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Posted to commits@spark.apache.org by do...@apache.org on 2023/06/23 17:18:55 UTC
[spark-website] branch asf-site updated: Add Apache Spark 3.4.1 docs (#464)
This is an automated email from the ASF dual-hosted git repository.
dongjoon 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 29f37323ed Add Apache Spark 3.4.1 docs (#464)
29f37323ed is described below
commit 29f37323edb3f282a0a9c2fc57147c03c2f6eb4b
Author: Dongjoon Hyun <do...@apache.org>
AuthorDate: Fri Jun 23 10:18:36 2023 -0700
Add Apache Spark 3.4.1 docs (#464)
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diff --git a/site/docs/3.4.1/404.html b/site/docs/3.4.1/404.html
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+
+<!DOCTYPE html>
+<!--[if lt IE 7]> <html class="no-js lt-ie9 lt-ie8 lt-ie7"> <![endif]-->
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+ <li class="nav-item"><a href="index.html" class="nav-link">Overview</a></li>
+
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+ <a href="#" class="nav-link dropdown-toggle" id="navbarQuickStart" role="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">Programming Guides</a>
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+
+ <li class="nav-item dropdown">
+ <a href="#" class="nav-link dropdown-toggle" id="navbarAPIDocs" role="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">API Docs</a>
+ <div class="dropdown-menu" aria-labelledby="navbarAPIDocs">
+ <a class="dropdown-item" href="api/scala/org/apache/spark/index.html">Scala</a>
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+ <a class="dropdown-item" href="api/R/index.html">R</a>
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+ <a class="dropdown-item" href="cluster-overview.html">Overview</a>
+ <a class="dropdown-item" href="submitting-applications.html">Submitting Applications</a>
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+ <a href="#" class="nav-link dropdown-toggle" id="navbarMore" role="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">More</a>
+ <div class="dropdown-menu" aria-labelledby="navbarMore">
+ <a class="dropdown-item" href="configuration.html">Configuration</a>
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+ <a class="dropdown-item" href="tuning.html">Tuning Guide</a>
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+ <a class="dropdown-item" href="security.html">Security</a>
+ <a class="dropdown-item" href="hardware-provisioning.html">Hardware Provisioning</a>
+ <a class="dropdown-item" href="migration-guide.html">Migration Guide</a>
+ <div class="dropdown-divider"></div>
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+ <a class="dropdown-item" href="https://spark.apache.org/contributing.html">Contributing to Spark</a>
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+ </div>
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+ <input type="text" id="docsearch-input" placeholder="Search the docs…">
+ </li>
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+
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+
+<p>It looks like this was the result of either:</p>
+
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+ <script src="js/vendor/bootstrap.bundle.min.js"></script>
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+ <script type="text/javascript">
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+ // 2. a JavaScript snippet to be inserted in your website that will bind this Algolia index
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+ // details on how works DocSearch, check the docs of DocSearch.
+ docsearch({
+ apiKey: 'd62f962a82bc9abb53471cb7b89da35e',
+ appId: 'RAI69RXRSK',
+ indexName: 'apache_spark',
+ inputSelector: '#docsearch-input',
+ enhancedSearchInput: true,
+ algoliaOptions: {
+ 'facetFilters': ["version:3.4.1"]
+ },
+ debug: false // Set debug to true if you want to inspect the dropdown
+});
+
+ </script>
+
+ <!-- MathJax Section -->
+ <script type="text/x-mathjax-config">
+ MathJax.Hub.Config({
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diff --git a/site/docs/3.4.1/README.html b/site/docs/3.4.1/README.html
new file mode 100644
index 0000000000..11d5f0fa24
--- /dev/null
+++ b/site/docs/3.4.1/README.html
@@ -0,0 +1,110 @@
+<p>Welcome to the Spark documentation!</p>
+
+<p>This readme will walk you through navigating and building the Spark documentation, which is included
+here with the Spark source code. You can also find documentation specific to release versions of
+Spark at https://spark.apache.org/documentation.html.</p>
+
+<p>Read on to learn more about viewing documentation in plain text (i.e., markdown) or building the
+documentation yourself. Why build it yourself? So that you have the docs that correspond to
+whichever version of Spark you currently have checked out of revision control.</p>
+
+<h2 id="prerequisites">Prerequisites</h2>
+
+<p>The Spark documentation build uses a number of tools to build HTML docs and API docs in Scala, Java,
+Python, R and SQL.</p>
+
+<p>You need to have <a href="https://www.ruby-lang.org/en/documentation/installation/">Ruby</a> and
+<a href="https://docs.python.org/2/using/unix.html#getting-and-installing-the-latest-version-of-python">Python</a>
+installed. Make sure the <code class="language-plaintext highlighter-rouge">bundle</code> command is available, if not install the Gem containing it:</p>
+
+<div class="language-sh highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="nv">$ </span><span class="nb">sudo </span>gem <span class="nb">install </span>bundler
+</code></pre></div></div>
+
+<p>After this all the required ruby dependencies can be installed from the <code class="language-plaintext highlighter-rouge">docs/</code> directory via the Bundler:</p>
+
+<div class="language-sh highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="nv">$ </span><span class="nb">cd </span>docs
+<span class="nv">$ </span>bundle <span class="nb">install</span>
+</code></pre></div></div>
+
+<p>Note: If you are on a system with both Ruby 1.9 and Ruby 2.0 you may need to replace gem with gem2.0.</p>
+
+<h3 id="sql-and-python-api-documentation-optional">SQL and Python API Documentation (Optional)</h3>
+
+<p>To generate SQL and Python API docs, you’ll need to install these libraries:</p>
+
+<!--
+TODO(SPARK-32407): Sphinx 3.1+ does not correctly index nested classes.
+See also https://github.com/sphinx-doc/sphinx/issues/7551.
+
+TODO(SPARK-35375): Jinja2 3.0.0+ causes error when building with Sphinx.
+See also https://issues.apache.org/jira/browse/SPARK-35375.
+-->
+<p>Run the following command from $SPARK_HOME:</p>
+<div class="language-sh highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="nv">$ </span><span class="nb">sudo </span>pip <span class="nb">install</span> <span class="nt">--upgrade</span> <span class="nt">-r</span> dev/requirements.txt
+</code></pre></div></div>
+
+<h3 id="r-api-documentation-optional">R API Documentation (Optional)</h3>
+
+<p>If you’d like to generate R API documentation, you’ll need to <a href="https://pandoc.org/installing.html">install Pandoc</a>
+and install these libraries:</p>
+
+<div class="language-sh highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="nv">$ </span><span class="nb">sudo </span>Rscript <span class="nt">-e</span> <span class="s1">'install.packages(c("knitr", "devtools", "testthat", "rmarkdown"), repos="https://cloud.r-project.org/")'</span>
+<span class="nv">$ </span><span class="nb">sudo </span>Rscript <span class="nt">-e</span> <span class="s1">'devtools::install_version("roxygen2", version = "7.1.2", repos="https://cloud.r-project.org/")'</span>
+<span class="nv">$ </span><span class="nb">sudo </span>Rscript <span class="nt">-e</span> <span class="s2">"devtools::install_version('pkgdown', version='2.0.1', repos='https://cloud.r-project.org')"</span>
+<span class="nv">$ </span><span class="nb">sudo </span>Rscript <span class="nt">-e</span> <span class="s2">"devtools::install_version('preferably', version='0.4', repos='https://cloud.r-project.org')"</span>
+</code></pre></div></div>
+
+<p>Note: Other versions of roxygen2 might work in SparkR documentation generation but <code class="language-plaintext highlighter-rouge">RoxygenNote</code> field in <code class="language-plaintext highlighter-rouge">$SPARK_HOME/R/pkg/DESCRIPTION</code> is 7.1.2, which is updated if the version is mismatched.</p>
+
+<h2 id="generating-the-documentation-html">Generating the Documentation HTML</h2>
+
+<p>We include the Spark documentation as part of the source (as opposed to using a hosted wiki, such as
+the github wiki, as the definitive documentation) to enable the documentation to evolve along with
+the source code and be captured by revision control (currently git). This way the code automatically
+includes the version of the documentation that is relevant regardless of which version or release
+you have checked out or downloaded.</p>
+
+<p>In this directory you will find text files formatted using Markdown, with an “.md” suffix. You can
+read those text files directly if you want. Start with <code class="language-plaintext highlighter-rouge">index.md</code>.</p>
+
+<p>Execute <code class="language-plaintext highlighter-rouge">SKIP_API=1 bundle exec jekyll build</code> from the <code class="language-plaintext highlighter-rouge">docs/</code> directory to compile the site. Compiling the site with
+Jekyll will create a directory called <code class="language-plaintext highlighter-rouge">_site</code> containing <code class="language-plaintext highlighter-rouge">index.html</code> as well as the rest of the
+compiled files.</p>
+
+<div class="language-sh highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="nv">$ </span><span class="nb">cd </span>docs
+<span class="c"># Skip generating API docs (which takes a while)</span>
+<span class="nv">$ SKIP_API</span><span class="o">=</span>1 bundle <span class="nb">exec </span>jekyll build
+</code></pre></div></div>
+
+<p>You can also generate the default Jekyll build with API Docs as follows:</p>
+
+<div class="language-sh highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="nv">$ </span>bundle <span class="nb">exec </span>jekyll build
+
+<span class="c"># Serve content locally on port 4000</span>
+<span class="nv">$ </span>bundle <span class="nb">exec </span>jekyll serve <span class="nt">--watch</span>
+
+<span class="c"># Build the site with extra features used on the live page</span>
+<span class="nv">$ PRODUCTION</span><span class="o">=</span>1 bundle <span class="nb">exec </span>jekyll build
+</code></pre></div></div>
+
+<h2 id="api-docs-scaladoc-javadoc-sphinx-roxygen2-mkdocs">API Docs (Scaladoc, Javadoc, Sphinx, roxygen2, MkDocs)</h2>
+
+<p>You can build just the Spark scaladoc and javadoc by running <code class="language-plaintext highlighter-rouge">./build/sbt unidoc</code> from the <code class="language-plaintext highlighter-rouge">$SPARK_HOME</code> directory.</p>
+
+<p>Similarly, you can build just the PySpark docs by running <code class="language-plaintext highlighter-rouge">make html</code> from the
+<code class="language-plaintext highlighter-rouge">$SPARK_HOME/python/docs</code> directory. Documentation is only generated for classes that are listed as
+public in <code class="language-plaintext highlighter-rouge">__init__.py</code>. The SparkR docs can be built by running <code class="language-plaintext highlighter-rouge">$SPARK_HOME/R/create-docs.sh</code>, and
+the SQL docs can be built by running <code class="language-plaintext highlighter-rouge">$SPARK_HOME/sql/create-docs.sh</code>
+after <a href="https://github.com/apache/spark#building-spark">building Spark</a> first.</p>
+
+<p>When you run <code class="language-plaintext highlighter-rouge">bundle exec jekyll build</code> in the <code class="language-plaintext highlighter-rouge">docs</code> directory, it will also copy over the scaladoc and javadoc for the various
+Spark subprojects into the <code class="language-plaintext highlighter-rouge">docs</code> directory (and then also into the <code class="language-plaintext highlighter-rouge">_site</code> directory). We use a
+jekyll plugin to run <code class="language-plaintext highlighter-rouge">./build/sbt unidoc</code> before building the site so if you haven’t run it (recently) it
+may take some time as it generates all of the scaladoc and javadoc using <a href="https://github.com/sbt/sbt-unidoc">Unidoc</a>.
+The jekyll plugin also generates the PySpark docs using <a href="http://sphinx-doc.org/">Sphinx</a>, SparkR docs
+using <a href="https://cran.r-project.org/web/packages/roxygen2/index.html">roxygen2</a> and SQL docs
+using <a href="https://www.mkdocs.org/">MkDocs</a>.</p>
+
+<p>NOTE: To skip the step of building and copying over the Scala, Java, Python, R and SQL API docs, run <code class="language-plaintext highlighter-rouge">SKIP_API=1
+bundle exec jekyll build</code>. In addition, <code class="language-plaintext highlighter-rouge">SKIP_SCALADOC=1</code>, <code class="language-plaintext highlighter-rouge">SKIP_PYTHONDOC=1</code>, <code class="language-plaintext highlighter-rouge">SKIP_RDOC=1</code> and <code class="language-plaintext highlighter-rouge">SKIP_SQLDOC=1</code> can be used
+to skip a single step of the corresponding language. <code class="language-plaintext highlighter-rouge">SKIP_SCALADOC</code> indicates skipping both the Scala and Java docs.</p>
diff --git a/site/docs/3.4.1/api/R/404.html b/site/docs/3.4.1/api/R/404.html
new file mode 100644
index 0000000000..8b1be1ff70
--- /dev/null
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+ <img src="" class="logo" alt=""><h1>SparkR - Practical Guide</h1>
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+
+
+ <div class="d-none name"><code>sparkr-vignettes.Rmd</code></div>
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+
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+<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 3.4.1, 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.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 3.4.1"</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>
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