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<li><a href="#your-first-project-the-uk-census" id="toc-your-first-project-the-uk-census" class="nav-link active" data-scroll-target="#your-first-project-the-uk-census"><span class="header-section-number">2.1</span> Your first project: the UK Census</a></li>
<li><a href="#examining-data" id="toc-examining-data" class="nav-link" data-scroll-target="#examining-data"><span class="header-section-number">2.2</span> Examining data:</a></li>
<li><a href="#parsing-and-exploring-your-data" id="toc-parsing-and-exploring-your-data" class="nav-link" data-scroll-target="#parsing-and-exploring-your-data"><span class="header-section-number">2.3</span> Parsing and Exploring your data</a></li>
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<li><a href="#multifactor-visualisation" id="toc-multifactor-visualisation" class="nav-link" data-scroll-target="#multifactor-visualisation"><span class="header-section-number">2.6</span> Multifactor Visualisation</a></li>
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<h1 class="title"><span class="chapter-number">2</span>&nbsp; <span class="chapter-title">The 2021 UK Census</span></h1>
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<section id="your-first-project-the-uk-census" class="level2 page-columns page-full" data-number="2.1">
<h2 data-number="2.1" class="anchored" data-anchor-id="your-first-project-the-uk-census"><span class="header-section-number">2.1</span> Your first project: the UK Census</h2>
<p>Let’s start by importing some data into R. Because R is what is called an object-oriented programming language, we’ll always take our information and give it a home inside a named object. There are many different kinds of objects, which you can specify, but usually R will assign a type that seems to fit best.</p>
<div class="page-columns page-full"><p></p><div class="no-row-height column-margin column-container"><span class="">If you’d like to explore this all in a bit more depth, you can find a very helpful summary in R for Data Science, chapter 8, <a href="https://r4ds.hadley.nz/data-import#reading-data-from-a-file">“data import”</a>.</span></div></div>
<p>In the example below, we’re going to read in data from a comma separated value file (“csv”) which has rows of information on separate lines in a text file with each column separated by a comma. This is one of the standard plain text file formats. R has a function you can use to import this efficiently called “read.csv”. Each line of code in R usually starts with the object, and then follows with instructions on what we’re going to put inside it, where that comes from, and how to format it:</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">setwd</span>(<span class="st">"/Users/kidwellj/gits/hacking_religion_textbook/hacking_religion"</span>)</span>
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(here) <span class="co"># much better way to manage working paths in R across multiple instances</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-stderr">
<pre><code>here() starts at /Users/kidwellj/gits/hacking_religion_textbook</code></pre>
</div>
<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(tidyverse)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stderr">
<pre><code>-- Attaching core tidyverse packages ------------------------ tidyverse 2.0.0 --
v dplyr 1.1.3 v readr 2.1.4
v forcats 1.0.0 v stringr 1.5.0
v ggplot2 3.4.3 v tibble 3.2.1
v lubridate 1.9.3 v tidyr 1.3.0
v purrr 1.0.2 </code></pre>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>-- Conflicts ------------------------------------------ tidyverse_conflicts() --
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()
i Use the conflicted package (&lt;http://conflicted.r-lib.org/&gt;) to force all conflicts to become errors</code></pre>
</div>
<div class="sourceCode cell-code" id="cb6"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>here<span class="sc">::</span><span class="fu">i_am</span>(<span class="st">"chapter_1.qmd"</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-stderr">
<pre><code>here() starts at /Users/kidwellj/gits/hacking_religion_textbook/hacking_religion</code></pre>
</div>
<div class="sourceCode cell-code" id="cb8"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>uk_census_2021_religion <span class="ot">&lt;-</span> <span class="fu">read.csv</span>(<span class="fu">here</span>(<span class="st">"example_data"</span>, <span class="st">"census2021-ts030-rgn.csv"</span>)) </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</section>
<section id="examining-data" class="level2" data-number="2.2">
<h2 data-number="2.2" class="anchored" data-anchor-id="examining-data"><span class="header-section-number">2.2</span> Examining data:</h2>
<p>What’s in the table? You can take a quick look at either the top of the data frame, or the bottom using one of the following commands:</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb9"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(uk_census_2021_religion)</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> geography total no_religion christian buddhist hindu jewish
1 North East 2647012 1058122 1343948 7026 10924 4389
2 North West 7417397 2419624 3895779 23028 49749 33285
3 Yorkshire and The Humber 5480774 2161185 2461519 15803 29243 9355
4 East Midlands 4880054 1950354 2214151 14521 120345 4313
5 West Midlands 5950756 1955003 2770559 18804 88116 4394
6 East 6335072 2544509 2955071 26814 86631 42012
muslim sikh other no_response
1 72102 7206 9950 133345
2 563105 11862 28103 392862
3 442533 24034 23618 313484
4 210766 53950 24813 286841
5 569963 172398 31805 339714
6 234744 24284 36380 384627</code></pre>
</div>
</div>
<p>This is actually a fairly ugly table, so I’ll use an R tool called kable to give you prettier tables in the future, like this:</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb11"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a>knitr<span class="sc">::</span><span class="fu">kable</span>(<span class="fu">head</span>(uk_census_2021_religion))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<table class="table table-sm table-striped small">
<colgroup>
<col style="width: 22%">
<col style="width: 7%">
<col style="width: 10%">
<col style="width: 9%">
<col style="width: 8%">
<col style="width: 6%">
<col style="width: 6%">
<col style="width: 6%">
<col style="width: 6%">
<col style="width: 5%">
<col style="width: 10%">
</colgroup>
<thead>
<tr class="header">
<th style="text-align: left;">geography</th>
<th style="text-align: right;">total</th>
<th style="text-align: right;">no_religion</th>
<th style="text-align: right;">christian</th>
<th style="text-align: right;">buddhist</th>
<th style="text-align: right;">hindu</th>
<th style="text-align: right;">jewish</th>
<th style="text-align: right;">muslim</th>
<th style="text-align: right;">sikh</th>
<th style="text-align: right;">other</th>
<th style="text-align: right;">no_response</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">North East</td>
<td style="text-align: right;">2647012</td>
<td style="text-align: right;">1058122</td>
<td style="text-align: right;">1343948</td>
<td style="text-align: right;">7026</td>
<td style="text-align: right;">10924</td>
<td style="text-align: right;">4389</td>
<td style="text-align: right;">72102</td>
<td style="text-align: right;">7206</td>
<td style="text-align: right;">9950</td>
<td style="text-align: right;">133345</td>
</tr>
<tr class="even">
<td style="text-align: left;">North West</td>
<td style="text-align: right;">7417397</td>
<td style="text-align: right;">2419624</td>
<td style="text-align: right;">3895779</td>
<td style="text-align: right;">23028</td>
<td style="text-align: right;">49749</td>
<td style="text-align: right;">33285</td>
<td style="text-align: right;">563105</td>
<td style="text-align: right;">11862</td>
<td style="text-align: right;">28103</td>
<td style="text-align: right;">392862</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Yorkshire and The Humber</td>
<td style="text-align: right;">5480774</td>
<td style="text-align: right;">2161185</td>
<td style="text-align: right;">2461519</td>
<td style="text-align: right;">15803</td>
<td style="text-align: right;">29243</td>
<td style="text-align: right;">9355</td>
<td style="text-align: right;">442533</td>
<td style="text-align: right;">24034</td>
<td style="text-align: right;">23618</td>
<td style="text-align: right;">313484</td>
</tr>
<tr class="even">
<td style="text-align: left;">East Midlands</td>
<td style="text-align: right;">4880054</td>
<td style="text-align: right;">1950354</td>
<td style="text-align: right;">2214151</td>
<td style="text-align: right;">14521</td>
<td style="text-align: right;">120345</td>
<td style="text-align: right;">4313</td>
<td style="text-align: right;">210766</td>
<td style="text-align: right;">53950</td>
<td style="text-align: right;">24813</td>
<td style="text-align: right;">286841</td>
</tr>
<tr class="odd">
<td style="text-align: left;">West Midlands</td>
<td style="text-align: right;">5950756</td>
<td style="text-align: right;">1955003</td>
<td style="text-align: right;">2770559</td>
<td style="text-align: right;">18804</td>
<td style="text-align: right;">88116</td>
<td style="text-align: right;">4394</td>
<td style="text-align: right;">569963</td>
<td style="text-align: right;">172398</td>
<td style="text-align: right;">31805</td>
<td style="text-align: right;">339714</td>
</tr>
<tr class="even">
<td style="text-align: left;">East</td>
<td style="text-align: right;">6335072</td>
<td style="text-align: right;">2544509</td>
<td style="text-align: right;">2955071</td>
<td style="text-align: right;">26814</td>
<td style="text-align: right;">86631</td>
<td style="text-align: right;">42012</td>
<td style="text-align: right;">234744</td>
<td style="text-align: right;">24284</td>
<td style="text-align: right;">36380</td>
<td style="text-align: right;">384627</td>
</tr>
</tbody>
</table>
</div>
</div>
<p>You can see how I’ve nested the previous command inside the <code>kable</code> command. For reference, in some cases when you’re working with really complex scripts with many different libraries and functions, they may end up with functions that have the same name. You can specify the library where the function is meant to come from by preceding it with :: as we’ve done <code>knitr::</code> above. The same kind of output can be gotten using <code>tail</code>:</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb12"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a>knitr<span class="sc">::</span><span class="fu">kable</span>(<span class="fu">tail</span>(uk_census_2021_religion))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<table class="table table-sm table-striped small">
<colgroup>
<col style="width: 2%">
<col style="width: 13%">
<col style="width: 7%">
<col style="width: 11%">
<col style="width: 9%">
<col style="width: 8%">
<col style="width: 6%">
<col style="width: 6%">
<col style="width: 7%">
<col style="width: 6%">
<col style="width: 5%">
<col style="width: 11%">
</colgroup>
<thead>
<tr class="header">
<th style="text-align: left;"></th>
<th style="text-align: left;">geography</th>
<th style="text-align: right;">total</th>
<th style="text-align: right;">no_religion</th>
<th style="text-align: right;">christian</th>
<th style="text-align: right;">buddhist</th>
<th style="text-align: right;">hindu</th>
<th style="text-align: right;">jewish</th>
<th style="text-align: right;">muslim</th>
<th style="text-align: right;">sikh</th>
<th style="text-align: right;">other</th>
<th style="text-align: right;">no_response</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">5</td>
<td style="text-align: left;">West Midlands</td>
<td style="text-align: right;">5950756</td>
<td style="text-align: right;">1955003</td>
<td style="text-align: right;">2770559</td>
<td style="text-align: right;">18804</td>
<td style="text-align: right;">88116</td>
<td style="text-align: right;">4394</td>
<td style="text-align: right;">569963</td>
<td style="text-align: right;">172398</td>
<td style="text-align: right;">31805</td>
<td style="text-align: right;">339714</td>
</tr>
<tr class="even">
<td style="text-align: left;">6</td>
<td style="text-align: left;">East</td>
<td style="text-align: right;">6335072</td>
<td style="text-align: right;">2544509</td>
<td style="text-align: right;">2955071</td>
<td style="text-align: right;">26814</td>
<td style="text-align: right;">86631</td>
<td style="text-align: right;">42012</td>
<td style="text-align: right;">234744</td>
<td style="text-align: right;">24284</td>
<td style="text-align: right;">36380</td>
<td style="text-align: right;">384627</td>
</tr>
<tr class="odd">
<td style="text-align: left;">7</td>
<td style="text-align: left;">London</td>
<td style="text-align: right;">8799728</td>
<td style="text-align: right;">2380404</td>
<td style="text-align: right;">3577681</td>
<td style="text-align: right;">77425</td>
<td style="text-align: right;">453034</td>
<td style="text-align: right;">145466</td>
<td style="text-align: right;">1318754</td>
<td style="text-align: right;">144543</td>
<td style="text-align: right;">86759</td>
<td style="text-align: right;">615662</td>
</tr>
<tr class="even">
<td style="text-align: left;">8</td>
<td style="text-align: left;">South East</td>
<td style="text-align: right;">9278068</td>
<td style="text-align: right;">3733094</td>
<td style="text-align: right;">4313319</td>
<td style="text-align: right;">54433</td>
<td style="text-align: right;">154748</td>
<td style="text-align: right;">18682</td>
<td style="text-align: right;">309067</td>
<td style="text-align: right;">74348</td>
<td style="text-align: right;">54098</td>
<td style="text-align: right;">566279</td>
</tr>
<tr class="odd">
<td style="text-align: left;">9</td>
<td style="text-align: left;">South West</td>
<td style="text-align: right;">5701186</td>
<td style="text-align: right;">2513369</td>
<td style="text-align: right;">2635872</td>
<td style="text-align: right;">24579</td>
<td style="text-align: right;">27746</td>
<td style="text-align: right;">7387</td>
<td style="text-align: right;">80152</td>
<td style="text-align: right;">7465</td>
<td style="text-align: right;">36884</td>
<td style="text-align: right;">367732</td>
</tr>
<tr class="even">
<td style="text-align: left;">10</td>
<td style="text-align: left;">Wales</td>
<td style="text-align: right;">3107494</td>
<td style="text-align: right;">1446398</td>
<td style="text-align: right;">1354773</td>
<td style="text-align: right;">10075</td>
<td style="text-align: right;">12242</td>
<td style="text-align: right;">2044</td>
<td style="text-align: right;">66947</td>
<td style="text-align: right;">4048</td>
<td style="text-align: right;">15926</td>
<td style="text-align: right;">195041</td>
</tr>
</tbody>
</table>
</div>
</div>
</section>
<section id="parsing-and-exploring-your-data" class="level2 page-columns page-full" data-number="2.3">
<h2 data-number="2.3" class="anchored" data-anchor-id="parsing-and-exploring-your-data"><span class="header-section-number">2.3</span> Parsing and Exploring your data</h2>
<p>The first thing you’re going to want to do is to take a smaller subset of a large data set, either by filtering out certain columns or rows. Now let’s say we want to just work with the data from the West Midlands, and we’d like to omit some of the columns. We can choose a specific range of columns using <code>select</code>, like this:</p>
<p>You can use the <code>filter</code> command to do this. To give an example, <code>filter</code> can pick a single row in the following way:</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb13"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a>uk_census_2021_religion_wmids <span class="ot">&lt;-</span> uk_census_2021_religion <span class="sc">%&gt;%</span> <span class="fu">filter</span>(geography<span class="sc">==</span><span class="st">"West Midlands"</span>) </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p>Now we’ll use select in a different way to narrow our data to specific columns that are needed (no totals!).</p>
<div class="page-columns page-full"><p></p><div class="no-row-height column-margin column-container"><span class="">Some readers will want to pause here and check out Hadley Wickham’s “R For Data Science” book, in the section, <a href="https://r4ds.hadley.nz/data-visualize#introduction">“Data visualisation”</a> to get a fuller explanation of how to explore your data.</span></div></div>
<p>In keeping with my goal to demonstrate data science through examples, we’re going to move on to producing some snappy looking charts for this data.</p>
</section>
<section id="making-your-first-data-visulation-the-humble-bar-chart" class="level2 page-columns page-full" data-number="2.4">
<h2 data-number="2.4" class="anchored" data-anchor-id="making-your-first-data-visulation-the-humble-bar-chart"><span class="header-section-number">2.4</span> Making your first data visulation: the humble bar chart</h2>
<p>We’ve got a nice lean set of data, so now it’s time to visualise this. We’ll start by making a pie chart:</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb14"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a>uk_census_2021_religion_wmids <span class="ot">&lt;-</span> uk_census_2021_religion_wmids <span class="sc">%&gt;%</span> <span class="fu">select</span>(no_religion<span class="sc">:</span>no_response)</span>
<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a>uk_census_2021_religion_wmids <span class="ot">&lt;-</span> <span class="fu">gather</span>(uk_census_2021_religion_wmids)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p>There are two basic ways to do visualisations in R. You can work with basic functions in R, often called “base R” or you can work with an alternative library called ggplot:</p>
<section id="base-r" class="level3" data-number="2.4.1">
<h3 data-number="2.4.1" class="anchored" data-anchor-id="base-r"><span class="header-section-number">2.4.1</span> Base R</h3>
<div class="cell">
<div class="sourceCode cell-code" id="cb15"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a>df <span class="ot">&lt;-</span> uk_census_2021_religion_wmids[<span class="fu">order</span>(uk_census_2021_religion_wmids<span class="sc">$</span>value,<span class="at">decreasing =</span> <span class="cn">TRUE</span>),]</span>
<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a><span class="fu">barplot</span>(<span class="at">height=</span>df<span class="sc">$</span>value, <span class="at">names=</span>df<span class="sc">$</span>key)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<p><img src="chapter_1_files/figure-html/unnamed-chunk-6-1.png" class="img-fluid" width="672"></p>
</div>
</div>
</section>
<section id="ggplot" class="level3 page-columns page-full" data-number="2.4.2">
<h3 data-number="2.4.2" class="anchored" data-anchor-id="ggplot"><span class="header-section-number">2.4.2</span> GGPlot</h3>
<div class="cell">
<div class="sourceCode cell-code" id="annotated-cell-11"><pre class="sourceCode r code-annotation-code code-with-copy"><code class="sourceCode r"><span id="annotated-cell-11-1"><a href="#annotated-cell-11-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(uk_census_2021_religion_wmids, <span class="fu">aes</span>(<span class="at">x =</span> key, <span class="at">y =</span> value)) <span class="sc">+</span></span>
<span id="annotated-cell-11-2"><a href="#annotated-cell-11-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_bar</span>(<span class="at">stat =</span> <span class="st">"identity"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-annotation">
<dl class="code-annotation-container-grid">
<dt data-target-cell="annotated-cell-12" data-target-annotation="2">2</dt>
<dd>
<span data-code-lines="1" data-code-annotation="2" data-code-cell="annotated-cell-12">We’ll re-order the column by size.</span>
</dd>
</dl>
</div>
<div class="cell-output-display">
<p><img src="chapter_1_files/figure-html/unnamed-chunk-7-1.png" class="img-fluid" width="672"></p>
</div>
<div class="sourceCode cell-code" id="annotated-cell-12"><pre class="sourceCode r code-annotation-code code-with-copy code-annotated"><code class="sourceCode r"><a class="code-annotation-anchor" data-target-cell="annotated-cell-12" data-target-annotation="2" onclick="event.preventDefault();">2</a><span id="annotated-cell-12-1" class="code-annotation-target"><a href="#annotated-cell-12-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(uk_census_2021_religion_wmids, <span class="fu">aes</span>(<span class="at">x=</span> <span class="fu">reorder</span>(key,<span class="sc">-</span>value),value)) <span class="sc">+</span> <span class="fu">geom_bar</span>(<span class="at">stat =</span><span class="st">"identity"</span>)</span><div class="code-annotation-gutter-bg"></div><div class="code-annotation-gutter"></div></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<p><img src="chapter_1_files/figure-html/unnamed-chunk-7-2.png" class="img-fluid" width="672"></p>
</div>
</div>
<p>Let’s assume we’re working with a data set that doesn’t include a “totals” column and that we might want to get sums for each column. This is pretty easy to do in R:</p>
<div class="cell">
<div class="sourceCode cell-code" id="annotated-cell-13"><pre class="sourceCode r code-annotation-code code-with-copy code-annotated"><code class="sourceCode r"><a class="code-annotation-anchor" data-target-cell="annotated-cell-13" data-target-annotation="1" onclick="event.preventDefault();">1</a><span id="annotated-cell-13-1" class="code-annotation-target"><a href="#annotated-cell-13-1" aria-hidden="true" tabindex="-1"></a>uk_census_2021_religion_totals <span class="ot">&lt;-</span> uk_census_2021_religion <span class="sc">%&gt;%</span> <span class="fu">select</span>(no_religion<span class="sc">:</span>no_response)</span>
<span id="annotated-cell-13-2"><a href="#annotated-cell-13-2" aria-hidden="true" tabindex="-1"></a>uk_census_2021_religion_totals <span class="ot">&lt;-</span> uk_census_2021_religion_totals <span class="sc">%&gt;%</span></span>
<a class="code-annotation-anchor" data-target-cell="annotated-cell-13" data-target-annotation="2" onclick="event.preventDefault();">2</a><span id="annotated-cell-13-3" class="code-annotation-target"><a href="#annotated-cell-13-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">summarise</span>(<span class="fu">across</span>(<span class="fu">everything</span>(), <span class="sc">~</span> <span class="fu">sum</span>(., <span class="at">na.rm =</span> <span class="cn">TRUE</span>)))</span>
<a class="code-annotation-anchor" data-target-cell="annotated-cell-13" data-target-annotation="3" onclick="event.preventDefault();">3</a><span id="annotated-cell-13-4" class="code-annotation-target"><a href="#annotated-cell-13-4" aria-hidden="true" tabindex="-1"></a>uk_census_2021_religion_totals <span class="ot">&lt;-</span> <span class="fu">gather</span>(uk_census_2021_religion_totals)</span>
<a class="code-annotation-anchor" data-target-cell="annotated-cell-13" data-target-annotation="4" onclick="event.preventDefault();">4</a><span id="annotated-cell-13-5" class="code-annotation-target"><a href="#annotated-cell-13-5" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(uk_census_2021_religion_totals, <span class="fu">aes</span>(<span class="at">x=</span> <span class="fu">reorder</span>(key,<span class="sc">-</span>value),value)) <span class="sc">+</span> <span class="fu">geom_bar</span>(<span class="at">stat =</span><span class="st">"identity"</span>)</span><div class="code-annotation-gutter-bg"></div><div class="code-annotation-gutter"></div></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-annotation">
<dl class="code-annotation-container-grid">
<dt data-target-cell="annotated-cell-13" data-target-annotation="1">1</dt>
<dd>
<span data-code-lines="1" data-code-annotation="1" data-code-cell="annotated-cell-13">First, remove the column with region names and the totals for the regions as we want just integer data.</span>
</dd>
<dt data-target-cell="annotated-cell-13" data-target-annotation="2">2</dt>
<dd>
<span data-code-lines="3" data-code-annotation="2" data-code-cell="annotated-cell-13">Second calculate the totals. In this example we use the tidyverse library <code>dplyr()</code>, but you can also do this using base R with <code>colsums()</code> like this: <code>uk_census_2021_religion_totals &lt;- colSums(uk_census_2021_religion_totals, na.rm = TRUE)</code>. The downside with base R is that you’ll also need to convert the result into a dataframe for <code>ggplot</code> like this: <code>uk_census_2021_religion_totals &lt;- as.data.frame(uk_census_2021_religion_totals)</code></span>
</dd>
<dt data-target-cell="annotated-cell-13" data-target-annotation="3">3</dt>
<dd>
<span data-code-lines="4" data-code-annotation="3" data-code-cell="annotated-cell-13">In order to visualise this data using ggplot, we need to shift this data from wide to long format. This is a quick job using gather()</span>
</dd>
<dt data-target-cell="annotated-cell-13" data-target-annotation="4">4</dt>
<dd>
<span data-code-lines="5" data-code-annotation="4" data-code-cell="annotated-cell-13">Now plot it out and have a look!</span>
</dd>
</dl>
</div>
<div class="cell-output-display">
<p><img src="chapter_1_files/figure-html/unnamed-chunk-8-1.png" class="img-fluid" width="672"></p>
</div>
</div>
<p>You might have noticed that these two dataframes give us somewhat different results. But with data science, it’s much more interesting to compare these two side-by-side in a visualisation. We can join these two dataframes and plot the bars side by side using <code>bind()</code> - which can be done by columns with cbind() and rows using rbind():</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb16"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a>uk_census_2021_religion_merged <span class="ot">&lt;-</span> <span class="fu">rbind</span>(uk_census_2021_religion_totals, uk_census_2021_religion_wmids)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p>Do you notice there’s going to be a problem here? How can we tell one set from the other? We need to add in something idenfiable first! This isn’t too hard to do as we can simply create a new column for each with identifiable information before we bind them:</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb17"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a>uk_census_2021_religion_totals<span class="sc">$</span>dataset <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">"totals"</span>)</span>
<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a>uk_census_2021_religion_wmids<span class="sc">$</span>dataset <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">"wmids"</span>)</span>
<span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a>uk_census_2021_religion_merged <span class="ot">&lt;-</span> <span class="fu">rbind</span>(uk_census_2021_religion_totals, uk_census_2021_religion_wmids)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p>Now we’re ready to plot out our data as a grouped barplot:</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb18"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(uk_census_2021_religion_merged, <span class="fu">aes</span>(<span class="at">fill=</span>dataset, <span class="at">x=</span> <span class="fu">reorder</span>(key,<span class="sc">-</span>value), value)) <span class="sc">+</span> <span class="fu">geom_bar</span>(<span class="at">position=</span><span class="st">"dodge"</span>, <span class="at">stat =</span><span class="st">"identity"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<p><img src="chapter_1_files/figure-html/unnamed-chunk-11-1.png" class="img-fluid" width="672"></p>
</div>
</div>
<p>If you’re looking closely, you will notice that I’ve added two elements to our previous ggplot. I’ve asked ggplot to fill in the columns with reference to the <code>dataset</code> column we’ve just created. Then I’ve also asked ggplot to alter the <code>position="dodge"</code> which places bars side by side rather than stacked on top of one another. You can give it a try without this instruction to see how this works. We will use stacked bars in a later chapter, so remember this feature.</p>
<p>If you inspect our chart, you can see that we’re getting closer, but it’s not really that helpful to compare the totals. What we need to do is get percentages that can be compared side by side. This is easy to do using another <code>dplyr</code> feature <code>mutate</code>:</p>
<div class="cell">
<div class="sourceCode cell-code" id="annotated-cell-17"><pre class="sourceCode r code-annotation-code code-with-copy"><code class="sourceCode r"><span id="annotated-cell-17-1"><a href="#annotated-cell-17-1" aria-hidden="true" tabindex="-1"></a>uk_census_2021_religion_totals <span class="ot">&lt;-</span> uk_census_2021_religion_totals <span class="sc">%&gt;%</span> </span>
<span id="annotated-cell-17-2"><a href="#annotated-cell-17-2" aria-hidden="true" tabindex="-1"></a> dplyr<span class="sc">::</span><span class="fu">mutate</span>(<span class="at">perc =</span> scales<span class="sc">::</span><span class="fu">percent</span>(value <span class="sc">/</span> <span class="fu">sum</span>(value), <span class="at">accuracy =</span> <span class="fl">0.1</span>, <span class="at">trim =</span> <span class="cn">FALSE</span>))</span>
<span id="annotated-cell-17-3"><a href="#annotated-cell-17-3" aria-hidden="true" tabindex="-1"></a>uk_census_2021_religion_wmids <span class="ot">&lt;-</span> uk_census_2021_religion_wmids <span class="sc">%&gt;%</span> </span>
<span id="annotated-cell-17-4"><a href="#annotated-cell-17-4" aria-hidden="true" tabindex="-1"></a> dplyr<span class="sc">::</span><span class="fu">mutate</span>(<span class="at">perc =</span> scales<span class="sc">::</span><span class="fu">percent</span>(value <span class="sc">/</span> <span class="fu">sum</span>(value), <span class="at">accuracy =</span> <span class="fl">0.1</span>, <span class="at">trim =</span> <span class="cn">FALSE</span>))</span>
<span id="annotated-cell-17-5"><a href="#annotated-cell-17-5" aria-hidden="true" tabindex="-1"></a>uk_census_2021_religion_merged <span class="ot">&lt;-</span> <span class="fu">rbind</span>(uk_census_2021_religion_totals, uk_census_2021_religion_wmids)</span>
<span id="annotated-cell-17-6"><a href="#annotated-cell-17-6" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(uk_census_2021_religion_merged, <span class="fu">aes</span>(<span class="at">fill=</span>dataset, <span class="at">x=</span>key, <span class="at">y=</span>perc)) <span class="sc">+</span> <span class="fu">geom_bar</span>(<span class="at">position=</span><span class="st">"dodge"</span>, <span class="at">stat =</span><span class="st">"identity"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<p><img src="chapter_1_files/figure-html/unnamed-chunk-12-1.png" class="img-fluid" width="672"></p>
</div>
</div>
<p>Now you can see a very rough comparison, which sets bars from the W Midlands data and overall data side by side for each category. The same principles that we’ve used here can be applied to draw in more data. You could, for example, compare census data from different years, e.g.&nbsp;2001 2011 and 2021. Our use of <code>dplyr::mutate</code> above can be repeated to add an infinite number of further series’ which can be plotted in bar groups.</p>
<p>We’ll draw this data into comparison with later sets in the next chapter. But the one glaring issue which remains for our chart is that it’s lacking in really any aesthetic refinements. This is where <code>ggplot</code> really shines as a tool as you can add all sorts of things.</p>
<p>These are basically just added to our <code>ggplot</code> code. So, for example, let’s say we want to improve the colours used for our bars. You can specify the formatting for the fill on the <code>scale</code> using <code>scale_fill_brewer</code>. This uses a particular tool (and a personal favourite of mine) called <code>colorbrewer</code>. Part of my appreciation of this tool is that you can pick colours which are not just visually pleasing, and produce useful contrast / complementary schemes, but you can also work proactively to accommodate colourblindness. Working with colour schemes which can be divergent in a visually obvious way will be even more important when we work on geospatial data and maps in a later chapter.</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb19"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(uk_census_2021_religion_merged, <span class="fu">aes</span>(<span class="at">fill=</span>dataset, <span class="at">x=</span>key, <span class="at">y=</span>perc)) <span class="sc">+</span> <span class="fu">geom_bar</span>(<span class="at">position=</span><span class="st">"dodge"</span>, <span class="at">stat =</span><span class="st">"identity"</span>) <span class="sc">+</span> <span class="fu">scale_fill_brewer</span>(<span class="at">palette =</span> <span class="st">"Set1"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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<p>We might also want to add a border to our bars to make them more visually striking (notice the addition of <code>color</code> to the geom_bar below. I’ve also added <code>reorder()</code> to the x value to sort descending from the largest to smallest.</p>
<div class="page-columns page-full"><p></p><div class="no-row-height column-margin column-container"><span class="">You can find more information about reordering ggplots on the <a href="https://r-graph-gallery.com/267-reorder-a-variable-in-ggplot2.html">R Graph gallery</a>.</span></div></div>
<div class="cell">
<div class="sourceCode cell-code" id="cb20"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a>uk_census_2021_religion_merged<span class="sc">$</span>dataset <span class="ot">&lt;-</span> <span class="fu">factor</span>(uk_census_2021_religion_merged<span class="sc">$</span>dataset, <span class="at">levels =</span> <span class="fu">c</span>(<span class="st">'wmids'</span>, <span class="st">'totals'</span>))</span>
<span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(uk_census_2021_religion_merged, <span class="fu">aes</span>(<span class="at">fill=</span><span class="fu">fct_reorder</span>(dataset, value), <span class="at">x=</span><span class="fu">reorder</span>(key,<span class="sc">-</span>value),value, <span class="at">y=</span>perc)) <span class="sc">+</span> <span class="fu">geom_bar</span>(<span class="at">position=</span><span class="st">"dodge"</span>, <span class="at">stat =</span><span class="st">"identity"</span>, <span class="at">colour =</span> <span class="st">"black"</span>) <span class="sc">+</span> <span class="fu">scale_fill_brewer</span>(<span class="at">palette =</span> <span class="st">"Set1"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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<p><img src="chapter_1_files/figure-html/unnamed-chunk-14-1.png" class="img-fluid" width="672"></p>
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<p>We can fine tune a few other visual features here as well, like adding a title with <code>ggtitle</code> and a them with some prettier fonts with <code>theme_ipsum()</code> (which requires the <code>hrbrthemes()</code> library). We can also remove the x and y axis labels (not the data labels, which are rather important).</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb21"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(uk_census_2021_religion_merged, <span class="fu">aes</span>(<span class="at">fill=</span><span class="fu">fct_reorder</span>(dataset, value), <span class="at">x=</span><span class="fu">reorder</span>(key,<span class="sc">-</span>value),value, <span class="at">y=</span>perc)) <span class="sc">+</span> <span class="fu">geom_bar</span>(<span class="at">position=</span><span class="st">"dodge"</span>, <span class="at">stat =</span><span class="st">"identity"</span>, <span class="at">colour =</span> <span class="st">"black"</span>) <span class="sc">+</span> <span class="fu">scale_fill_brewer</span>(<span class="at">palette =</span> <span class="st">"Set1"</span>) <span class="sc">+</span> <span class="fu">ggtitle</span>(<span class="st">"Religious Affiliation in the UK: 2021"</span>) <span class="sc">+</span> <span class="fu">xlab</span>(<span class="st">""</span>) <span class="sc">+</span> <span class="fu">ylab</span>(<span class="st">""</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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<p><img src="chapter_1_files/figure-html/unnamed-chunk-15-1.png" class="img-fluid" width="672"></p>
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</section>
<section id="is-your-chart-accurate-telling-the-truth-in-data-science" class="level2" data-number="2.5">
<h2 data-number="2.5" class="anchored" data-anchor-id="is-your-chart-accurate-telling-the-truth-in-data-science"><span class="header-section-number">2.5</span> Is your chart accurate? Telling the truth in data science</h2>
<p>There is some technical work yet to be done fine-tuning the visualisation of our chart here. But I’d like to pause for a moment and consider an ethical question. Is the title of this chart truthful and accurate? On one hand, it is a straight-forward reference to the nature of the question asked on the 2021 census survey instrument. However, as you will see in the next chapter, large data sets from the same year which asked a fairly similar question yield different results. Part of this could be attributed to the amount of non-respose to this specific question which, in the 2021 census is between 5-6% across many demographics. It’s possible (though perhaps unlikely) that all those non-responses were Sikh respondents who felt uncomfortable identifying themselves on such a survey. If even half of the non-responses were of this nature, this would dramatically shift the results especially in comparison to other minority groups. So there is some work for us to do here in representing non-response as a category on the census. But it’s equally possible that someone might feel uncertain when answering, but nonetheless land on a particular decision marking “Christian” when they wondered if they should instead tick “no religion. Some surveys attempt to capture uncertainty in this way, asking respondents to mark how confident they are about their answers, but the census hasn’t capture this so we simply don’t know. If a large portion of respondents in the”Christian” category were hovering between this and another response, again, they might shift their answers when responding on a different day, perhaps having just had a conversation with a friend which shifted their thinking. Even the inertia of survey design can have an effect on this, so responding to other questions in a particular way, thinking about ethnic identity, for example, can prime a person to think about their religious identity in a different or more focussed way, altering their response to the question. For this reason, some survey instruments randomise the order of questions. This hasn’t been done on the census (which would have been quite hard work given that most of the instruments were printed hard copies!), so again, we can’t really be sure if those answers given are stable. Finally, researchers have also found that when people are asked to mark their religious affiliation, sometimes they can prefer to mark more than one answer. A person might consider themselves to be “Muslim” but also “Spiritual but not religious” preferring the combination of those identities. It is also the case that respondents can identify with more unexpected hybrid religious identities, such as “Christian” and “Hindu”. The census only allows respondents to tick a single box for the religion category. It is worth noting that, in contrast, the responses for ethnicity allow for combinations. Given that this is the case, it’s impossible to know which way a person went at the fork in the road as they were forced to choose just one half of this kind of hybrid identity. Finally, it is interesting to wonder exactly what it means for a person when they tick a box like this. Is it because they attend synagogue on a weekly basis? Some persons would consider weekly attendance at workship a prerequisite for membership in a group, but others would not. Indeed we can infer from surveys and research which aims to track rates of participation in weekly worship that many people who tick boxes for particular religious identities on the census have never attended a worship service at all.</p>
<p>What does this mean for our results? Are they completely unreliable and invalid? I don’t think this is the case or that taking a clear-eyed look at the force and stability of our underlying data should be cause for despair. Instead, the most appropriate response is humility. Someone has made a statement which is recorded in the census, of this we can be sure. They felt it to be an accurate response on some level based on the information they had at the time. And with regard to the census, it is a massive, almost completely population level, sample so there is additional validity there. The easiest way to represent all this reality in the form of speaking truthfully about our data is to acknowledge that however valid it may seem, it is nonetheless a snapshot. For this reason, I would always advise that the best title for a chart is one which specifies the data set. We should also probably do something different with those non-responses:</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb22"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(uk_census_2021_religion_merged, <span class="fu">aes</span>(<span class="at">fill=</span><span class="fu">fct_reorder</span>(dataset, value), <span class="at">x=</span><span class="fu">reorder</span>(key,<span class="sc">-</span>value),value, <span class="at">y=</span>perc)) <span class="sc">+</span> <span class="fu">geom_bar</span>(<span class="at">position=</span><span class="st">"dodge"</span>, <span class="at">stat =</span><span class="st">"identity"</span>, <span class="at">colour =</span> <span class="st">"black"</span>) <span class="sc">+</span> <span class="fu">scale_fill_brewer</span>(<span class="at">palette =</span> <span class="st">"Set1"</span>) <span class="sc">+</span> <span class="fu">ggtitle</span>(<span class="st">"Religious Affiliation in the 2021 Census of England and Wales"</span>) <span class="sc">+</span> <span class="fu">xlab</span>(<span class="st">""</span>) <span class="sc">+</span> <span class="fu">ylab</span>(<span class="st">""</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<p><img src="chapter_1_files/figure-html/unnamed-chunk-16-1.png" class="img-fluid" width="672"></p>
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</div>
<p>Change orientation of X axis labels + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))</p>
<p>Relabel fields Simplify y-axis labels Add percentage text to bars (or maybe save for next chapter?)</p>
</section>
<section id="multifactor-visualisation" class="level2" data-number="2.6">
<h2 data-number="2.6" class="anchored" data-anchor-id="multifactor-visualisation"><span class="header-section-number">2.6</span> Multifactor Visualisation</h2>
<p>One element of R data analysis that can get really interesting is working with multiple variables. Above we’ve looked at the breakdown of religious affiliation across the whole of England and Wales (Scotland operates an independent census), and by placing this data alongside a specific region, we’ve already made a basic entry into working with multiple variables but this can get much more interesting. Adding an additional quantative variable (also known as bivariate data) into the mix, however can also generate a lot more information and we have to think about visualising it in different ways which can still communicate with visual clarity in spite of the additional visual noise which is inevitable with enhanced complexity. Let’s have a look at the way that religion in England and Wales breaks down by ethnicity.</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb23"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(nomisr)</span>
<span id="cb23-2"><a href="#cb23-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb23-3"><a href="#cb23-3" aria-hidden="true" tabindex="-1"></a><span class="co"># Process to explore nomis() data for specific datasets</span></span>
<span id="cb23-4"><a href="#cb23-4" aria-hidden="true" tabindex="-1"></a>religion_search <span class="ot">&lt;-</span> <span class="fu">nomis_search</span>(<span class="at">name =</span> <span class="st">"*Religion*"</span>)</span>
<span id="cb23-5"><a href="#cb23-5" aria-hidden="true" tabindex="-1"></a>religion_measures <span class="ot">&lt;-</span> <span class="fu">nomis_get_metadata</span>(<span class="st">"NM_529_1"</span>, <span class="st">"measures"</span>)</span>
<span id="cb23-6"><a href="#cb23-6" aria-hidden="true" tabindex="-1"></a>tibble<span class="sc">::</span><span class="fu">glimpse</span>(religion_measures)</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: 2
Columns: 3
$ id &lt;chr&gt; "20100", "20301"
$ label.en &lt;chr&gt; "value", "percent"
$ description.en &lt;chr&gt; "value", "percent"</code></pre>
</div>
<div class="sourceCode cell-code" id="cb25"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1" aria-hidden="true" tabindex="-1"></a>religion_geography <span class="ot">&lt;-</span> <span class="fu">nomis_get_metadata</span>(<span class="st">"NM_529_1"</span>, <span class="st">"geography"</span>, <span class="st">"TYPE"</span>)</span>
<span id="cb25-2"><a href="#cb25-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb25-3"><a href="#cb25-3" aria-hidden="true" tabindex="-1"></a><span class="co"># Get table of Census 2011 religion data from nomis</span></span>
<span id="cb25-4"><a href="#cb25-4" aria-hidden="true" tabindex="-1"></a>z <span class="ot">&lt;-</span> <span class="fu">nomis_get_data</span>(<span class="at">id =</span> <span class="st">"NM_529_1"</span>, <span class="at">time =</span> <span class="st">"latest"</span>, <span class="at">geography =</span> <span class="st">"TYPE499"</span>, <span class="at">measures=</span><span class="fu">c</span>(<span class="dv">20301</span>))</span>
<span id="cb25-5"><a href="#cb25-5" aria-hidden="true" tabindex="-1"></a><span class="co"># Filter down to simplified dataset with England / Wales and percentages without totals</span></span>
<span id="cb25-6"><a href="#cb25-6" aria-hidden="true" tabindex="-1"></a>uk_census_2011_religion <span class="ot">&lt;-</span> <span class="fu">filter</span>(z, GEOGRAPHY_NAME<span class="sc">==</span><span class="st">"England and Wales"</span> <span class="sc">&amp;</span> RURAL_URBAN_NAME<span class="sc">==</span><span class="st">"Total"</span> <span class="sc">&amp;</span> C_RELPUK11_NAME <span class="sc">!=</span> <span class="st">"All categories: Religion"</span>)</span>
<span id="cb25-7"><a href="#cb25-7" aria-hidden="true" tabindex="-1"></a><span class="co"># Drop unnecessary columns</span></span>
<span id="cb25-8"><a href="#cb25-8" aria-hidden="true" tabindex="-1"></a>uk_census_2011_religion <span class="ot">&lt;-</span> <span class="fu">select</span>(uk_census_2011_religion, C_RELPUK11_NAME, OBS_VALUE)</span>
<span id="cb25-9"><a href="#cb25-9" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot results</span></span>
<span id="cb25-10"><a href="#cb25-10" aria-hidden="true" tabindex="-1"></a>plot1 <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(uk_census_2011_religion, <span class="fu">aes</span>(<span class="at">x =</span> C_RELPUK11_NAME, <span class="at">y =</span> OBS_VALUE)) <span class="sc">+</span> <span class="fu">geom_bar</span>(<span class="at">stat =</span> <span class="st">"identity"</span>) <span class="sc">+</span> <span class="fu">theme</span>(<span class="at">axis.text.x =</span> <span class="fu">element_text</span>(<span class="at">angle =</span> <span class="dv">90</span>, <span class="at">vjust =</span> <span class="fl">0.5</span>, <span class="at">hjust=</span><span class="dv">1</span>))</span>
<span id="cb25-11"><a href="#cb25-11" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="at">filename =</span> <span class="st">"plot.png"</span>, <span class="at">plot =</span> plot1)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div class="sourceCode cell-code" id="cb27"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1" aria-hidden="true" tabindex="-1"></a><span class="co"># grab data from nomis for 2011 census religion / ethnicity table</span></span>
<span id="cb27-2"><a href="#cb27-2" aria-hidden="true" tabindex="-1"></a>z1 <span class="ot">&lt;-</span> <span class="fu">nomis_get_data</span>(<span class="at">id =</span> <span class="st">"NM_659_1"</span>, <span class="at">time =</span> <span class="st">"latest"</span>, <span class="at">geography =</span> <span class="st">"TYPE499"</span>, <span class="at">measures=</span><span class="fu">c</span>(<span class="dv">20100</span>))</span>
<span id="cb27-3"><a href="#cb27-3" aria-hidden="true" tabindex="-1"></a><span class="co"># select relevant columns</span></span>
<span id="cb27-4"><a href="#cb27-4" aria-hidden="true" tabindex="-1"></a>uk_census_2011_religion_ethnicitity <span class="ot">&lt;-</span> <span class="fu">select</span>(z1, GEOGRAPHY_NAME, C_RELPUK11_NAME, C_ETHPUK11_NAME, OBS_VALUE)</span>
<span id="cb27-5"><a href="#cb27-5" aria-hidden="true" tabindex="-1"></a><span class="co"># Filter down to simplified dataset with England / Wales and percentages without totals</span></span>
<span id="cb27-6"><a href="#cb27-6" aria-hidden="true" tabindex="-1"></a>uk_census_2011_religion_ethnicitity <span class="ot">&lt;-</span> <span class="fu">filter</span>(uk_census_2011_religion_ethnicitity, GEOGRAPHY_NAME<span class="sc">==</span><span class="st">"England and Wales"</span> <span class="sc">&amp;</span> C_RELPUK11_NAME <span class="sc">!=</span> <span class="st">"All categories: Religion"</span> <span class="sc">&amp;</span> C_ETHPUK11_NAME <span class="sc">!=</span> <span class="st">"All categories: Ethnic group"</span>)</span>
<span id="cb27-7"><a href="#cb27-7" aria-hidden="true" tabindex="-1"></a><span class="co"># Simplify data to only include general totals and omit subcategories</span></span>
<span id="cb27-8"><a href="#cb27-8" aria-hidden="true" tabindex="-1"></a>uk_census_2011_religion_ethnicitity <span class="ot">&lt;-</span> uk_census_2011_religion_ethnicitity <span class="sc">%&gt;%</span> <span class="fu">filter</span>(<span class="fu">grepl</span>(<span class="st">'Total'</span>, C_ETHPUK11_NAME))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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<section id="references" class="level1 unnumbered">
<h1 class="unnumbered">References</h1>
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