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index.html
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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-1.5.53">
<title>index</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; }
.sourceCode { overflow: visible; }
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 { display: inline-block; 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; font-weight: bold; } /* 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 */
code span.fl { color: #ad0000; } /* Float */
code span.fu { color: #4758ab; } /* Function */
code span.im { color: #00769e; } /* Import */
code span.in { color: #5e5e5e; } /* Information */
code span.kw { color: #003b4f; font-weight: bold; } /* 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">
<link href="index_files/libs/revealjs/plugin/reveal-menu/menu.css" rel="stylesheet">
<link href="index_files/libs/revealjs/plugin/reveal-menu/quarto-menu.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;
}
.callout.callout-style-default {
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;
}
.callout.callout-style-default .callout-body {
font-size: 0.9rem;
font-weight: 400;
}
.callout.callout-titled.callout-style-simple .callout-body {
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%;
}
.callout.callout-style-default div.callout-title {
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 {
background-image: url('data:image/png;base64,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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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</head>
<body class="quarto-light">
<div class="reveal">
<div class="slides">
<section id="introduction-to-arrow-in-r" class="slide level2">
<h2>Introduction to Arrow in R</h2>
<p>NEDs Workshop (18th July 2024)</p>
<div class="quarto-figure quarto-figure-center">
<figure>
<p><img data-src="images/logo.png" class="quarto-figure quarto-figure-center" width="415" height="500"></p>
</figure>
</div>
</section>
<section id="introductions" class="slide level2">
<h2>Introductions</h2>
<ul>
<li>Nic Crane</li>
</ul>
<p>Arrow contributor!</p>
<aside class="notes">
<p>Have participants introduce themselves in the chat</p>
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</section>
<section id="welcome" class="slide level2">
<h2>Welcome</h2>
<p>Today we’re going to cover:</p>
<ul>
<li>Working with larger-than-memory datasets with Arrow</li>
<li>How to get the best performance with your tabular data</li>
<li>Where to find more information</li>
</ul>
</section>
<section id="workshop-format" class="slide level2">
<h2>Workshop format</h2>
<ul>
<li>Slides available at <a href="https://tinyurl.com/introtoarrowneds" class="uri">https://tinyurl.com/introtoarrowneds</a></li>
<li>Follow-along coding</li>
<li>Time to ask questions</li>
</ul>
<aside class="notes">
<ul>
<li>feel free to ask questions as we go along!</li>
</ul>
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</section>
<section id="getting-set-up" class="slide level2">
<h2>Getting set up</h2>
<img data-src="images/newproj.png" class="r-stretch"><p>Repository URL: <a href="https://github.com/thisisnic/introtoarrowneds" class="uri">https://github.com/thisisnic/introtoarrowneds</a></p>
</section>
<section id="dataset-to-follow-along-with" class="slide level2">
<h2>Dataset to follow along with</h2>
<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">options</span>(<span class="at">timeout =</span> <span class="dv">18000</span>)</span>
<span id="cb1-2"><a href=""></a>curl<span class="sc">::</span><span class="fu">multi_download</span>(</span>
<span id="cb1-3"><a href=""></a> <span class="st">"https://r4ds.s3.us-west-2.amazonaws.com/seattle-library-checkouts.csv"</span>,</span>
<span id="cb1-4"><a href=""></a> <span class="st">"data/seattle-library-checkouts.csv"</span>,</span>
<span id="cb1-5"><a href=""></a> <span class="at">resume =</span> <span class="cn">TRUE</span></span>
<span id="cb1-6"><a href=""></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p>If your download stops partway through, you can stop and resume from the same place.</p>
<aside class="notes">
<ul>
<li>dataset from R For Data Science</li>
<li>run this from within the project you’ve got set up</li>
<li>first line of code increase the download timeout to 30 mins - essential when downloading larger datasets</li>
<li>if you don’t have the data yet, download it now - next section talking about arrow</li>
</ul>
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</section>
<section id="dataset-to-follow-along-with---tiny-version" class="slide level2">
<h2>Dataset to follow along with - tiny version</h2>
<div class="cell">
<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb2-1"><a href=""></a><span class="fu">options</span>(<span class="at">timeout =</span> <span class="dv">18000</span>)</span>
<span id="cb2-2"><a href=""></a>curl<span class="sc">::</span><span class="fu">multi_download</span>(</span>
<span id="cb2-3"><a href=""></a> <span class="st">"https://github.com/posit-conf-2023/arrow/releases/download/v0.1.0/seattle-library-checkouts-tiny.csv"</span>,</span>
<span id="cb2-4"><a href=""></a> <span class="st">"data/seattle-library-checkouts-tiny.csv"</span>,</span>
<span id="cb2-5"><a href=""></a> <span class="at">resume =</span> <span class="cn">TRUE</span></span>
<span id="cb2-6"><a href=""></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</section>
<section>
<section id="part-1---arrow" class="title-slide slide level1 center">
<h1>Part 1 - Arrow</h1>
<aside class="notes">
<ul>
<li>any questions before we get started?</li>
<li>this section will cover a bit of background info about Apache Arrow</li>
</ul>
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<section id="what-is-apache-arrow" class="slide level2">
<h2>What is Apache Arrow?</h2>
<div class="columns">
<div class="column" style="width:50%;">
<blockquote>
<p>A multi-language toolbox for accelerated data interchange and in-memory processing</p>
</blockquote>
</div><div class="column" style="width:50%;">
<blockquote>
<p>Arrow is designed to both improve the performance of analytical algorithms and the efficiency of moving data from one system or programming language to another</p>
</blockquote>
</div></div>
<div style="font-size: 70%;">
<p><a href="https://arrow.apache.org/overview/" class="uri">https://arrow.apache.org/overview/</a></p>
</div>
<aside class="notes">
<ul>
<li>TODO: add main points want to hit here</li>
</ul>
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</section>
<section id="apache-arrow-specification" class="slide level2">
<h2>Apache Arrow Specification</h2>
<p>In-memory <span style="background-color: #F3D1FF;">columnar format</span>: a <span style="background-color: #FFDCB7;">standardized, language-agnostic specification</span> for representing structured, <span style="background-color: #DCFFC9;">table-like datasets</span> in-memory.</p>
<p><br></p>
<p><img data-src="images/arrow-rectangle.png" class="absolute" style="left: 200px; "></p>
<aside class="notes">
<ul>
<li>brief overview of these points but coming back to later</li>
</ul>
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</section>
<section id="a-multi-language-toolbox" class="slide level2">
<h2>A Multi-Language Toolbox</h2>
<img data-src="images/arrow-libraries-structure.png" class="r-stretch"></section>
<section id="accelerated-data-interchange" class="slide level2">
<h2>Accelerated Data Interchange</h2>
<img data-src="images/data-interchange-with-arrow.png" class="r-stretch"><aside class="notes">
<ul>
<li>standardisation means multiple things speaking Arrow prevents copying back and forth</li>
</ul>
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</section>
<section id="accelerated-in-memory-processing" class="slide level2">
<h2>Accelerated In-Memory Processing</h2>
<p>Arrow’s Columnar Format is Fast</p>
<p><img data-src="images/columnar-fast.png" class="absolute" style="top: 120px; left: 200px; height: 550px; "></p>
<aside class="notes">
<ul>
<li>intro the toy dataset</li>
<li>memory buffers are a 1-dimension structure, not like a 2D table/data.frame</li>
<li>walk through this in the 2 diagrams</li>
<li>analytic workflows typically have filtering, grouping by columns etc; give examples</li>
<li>faster to scan adjacent areas than picking through it all + taking advantage of vectorization available on modern processes speeds up faster</li>
</ul>
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</section>
<section id="arrow" class="slide level2">
<h2>arrow 📦</h2>
<p><br></p>
<p><img data-src="images/arrow-r-pkg-highlights.png" class="absolute" style="top: 0px; left: 300px; width: 700px; height: 900px; "></p>
</section>
<section id="arrow-1" class="slide level2">
<h2>arrow 📦</h2>
<img data-src="images/arrow-read-write-updated.png" class="r-stretch"><aside class="notes">
<ul>
<li>different types of objects</li>
<li>different file formats</li>
<li>different storage locations</li>
</ul>
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</section></section>
<section>
<section id="part-2---working-with-arrow-datasets" class="title-slide slide level1 center">
<h1>Part 2 - Working with Arrow Datasets</h1>
<aside class="notes">
<ul>
<li>any questions so far?</li>
<li>maybe a poll - who’s worked with dplyr/arrow/parquet before?</li>
</ul>
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</section>
<section id="seattle-checkouts---big-csv" class="slide level2">
<h2>Seattle Checkouts - Big CSV</h2>
<p><img data-src="images/seattle-checkouts.png" class="absolute" style="top: 120px; left: 200px; height: 550px; "></p>
<div style="font-size: 70%;">
<p><a href="https://data.seattle.gov/Community/Checkouts-by-Title/tmmm-ytt6/about_data" class="uri">https://data.seattle.gov/Community/Checkouts-by-Title/tmmm-ytt6/about_data</a></p>
</div>
</section>
<section id="dataset-contents" class="slide level2">
<h2>Dataset contents</h2>
<p><img data-src="images/datapreview.png" height="550"></p>
<aside class="notes">
<ul>
<li>LIVE CODING
<ul>
<li>path to the data depending on where you’ve downloaded it</li>
<li>impact of data size on performance</li>
<li>walk through subcomponents of the output when print the <code>seattle_csv</code> object
<ul>
<li>explicitly mention the term “schema”</li>
<li>string and character are direct equivalents</li>
<li>why we have e.g. 64-bit integers</li>
<li>arrow automatically handles the conversion between R and Arrow data types</li>
</ul></li>
<li>these types have been guessed from first 1MB of rows (can’t say how many as varies with ncol)</li>
<li>can anyone spot something odd here?</li>
<li>what is an ISBN; what is the null type?</li>
<li>pulling out the schema</li>
<li>updating the schema</li>
<li>using the e.g. <code>string()</code> functions to create different data types and where in the docs?</li>
<li>show <em>both</em> schema updating and <code>col_types</code></li>
<li>checking out the new schema</li>
<li>how often is this necessary? Often not. Good practice. Only important with CSVs not Parquet.</li>
<li>run <code>glimpse()</code> but remember it’ll take a moment
<ul>
<li>TODO: find something to talk about here or don’t use glimpse!</li>
<li>42 millions rows</li>
<li>UsageClass column data</li>
<li>ISBN column data</li>
<li>PublicationYear + why it’s a string</li>
</ul></li>
</ul></li>
<li>Recap this section!</li>
<li>Questions?</li>
</ul>
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</section>
<section id="how-big-is-the-dataset" class="slide level2">
<h2>How big is the dataset?</h2>
<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>(arrow)</span>
<span id="cb3-2"><a href=""></a><span class="fu">library</span>(dplyr)</span>
<span id="cb3-3"><a href=""></a><span class="fu">file.size</span>(<span class="st">"./data/seattle-library-checkouts.csv"</span>) <span class="sc">/</span> <span class="dv">10</span> <span class="sc">**</span><span class="dv">9</span></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>[1] 9.211969</code></pre>
</div>
</div>
</section>
<section id="opening-in-arrow" class="slide level2">
<h2>Opening in Arrow</h2>
<div class="cell">
<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb5-1"><a href=""></a>seattle_csv <span class="ot"><-</span> <span class="fu">open_dataset</span>(</span>
<span id="cb5-2"><a href=""></a> <span class="at">sources =</span> <span class="st">"./data/seattle-library-checkouts.csv"</span>, </span>
<span id="cb5-3"><a href=""></a> <span class="at">format =</span> <span class="st">"csv"</span></span>
<span id="cb5-4"><a href=""></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</section>
<section id="how-many-rows-of-data" class="slide level2">
<h2>How many rows of data?</h2>
<div class="cell">
<div class="sourceCode cell-code" id="cb6"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb6-1"><a href=""></a><span class="fu">nrow</span>(seattle_csv)</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>[1] 41389465</code></pre>
</div>
</div>
</section>
<section id="extract-schema" class="slide level2">
<h2>Extract schema</h2>
<div class="cell">
<div class="sourceCode cell-code" id="cb8"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb8-1"><a href=""></a><span class="fu">schema</span>(seattle_csv)</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>Schema
UsageClass: string
CheckoutType: string
MaterialType: string
CheckoutYear: int64
CheckoutMonth: int64
Checkouts: int64
Title: string
ISBN: null
Creator: string
Subjects: string
Publisher: string
PublicationYear: string</code></pre>
</div>
</div>
</section>
<section id="arrow-data-types" class="slide level2">
<h2>Arrow Data Types</h2>
<p>Arrow has a rich data type system, including direct analogs of many R data types</p>
<ul>
<li><code><dbl></code> == <code><double></code></li>
<li><code><chr></code> == <code><string></code> or <code><utf8></code></li>
<li><code><int></code> == <code><int32></code></li>
</ul>
<p><br></p>
<p><a href="https://arrow.apache.org/docs/r/articles/data_types.html" class="uri">https://arrow.apache.org/docs/r/articles/data_types.html</a></p>
</section>
<section id="parsing-the-metadata" class="slide level2">
<h2>Parsing the Metadata</h2>
<p><br></p>
<p>Arrow scans 👀 1MB of the file(s) to impute or “guess” the data types</p>
<div style="font-size: 80%; margin-top: 200px;">
<p>📚 arrow vs readr blog post: <a href="https://thisisnic.github.io/2022/11/21/type-inference-in-readr-and-arrow/" class="uri">https://thisisnic.github.io/2022/11/21/type-inference-in-readr-and-arrow/</a></p>
</div>
</section>
<section id="parsers-are-not-always-right" class="slide level2">
<h2>Parsers Are Not Always Right</h2>
<div class="cell">
<div class="sourceCode cell-code" id="cb10"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb10-1"><a href=""></a><span class="fu">schema</span>(seattle_csv)</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>Schema
UsageClass: string
CheckoutType: string
MaterialType: string
CheckoutYear: int64
CheckoutMonth: int64
Checkouts: int64
Title: string
ISBN: null
Creator: string
Subjects: string
Publisher: string
PublicationYear: string</code></pre>
</div>
</div>
<p><img data-src="images/data-dict.png" class="absolute" style="top: 200px; left: 330px; width: 700px; "></p>
<aside class="notes">
<p>International Standard Book Number (ISBN) is a 13-digit number that uniquely identifies books and book-like products published internationally.</p>
<p>Data Dictionaries, metadata in data catalogues should provide this info.</p>
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</section>
<section id="lets-control-the-schema" class="slide level2">
<h2>Let’s Control the Schema</h2>
<div class="cell">
<div class="sourceCode cell-code" id="cb12"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb12-1"><a href=""></a>seattle_csv <span class="ot"><-</span> <span class="fu">open_dataset</span>(</span>
<span id="cb12-2"><a href=""></a> <span class="at">sources =</span> <span class="st">"./data/seattle-library-checkouts.csv"</span>, </span>
<span id="cb12-3"><a href=""></a> <span class="at">col_types =</span> <span class="fu">schema</span>(<span class="at">ISBN =</span> <span class="fu">string</span>()),</span>
<span id="cb12-4"><a href=""></a> <span class="at">format =</span> <span class="st">"csv"</span></span>
<span id="cb12-5"><a href=""></a>)</span>
<span id="cb12-6"><a href=""></a></span>
<span id="cb12-7"><a href=""></a><span class="fu">schema</span>(seattle_csv)</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>Schema
UsageClass: string
CheckoutType: string
MaterialType: string
CheckoutYear: int64
CheckoutMonth: int64
Checkouts: int64
Title: string
ISBN: string
Creator: string
Subjects: string
Publisher: string
PublicationYear: string</code></pre>
</div>
</div>
</section></section>
<section>
<section id="part-3---data-manipulation-with-arrow" class="title-slide slide level1 center">
<h1>Part 3 - Data Manipulation with Arrow</h1>
<aside class="notes">
<ul>
<li><Up to here was about 28 mins in first practice run (no time for questions)></li>
<li>Question - how many people here have used dbplyr to connect to a database in R?</li>
</ul>
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</section>
<section id="arrow-dplyr-backend" class="slide level2">
<h2>Arrow dplyr backend</h2>
<img data-src="images/dplyr-backend.png" class="r-stretch"><aside class="notes">
<p>LIVE CODING - Data contains book, ebooks, things which aren’t book - Do <em>not</em> call collect() after first query - talk about lazy eval and show the query - endsWith to ends_with; this is actually an arrow C++ lib func - Don’t want to pull all into memory as it’s a lot of data; preview using head - Now look at the outputs of that - Next: How many books and ebooks were checked out each year? - Don’t need to call head() to preview as the data returned is just a row for each year - Run again with a timer set, then walk through results - Walk through the data, drop in 2020 - pandemic? - Not bad, it can be faster and this is what we’ll talk about in part 3 RECAP - Any questions?</p>
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</section>
<section id="querying-the-data---new-column-is-this-a-book" class="slide level2">
<h2>Querying the data - new column: is this a book?</h2>
<div class="cell">
<div class="sourceCode cell-code" id="cb14"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb14-1"><a href=""></a>seattle_csv <span class="sc">|></span></span>
<span id="cb14-2"><a href=""></a> <span class="fu">mutate</span>(<span class="at">IsBook =</span> <span class="fu">endsWith</span>(MaterialType, <span class="st">"BOOK"</span>)) <span class="sc">|></span></span>
<span id="cb14-3"><a href=""></a> <span class="fu">select</span>(MaterialType, IsBook)</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>FileSystemDataset (query)
MaterialType: string
IsBook: bool (ends_with(MaterialType, {pattern="BOOK", ignore_case=false}))
See $.data for the source Arrow object</code></pre>
</div>
</div>
<p>Nothing is pulled into memory yet!</p>
</section>
<section id="preview-the-query" class="slide level2">
<h2>Preview the query</h2>
<div class="cell">
<div class="sourceCode cell-code" id="cb16" data-code-line-numbers="|2,5"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb16-1"><a href=""></a>seattle_csv <span class="sc">|></span></span>
<span id="cb16-2"><a href=""></a> <span class="fu">head</span>(<span class="dv">20</span>) <span class="sc">|></span></span>
<span id="cb16-3"><a href=""></a> <span class="fu">mutate</span>(<span class="at">IsBook =</span> <span class="fu">endsWith</span>(MaterialType, <span class="st">"BOOK"</span>)) <span class="sc">|></span></span>
<span id="cb16-4"><a href=""></a> <span class="fu">select</span>(MaterialType, IsBook) <span class="sc">|></span></span>
<span id="cb16-5"><a href=""></a> <span class="fu">collect</span>()</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># A tibble: 20 × 2
MaterialType IsBook
<chr> <lgl>
1 BOOK TRUE
2 BOOK TRUE
3 EBOOK TRUE
4 BOOK TRUE
5 SOUNDDISC FALSE
6 BOOK TRUE
7 BOOK TRUE
8 EBOOK TRUE
9 BOOK TRUE
10 EBOOK TRUE
11 BOOK TRUE
12 BOOK TRUE
13 BOOK TRUE
14 AUDIOBOOK TRUE
15 BOOK TRUE
16 EBOOK TRUE
17 SOUNDDISC FALSE
18 VIDEODISC FALSE
19 SOUNDDISC FALSE
20 AUDIOBOOK TRUE </code></pre>
</div>
</div>
</section>
<section id="how-many-books-were-checked-out-each-year" class="slide level2">
<h2>How many books were checked out each year?</h2>
<div class="cell">
<div class="sourceCode cell-code" id="cb18"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb18-1"><a href=""></a>seattle_csv <span class="sc">|></span></span>
<span id="cb18-2"><a href=""></a> <span class="fu">filter</span>(<span class="fu">endsWith</span>(MaterialType, <span class="st">"BOOK"</span>)) <span class="sc">|></span></span>
<span id="cb18-3"><a href=""></a> <span class="fu">group_by</span>(CheckoutYear) <span class="sc">|></span></span>
<span id="cb18-4"><a href=""></a> <span class="fu">summarise</span>(<span class="at">Checkouts =</span> <span class="fu">sum</span>(Checkouts)) <span class="sc">|></span></span>
<span id="cb18-5"><a href=""></a> <span class="fu">arrange</span>(CheckoutYear) <span class="sc">|></span> </span>
<span id="cb18-6"><a href=""></a> <span class="fu">collect</span>()</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># A tibble: 18 × 2
CheckoutYear Checkouts
<int> <int>
1 2005 2129128
2 2006 3385869
3 2007 3679981
4 2008 4156859
5 2009 4500788
6 2010 4389760
7 2011 4484366
8 2012 4696376
9 2013 5394411
10 2014 5606168
11 2015 5784864
12 2016 5915722
13 2017 6280679
14 2018 6831226
15 2019 7339010
16 2020 5549585
17 2021 6659627
18 2022 6301822</code></pre>
</div>
</div>
</section>
<section id="how-long-did-it-take" class="slide level2">
<h2>How long did it take?</h2>
<div class="cell">
<div class="sourceCode cell-code" id="cb20" data-code-line-numbers="6"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb20-1"><a href=""></a>seattle_csv <span class="sc">|></span></span>
<span id="cb20-2"><a href=""></a> <span class="fu">filter</span>(<span class="fu">endsWith</span>(MaterialType, <span class="st">"BOOK"</span>)) <span class="sc">|></span></span>
<span id="cb20-3"><a href=""></a> <span class="fu">group_by</span>(CheckoutYear) <span class="sc">|></span></span>
<span id="cb20-4"><a href=""></a> <span class="fu">summarise</span>(<span class="at">Checkouts =</span> <span class="fu">sum</span>(Checkouts)) <span class="sc">|></span></span>
<span id="cb20-5"><a href=""></a> <span class="fu">arrange</span>(CheckoutYear) <span class="sc">|></span> </span>
<span id="cb20-6"><a href=""></a> <span class="fu">collect</span>() <span class="sc">|></span></span>
<span id="cb20-7"><a href=""></a> <span class="fu">system.time</span>()</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> user system elapsed
13.120 1.162 11.797 </code></pre>
</div>
</div>
<p>42 million rows – not bad, but could be faster….</p>
</section></section>
<section>
<section id="part-4---engineering-the-data" class="title-slide slide level1 center">
<h1>Part 4 - Engineering the Data</h1>
</section>
<section id="norm-files" class="slide level2">
<h2>.NORM Files</h2>
<p><img data-src="images/norm_normal_file_format_2x.png" class="absolute" style="top: 0px; left: 400px; "></p>
<p><br></p>