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docs/chapter_1.html
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@ -246,9 +246,8 @@ div.csl-indent {
<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="co"># R Setup -----------------------------------------------------------------</span></span>
<span id="cb1-2"><a href="#cb1-2" 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="cb1-3"><a href="#cb1-3" 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="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>
@ -576,7 +575,7 @@ i Use the conflicted package (&lt;http://conflicted.r-lib.org/&gt;) to force all
<dl class="code-annotation-container-grid">
<dt data-target-cell="annotated-cell-12" data-target-annotation="2">2</dt>
<dd>
<span data-code-cell="annotated-cell-12" data-code-annotation="2" data-code-lines="1">We’ll re-order the column by size.</span>
<span data-code-annotation="2" data-code-lines="1" data-code-cell="annotated-cell-12">We’ll re-order the column by size.</span>
</dd>
</dl>
</div>
@ -599,19 +598,19 @@ i Use the conflicted package (&lt;http://conflicted.r-lib.org/&gt;) to force all
<dl class="code-annotation-container-grid">
<dt data-target-cell="annotated-cell-13" data-target-annotation="1">1</dt>
<dd>
<span data-code-cell="annotated-cell-13" data-code-annotation="1" data-code-lines="1">First, remove the column with region names and the totals for the regions as we want just integer data.</span>
<span data-code-annotation="1" data-code-lines="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-cell="annotated-cell-13" data-code-annotation="2" data-code-lines="3">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>
<span data-code-annotation="2" data-code-lines="3" 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-cell="annotated-cell-13" data-code-annotation="3" data-code-lines="4">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>
<span data-code-annotation="3" data-code-lines="4" 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-cell="annotated-cell-13" data-code-annotation="4" data-code-lines="5">Now plot it out and have a look!</span>
<span data-code-annotation="4" data-code-lines="5" data-code-cell="annotated-cell-13">Now plot it out and have a look!</span>
</dd>
</dl>
</div>

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@ -355,15 +355,15 @@ So <em>who’s</em> religious?
<dl class="code-annotation-container-grid">
<dt data-target-cell="annotated-cell-6" data-target-annotation="1">1</dt>
<dd>
<span data-code-cell="annotated-cell-6" data-code-annotation="1" data-code-lines="2">First we generate new a dataframe with sums per category and</span>
<span data-code-cell="annotated-cell-6" data-code-lines="2" data-code-annotation="1">First we generate new a dataframe with sums per category and</span>
</dd>
<dt data-target-cell="annotated-cell-6" data-target-annotation="2">2</dt>
<dd>
<span data-code-cell="annotated-cell-6" data-code-annotation="2" data-code-lines="3">…sort in descending order</span>
<span data-code-cell="annotated-cell-6" data-code-lines="3" data-code-annotation="2">…sort in descending order</span>
</dd>
<dt data-target-cell="annotated-cell-6" data-target-annotation="3">3</dt>
<dd>
<span data-code-cell="annotated-cell-6" data-code-annotation="3" data-code-lines="5">Then we add new column with percentages based on the sums you’ve just generated</span>
<span data-code-cell="annotated-cell-6" data-code-lines="5" data-code-annotation="3">Then we add new column with percentages based on the sums you’ve just generated</span>
</dd>
</dl>
</div>

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@ -246,9 +246,8 @@ div.csl-indent {
<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="co"># R Setup -----------------------------------------------------------------</span></span>
<span id="cb1-2"><a href="#cb1-2" 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="cb1-3"><a href="#cb1-3" 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="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>
@ -576,7 +575,7 @@ i Use the conflicted package (&lt;http://conflicted.r-lib.org/&gt;) to force all
<dl class="code-annotation-container-grid">
<dt data-target-cell="annotated-cell-12" data-target-annotation="2">2</dt>
<dd>
<span data-code-cell="annotated-cell-12" data-code-annotation="2" data-code-lines="1">We’ll re-order the column by size.</span>
<span data-code-annotation="2" data-code-lines="1" data-code-cell="annotated-cell-12">We’ll re-order the column by size.</span>
</dd>
</dl>
</div>
@ -599,19 +598,19 @@ i Use the conflicted package (&lt;http://conflicted.r-lib.org/&gt;) to force all
<dl class="code-annotation-container-grid">
<dt data-target-cell="annotated-cell-13" data-target-annotation="1">1</dt>
<dd>
<span data-code-cell="annotated-cell-13" data-code-annotation="1" data-code-lines="1">First, remove the column with region names and the totals for the regions as we want just integer data.</span>
<span data-code-annotation="1" data-code-lines="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-cell="annotated-cell-13" data-code-annotation="2" data-code-lines="3">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>
<span data-code-annotation="2" data-code-lines="3" 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-cell="annotated-cell-13" data-code-annotation="3" data-code-lines="4">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>
<span data-code-annotation="3" data-code-lines="4" 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-cell="annotated-cell-13" data-code-annotation="4" data-code-lines="5">Now plot it out and have a look!</span>
<span data-code-annotation="4" data-code-lines="5" data-code-cell="annotated-cell-13">Now plot it out and have a look!</span>
</dd>
</dl>
</div>

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@ -20,6 +20,7 @@ bibliography: references.bib
format:
html:
theme: cosmo
code-overflow: wrap
pdf:
documentclass: scrreprt

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hacking_religion/chapter_1.qmd
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@ -10,7 +10,8 @@ In the example below, we're going to read in data from a comma separated value f
```{r}
# R Setup -----------------------------------------------------------------
#| include: true
#| label: fig-polar
setwd("/Users/kidwellj/gits/hacking_religion_textbook/hacking_religion")
library(here) # much better way to manage working paths in R across multiple instances
library(tidyverse)