">carsRank</span> <span class="op"><-</span> <span class="fu"><a href="../reference/withColumn.html">withColumn</a></span><span class="op">(</span><span class="va">carsSubDF</span>, <span class="st">"rank"</span>, <span class="fu"><a href="../reference/over.html">over</a></span><span class="op">(</span><span class="fu"><a href="../reference/column_window_functions.html">rank</a></span><span class="op">(</span><span class="op">)</span>, <span class="va">ws</span><s [...]
+<span><span class="fu"><a href="../reference/head.html">head</a></span><span class="op">(</span><span class="va">carsRank</span>, n <span class="op">=</span> <span class="fl">20L</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## model mpg cyl rank</span></span>
+<span><span class="co">## 1 Volvo 142E 21.4 4 1</span></span>
+<span><span class="co">## 2 Toyota Corona 21.5 4 2</span></span>
+<span><span class="co">## 3 Datsun 710 22.8 4 3</span></span>
+<span><span class="co">## 4 Merc 230 22.8 4 3</span></span>
+<span><span class="co">## 5 Merc 240D 24.4 4 5</span></span>
+<span><span class="co">## 6 Porsche 914-2 26.0 4 6</span></span>
+<span><span class="co">## 7 Fiat X1-9 27.3 4 7</span></span>
+<span><span class="co">## 8 Honda Civic 30.4 4 8</span></span>
+<span><span class="co">## 9 Lotus Europa 30.4 4 8</span></span>
+<span><span class="co">## 10 Fiat 128 32.4 4 10</span></span>
+<span><span class="co">## 11 Toyota Corolla 33.9 4 11</span></span>
+<span><span class="co">## 12 Merc 280C 17.8 6 1</span></span>
+<span><span class="co">## 13 Valiant 18.1 6 2</span></span>
+<span><span class="co">## 14 Merc 280 19.2 6 3</span></span>
+<span><span class="co">## 15 Ferrari Dino 19.7 6 4</span></span>
+<span><span class="co">## 16 Mazda RX4 21.0 6 5</span></span>
+<span><span class="co">## 17 Mazda RX4 Wag 21.0 6 5</span></span>
+<span><span class="co">## 18 Hornet 4 Drive 21.4 6 7</span></span>
+<span><span class="co">## 19 Cadillac Fleetwood 10.4 8 1</span></span>
+<span><span class="co">## 20 Lincoln Continental 10.4 8 1</span></span></code></pre>
+<p>We explain in detail the above steps.</p>
+<ul>
+<li>
+<code>windowPartitionBy</code> creates a window specification object <code>WindowSpec</code> that defines the partition. It controls which rows will be in the same partition as the given row. In this case, rows with the same value in <code>cyl</code> will be put in the same partition. <code>orderBy</code> further defines the ordering - the position a given row is in the partition. The resulting <code>WindowSpec</code> is returned as <code>ws</code>.</li>
+</ul>
+<p>More window specification methods include <code>rangeBetween</code>, which can define boundaries of the frame by value, and <code>rowsBetween</code>, which can define the boundaries by row indices.</p>
+<ul>
+<li>
+<code>withColumn</code> appends a Column called <code>rank</code> to the <code>SparkDataFrame</code>. <code>over</code> returns a windowing column. The first argument is usually a Column returned by window function(s) such as <code><a href="../reference/column_window_functions.html">rank()</a></code>, <code>lead(carsDF$wt)</code>. That calculates the corresponding values according to the partitioned-and-ordered table.</li>
+</ul>
+</div>
+<div class="section level3">
+<h3 id="user-defined-function">User-Defined Function<a class="anchor" aria-label="anchor" href="#user-defined-function"></a>
+</h3>
+<p>In SparkR, we support several kinds of user-defined functions (UDFs).</p>
+<div class="section level4">
+<h4 id="apply-by-partition">Apply by Partition<a class="anchor" aria-label="anchor" href="#apply-by-partition"></a>
+</h4>
+<p><code>dapply</code> can apply a function to each partition of a <code>SparkDataFrame</code>. The function to be applied to each partition of the <code>SparkDataFrame</code> should have only one parameter, a <code>data.frame</code> corresponding to a partition, and the output should be a <code>data.frame</code> as well. Schema specifies the row format of the resulting a <code>SparkDataFrame</code>. It must match to data types of returned value. See <a href="#DataTypes">here</a> for map [...]
+<p>We convert <code>mpg</code> to <code>kmpg</code> (kilometers per gallon). <code>carsSubDF</code> is a <code>SparkDataFrame</code> with a subset of <code>carsDF</code> columns.</p>
+<div class="sourceCode" id="cb58"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">carsSubDF</span> <span class="op"><-</span> <span class="fu"><a href="../reference/select.html">select</a></span><span class="op">(</span><span class="va">carsDF</span>, <span class="st">"model"</span>, <span class="st">"mpg"</span><span class="op">)</span></span>
+<span><span class="va">schema</span> <span class="op"><-</span> <span class="st">"model STRING, mpg DOUBLE, kmpg DOUBLE"</span></span>
+<span><span class="va">out</span> <span class="op"><-</span> <span class="fu"><a href="../reference/dapply.html">dapply</a></span><span class="op">(</span><span class="va">carsSubDF</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="va">x</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">cbind</a></span><span clas [...]
+<span><span class="fu"><a href="../reference/head.html">head</a></span><span class="op">(</span><span class="fu"><a href="../reference/collect.html">collect</a></span><span class="op">(</span><span class="va">out</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## model mpg kmpg</span></span>
+<span><span class="co">## 1 Mazda RX4 21.0 33.810</span></span>
+<span><span class="co">## 2 Mazda RX4 Wag 21.0 33.810</span></span>
+<span><span class="co">## 3 Datsun 710 22.8 36.708</span></span>
+<span><span class="co">## 4 Hornet 4 Drive 21.4 34.454</span></span>
+<span><span class="co">## 5 Hornet Sportabout 18.7 30.107</span></span>
+<span><span class="co">## 6 Valiant 18.1 29.141</span></span></code></pre>
+<p>Like <code>dapply</code>, <code>dapplyCollect</code> can apply a function to each partition of a <code>SparkDataFrame</code> and collect the result back. The output of the function should be a <code>data.frame</code>, but no schema is required in this case. Note that <code>dapplyCollect</code> can fail if the output of the UDF on all partitions cannot be pulled into the driver’s memory.</p>
+<div class="sourceCode" id="cb60"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">out</span> <span class="op"><-</span> <span class="fu"><a href="../reference/dapplyCollect.html">dapplyCollect</a></span><span class="op">(</span></span>
+<span> <span class="va">carsSubDF</span>,</span>
+<span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="op">{</span></span>
+<span> <span class="va">x</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">cbind</a></span><span class="op">(</span><span class="va">x</span>, <span class="st">"kmpg"</span> <span class="op">=</span> <span class="va">x</span><span class="op">$</span><span class="va">mpg</span> <span class="op">*</span> <span class="fl">1.61</span><span class="op">)</span></span>
+<span> <span class="op">}</span><span class="op">)</span></span>
+<span><span class="fu"><a href="../reference/head.html">head</a></span><span class="op">(</span><span class="va">out</span>, <span class="fl">3</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## model mpg kmpg</span></span>
+<span><span class="co">## 1 Mazda RX4 21.0 33.810</span></span>
+<span><span class="co">## 2 Mazda RX4 Wag 21.0 33.810</span></span>
+<span><span class="co">## 3 Datsun 710 22.8 36.708</span></span></code></pre>
+</div>
+<div class="section level4">
+<h4 id="apply-by-group">Apply by Group<a class="anchor" aria-label="anchor" href="#apply-by-group"></a>
+</h4>
+<p><code>gapply</code> can apply a function to each group of a <code>SparkDataFrame</code>. The function is to be applied to each group of the <code>SparkDataFrame</code> and should have only two parameters: grouping key and R <code>data.frame</code> corresponding to that key. The groups are chosen from <code>SparkDataFrames</code> column(s). The output of function should be a <code>data.frame</code>. Schema specifies the row format of the resulting <code>SparkDataFrame</code>. It must r [...]
+<div class="sourceCode" id="cb62"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">schema</span> <span class="op"><-</span> <span class="fu"><a href="../reference/structType.html">structType</a></span><span class="op">(</span><span class="fu"><a href="../reference/structField.html">structField</a></span><span class="op">(</span><span class="st">"cyl"</span>, <span class="st">"double"</span><span class="op">)</span>, <span class="fu"><a href="../reference/structField.html">structField</a></span><span class="op">(</spa [...]
+<span><span class="va">result</span> <span class="op"><-</span> <span class="fu"><a href="../reference/gapply.html">gapply</a></span><span class="op">(</span></span>
+<span> <span class="va">carsDF</span>,</span>
+<span> <span class="st">"cyl"</span>,</span>
+<span> <span class="kw">function</span><span class="op">(</span><span class="va">key</span>, <span class="va">x</span><span class="op">)</span> <span class="op">{</span></span>
+<span> <span class="va">y</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span><span class="va">key</span>, <span class="fu"><a href="../reference/column_aggregate_functions.html">max</a></span><span class="op">(</span><span class="va">x</span><span class="op">$</span><span class="va">mpg</span><span class="op">)</span><span class="op">)</span></span>
+<span> <span class="op">}</span>,</span>
+<span> <span class="va">schema</span><span class="op">)</span></span>
+<span><span class="fu"><a href="../reference/head.html">head</a></span><span class="op">(</span><span class="fu"><a href="../reference/arrange.html">arrange</a></span><span class="op">(</span><span class="va">result</span>, <span class="st">"max_mpg"</span>, decreasing <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## cyl max_mpg</span></span>
+<span><span class="co">## 1 4 33.9</span></span>
+<span><span class="co">## 2 6 21.4</span></span>
+<span><span class="co">## 3 8 19.2</span></span></code></pre>
+<p>Like <code>gapply</code>, <code>gapplyCollect</code> can apply a function to each partition of a <code>SparkDataFrame</code> and collect the result back to R <code>data.frame</code>. The output of the function should be a <code>data.frame</code> but no schema is required in this case. Note that <code>gapplyCollect</code> can fail if the output of the UDF on all partitions cannot be pulled into the driver’s memory.</p>
+<div class="sourceCode" id="cb64"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">result</span> <span class="op"><-</span> <span class="fu"><a href="../reference/gapplyCollect.html">gapplyCollect</a></span><span class="op">(</span></span>
+<span> <span class="va">carsDF</span>,</span>
+<span> <span class="st">"cyl"</span>,</span>
+<span> <span class="kw">function</span><span class="op">(</span><span class="va">key</span>, <span class="va">x</span><span class="op">)</span> <span class="op">{</span></span>
+<span> <span class="va">y</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span><span class="va">key</span>, <span class="fu"><a href="../reference/column_aggregate_functions.html">max</a></span><span class="op">(</span><span class="va">x</span><span class="op">$</span><span class="va">mpg</span><span class="op">)</span><span class="op">)</span></span>
+<span> <span class="fu"><a href="../reference/columns.html">colnames</a></span><span class="op">(</span><span class="va">y</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"cyl"</span>, <span class="st">"max_mpg"</span><span class="op">)</span></span>
+<span> <span class="va">y</span></span>
+<span> <span class="op">}</span><span class="op">)</span></span>
+<span><span class="fu"><a href="../reference/head.html">head</a></span><span class="op">(</span><span class="va">result</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/order.html" class="external-link">order</a></span><span class="op">(</span><span class="va">result</span><span class="op">$</span><span class="va">max_mpg</span>, decreasing <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>, <span class="op">]</span><span class= [...]
+<pre><code><span><span class="co">## cyl max_mpg</span></span>
+<span><span class="co">## 1 4 33.9</span></span>
+<span><span class="co">## 2 6 21.4</span></span>
+<span><span class="co">## 3 8 19.2</span></span></code></pre>
+</div>
+<div class="section level4">
+<h4 id="distribute-local-functions">Distribute Local Functions<a class="anchor" aria-label="anchor" href="#distribute-local-functions"></a>
+</h4>
+<p>Similar to <code>lapply</code> in native R, <code>spark.lapply</code> runs a function over a list of elements and distributes the computations with Spark. <code>spark.lapply</code> works in a manner that is similar to <code>doParallel</code> or <code>lapply</code> to elements of a list. The results of all the computations should fit in a single machine. If that is not the case you can do something like <code>df <- createDataFrame(list)</code> and then use <code>dapply</code>.</p>
+<p>We use <code>svm</code> in package <code>e1071</code> as an example. We use all default settings except for varying costs of constraints violation. <code>spark.lapply</code> can train those different models in parallel.</p>
+<div class="sourceCode" id="cb66"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">costs</span> <span class="op"><-</span> <span class="fu"><a href="../reference/column_math_functions.html">exp</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span>from <span class="op">=</span> <span class="fu"><a href="../reference/column_math_functions.html">log</a></span><span class="op">(</span><span class="fl">1</span><span class [...]
+<span><span class="va">train</span> <span class="op"><-</span> <span class="kw">function</span><span class="op">(</span><span class="va">cost</span><span class="op">)</span> <span class="op">{</span></span>
+<span> <span class="fu"><a href="https://rdrr.io/r/base/stopifnot.html" class="external-link">stopifnot</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/ns-load.html" class="external-link">requireNamespace</a></span><span class="op">(</span><span class="st">"e1071"</span>, quietly <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span>
+<span> <span class="va">model</span> <span class="op"><-</span> <span class="fu">e1071</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/e1071/man/svm.html" class="external-link">svm</a></span><span class="op">(</span><span class="va">Species</span> <span class="op">~</span> <span class="va">.</span>, data <span class="op">=</span> <span class="va">iris</span>, cost <span class="op">=</span> <span class="va">cost</span><span class="op">)</span></span>
+<span> <span class="fu"><a href="../reference/summary.html">summary</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span></span>
+<span><span class="op">}</span></span></code></pre></div>
+<p>Return a list of model’s summaries.</p>
+<div class="sourceCode" id="cb67"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">model.summaries</span> <span class="op"><-</span> <span class="fu"><a href="../reference/spark.lapply.html">spark.lapply</a></span><span class="op">(</span><span class="va">costs</span>, <span class="va">train</span><span class="op">)</span></span></code></pre></div>
+<div class="sourceCode" id="cb68"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/class.html" class="external-link">class</a></span><span class="op">(</span><span class="va">model.summaries</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## [1] "list"</span></span></code></pre>
+<p>To avoid lengthy display, we only present the partial result of the second fitted model. You are free to inspect other models as well.</p>
+<div class="sourceCode" id="cb70"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">model.summaries</span><span class="op">[[</span><span class="fl">2</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## $call</span></span>
+<span><span class="co">## svm(formula = Species ~ ., data = iris, cost = cost)</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $type</span></span>
+<span><span class="co">## [1] 0</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $kernel</span></span>
+<span><span class="co">## [1] 2</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $cost</span></span>
+<span><span class="co">## [1] 5.623413</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $degree</span></span>
+<span><span class="co">## [1] 3</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $gamma</span></span>
+<span><span class="co">## [1] 0.25</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $coef0</span></span>
+<span><span class="co">## [1] 0</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $nu</span></span>
+<span><span class="co">## [1] 0.5</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $epsilon</span></span>
+<span><span class="co">## [1] 0.1</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $sparse</span></span>
+<span><span class="co">## [1] FALSE</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $scaled</span></span>
+<span><span class="co">## [1] TRUE TRUE TRUE TRUE</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $x.scale</span></span>
+<span><span class="co">## $x.scale$`scaled:center`</span></span>
+<span><span class="co">## Sepal.Length Sepal.Width Petal.Length Petal.Width </span></span>
+<span><span class="co">## 5.843333 3.057333 3.758000 1.199333 </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $x.scale$`scaled:scale`</span></span>
+<span><span class="co">## Sepal.Length Sepal.Width Petal.Length Petal.Width </span></span>
+<span><span class="co">## 0.8280661 0.4358663 1.7652982 0.7622377 </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $y.scale</span></span>
+<span><span class="co">## NULL</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $nclasses</span></span>
+<span><span class="co">## [1] 3</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $levels</span></span>
+<span><span class="co">## [1] "setosa" "versicolor" "virginica" </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $tot.nSV</span></span>
+<span><span class="co">## [1] 35</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $nSV</span></span>
+<span><span class="co">## [1] 6 15 14</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $labels</span></span>
+<span><span class="co">## [1] 1 2 3</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $SV</span></span>
+<span><span class="co">## Sepal.Length Sepal.Width Petal.Length Petal.Width</span></span>
+<span><span class="co">## 14 -1.86378030 -0.13153881 -1.5056946 -1.4422448</span></span>
+<span><span class="co">## 16 -0.17309407 3.08045544 -1.2791040 -1.0486668</span></span>
+<span><span class="co">## 21 -0.53538397 0.78617383 -1.1658087 -1.3110521</span></span>
+<span><span class="co">## 23 -1.50149039 1.24503015 -1.5623422 -1.3110521</span></span>
+<span><span class="co">## 24 -0.89767388 0.55674567 -1.1658087 -0.9174741</span></span>
+<span><span class="co">## 42 -1.62225369 -1.73753594 -1.3923993 -1.1798595</span></span>
+<span><span class="co">## 51 1.39682886 0.32731751 0.5336209 0.2632600</span></span>
+<span><span class="co">## 53 1.27606556 0.09788935 0.6469162 0.3944526</span></span>
+<span><span class="co">## 54 -0.41462067 -1.73753594 0.1370873 0.1320673</span></span>
+<span><span class="co">## 55 0.79301235 -0.59039513 0.4769732 0.3944526</span></span>
+<span><span class="co">## [ reached getOption("max.print") -- omitted 25 rows ]</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $index</span></span>
+<span><span class="co">## [1] 14 16 21 23 24 42 51 53 54 55 58 61 69 71 73 78 79 84 85</span></span>
+<span><span class="co">## [20] 86 99 107 111 119 120 124 127 128 130 132 134 135 139 149 150</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $rho</span></span>
+<span><span class="co">## [1] -0.10346530 0.12160294 -0.09540346</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $compprob</span></span>
+<span><span class="co">## [1] FALSE</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $probA</span></span>
+<span><span class="co">## NULL</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $probB</span></span>
+<span><span class="co">## NULL</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $sigma</span></span>
+<span><span class="co">## NULL</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $coefs</span></span>
+<span><span class="co">## [,1] [,2]</span></span>
+<span><span class="co">## [1,] 0.00000000 0.06561739</span></span>
+<span><span class="co">## [2,] 0.76813720 0.93378721</span></span>
+<span><span class="co">## [3,] 0.00000000 0.12123270</span></span>
+<span><span class="co">## [4,] 0.00000000 0.31170741</span></span>
+<span><span class="co">## [5,] 1.11614066 0.46397392</span></span>
+<span><span class="co">## [6,] 1.88141600 1.10392128</span></span>
+<span><span class="co">## [7,] -0.55872622 0.00000000</span></span>
+<span><span class="co">## [8,] 0.00000000 5.62341325</span></span>
+<span><span class="co">## [9,] 0.00000000 0.27711792</span></span>
+<span><span class="co">## [10,] 0.00000000 5.28440007</span></span>
+<span><span class="co">## [11,] -1.06596713 0.00000000</span></span>
+<span><span class="co">## [12,] -0.57076709 1.09019756</span></span>
+<span><span class="co">## [13,] -0.03365904 5.62341325</span></span>
+<span><span class="co">## [14,] 0.00000000 5.62341325</span></span>
+<span><span class="co">## [15,] 0.00000000 5.62341325</span></span>
+<span><span class="co">## [16,] 0.00000000 5.62341325</span></span>
+<span><span class="co">## [17,] 0.00000000 4.70398738</span></span>
+<span><span class="co">## [18,] 0.00000000 5.62341325</span></span>
+<span><span class="co">## [19,] 0.00000000 4.97981371</span></span>
+<span><span class="co">## [20,] -0.77497987 0.00000000</span></span>
+<span><span class="co">## [ reached getOption("max.print") -- omitted 15 rows ]</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $na.action</span></span>
+<span><span class="co">## NULL</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $xlevels</span></span>
+<span><span class="co">## named list()</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $fitted</span></span>
+<span><span class="co">## 1 2 3 4 5 6 7 8 9 10 11 </span></span>
+<span><span class="co">## setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa </span></span>
+<span><span class="co">## 12 13 14 15 16 17 18 19 20 21 22 </span></span>
+<span><span class="co">## setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa </span></span>
+<span><span class="co">## 23 24 25 26 27 28 29 30 31 32 33 </span></span>
+<span><span class="co">## setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa </span></span>
+<span><span class="co">## 34 35 36 37 38 39 40 </span></span>
+<span><span class="co">## setosa setosa setosa setosa setosa setosa setosa </span></span>
+<span><span class="co">## [ reached getOption("max.print") -- omitted 110 entries ]</span></span>
+<span><span class="co">## Levels: setosa versicolor virginica</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $decision.values</span></span>
+<span><span class="co">## setosa/versicolor setosa/virginica versicolor/virginica</span></span>
+<span><span class="co">## 1 1.1911739 1.0908424 1.1275805</span></span>
+<span><span class="co">## 2 1.1336557 1.0619543 1.3260964</span></span>
+<span><span class="co">## 3 1.2085065 1.0698101 1.0511345</span></span>
+<span><span class="co">## 4 1.1646153 1.0505915 1.0806874</span></span>
+<span><span class="co">## 5 1.1880814 1.0950348 0.9542815</span></span>
+<span><span class="co">## 6 1.0990761 1.0984626 0.9326361</span></span>
+<span><span class="co">## 7 1.1573474 1.0343287 0.9726843</span></span>
+<span><span class="co">## 8 1.1851598 1.0815750 1.2206802</span></span>
+<span><span class="co">## 9 1.1673499 1.0406734 0.8837945</span></span>
+<span><span class="co">## 10 1.1629911 1.0560925 1.2430067</span></span>
+<span><span class="co">## 11 1.1339282 1.0803946 1.0338357</span></span>
+<span><span class="co">## 12 1.1724182 1.0641469 1.1190423</span></span>
+<span><span class="co">## 13 1.1827355 1.0667956 1.1414844</span></span>
+<span><span class="co">## [ reached getOption("max.print") -- omitted 137 rows ]</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $terms</span></span>
+<span><span class="co">## Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width</span></span>
+<span><span class="co">## attr(,"variables")</span></span>
+<span><span class="co">## list(Species, Sepal.Length, Sepal.Width, Petal.Length, Petal.Width)</span></span>
+<span><span class="co">## attr(,"factors")</span></span>
+<span><span class="co">## Sepal.Length Sepal.Width Petal.Length Petal.Width</span></span>
+<span><span class="co">## Species 0 0 0 0</span></span>
+<span><span class="co">## Sepal.Length 1 0 0 0</span></span>
+<span><span class="co">## Sepal.Width 0 1 0 0</span></span>
+<span><span class="co">## Petal.Length 0 0 1 0</span></span>
+<span><span class="co">## Petal.Width 0 0 0 1</span></span>
+<span><span class="co">## attr(,"term.labels")</span></span>
+<span><span class="co">## [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" </span></span>
+<span><span class="co">## attr(,"order")</span></span>
+<span><span class="co">## [1] 1 1 1 1</span></span>
+<span><span class="co">## attr(,"intercept")</span></span>
+<span><span class="co">## [1] 0</span></span>
+<span><span class="co">## attr(,"response")</span></span>
+<span><span class="co">## [1] 1</span></span>
+<span><span class="co">## attr(,".Environment")</span></span>
+<span><span class="co">## <environment: 0x5580e90fd1d8></span></span>
+<span><span class="co">## attr(,"predvars")</span></span>
+<span><span class="co">## list(Species, Sepal.Length, Sepal.Width, Petal.Length, Petal.Width)</span></span>
+<span><span class="co">## attr(,"dataClasses")</span></span>
+<span><span class="co">## Species Sepal.Length Sepal.Width Petal.Length Petal.Width </span></span>
+<span><span class="co">## "factor" "numeric" "numeric" "numeric" "numeric" </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## attr(,"class")</span></span>
+<span><span class="co">## [1] "summary.svm"</span></span></code></pre>
+</div>
+</div>
+<div class="section level3">
+<h3 id="sql-queries">SQL Queries<a class="anchor" aria-label="anchor" href="#sql-queries"></a>
+</h3>
+<p>A <code>SparkDataFrame</code> can also be registered as a temporary view in Spark SQL so that one can run SQL queries over its data. The sql function enables applications to run SQL queries programmatically and returns the result as a <code>SparkDataFrame</code>.</p>
+<div class="sourceCode" id="cb72"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">people</span> <span class="op"><-</span> <span class="fu"><a href="../reference/read.df.html">read.df</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="fu"><a href="../reference/sparkR.conf.html">sparkR.conf</a></span><span class="op">(</span><span class="st">"spark.home"</span><span class="op">)</span>,</span>
+<span> <span class="st">"/examples/src/main/resources/people.json"</span><span class="op">)</span>, <span class="st">"json"</span><span class="op">)</span></span></code></pre></div>
+<p>Register this <code>SparkDataFrame</code> as a temporary view.</p>
+<div class="sourceCode" id="cb73"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/createOrReplaceTempView.html">createOrReplaceTempView</a></span><span class="op">(</span><span class="va">people</span>, <span class="st">"people"</span><span class="op">)</span></span></code></pre></div>
+<p>SQL statements can be run using the sql method.</p>
+<div class="sourceCode" id="cb74"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">teenagers</span> <span class="op"><-</span> <span class="fu"><a href="../reference/sql.html">sql</a></span><span class="op">(</span><span class="st">"SELECT name FROM people WHERE age >= 13 AND age <= 19"</span><span class="op">)</span></span>
+<span><span class="fu"><a href="../reference/head.html">head</a></span><span class="op">(</span><span class="va">teenagers</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## name</span></span>
+<span><span class="co">## 1 Justin</span></span></code></pre>
+</div>
+</div>
+<div class="section level2">
+<h2 id="machine-learning">Machine Learning<a class="anchor" aria-label="anchor" href="#machine-learning"></a>
+</h2>
+<p>SparkR supports the following machine learning models and algorithms.</p>
+<div class="section level4">
+<h4 id="classification">Classification<a class="anchor" aria-label="anchor" href="#classification"></a>
+</h4>
+<ul>
+<li><p>Linear Support Vector Machine (SVM) Classifier</p></li>
+<li><p>Logistic Regression</p></li>
+<li><p>Multilayer Perceptron (MLP)</p></li>
+<li><p>Naive Bayes</p></li>
+<li><p>Factorization Machines (FM) Classifier</p></li>
+</ul>
+</div>
+<div class="section level4">
+<h4 id="regression">Regression<a class="anchor" aria-label="anchor" href="#regression"></a>
+</h4>
+<ul>
+<li><p>Accelerated Failure Time (AFT) Survival Model</p></li>
+<li><p>Generalized Linear Model (GLM)</p></li>
+<li><p>Isotonic Regression</p></li>
+<li><p>Linear Regression</p></li>
+<li><p>Factorization Machines (FM) Regressor</p></li>
+</ul>
+</div>
+<div class="section level4">
+<h4 id="tree---classification-and-regression">Tree - Classification and Regression<a class="anchor" aria-label="anchor" href="#tree---classification-and-regression"></a>
+</h4>
+<ul>
+<li><p>Decision Tree</p></li>
+<li><p>Gradient-Boosted Trees (GBT)</p></li>
+<li><p>Random Forest</p></li>
+</ul>
+</div>
+<div class="section level4">
+<h4 id="clustering">Clustering<a class="anchor" aria-label="anchor" href="#clustering"></a>
+</h4>
+<ul>
+<li><p>Bisecting <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>k</mi><annotation encoding="application/x-tex">k</annotation></semantics></math>-means</p></li>
+<li><p>Gaussian Mixture Model (GMM)</p></li>
+<li><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>k</mi><annotation encoding="application/x-tex">k</annotation></semantics></math>-means Clustering</p></li>
+<li><p>Latent Dirichlet Allocation (LDA)</p></li>
+<li><p>Power Iteration Clustering (PIC)</p></li>
+</ul>
+</div>
+<div class="section level4">
+<h4 id="collaborative-filtering">Collaborative Filtering<a class="anchor" aria-label="anchor" href="#collaborative-filtering"></a>
+</h4>
+<ul>
+<li>Alternating Least Squares (ALS)</li>
+</ul>
+</div>
+<div class="section level4">
+<h4 id="frequent-pattern-mining">Frequent Pattern Mining<a class="anchor" aria-label="anchor" href="#frequent-pattern-mining"></a>
+</h4>
+<ul>
+<li>FP-growth</li>
+<li>PrefixSpan</li>
+</ul>
+</div>
+<div class="section level4">
+<h4 id="statistics">Statistics<a class="anchor" aria-label="anchor" href="#statistics"></a>
+</h4>
+<ul>
+<li>Kolmogorov-Smirnov Test</li>
+</ul>
+</div>
+<div class="section level3">
+<h3 id="r-formula">R Formula<a class="anchor" aria-label="anchor" href="#r-formula"></a>
+</h3>
+<p>For most above, SparkR supports <strong>R formula operators</strong>, including <code>~</code>, <code>.</code>, <code>:</code>, <code>+</code> and <code>-</code> for model fitting. This makes it a similar experience as using R functions.</p>
+</div>
+<div class="section level3">
+<h3 id="training-and-test-sets">Training and Test Sets<a class="anchor" aria-label="anchor" href="#training-and-test-sets"></a>
+</h3>
+<p>We can easily split <code>SparkDataFrame</code> into random training and test sets by the <code>randomSplit</code> function. It returns a list of split <code>SparkDataFrames</code> with provided <code>weights</code>. We use <code>carsDF</code> as an example and want to have about <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mn>70</mn><annotation encoding="application/x-tex">70%</annotation></semantics></math> training data and <math display="inline" xm [...]
+<div class="sourceCode" id="cb76"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">splitDF_list</span> <span class="op"><-</span> <span class="fu"><a href="../reference/randomSplit.html">randomSplit</a></span><span class="op">(</span><span class="va">carsDF</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.7</span>, <span class="fl">0.3</span><span class="op">)</span>, seed <span class="op">=</span> <span class="fl">0</span>< [...]
+<span><span class="va">carsDF_train</span> <span class="op"><-</span> <span class="va">splitDF_list</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span></span>
+<span><span class="va">carsDF_test</span> <span class="op"><-</span> <span class="va">splitDF_list</span><span class="op">[[</span><span class="fl">2</span><span class="op">]</span><span class="op">]</span></span></code></pre></div>
+<div class="sourceCode" id="cb77"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/count.html">count</a></span><span class="op">(</span><span class="va">carsDF_train</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## [1] 24</span></span></code></pre>
+<div class="sourceCode" id="cb79"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/head.html">head</a></span><span class="op">(</span><span class="va">carsDF_train</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## model mpg cyl disp hp drat wt qsec vs am gear carb</span></span>
+<span><span class="co">## 1 Cadillac Fleetwood 10.4 8 472 205 2.93 5.250 17.98 0 0 3 4</span></span>
+<span><span class="co">## 2 Camaro Z28 13.3 8 350 245 3.73 3.840 15.41 0 0 3 4</span></span>
+<span><span class="co">## 3 Chrysler Imperial 14.7 8 440 230 3.23 5.345 17.42 0 0 3 4</span></span>
+<span><span class="co">## 4 Dodge Challenger 15.5 8 318 150 2.76 3.520 16.87 0 0 3 2</span></span>
+<span><span class="co">## 5 Duster 360 14.3 8 360 245 3.21 3.570 15.84 0 0 3 4</span></span>
+<span><span class="co">## 6 Ferrari Dino 19.7 6 145 175 3.62 2.770 15.50 0 1 5 6</span></span></code></pre>
+<div class="sourceCode" id="cb81"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/count.html">count</a></span><span class="op">(</span><span class="va">carsDF_test</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## [1] 8</span></span></code></pre>
+<div class="sourceCode" id="cb83"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/head.html">head</a></span><span class="op">(</span><span class="va">carsDF_test</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## model mpg cyl disp hp drat wt qsec vs am gear carb</span></span>
+<span><span class="co">## 1 AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2</span></span>
+<span><span class="co">## 2 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1</span></span>
+<span><span class="co">## 3 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1</span></span>
+<span><span class="co">## 4 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2</span></span>
+<span><span class="co">## 5 Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4</span></span>
+<span><span class="co">## 6 Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1</span></span></code></pre>
+</div>
+<div class="section level3">
+<h3 id="models-and-algorithms">Models and Algorithms<a class="anchor" aria-label="anchor" href="#models-and-algorithms"></a>
+</h3>
+<div class="section level4">
+<h4 id="linear-support-vector-machine-svm-classifier">Linear Support Vector Machine (SVM) Classifier<a class="anchor" aria-label="anchor" href="#linear-support-vector-machine-svm-classifier"></a>
+</h4>
+<p><a href="https://en.wikipedia.org/wiki/Support_vector_machine#Linear_SVM" class="external-link">Linear Support Vector Machine (SVM)</a> classifier is an SVM classifier with linear kernels. This is a binary classifier. We use a simple example to show how to use <code>spark.svmLinear</code> for binary classification.</p>
+<div class="sourceCode" id="cb85"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># load training data and create a DataFrame</span></span>
+<span><span class="va">t</span> <span class="op"><-</span> <span class="fu"><a href="../reference/as.data.frame.html">as.data.frame</a></span><span class="op">(</span><span class="va">Titanic</span><span class="op">)</span></span>
+<span><span class="va">training</span> <span class="op"><-</span> <span class="fu"><a href="../reference/createDataFrame.html">createDataFrame</a></span><span class="op">(</span><span class="va">t</span><span class="op">)</span></span>
+<span><span class="co"># fit a Linear SVM classifier model</span></span>
+<span><span class="va">model</span> <span class="op"><-</span> <span class="fu"><a href="../reference/spark.svmLinear.html">spark.svmLinear</a></span><span class="op">(</span><span class="va">training</span>, <span class="va">Survived</span> <span class="op">~</span> <span class="va">.</span>, regParam <span class="op">=</span> <span class="fl">0.01</span>, maxIter <span class="op">=</span> <span class="fl">10</span><span class="op">)</span></span>
+<span><span class="fu"><a href="../reference/summary.html">summary</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## $coefficients</span></span>
+<span><span class="co">## Estimate</span></span>
+<span><span class="co">## (Intercept) 0.993131388</span></span>
+<span><span class="co">## Class_1st -0.386500359</span></span>
+<span><span class="co">## Class_2nd -0.622627816</span></span>
+<span><span class="co">## Class_3rd -0.204446602</span></span>
+<span><span class="co">## Sex_Female -0.589950309</span></span>
+<span><span class="co">## Age_Adult 0.741676902</span></span>
+<span><span class="co">## Freq -0.006582887</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $numClasses</span></span>
+<span><span class="co">## [1] 2</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $numFeatures</span></span>
+<span><span class="co">## [1] 6</span></span></code></pre>
+<p>Predict values on training data</p>
+<div class="sourceCode" id="cb87"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">prediction</span> <span class="op"><-</span> <span class="fu"><a href="../reference/predict.html">predict</a></span><span class="op">(</span><span class="va">model</span>, <span class="va">training</span><span class="op">)</span></span>
+<span><span class="fu"><a href="../reference/head.html">head</a></span><span class="op">(</span><span class="fu"><a href="../reference/select.html">select</a></span><span class="op">(</span><span class="va">prediction</span>, <span class="st">"Class"</span>, <span class="st">"Sex"</span>, <span class="st">"Age"</span>, <span class="st">"Freq"</span>, <span class="st">"Survived"</span>, <span class="st">"prediction"</span><span class="op">)</span><span class="op">)</span></span></code></p [...]
+<pre><code><span><span class="co">## Class Sex Age Freq Survived prediction</span></span>
+<span><span class="co">## 1 1st Male Child 0 No Yes</span></span>
+<span><span class="co">## 2 2nd Male Child 0 No Yes</span></span>
+<span><span class="co">## 3 3rd Male Child 35 No Yes</span></span>
+<span><span class="co">## 4 Crew Male Child 0 No Yes</span></span>
+<span><span class="co">## 5 1st Female Child 0 No Yes</span></span>
+<span><span class="co">## 6 2nd Female Child 0 No No</span></span></code></pre>
+</div>
+<div class="section level4">
+<h4 id="logistic-regression">Logistic Regression<a class="anchor" aria-label="anchor" href="#logistic-regression"></a>
+</h4>
+<p><a href="https://en.wikipedia.org/wiki/Logistic_regression" class="external-link">Logistic regression</a> is a widely-used model when the response is categorical. It can be seen as a special case of the <a href="https://en.wikipedia.org/wiki/Generalized_linear_model" class="external-link">Generalized Linear Predictive Model</a>. We provide <code>spark.logit</code> on top of <code>spark.glm</code> to support logistic regression with advanced hyper-parameters. It supports both binary an [...]
+<p>We use a simple example to demonstrate <code>spark.logit</code> usage. In general, there are three steps of using <code>spark.logit</code>: 1). Create a dataframe from a proper data source; 2). Fit a logistic regression model using <code>spark.logit</code> with a proper parameter setting; and 3). Obtain the coefficient matrix of the fitted model using <code>summary</code> and use the model for prediction with <code>predict</code>.</p>
+<p>Binomial logistic regression</p>
+<div class="sourceCode" id="cb89"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">t</span> <span class="op"><-</span> <span class="fu"><a href="../reference/as.data.frame.html">as.data.frame</a></span><span class="op">(</span><span class="va">Titanic</span><span class="op">)</span></span>
+<span><span class="va">training</span> <span class="op"><-</span> <span class="fu"><a href="../reference/createDataFrame.html">createDataFrame</a></span><span class="op">(</span><span class="va">t</span><span class="op">)</span></span>
+<span><span class="va">model</span> <span class="op"><-</span> <span class="fu"><a href="../reference/spark.logit.html">spark.logit</a></span><span class="op">(</span><span class="va">training</span>, <span class="va">Survived</span> <span class="op">~</span> <span class="va">.</span>, regParam <span class="op">=</span> <span class="fl">0.04741301</span><span class="op">)</span></span>
+<span><span class="fu"><a href="../reference/summary.html">summary</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## $coefficients</span></span>
+<span><span class="co">## Estimate</span></span>
+<span><span class="co">## (Intercept) 0.2255014282</span></span>
+<span><span class="co">## Class_1st -0.1338856652</span></span>
+<span><span class="co">## Class_2nd -0.1479826947</span></span>
+<span><span class="co">## Class_3rd 0.0005674937</span></span>
+<span><span class="co">## Sex_Female -0.2011183871</span></span>
+<span><span class="co">## Age_Adult 0.3263186885</span></span>
+<span><span class="co">## Freq -0.0033111157</span></span></code></pre>
+<p>Predict values on training data</p>
+<div class="sourceCode" id="cb91"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">fitted</span> <span class="op"><-</span> <span class="fu"><a href="../reference/predict.html">predict</a></span><span class="op">(</span><span class="va">model</span>, <span class="va">training</span><span class="op">)</span></span>
+<span><span class="fu"><a href="../reference/head.html">head</a></span><span class="op">(</span><span class="fu"><a href="../reference/select.html">select</a></span><span class="op">(</span><span class="va">fitted</span>, <span class="st">"Class"</span>, <span class="st">"Sex"</span>, <span class="st">"Age"</span>, <span class="st">"Freq"</span>, <span class="st">"Survived"</span>, <span class="st">"prediction"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## Class Sex Age Freq Survived prediction</span></span>
+<span><span class="co">## 1 1st Male Child 0 No Yes</span></span>
+<span><span class="co">## 2 2nd Male Child 0 No Yes</span></span>
+<span><span class="co">## 3 3rd Male Child 35 No Yes</span></span>
+<span><span class="co">## 4 Crew Male Child 0 No Yes</span></span>
+<span><span class="co">## 5 1st Female Child 0 No No</span></span>
+<span><span class="co">## 6 2nd Female Child 0 No No</span></span></code></pre>
+<p>Multinomial logistic regression against three classes</p>
+<div class="sourceCode" id="cb93"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">t</span> <span class="op"><-</span> <span class="fu"><a href="../reference/as.data.frame.html">as.data.frame</a></span><span class="op">(</span><span class="va">Titanic</span><span class="op">)</span></span>
+<span><span class="va">training</span> <span class="op"><-</span> <span class="fu"><a href="../reference/createDataFrame.html">createDataFrame</a></span><span class="op">(</span><span class="va">t</span><span class="op">)</span></span>
+<span><span class="co"># Note in this case, Spark infers it is multinomial logistic regression, so family = "multinomial" is optional.</span></span>
+<span><span class="va">model</span> <span class="op"><-</span> <span class="fu"><a href="../reference/spark.logit.html">spark.logit</a></span><span class="op">(</span><span class="va">training</span>, <span class="va">Class</span> <span class="op">~</span> <span class="va">.</span>, regParam <span class="op">=</span> <span class="fl">0.07815179</span><span class="op">)</span></span>
+<span><span class="fu"><a href="../reference/summary.html">summary</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## $coefficients</span></span>
+<span><span class="co">## 1st 2nd 3rd Crew</span></span>
+<span><span class="co">## (Intercept) 0.051662845 0.062998145 -0.039083689 -0.075577300</span></span>
+<span><span class="co">## Sex_Female -0.088030587 -0.102528148 0.059233106 0.131325629</span></span>
+<span><span class="co">## Age_Adult 0.141935316 0.169492058 -0.102562719 -0.208864654</span></span>
+<span><span class="co">## Survived_No 0.052721020 0.057980057 -0.029408423 -0.081292653</span></span>
+<span><span class="co">## Freq -0.001555912 -0.001970377 0.001303836 0.002222453</span></span></code></pre>
+</div>
+<div class="section level4">
+<h4 id="multilayer-perceptron">Multilayer Perceptron<a class="anchor" aria-label="anchor" href="#multilayer-perceptron"></a>
+</h4>
+<p>Multilayer perceptron classifier (MLPC) is a classifier based on the <a href="https://en.wikipedia.org/wiki/Feedforward_neural_network" class="external-link">feedforward artificial neural network</a>. MLPC consists of multiple layers of nodes. Each layer is fully connected to the next layer in the network. Nodes in the input layer represent the input data. All other nodes map inputs to outputs by a linear combination of the inputs with the node’s weights <math display="inline" xmlns=" [...]
+y(x)=f_K(\ldots f_2(w_2^T f_1(w_1^T x + b_1) + b_2) \ldots + b_K).
+</annotation></semantics></math></p>
+<p>Nodes in intermediate layers use sigmoid (logistic) function: <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>f</mi><mo stretchy="false" form="prefix">(</mo><msub><mi>z</mi><mi>i</mi></msub><mo stretchy="false" form="postfix">)</mo><mo>=</mo><mfrac><mn>1</mn><mrow><mn>1</mn><mo>+</mo><msup><mi>e</mi><mrow><mo>−</mo><msub><mi>z</mi><mi>i</mi></msub></mrow></msup></mrow></mfrac><mi>.</mi></mrow><annotation encoding="application/x-tex">
+f(z_i) = \frac{1}{1+e^{-z_i}}.
+</annotation></semantics></math></p>
+<p>Nodes in the output layer use softmax function: <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>f</mi><mo stretchy="false" form="prefix">(</mo><msub><mi>z</mi><mi>i</mi></msub><mo stretchy="false" form="postfix">)</mo><mo>=</mo><mfrac><msup><mi>e</mi><msub><mi>z</mi><mi>i</mi></msub></msup><mrow><munderover><mo>∑</mo><mrow><mi>k</mi><mo>=</mo><mn>1</mn></mrow><mi>N</mi></munderover><msup><mi>e</mi><msub><mi>z</mi><mi>k</mi></msub></msup></mrow></m [...]
+f(z_i) = \frac{e^{z_i}}{\sum_{k=1}^N e^{z_k}}.
+</annotation></semantics></math></p>
+<p>The number of nodes <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>N</mi><annotation encoding="application/x-tex">N</annotation></semantics></math> in the output layer corresponds to the number of classes.</p>
+<p>MLPC employs backpropagation for learning the model. We use the logistic loss function for optimization and L-BFGS as an optimization routine.</p>
+<p><code>spark.mlp</code> requires at least two columns in <code>data</code>: one named <code>"label"</code> and the other one <code>"features"</code>. The <code>"features"</code> column should be in libSVM-format.</p>
+<p>We use Titanic data set to show how to use <code>spark.mlp</code> in classification.</p>
+<div class="sourceCode" id="cb95"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">t</span> <span class="op"><-</span> <span class="fu"><a href="../reference/as.data.frame.html">as.data.frame</a></span><span class="op">(</span><span class="va">Titanic</span><span class="op">)</span></span>
+<span><span class="va">training</span> <span class="op"><-</span> <span class="fu"><a href="../reference/createDataFrame.html">createDataFrame</a></span><span class="op">(</span><span class="va">t</span><span class="op">)</span></span>
+<span><span class="co"># fit a Multilayer Perceptron Classification Model</span></span>
+<span><span class="va">model</span> <span class="op"><-</span> <span class="fu"><a href="../reference/spark.mlp.html">spark.mlp</a></span><span class="op">(</span><span class="va">training</span>, <span class="va">Survived</span> <span class="op">~</span> <span class="va">Age</span> <span class="op">+</span> <span class="va">Sex</span>, blockSize <span class="op">=</span> <span class="fl">128</span>, layers <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.h [...]
+<p>To avoid lengthy display, we only present partial results of the model summary. You can check the full result from your sparkR shell.</p>
+<div class="sourceCode" id="cb96"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># check the summary of the fitted model</span></span>
+<span><span class="fu"><a href="../reference/summary.html">summary</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## $numOfInputs</span></span>
+<span><span class="co">## [1] 2</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $numOfOutputs</span></span>
+<span><span class="co">## [1] 2</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $layers</span></span>
+<span><span class="co">## [1] 2 2</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $weights</span></span>
+<span><span class="co">## $weights[[1]]</span></span>
+<span><span class="co">## [1] 0</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $weights[[2]]</span></span>
+<span><span class="co">## [1] 0</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $weights[[3]]</span></span>
+<span><span class="co">## [1] 5</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $weights[[4]]</span></span>
+<span><span class="co">## [1] 5</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $weights[[5]]</span></span>
+<span><span class="co">## [1] 9</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $weights[[6]]</span></span>
+<span><span class="co">## [1] 9</span></span></code></pre>
+<div class="sourceCode" id="cb98"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># make predictions use the fitted model</span></span>
+<span><span class="va">predictions</span> <span class="op"><-</span> <span class="fu"><a href="../reference/predict.html">predict</a></span><span class="op">(</span><span class="va">model</span>, <span class="va">training</span><span class="op">)</span></span>
+<span><span class="fu"><a href="../reference/head.html">head</a></span><span class="op">(</span><span class="fu"><a href="../reference/select.html">select</a></span><span class="op">(</span><span class="va">predictions</span>, <span class="va">predictions</span><span class="op">$</span><span class="va">prediction</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## prediction</span></span>
+<span><span class="co">## 1 No</span></span>
+<span><span class="co">## 2 No</span></span>
+<span><span class="co">## 3 No</span></span>
+<span><span class="co">## 4 No</span></span>
+<span><span class="co">## 5 No</span></span>
+<span><span class="co">## 6 No</span></span></code></pre>
+</div>
+<div class="section level4">
+<h4 id="naive-bayes">Naive Bayes<a class="anchor" aria-label="anchor" href="#naive-bayes"></a>
+</h4>
+<p>Naive Bayes model assumes independence among the features. <code>spark.naiveBayes</code> fits a <a href="https://en.wikipedia.org/wiki/Naive_Bayes_classifier#Bernoulli_naive_Bayes" class="external-link">Bernoulli naive Bayes model</a> against a SparkDataFrame. The data should be all categorical. These models are often used for document classification.</p>
+<div class="sourceCode" id="cb100"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">titanic</span> <span class="op"><-</span> <span class="fu"><a href="../reference/as.data.frame.html">as.data.frame</a></span><span class="op">(</span><span class="va">Titanic</span><span class="op">)</span></span>
+<span><span class="va">titanicDF</span> <span class="op"><-</span> <span class="fu"><a href="../reference/createDataFrame.html">createDataFrame</a></span><span class="op">(</span><span class="va">titanic</span><span class="op">[</span><span class="va">titanic</span><span class="op">$</span><span class="va">Freq</span> <span class="op">></span> <span class="fl">0</span>, <span class="op">-</span><span class="fl">5</span><span class="op">]</span><span class="op">)</span></span>
+<span><span class="va">naiveBayesModel</span> <span class="op"><-</span> <span class="fu"><a href="../reference/spark.naiveBayes.html">spark.naiveBayes</a></span><span class="op">(</span><span class="va">titanicDF</span>, <span class="va">Survived</span> <span class="op">~</span> <span class="va">Class</span> <span class="op">+</span> <span class="va">Sex</span> <span class="op">+</span> <span class="va">Age</span><span class="op">)</span></span>
+<span><span class="fu"><a href="../reference/summary.html">summary</a></span><span class="op">(</span><span class="va">naiveBayesModel</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## $apriori</span></span>
+<span><span class="co">## Yes No</span></span>
+<span><span class="co">## [1,] 0.5769231 0.4230769</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $tables</span></span>
+<span><span class="co">## Class_3rd Class_1st Class_2nd Sex_Female Age_Adult</span></span>
+<span><span class="co">## Yes 0.3125 0.3125 0.3125 0.5 0.5625 </span></span>
+<span><span class="co">## No 0.4166667 0.25 0.25 0.5 0.75</span></span></code></pre>
+<div class="sourceCode" id="cb102"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">naiveBayesPrediction</span> <span class="op"><-</span> <span class="fu"><a href="../reference/predict.html">predict</a></span><span class="op">(</span><span class="va">naiveBayesModel</span>, <span class="va">titanicDF</span><span class="op">)</span></span>
+<span><span class="fu"><a href="../reference/head.html">head</a></span><span class="op">(</span><span class="fu"><a href="../reference/select.html">select</a></span><span class="op">(</span><span class="va">naiveBayesPrediction</span>, <span class="st">"Class"</span>, <span class="st">"Sex"</span>, <span class="st">"Age"</span>, <span class="st">"Survived"</span>, <span class="st">"prediction"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## Class Sex Age Survived prediction</span></span>
+<span><span class="co">## 1 3rd Male Child No Yes</span></span>
+<span><span class="co">## 2 3rd Female Child No Yes</span></span>
+<span><span class="co">## 3 1st Male Adult No Yes</span></span>
+<span><span class="co">## 4 2nd Male Adult No Yes</span></span>
+<span><span class="co">## 5 3rd Male Adult No No</span></span>
+<span><span class="co">## 6 Crew Male Adult No Yes</span></span></code></pre>
+</div>
+<div class="section level4">
+<h4 id="factorization-machines-classifier">Factorization Machines Classifier<a class="anchor" aria-label="anchor" href="#factorization-machines-classifier"></a>
+</h4>
+<p>Factorization Machines for classification problems.</p>
+<p>For background and details about the implementation of factorization machines, refer to the <a href="https://spark.apache.org/docs/latest/ml-classification-regression.html#factorization-machines" class="external-link">Factorization Machines section</a>.</p>
+<div class="sourceCode" id="cb104"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">t</span> <span class="op"><-</span> <span class="fu"><a href="../reference/as.data.frame.html">as.data.frame</a></span><span class="op">(</span><span class="va">Titanic</span><span class="op">)</span></span>
+<span><span class="va">training</span> <span class="op"><-</span> <span class="fu"><a href="../reference/createDataFrame.html">createDataFrame</a></span><span class="op">(</span><span class="va">t</span><span class="op">)</span></span>
+<span></span>
+<span><span class="va">model</span> <span class="op"><-</span> <span class="fu"><a href="../reference/spark.fmClassifier.html">spark.fmClassifier</a></span><span class="op">(</span><span class="va">training</span>, <span class="va">Survived</span> <span class="op">~</span> <span class="va">Age</span> <span class="op">+</span> <span class="va">Sex</span><span class="op">)</span></span>
+<span><span class="fu"><a href="../reference/summary.html">summary</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## $coefficients</span></span>
+<span><span class="co">## Estimate</span></span>
+<span><span class="co">## (Intercept) 0.0064275991</span></span>
+<span><span class="co">## Age_Adult 0.0001294448</span></span>
+<span><span class="co">## Sex_Female 0.0001294448</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $factors</span></span>
+<span><span class="co">## [,1] [,2] [,3] [,4] [,5] [,6]</span></span>
+<span><span class="co">## [1,] -0.3256224 0.11912568 0.1460235 0.1620567 0.13153516 0.06403695</span></span>
+<span><span class="co">## [2,] -0.1382155 -0.03658261 0.1717808 -0.1602241 -0.08446129 -0.19287098</span></span>
+<span><span class="co">## [,7] [,8]</span></span>
+<span><span class="co">## [1,] -0.03292446 -0.05166818</span></span>
+<span><span class="co">## [2,] 0.19252571 0.06237194</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $numClasses</span></span>
+<span><span class="co">## [1] 2</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $numFeatures</span></span>
+<span><span class="co">## [1] 2</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## $factorSize</span></span>
+<span><span class="co">## [1] 8</span></span></code></pre>
+<div class="sourceCode" id="cb106"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">predictions</span> <span class="op"><-</span> <span class="fu"><a href="../reference/predict.html">predict</a></span><span class="op">(</span><span class="va">model</span>, <span class="va">training</span><span class="op">)</span></span>
+<span><span class="fu"><a href="../reference/head.html">head</a></span><span class="op">(</span><span class="fu"><a href="../reference/select.html">select</a></span><span class="op">(</span><span class="va">predictions</span>, <span class="va">predictions</span><span class="op">$</span><span class="va">prediction</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## prediction</span></span>
+<span><span class="co">## 1 Yes</span></span>
+<span><span class="co">## 2 Yes</span></span>
+<span><span class="co">## 3 Yes</span></span>
+<span><span class="co">## 4 Yes</span></span>
+<span><span class="co">## 5 Yes</span></span>
+<span><span class="co">## 6 Yes</span></span></code></pre>
+</div>
+<div class="section level4">
+<h4 id="accelerated-failure-time-survival-model">Accelerated Failure Time Survival Model<a class="anchor" aria-label="anchor" href="#accelerated-failure-time-survival-model"></a>
+</h4>
+<p>Survival analysis studies the expected duration of time until an event happens, and often the relationship with risk factors or treatment taken on the subject. In contrast to standard regression analysis, survival modeling has to deal with special characteristics in the data including non-negative survival time and censoring.</p>
+<p>Accelerated Failure Time (AFT) model is a parametric survival model for censored data that assumes the effect of a covariate is to accelerate or decelerate the life course of an event by some constant. For more information, refer to the Wikipedia page <a href="https://en.wikipedia.org/wiki/Accelerated_failure_time_model" class="external-link">AFT Model</a> and the references there. Different from a <a href="https://en.wikipedia.org/wiki/Proportional_hazards_model" class="external-link [...]
+<div class="sourceCode" id="cb108"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><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="https://github.com/therneau/survival" class="external-link">survival</a></span><span class="op">)</span></span>
+<span><span class="va">ovarianDF</span> <span class="op"><-</span> <span class="fu"><a href="../reference/createDataFrame.html">createDataFrame</a></span><span class="op">(</span><span class="va">ovarian</span><span class="op">)</span></span>
+<span><span class="va">aftModel</span> <span class="op"><-</span> <span class="fu"><a href="../reference/spark.survreg.html">spark.survreg</a></span><span class="op">(</span><span class="va">ovarianDF</span>, <span class="fu"><a href="https://rdrr.io/pkg/survival/man/Surv.html" class="external-link">Surv</a></span><span class="op">(</span><span class="va">futime</span>, <span class="va">fustat</span><span class="op">)</span> <span class="op">~</span> <span class="va">ecog_ps</span> <s [...]
+<span><span class="fu"><a href="../reference/summary.html">summary</a></span><span class="op">(</span><span class="va">aftModel</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## $coefficients</span></span>
+<span><span class="co">## Value</span></span>
+<span><span class="co">## (Intercept) 6.8966910</span></span>
+<span><span class="co">## ecog_ps -0.3850414</span></span>
+<span><span class="co">## rx 0.5286455</span></span>
+<span><span class="co">## Log(scale) -0.1234429</span></span></code></pre>
+<div class="sourceCode" id="cb110"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">aftPredictions</span> <span class="op"><-</span> <span class="fu"><a href="../reference/predict.html">predict</a></span><span class="op">(</span><span class="va">aftModel</span>, <span class="va">ovarianDF</span><span class="op">)</span></span>
+<span><span class="fu"><a href="../reference/head.html">head</a></span><span class="op">(</span><span class="va">aftPredictions</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## futime fustat age resid_ds rx ecog_ps label prediction</span></span>
+<span><span class="co">## 1 59 1 72.3315 2 1 1 59 1141.724</span></span>
+<span><span class="co">## 2 115 1 74.4932 2 1 1 115 1141.724</span></span>
+<span><span class="co">## 3 156 1 66.4658 2 1 2 156 776.855</span></span>
+<span><span class="co">## 4 421 0 53.3644 2 2 1 421 1937.087</span></span>
+<span><span class="co">## 5 431 1 50.3397 2 1 1 431 1141.724</span></span>
+<span><span class="co">## 6 448 0 56.4301 1 1 2 448 776.855</span></span></code></pre>
+</div>
+<div class="section level4">
+<h4 id="generalized-linear-model">Generalized Linear Model<a class="anchor" aria-label="anchor" href="#generalized-linear-model"></a>
+</h4>
+<p>The main function is <code>spark.glm</code>. The following families and link functions are supported. The default is gaussian.</p>
+<table class="table">
+<thead><tr class="header">
+<th>Family</th>
+<th>Link Function</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td>gaussian</td>
+<td>identity, log, inverse</td>
+</tr>
+<tr class="even">
+<td>binomial</td>
+<td>logit, probit, cloglog (complementary log-log)</td>
+</tr>
+<tr class="odd">
+<td>poisson</td>
+<td>log, identity, sqrt</td>
+</tr>
+<tr class="even">
+<td>gamma</td>
+<td>inverse, identity, log</td>
+</tr>
+<tr class="odd">
+<td>tweedie</td>
+<td>power link function</td>
+</tr>
+</tbody>
+</table>
+<p>There are three ways to specify the <code>family</code> argument.</p>
+<ul>
+<li><p>Family name as a character string, e.g. <code>family = "gaussian"</code>.</p></li>
+<li><p>Family function, e.g. <code>family = binomial</code>.</p></li>
+<li><p>Result returned by a family function, e.g. <code>family = poisson(link = log)</code>.</p></li>
+<li>
+<p>Note that there are two ways to specify the tweedie family:</p>
+<ol style="list-style-type: lower-alpha">
+<li>Set <code>family = "tweedie"</code> and specify the <code>var.power</code> and <code>link.power</code>
+</li>
+<li>When package <code>statmod</code> is loaded, the tweedie family is specified using the family definition therein, i.e., <code><a href="https://rdrr.io/pkg/statmod/man/tweedie.html" class="external-link">tweedie()</a></code>.</li>
+</ol>
+</li>
+</ul>
+<p>For more information regarding the families and their link functions, see the Wikipedia page <a href="https://en.wikipedia.org/wiki/Generalized_linear_model" class="external-link">Generalized Linear Model</a>.</p>
+<p>We use the <code>mtcars</code> dataset as an illustration. The corresponding <code>SparkDataFrame</code> is <code>carsDF</code>. After fitting the model, we print out a summary and see the fitted values by making predictions on the original dataset. We can also pass into a new <code>SparkDataFrame</code> of same schema to predict on new data.</p>
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