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<!DOCTYPE html>
<html lang="en"><head>
<script src="index_files/libs/clipboard/clipboard.min.js"></script>
<script src="index_files/libs/quarto-html/tabby.min.js"></script>
<script src="index_files/libs/quarto-html/popper.min.js"></script>
<script src="index_files/libs/quarto-html/tippy.umd.min.js"></script>
<link href="index_files/libs/quarto-html/tippy.css" rel="stylesheet">
<link href="index_files/libs/quarto-html/light-border.css" rel="stylesheet">
<link href="index_files/libs/quarto-html/quarto-html.min.css" rel="stylesheet" data-mode="light">
<link href="index_files/libs/quarto-html/quarto-syntax-highlighting.css" rel="stylesheet" id="quarto-text-highlighting-styles"><meta charset="utf-8">
<meta name="generator" content="quarto-1.4.549">
<meta name="author" content="Javier Orraca-Deatcurrr.is/ArrowHackathonTalk">
<title>ETL with Arrow and DuckDB</title>
<meta name="apple-mobile-web-app-capable" content="yes">
<meta name="apple-mobile-web-app-status-bar-style" content="black-translucent">
<meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no, minimal-ui">
<link rel="stylesheet" href="index_files/libs/revealjs/dist/reset.css">
<link rel="stylesheet" href="index_files/libs/revealjs/dist/reveal.css">
<style>
code{white-space: pre-wrap;}
span.smallcaps{font-variant: small-caps;}
div.columns{display: flex; gap: min(4vw, 1.5em);}
div.column{flex: auto; overflow-x: auto;}
div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
ul.task-list{list-style: none;}
ul.task-list li input[type="checkbox"] {
width: 0.8em;
margin: 0 0.8em 0.2em -1em; /* quarto-specific, see https://github.com/quarto-dev/quarto-cli/issues/4556 */
vertical-align: middle;
}
/* CSS for syntax highlighting */
pre > code.sourceCode { white-space: pre; position: relative; }
pre > code.sourceCode > span { line-height: 1.25; }
pre > code.sourceCode > span:empty { height: 1.2em; }
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code.sourceCode > span { color: inherit; text-decoration: inherit; }
div.sourceCode { margin: 1em 0; }
pre.sourceCode { margin: 0; }
@media screen {
div.sourceCode { overflow: auto; }
}
@media print {
pre > code.sourceCode { white-space: pre-wrap; }
pre > code.sourceCode > span { text-indent: -5em; padding-left: 5em; }
}
pre.numberSource code
{ counter-reset: source-line 0; }
pre.numberSource code > span
{ position: relative; left: -4em; counter-increment: source-line; }
pre.numberSource code > span > a:first-child::before
{ content: counter(source-line);
position: relative; left: -1em; text-align: right; vertical-align: baseline;
border: none; display: inline-block;
-webkit-touch-callout: none; -webkit-user-select: none;
-khtml-user-select: none; -moz-user-select: none;
-ms-user-select: none; user-select: none;
padding: 0 4px; width: 4em;
color: #aaaaaa;
}
pre.numberSource { margin-left: 3em; border-left: 1px solid #aaaaaa; padding-left: 4px; }
div.sourceCode
{ color: #003b4f; background-color: #f1f3f5; }
@media screen {
pre > code.sourceCode > span > a:first-child::before { text-decoration: underline; }
}
code span { color: #003b4f; } /* Normal */
code span.al { color: #ad0000; } /* Alert */
code span.an { color: #5e5e5e; } /* Annotation */
code span.at { color: #657422; } /* Attribute */
code span.bn { color: #ad0000; } /* BaseN */
code span.bu { } /* BuiltIn */
code span.cf { color: #003b4f; } /* ControlFlow */
code span.ch { color: #20794d; } /* Char */
code span.cn { color: #8f5902; } /* Constant */
code span.co { color: #5e5e5e; } /* Comment */
code span.cv { color: #5e5e5e; font-style: italic; } /* CommentVar */
code span.do { color: #5e5e5e; font-style: italic; } /* Documentation */
code span.dt { color: #ad0000; } /* DataType */
code span.dv { color: #ad0000; } /* DecVal */
code span.er { color: #ad0000; } /* Error */
code span.ex { } /* Extension */
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code span.fu { color: #4758ab; } /* Function */
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code span.kw { color: #003b4f; } /* Keyword */
code span.op { color: #5e5e5e; } /* Operator */
code span.ot { color: #003b4f; } /* Other */
code span.pp { color: #ad0000; } /* Preprocessor */
code span.sc { color: #5e5e5e; } /* SpecialChar */
code span.ss { color: #20794d; } /* SpecialString */
code span.st { color: #20794d; } /* String */
code span.va { color: #111111; } /* Variable */
code span.vs { color: #20794d; } /* VerbatimString */
code span.wa { color: #5e5e5e; font-style: italic; } /* Warning */
</style>
<link rel="stylesheet" href="index_files/libs/revealjs/dist/theme/quarto.css">
<link href="index_files/libs/revealjs/plugin/quarto-line-highlight/line-highlight.css" rel="stylesheet">
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<link href="index_files/libs/revealjs/plugin/reveal-codewindow/codewindow.css" rel="stylesheet">
<link href="index_files/libs/revealjs/plugin/quarto-support/footer.css" rel="stylesheet">
<style type="text/css">
.callout {
margin-top: 1em;
margin-bottom: 1em;
border-radius: .25rem;
}
.callout.callout-style-simple {
padding: 0em 0.5em;
border-left: solid #acacac .3rem;
border-right: solid 1px silver;
border-top: solid 1px silver;
border-bottom: solid 1px silver;
display: flex;
}
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border-left: solid #acacac .3rem;
border-right: solid 1px silver;
border-top: solid 1px silver;
border-bottom: solid 1px silver;
}
.callout .callout-body-container {
flex-grow: 1;
}
.callout.callout-style-simple .callout-body {
font-size: 1rem;
font-weight: 400;
}
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font-size: 0.9rem;
font-weight: 400;
}
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margin-top: 0.2em;
}
.callout:not(.callout-titled) .callout-body {
display: flex;
}
.callout:not(.no-icon).callout-titled.callout-style-simple .callout-content {
padding-left: 1.6em;
}
.callout.callout-titled .callout-header {
padding-top: 0.2em;
margin-bottom: -0.2em;
}
.callout.callout-titled .callout-title p {
margin-top: 0.5em;
margin-bottom: 0.5em;
}
.callout.callout-titled.callout-style-simple .callout-content p {
margin-top: 0;
}
.callout.callout-titled.callout-style-default .callout-content p {
margin-top: 0.7em;
}
.callout.callout-style-simple div.callout-title {
border-bottom: none;
font-size: .9rem;
font-weight: 600;
opacity: 75%;
}
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border-bottom: none;
font-weight: 600;
opacity: 85%;
font-size: 0.9rem;
padding-left: 0.5em;
padding-right: 0.5em;
}
.callout.callout-style-default div.callout-content {
padding-left: 0.5em;
padding-right: 0.5em;
}
.callout.callout-style-simple .callout-icon::before {
height: 1rem;
width: 1rem;
display: inline-block;
content: "";
background-repeat: no-repeat;
background-size: 1rem 1rem;
}
.callout.callout-style-default .callout-icon::before {
height: 0.9rem;
width: 0.9rem;
display: inline-block;
content: "";
background-repeat: no-repeat;
background-size: 0.9rem 0.9rem;
}
.callout-title {
display: flex
}
.callout-icon::before {
margin-top: 1rem;
padding-right: .5rem;
}
.callout.no-icon::before {
display: none !important;
}
.callout.callout-titled .callout-body > .callout-content > :last-child {
padding-bottom: 0.5rem;
margin-bottom: 0;
}
.callout.callout-titled .callout-icon::before {
margin-top: .5rem;
padding-right: .5rem;
}
.callout:not(.callout-titled) .callout-icon::before {
margin-top: 1rem;
padding-right: .5rem;
}
/* Callout Types */
div.callout-note {
border-left-color: #4582ec !important;
}
div.callout-note .callout-icon::before {
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}
div.callout-note.callout-style-default .callout-title {
background-color: #dae6fb
}
div.callout-important {
border-left-color: #d9534f !important;
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<div class="reveal">
<div class="slides">
<section id="title-slide" data-background-color="#000000" data-background-image="images/SoCal_RUG_logo.png" data-background-position="bottom 50px right 50px" data-background-size="19%" class="quarto-title-block center">
<h1 class="title">ETL with <font color="#fff100">Arrow</font> and <font color="#fff100">DuckDB</font></h1>
<p class="subtitle">SoCal RUG x UCI Hackathon - 2024.04.27</p>
<div class="quarto-title-authors">
<div class="quarto-title-author">
<div class="quarto-title-author-name">
<font color="#fff100">Javier Orraca-Deatcu</font><br><a href="https://rrr.is/ArrowHackathonTalk">rrr.is/ArrowHackathonTalk</a>
</div>
</div>
</div>
</section>
<section>
<section id="etl-basics-recap" class="title-slide slide level1 center">
<h1>1. ETL: Basics Recap</h1>
</section>
<section id="basic-etl-with-r-python" class="slide level2">
<h2>Basic ETL with R & Python</h2>
<h3 id="rs-dplyr">R’s <code>dplyr</code></h3>
<ul>
<li><code>dplyr</code> is a data manipulation package for R that offers SQL-like functions such as <code>select()</code>, <code>filter()</code>, <code>summarize()</code>, and more to simplify data tasks</li>
</ul>
<h3 id="pythons-pandas">Python’s <code>pandas</code></h3>
<ul>
<li><code>pandas</code> is a data analysis library in Python that supports the same kind of selecting, filtering, and manipulation tasks</li>
</ul>
<div class="footer">
<p><a href="https://dplyr.tidyverse.org/">dplyr</a> for R | <a href="https://pandas.pydata.org/">pandas</a> for Python</p>
</div>
</section>
<section id="basic-etl-with-r-python-1" class="slide level2">
<h2>Basic ETL with R & Python</h2>
<ul>
<li>Both libraries are essential for data manipulation and are designed to be intuitive and performant</li>
</ul>
<div class="fragment">
<ul>
<li>but first…</li>
</ul>
<div class="footer">
<p><a href="https://dplyr.tidyverse.org/">dplyr</a> for R | <a href="https://pandas.pydata.org/">pandas</a> for Python</p>
</div>
</div>
</section>
<section id="section" class="slide level2 center">
<h2></h2>
<div class="r-fit-text">
<p>we need</p>
</div>
</section>
<section id="section-1" class="slide level2 center">
<h2></h2>
<div class="r-fit-text">
<p>data!</p>
</div>
</section>
<section id="palmer-penguins" class="slide level2">
<h2>Palmer Penguins</h2>
<img data-src="images/lter_penguins.png" alt="Artwork by Allison Horst" class="r-stretch quarto-figure-center"><p class="caption">Artwork by Allison Horst | <a href="https://allisonhorst.github.io/palmerpenguins" class="uri">https://allisonhorst.github.io/palmerpenguins</a></p><div class="footer">
<p><code>palmerpenguins</code> | Package available on <a href="https://dplyr.tidyverse.org/">CRAN for R</a> and <a href="https://pypi.org/project/palmerpenguins/">PyPI for Python</a></p>
</div>
</section>
<section id="palmer-penguins-1" class="slide level2">
<h2>Palmer Penguins</h2>
<ul>
<li>The following slides show examples in R and Python that make use of the <code>palmerpenguins</code> package</li>
<li>This data contains measurements for three penguin species observed from 2007-2009 on three islands in the Palmer Archipelago, Antarctica</li>
<li>The data was collected by the <a href="https://pal.lternet.edu/">Palmer Station Long Term Ecological Research Program</a>, part of the <a href="https://lternet.edu/">US Long Term Ecological Research Network</a></li>
</ul>
<div class="footer">
<p><code>palmerpenguins</code> | Package available on <a href="https://dplyr.tidyverse.org/">CRAN for R</a> and <a href="https://pypi.org/project/palmerpenguins/">PyPI for Python</a></p>
</div>
</section>
<section id="palmer-penguins-2" class="slide level2">
<h2>Palmer Penguins</h2>
<ul>
<li>Install <code>palmerpenguins</code> and load the curated data set</li>
</ul>
<div class="fragment">
<div class="panel-tabset">
<ul id="tabset-1" class="panel-tabset-tabby"><li><a data-tabby-default="" href="#tabset-1-1">R</a></li><li><a href="#tabset-1-2">Python</a></li></ul>
<div class="tab-content">
<div id="tabset-1-1">
<div class="cell">
<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb1-1"><a href=""></a><span class="fu">install.packages</span>(<span class="st">"palmerpenguins"</span>)</span>
<span id="cb1-2"><a href=""></a></span>
<span id="cb1-3"><a href=""></a><span class="fu">library</span>(palmerpenguins)</span>
<span id="cb1-4"><a href=""></a></span>
<span id="cb1-5"><a href=""></a>penguins_df <span class="ot"><-</span> palmerpenguins<span class="sc">::</span>penguins</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</div>
<div id="tabset-1-2">
<div class="cell">
<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode numberSource python number-lines code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href=""></a>pip install palmerpenguins</span>
<span id="cb2-2"><a href=""></a></span>
<span id="cb2-3"><a href=""></a><span class="im">from</span> palmerpenguins <span class="im">import</span> load_penguins</span>
<span id="cb2-4"><a href=""></a></span>
<span id="cb2-5"><a href=""></a>penguins_df <span class="op">=</span> load_penguins()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</div>
</div>
</div>
<div class="footer">
<p><code>palmerpenguins</code> | Package available on <a href="https://dplyr.tidyverse.org/">CRAN for R</a> and <a href="https://pypi.org/project/palmerpenguins/">PyPI for Python</a></p>
</div>
</div>
</section>
<section id="take-a-glimpse-at-the-data" class="slide level2">
<h2>Take a <code>glimpse()</code> at the Data</h2>
<div class="panel-tabset">
<ul id="tabset-2" class="panel-tabset-tabby"><li><a data-tabby-default="" href="#tabset-2-1">R</a></li><li><a href="#tabset-2-2">Python</a></li></ul>
<div class="tab-content">
<div id="tabset-2-1">
<div class="cell">
<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb3-1"><a href=""></a><span class="fu">library</span>(dplyr)</span>
<span id="cb3-2"><a href=""></a></span>
<span id="cb3-3"><a href=""></a><span class="fu">glimpse</span>(penguins_df)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code>Rows: 344
Columns: 8
$ species <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…
$ island <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…
$ bill_length_mm <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …
$ bill_depth_mm <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …
$ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…
$ body_mass_g <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …
$ sex <fct> male, female, female, NA, female, male, female, male…
$ year <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…</code></pre>
</div>
</div>
</div>
<div id="tabset-2-2">
<div class="cell">
<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode numberSource python number-lines code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href=""></a><span class="bu">print</span>(penguins_df.head())</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</div>
</div>
</div>
<div class="footer">
<p><code>palmerpenguins</code> | Package available on <a href="https://dplyr.tidyverse.org/">CRAN for R</a> and <a href="https://pypi.org/project/palmerpenguins/">PyPI for Python</a></p>
</div>
</section>
<section id="functions-well-explore" class="slide level2">
<h2>Functions We’ll Explore</h2>
<div class="columns">
<div class="column" style="width:50%;">
<ul>
<li>select</li>
<li>filter</li>
<li>arrange</li>
</ul>
</div><div class="column" style="width:50%;">
<ul>
<li>mutate</li>
<li>group_by</li>
<li>summarize</li>
</ul>
</div>
</div>
<div class="footer">
<p>dplyr | Learn more about <a href="https://dplyr.tidyverse.org/">dplyr</a></p>
</div>
</section>
<section id="select" class="slide level2">
<h2>select</h2>
<ul>
<li>Subset <em>columns</em> with the <code>select()</code> function</li>
</ul>
<div class="fragment">
<div class="panel-tabset">
<ul id="tabset-3" class="panel-tabset-tabby"><li><a data-tabby-default="" href="#tabset-3-1">R</a></li><li><a href="#tabset-3-2">Python</a></li></ul>
<div class="tab-content">
<div id="tabset-3-1">
<div class="cell">
<div class="sourceCode cell-code" id="cb6" data-code-line-numbers="1-4|2"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb6-1"><a href=""></a>penguins_mod <span class="ot"><-</span> penguins_df <span class="sc">|></span> </span>
<span id="cb6-2"><a href=""></a> <span class="fu">select</span>(species, sex)</span>
<span id="cb6-3"><a href=""></a></span>
<span id="cb6-4"><a href=""></a><span class="fu">glimpse</span>(penguins_mod)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code>Rows: 344
Columns: 2
$ species <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adelie…
$ sex <fct> male, female, female, NA, female, male, female, male, NA, NA, …</code></pre>
</div>
</div>
</div>
<div id="tabset-3-2">
<div class="cell">
<div class="sourceCode cell-code" id="cb8"><pre class="sourceCode numberSource python number-lines code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href=""></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
<span id="cb8-2"><a href=""></a></span>
<span id="cb8-3"><a href=""></a>penguins_mod <span class="op">=</span> penguins_df[[<span class="st">'species'</span>, <span class="st">'sex'</span>]]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</div>
</div>
</div>
<div class="footer">
<p>dplyr | Learn more about <a href="https://dplyr.tidyverse.org/articles/dplyr.html#select-columns-with-select">select()</a></p>
</div>
</div>
</section>
<section id="select-1" class="slide level2">
<h2>select</h2>
<ul>
<li>Using the column (or “variable”) names, <code>select()</code> let us easily subset the data</li>
</ul>
<img data-src="images/select.svg" class="r-stretch quarto-figure-center"><p class="caption">Image credit: Duke University <a href="https://intro2r.library.duke.edu/wrangle.html" class="uri">https://intro2r.library.duke.edu/wrangle.html</a></p><div class="footer">
<p>dplyr | Learn more about <a href="https://dplyr.tidyverse.org/articles/dplyr.html#select-columns-with-select">select()</a></p>
</div>
</section>
<section id="filter" class="slide level2">
<h2>filter</h2>
<ul>
<li>Subset <em>rows</em> with the <code>filter()</code> function</li>
</ul>
<div class="fragment">
<div class="panel-tabset">
<ul id="tabset-4" class="panel-tabset-tabby"><li><a data-tabby-default="" href="#tabset-4-1">R</a></li><li><a href="#tabset-4-2">Python</a></li></ul>
<div class="tab-content">
<div id="tabset-4-1">
<div class="cell">
<div class="sourceCode cell-code" id="cb9" data-code-line-numbers="1-5|2-3"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb9-1"><a href=""></a>penguins_mod <span class="ot"><-</span> penguins_df <span class="sc">|></span> </span>
<span id="cb9-2"><a href=""></a> <span class="fu">filter</span>(sex <span class="sc">==</span> <span class="st">"female"</span>,</span>
<span id="cb9-3"><a href=""></a> bill_length_mm <span class="sc"><=</span> <span class="dv">40</span>)</span>
<span id="cb9-4"><a href=""></a></span>
<span id="cb9-5"><a href=""></a><span class="fu">glimpse</span>(penguins_mod)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code>Rows: 66
Columns: 8
$ species <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…
$ island <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…
$ bill_length_mm <dbl> 39.5, 36.7, 38.9, 36.6, 38.7, 34.4, 37.8, 35.9, 35.3…
$ bill_depth_mm <dbl> 17.4, 19.3, 17.8, 17.8, 19.0, 18.4, 18.3, 19.2, 18.9…
$ flipper_length_mm <int> 186, 193, 181, 185, 195, 184, 174, 189, 187, 172, 17…
$ body_mass_g <int> 3800, 3450, 3625, 3700, 3450, 3325, 3400, 3800, 3800…
$ sex <fct> female, female, female, female, female, female, fema…
$ year <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…</code></pre>
</div>
</div>
</div>
<div id="tabset-4-2">
<div class="cell">
<div class="sourceCode cell-code" id="cb11"><pre class="sourceCode numberSource python number-lines code-with-copy"><code class="sourceCode python"><span id="cb11-1"><a href=""></a>penguins_mod <span class="op">=</span> penguins_df[</span>
<span id="cb11-2"><a href=""></a> (penguins_df[<span class="st">'sex'</span>] <span class="op">==</span> <span class="st">'female'</span>) <span class="op">&</span></span>
<span id="cb11-3"><a href=""></a> (penguins_df[<span class="st">'bill_length_mm'</span>] <span class="op"><=</span> <span class="dv">40</span>)</span>
<span id="cb11-4"><a href=""></a>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</div>
</div>
</div>
<div class="footer">
<p>dplyr | Learn more about <a href="https://dplyr.tidyverse.org/articles/dplyr.html#filter-rows-with-filter">filter()</a></p>
</div>
</div>
</section>
<section id="filter-1" class="slide level2">
<h2>filter</h2>
<ul>
<li>The <code>filter()</code> function let’s us keep rows where the provided expression(s) are <code>TRUE</code></li>
</ul>
<img data-src="images/filter_by_rows.svg" class="r-stretch quarto-figure-center"><p class="caption">Image credit: Duke University <a href="https://intro2r.library.duke.edu/wrangle.html" class="uri">https://intro2r.library.duke.edu/wrangle.html</a></p><div class="footer">
<p>dplyr | Learn more about <a href="https://dplyr.tidyverse.org/articles/dplyr.html#filter-rows-with-filter">filter()</a></p>
</div>
</section>
<section id="arrange" class="slide level2">
<h2>arrange</h2>
<ul>
<li>Sorting rows by column names can be accomplished with the <code>arrange()</code> function</li>
<li>In the example on the next slide, we’ll sort the data by <code>bill_length_mm</code> ascending and by <code>year</code> descending</li>
</ul>
<div class="footer">
<p>dplyr | Learn more about <a href="https://dplyr.tidyverse.org/articles/dplyr.html#arrange-rows-with-arrange">arrange()</a></p>
</div>
</section>
<section id="arrange-1" class="slide level2">
<h2>arrange</h2>
<div class="panel-tabset">
<ul id="tabset-5" class="panel-tabset-tabby"><li><a data-tabby-default="" href="#tabset-5-1">R</a></li><li><a href="#tabset-5-2">Python</a></li></ul>
<div class="tab-content">
<div id="tabset-5-1">
<div class="cell">
<div class="sourceCode cell-code" id="cb12" data-code-line-numbers="1-4|2"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb12-1"><a href=""></a>penguins_mod <span class="ot"><-</span> penguins_df <span class="sc">|></span> </span>
<span id="cb12-2"><a href=""></a> <span class="fu">arrange</span>(bill_length_mm, <span class="fu">desc</span>(year))</span>
<span id="cb12-3"><a href=""></a></span>
<span id="cb12-4"><a href=""></a><span class="fu">glimpse</span>(penguins_mod)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code>Rows: 344
Columns: 8
$ species <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…
$ island <fct> Dream, Dream, Torgersen, Dream, Torgersen, Torgersen…
$ bill_length_mm <dbl> 32.1, 33.1, 33.5, 34.0, 34.1, 34.4, 34.5, 34.6, 34.6…
$ bill_depth_mm <dbl> 15.5, 16.1, 19.0, 17.1, 18.1, 18.4, 18.1, 17.2, 21.1…
$ flipper_length_mm <int> 188, 178, 190, 185, 193, 184, 187, 189, 198, 192, 19…
$ body_mass_g <int> 3050, 2900, 3600, 3400, 3475, 3325, 2900, 3200, 4400…
$ sex <fct> female, female, female, female, NA, female, female, …
$ year <int> 2009, 2008, 2008, 2008, 2007, 2007, 2008, 2008, 2007…</code></pre>
</div>
</div>
</div>
<div id="tabset-5-2">
<div class="cell">
<div class="sourceCode cell-code" id="cb14"><pre class="sourceCode numberSource python number-lines code-with-copy"><code class="sourceCode python"><span id="cb14-1"><a href=""></a>penguins_mod <span class="op">=</span> penguins_df.sort_values(</span>
<span id="cb14-2"><a href=""></a> by<span class="op">=</span>[<span class="st">'bill_length_mm'</span>, <span class="st">'year'</span>], </span>
<span id="cb14-3"><a href=""></a> ascending<span class="op">=</span>[<span class="va">True</span>, <span class="va">False</span>]</span>
<span id="cb14-4"><a href=""></a>)</span>
<span id="cb14-5"><a href=""></a></span>
<span id="cb14-6"><a href=""></a><span class="bu">print</span>(penguins_mod.head())</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</div>
</div>
</div>
<div class="footer">
<p>dplyr | Learn more about <a href="https://dplyr.tidyverse.org/articles/dplyr.html#arrange-rows-with-arrange">arrange()</a></p>
</div>
</section>
<section id="arrange-2" class="slide level2">
<h2>arrange</h2>
<ul>
<li>The <code>arrange()</code> function sorts rows in order of one or more columns</li>
<li>You can arrange in ascending order (the default) or descending order by wrapping the column name in <code>desc()</code></li>
</ul>
<img data-src="images/arrange_rows.svg" class="r-stretch quarto-figure-center"><p class="caption">Image credit: Duke University <a href="https://intro2r.library.duke.edu/wrangle.html" class="uri">https://intro2r.library.duke.edu/wrangle.html</a></p><div class="footer">
<p>dplyr | Learn more about <a href="https://dplyr.tidyverse.org/articles/dplyr.html#arrange-rows-with-arrange">arrange()</a></p>
</div>
</section>
<section id="mutate" class="slide level2">
<h2>mutate</h2>
<ul>
<li>Create or modify columns with <code>mutate()</code></li>
</ul>
<div class="fragment">
<div class="panel-tabset">
<ul id="tabset-6" class="panel-tabset-tabby"><li><a data-tabby-default="" href="#tabset-6-1">R</a></li><li><a href="#tabset-6-2">Python</a></li></ul>
<div class="tab-content">
<div id="tabset-6-1">
<div class="cell">
<div class="sourceCode cell-code" id="cb15" data-code-line-numbers="1-4|2"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb15-1"><a href=""></a>penguins_mod <span class="ot"><-</span> penguins_df <span class="sc">|></span> </span>
<span id="cb15-2"><a href=""></a> <span class="fu">mutate</span>(<span class="at">body_mass_kg =</span> body_mass_g <span class="sc">/</span> <span class="dv">1000</span>)</span>
<span id="cb15-3"><a href=""></a></span>
<span id="cb15-4"><a href=""></a><span class="fu">glimpse</span>(penguins_mod)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code>Rows: 344
Columns: 9
$ species <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…
$ island <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…
$ bill_length_mm <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …
$ bill_depth_mm <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …
$ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…
$ body_mass_g <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …
$ sex <fct> male, female, female, NA, female, male, female, male…
$ year <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…
$ body_mass_kg <dbl> 3.750, 3.800, 3.250, NA, 3.450, 3.650, 3.625, 4.675,…</code></pre>
</div>
</div>
</div>
<div id="tabset-6-2">
<div class="cell">
<div class="sourceCode cell-code" id="cb17"><pre class="sourceCode numberSource python number-lines code-with-copy"><code class="sourceCode python"><span id="cb17-1"><a href=""></a>penguins_mod <span class="op">=</span> penguins_df.assign(</span>
<span id="cb17-2"><a href=""></a> body_mass_kg <span class="op">=</span> penguins_df[<span class="st">'body_mass_g'</span>] <span class="op">/</span> <span class="dv">1000</span></span>
<span id="cb17-3"><a href=""></a>)</span>
<span id="cb17-4"><a href=""></a></span>
<span id="cb17-5"><a href=""></a><span class="bu">print</span>(penguins_mod.head())</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</div>
</div>
</div>
<div class="footer">
<p>dplyr | Learn more about <a href="https://dplyr.tidyverse.org/articles/dplyr.html#add-new-columns-with-mutate">mutate()</a></p>
</div>
</div>
</section>
<section id="mutate-1" class="slide level2">
<h2>mutate</h2>
<ul>
<li>Add new columns to your dataframe or tibble with <code>mutate()</code></li>
<li>dplyr’s <code>mutate()</code> is similar to base R’s <code>transform()</code> with the added benefit of being able to reference columns you’ve just created</li>
</ul>
<img data-src="images/mutate.svg" class="r-stretch quarto-figure-center"><p class="caption">Image credit: Duke University <a href="https://intro2r.library.duke.edu/wrangle.html" class="uri">https://intro2r.library.duke.edu/wrangle.html</a></p><div class="footer">
<p>dplyr | Learn more about <a href="https://dplyr.tidyverse.org/articles/dplyr.html#add-new-columns-with-mutate">mutate()</a></p>
</div>
</section>
<section id="group_by-and-summarize" class="slide level2">
<h2>group_by and summarize</h2>
<ul>
<li><code>group_by()</code> variables and <code>summarize()</code> results</li>
</ul>
<div class="fragment">
<div class="panel-tabset">
<ul id="tabset-7" class="panel-tabset-tabby"><li><a data-tabby-default="" href="#tabset-7-1">R</a></li><li><a href="#tabset-7-2">Python</a></li></ul>
<div class="tab-content">
<div id="tabset-7-1">
<div class="cell">
<div class="sourceCode cell-code" id="cb18" data-code-line-numbers="1-5|2-3"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb18-1"><a href=""></a>penguins_mod <span class="ot"><-</span> penguins_df <span class="sc">|></span> </span>
<span id="cb18-2"><a href=""></a> <span class="fu">group_by</span>(sex) <span class="sc">|></span> </span>
<span id="cb18-3"><a href=""></a> <span class="fu">summarize</span>(<span class="at">mean_mass_g =</span> <span class="fu">mean</span>(body_mass_g, <span class="at">na.rm =</span> <span class="cn">TRUE</span>))</span>
<span id="cb18-4"><a href=""></a></span>
<span id="cb18-5"><a href=""></a><span class="fu">glimpse</span>(penguins_mod)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code>Rows: 3
Columns: 2
$ sex <fct> female, male, NA
$ mean_mass_g <dbl> 3862.273, 4545.685, 4005.556</code></pre>
</div>
</div>
</div>
<div id="tabset-7-2">
<div class="cell">
<div class="sourceCode cell-code" id="cb20"><pre class="sourceCode numberSource python number-lines code-with-copy"><code class="sourceCode python"><span id="cb20-1"><a href=""></a>grouped <span class="op">=</span> penguins_df.groupby(<span class="st">'sex'</span>)[<span class="st">'body_mass_g'</span>].transform(<span class="kw">lambda</span> x: x.mean(skipna<span class="op">=</span><span class="va">True</span>))</span>
<span id="cb20-2"><a href=""></a></span>
<span id="cb20-3"><a href=""></a>penguins_mod <span class="op">=</span> penguins_df.assign(mean_mass_g<span class="op">=</span>grouped)</span>
<span id="cb20-4"><a href=""></a></span>
<span id="cb20-5"><a href=""></a><span class="bu">print</span>(penguins_mod.head())</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</div>
</div>
</div>
<div class="footer">
<p>dplyr | Learn more about <a href="https://dplyr.tidyverse.org/articles/dplyr.html#summarize">summarize()</a></p>
</div>
</div>
</section>
<section id="group_by-and-summarize-1" class="slide level2">
<h2>group_by and summarize</h2>
<ul>
<li><code>summarize()</code> creates a new data frame</li>
<li>it returns one row for each combination of grouping variables</li>
<li>if there are no grouping variables, the output will have a single row summarizing all observations in the input</li>
</ul>
<div class="footer">
<p>dplyr | Learn more about <a href="https://dplyr.tidyverse.org/articles/dplyr.html#summarize">summarize()</a></p>
</div>
</section>
<section id="chaining-methods" class="slide level2">
<h2>Chaining Methods</h2>
<ul>
<li>In R, the native pipe operator (<code>|></code>) or magrittr pipe (<code>%>%</code>) can be used to chain or pipe dplyr functions together</li>
</ul>
<div class="cell">
<div class="sourceCode cell-code" id="cb21"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb21-1"><a href=""></a>penguins_mod <span class="ot"><-</span> penguins_df <span class="sc">|></span> </span>
<span id="cb21-2"><a href=""></a> <span class="fu">select</span>(sex, bill_length_mm, body_mass_g) <span class="sc">|></span> </span>
<span id="cb21-3"><a href=""></a> <span class="fu">filter</span>(sex <span class="sc">==</span> <span class="st">"female"</span>,</span>
<span id="cb21-4"><a href=""></a> bill_length_mm <span class="sc">></span> <span class="dv">30</span>) <span class="sc">|></span> </span>
<span id="cb21-5"><a href=""></a> <span class="fu">group_by</span>(sex) <span class="sc">|></span> </span>
<span id="cb21-6"><a href=""></a> <span class="fu">summarize</span>(<span class="at">mean_mass_g =</span> <span class="fu">mean</span>(body_mass_g, <span class="at">na.rm =</span> <span class="cn">TRUE</span>))</span>
<span id="cb21-7"><a href=""></a></span>
<span id="cb21-8"><a href=""></a><span class="fu">glimpse</span>(penguins_mod)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code>Rows: 1
Columns: 2
$ sex <fct> female
$ mean_mass_g <dbl> 3862.273</code></pre>
</div>
</div>
<div class="footer">
<p>dplyr | Learn more about <a href="https://dplyr.tidyverse.org/articles/dplyr.html#the-pipe">piping</a></p>
</div>
</section>
<section id="chaining-methods-1" class="slide level2">
<h2>Chaining Methods</h2>
<ul>
<li>In Python, the period (<code>.</code>) is used as the chain functions together</li>
</ul>
<div class="cell">
<div class="sourceCode cell-code" id="cb23"><pre class="sourceCode numberSource python number-lines code-with-copy"><code class="sourceCode python"><span id="cb23-1"><a href=""></a>selected <span class="op">=</span> penguins_df[[<span class="st">'sex'</span>, <span class="st">'bill_length_mm'</span>, <span class="st">'body_mass_g'</span>]]</span>
<span id="cb23-2"><a href=""></a></span>
<span id="cb23-3"><a href=""></a>filtered <span class="op">=</span> selected[(selected[<span class="st">'sex'</span>] <span class="op">==</span> <span class="st">'female'</span>) <span class="op">&</span> (selected[<span class="st">'bill_length_mm'</span>] <span class="op">></span> <span class="dv">30</span>)]</span>
<span id="cb23-4"><a href=""></a></span>
<span id="cb23-5"><a href=""></a>penguins_mod <span class="op">=</span> filtered.groupby(<span class="st">'sex'</span>).agg(mean_mass_g<span class="op">=</span>(<span class="st">'body_mass_g'</span>, <span class="st">'mean'</span>)).reset_index()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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</section></section>
<section>
<section id="arrow" class="title-slide slide level1 center">
<h1>2. Arrow</h1>
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<p>Arrow | Learn more at <a href="https://arrow.apache.org/" class="uri">https://arrow.apache.org/</a></p>
</div>
</section>
<section id="larger-than-memory-data" class="slide level2">
<h2>Larger-than-Memory Data</h2>
<p><br> <img data-src="images/apache_arrow.png"></p>
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<p>Arrow | Learn more at <a href="https://arrow.apache.org/" class="uri">https://arrow.apache.org/</a></p>
</div>
</section>
<section id="arrow-and-parquet" class="slide level2">
<h2>Arrow and Parquet <img data-src="hexes/arrow.png" class="absolute" style="top: -20px; right: 0px; width: 64px; height: 74.24px; "></h2>
<ul>
<li>Columnar memory format for flat data</li>
<li>Ultra-fast read times from Parquet files</li>
<li>Easily convert data.frames and tibbles to Arrow tables in-line</li>
<li>Plays well with Pandas (Python) and dplyr (R)</li>
<li>Arrow will soon be able to process nested list data</li>
</ul>
<aside class="notes">
<p>Libraries are available for C, C++, C#, Go, Java, JavaScript, Julia, MATLAB, Python, R, Ruby, and Rust</p>
<p>Arrow is organized for efficient analytic operations on modern hardware like CPUs and GPUs.</p>
<p>The Arrow memory format also supports zero-copy reads for lightning-fast data access without serialization overhead</p>
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<p>Arrow | Learn more at <a href="https://arrow.apache.org/" class="uri">https://arrow.apache.org/</a></p>
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</section>
<section id="how-fast-is-fast" class="slide level2">
<h2>How fast is fast? <img data-src="hexes/arrow.png" class="absolute" style="top: -20px; right: 0px; width: 64px; height: 74.24px; "></h2>
<div>
<ul>
<li class="fragment">My personal laptop has 24 GB RAM</li>
<li class="fragment">To test Arrow’s capabilities, I read a <span class="fragment highlight-teal">40 GB dataset</span> with over <span class="fragment highlight-teal">1.1 billion rows</span> and 24 columns</li>
<li class="fragment">The <code>.parquet</code> dataset was partitioned by Year and Month (120 files)</li>
<li class="fragment">Important to note that my laptop would not be able to load this object entirely into memory as a data.frame or tibble given my laptop’s limited RAM</li>
</ul>
</div>
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<p>Arrow | Learn more at <a href="https://arrow.apache.org/" class="uri">https://arrow.apache.org/</a></p>
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</section>
<section id="benchmarking-read-times" class="slide level2">
<h2>Benchmarking Read Times <img data-src="hexes/ggplot2.png" class="absolute" style="top: -20px; right: 0px; width: 64px; height: 74.24px; "> <img data-src="hexes/arrow.png" class="absolute" style="top: -20px; right: 64px; width: 64px; height: 74.24px; "></h2>
<div class="cell">
<div class="sourceCode cell-code" id="cb24"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb24-1"><a href=""></a><span class="co"># Install full Arrow with S3 and GCS support:</span></span>
<span id="cb24-2"><a href=""></a><span class="co"># Sys.setenv("LIBARROW_MINIMAL" = FALSE)</span></span>
<span id="cb24-3"><a href=""></a><span class="co"># Sys.setenv("ARROW_S3" = "ON")</span></span>
<span id="cb24-4"><a href=""></a><span class="co"># Sys.setenv("ARROW_GCS" = "ON")</span></span>
<span id="cb24-5"><a href=""></a><span class="co"># install.packages("arrow")</span></span>
<span id="cb24-6"><a href=""></a></span>
<span id="cb24-7"><a href=""></a><span class="fu">library</span>(here)</span>
<span id="cb24-8"><a href=""></a><span class="fu">library</span>(arrow)</span>
<span id="cb24-9"><a href=""></a><span class="fu">library</span>(dplyr)</span>
<span id="cb24-10"><a href=""></a><span class="fu">library</span>(ggplot2)</span>
<span id="cb24-11"><a href=""></a><span class="fu">library</span>(bench)</span>
<span id="cb24-12"><a href=""></a></span>
<span id="cb24-13"><a href=""></a><span class="co"># NYC Taxi Data download (40 GB)</span></span>
<span id="cb24-14"><a href=""></a>data_path <span class="ot"><-</span> here<span class="sc">::</span><span class="fu">here</span>(<span class="st">"data/nyc-taxi"</span>)</span>
<span id="cb24-15"><a href=""></a></span>
<span id="cb24-16"><a href=""></a><span class="fu">open_dataset</span>(<span class="st">"s3://voltrondata-labs-datasets/nyc-taxi"</span>) <span class="sc">|></span></span>
<span id="cb24-17"><a href=""></a> <span class="fu">filter</span>(year <span class="sc">%in%</span> <span class="dv">2012</span><span class="sc">:</span><span class="dv">2021</span>) <span class="sc">|></span> </span>
<span id="cb24-18"><a href=""></a> <span class="fu">write_dataset</span>(data_path, <span class="at">partitioning =</span> <span class="fu">c</span>(<span class="st">"year"</span>, <span class="st">"month"</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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<p>Arrow | Learn more at <a href="https://arrow.apache.org/" class="uri">https://arrow.apache.org/</a></p>
</div>
</section>
<section id="benchmarking-read-times-1" class="slide level2">
<h2>Benchmarking Read Times <img data-src="hexes/ggplot2.png" class="absolute" style="top: -20px; right: 0px; width: 64px; height: 74.24px; "> <img data-src="hexes/arrow.png" class="absolute" style="top: -20px; right: 64px; width: 64px; height: 74.24px; "></h2>
<div class="cell">
<div class="sourceCode cell-code" id="cb25"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb25-1"><a href=""></a><span class="co"># 2. Benchmark Read Times</span></span>
<span id="cb25-2"><a href=""></a>bnch <span class="ot"><-</span> bench<span class="sc">::</span><span class="fu">mark</span>(</span>
<span id="cb25-3"><a href=""></a> <span class="at">min_iterations =</span> <span class="dv">1000</span>,</span>
<span id="cb25-4"><a href=""></a> <span class="at">arrow =</span> <span class="fu">open_dataset</span>(here<span class="sc">::</span><span class="fu">here</span>(<span class="st">"data/nyc-taxi"</span>))</span>
<span id="cb25-5"><a href=""></a>)</span>
<span id="cb25-6"><a href=""></a></span>
<span id="cb25-7"><a href=""></a><span class="fu">autoplot</span>(bnch)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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<p>Arrow | Learn more at <a href="https://arrow.apache.org/" class="uri">https://arrow.apache.org/</a></p>
</div>
</section>
<section id="benchmarking-read-times-2" class="slide level2">
<h2>Benchmarking Read Times <img data-src="hexes/ggplot2.png" class="absolute" style="top: -20px; right: 0px; width: 64px; height: 74.24px; "> <img data-src="hexes/arrow.png" class="absolute" style="top: -20px; right: 64px; width: 64px; height: 74.24px; "></h2>
<ul>
<li>Results show read times from a 40GB parquet, 1.1 billion row dataset (benchmarked over 1,000 iterations)</li>
</ul>
<div class="quarto-figure quarto-figure-center">
<figure>
<p><img data-src="images/arrow_read.png" class="quarto-figure quarto-figure-center" style="width:55.0%"></p>
</figure>
</div>
<div class="footer">
<p>Arrow | Learn more at <a href="https://arrow.apache.org/" class="uri">https://arrow.apache.org/</a></p>
</div>
</section>
<section id="arrow-dplyr-benchmark" class="slide level2">
<h2>arrow + dplyr Benchmark <img data-src="hexes/ggplot2.png" class="absolute" style="top: -20px; right: 0px; width: 64px; height: 74.24px; "> <img data-src="hexes/dplyr.png" class="absolute" style="top: -20px; right: 64px; width: 64px; height: 74.24px; "> <img data-src="hexes/arrow.png" class="absolute" style="top: -20px; right: 128px; width: 64px; height: 74.24px; "></h2>
<div class="cell">
<div class="sourceCode cell-code" id="cb26"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb26-1"><a href=""></a><span class="co"># 1. Open Arrow connection to dataset (40 GB)</span></span>
<span id="cb26-2"><a href=""></a>nyc_taxi <span class="ot"><-</span> <span class="fu">open_dataset</span>(here<span class="sc">::</span><span class="fu">here</span>(<span class="st">"data/nyc-taxi"</span>))</span>
<span id="cb26-3"><a href=""></a></span>
<span id="cb26-4"><a href=""></a><span class="co"># 2. Benchmark dplyr pipeline</span></span>
<span id="cb26-5"><a href=""></a>bnch <span class="ot"><-</span> bench<span class="sc">::</span><span class="fu">mark</span>(</span>
<span id="cb26-6"><a href=""></a> <span class="at">min_iterations =</span> <span class="dv">10</span>,</span>
<span id="cb26-7"><a href=""></a> <span class="at">arrow =</span> nyc_taxi <span class="sc">|></span> </span>
<span id="cb26-8"><a href=""></a> dplyr<span class="sc">::</span><span class="fu">group_by</span>(year) <span class="sc">|></span> </span>
<span id="cb26-9"><a href=""></a> dplyr<span class="sc">::</span><span class="fu">summarise</span>(<span class="at">all_trips =</span> <span class="fu">n</span>(),</span>
<span id="cb26-10"><a href=""></a> <span class="at">shared_trips =</span> <span class="fu">sum</span>(passenger_count <span class="sc">></span> <span class="dv">1</span>, <span class="at">na.rm =</span> T)) <span class="sc">|></span></span>
<span id="cb26-11"><a href=""></a> dplyr<span class="sc">::</span><span class="fu">mutate</span>(<span class="at">pct_shared =</span> shared_trips <span class="sc">/</span> all_trips <span class="sc">*</span> <span class="dv">100</span>) <span class="sc">|></span> </span>
<span id="cb26-12"><a href=""></a> dplyr<span class="sc">::</span><span class="fu">collect</span>()</span>
<span id="cb26-13"><a href=""></a>)</span>
<span id="cb26-14"><a href=""></a></span>
<span id="cb26-15"><a href=""></a><span class="fu">autoplot</span>(bnch)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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