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fixed religious affiliation plot ch.2
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@ -631,7 +631,7 @@ What is Secularisation?
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<p>In the last chapter we explored some high level data about religion in the UK. This was a census sample, which usually refers to an attempt to get as comprehensive a sample as possible. But this is actually fairly unusual in practice. Depending on how complex a subject is, and how representative we want our data to be, it’s much more common to use selective sampling, that is survey responses at n=100 or n=1000 at a maximum. The advantage of a census sample is that you can explore how a wide range of other factors - particularly demographics - intersect with your question. And this can be really valuable in the study of religion, particularly as you will see as we go along that responses to some questions are more strongly correlated to things like economic status or educational attainment than they are to religious affiliation. It can be hard to tell if this is the case unless you have enough of a sample to break down into a number of different kinds of subsets. But census samples are complex and expensive to gather, so they’re quite rare in practice.</p>
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<p>For this chapter, I’m going to walk you through a data set that a colleague (Charles Ogunbode) and I collected in 2021. Another problem with smaller, more selective samples is that researchers can often undersample minoritised ethnic groups. This is particularly the case with climate change research. Until the time we conducted this research, there had not been a single study investigating the specific experiences of people of colour in relation to climate change in the UK. Past researchers had been content to work with large samples, and assumed that if they had done 1000 surveys and 50 of these were completed by people of colour, they could “tick” the box. But 5% is actually well below levels of representation in the UK generally, and even more sharply the case for specific communities. And if we bear in mind that non-white respondents are (of course!) a highly heterogenous group, we’re even more behind in terms of collecting data that can improve our knowledge. Up until recently researchers just haven’t been paying close enough attention to catch the significant neglect of the empirical field that this represents.</p>
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<p>While I’ve framed my comments above in terms of climate change research, it is also the case that, especially in diverse societies like the USA, Canada, the UK etc., paying attention to non-majority groups and people and communities of colour automatically draws in a strongly religious sample. This is highlighted in one recent study done in the UK, the “<a href="https://www.cam.ac.uk/stories/black-british-voices-report">Black British Voices Report</a>” in which the researchers observed that “84% of respondents described themselves as religious and/or spiritual”. My comments above in terms of controlling for other factors remains important here - these same researchers also note that “despire their significant important to the lives of Black Britons, only 7% of survey respondents reported that their religion was more defining of their identity than their race”.</p>
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<p>We’ve decided to open up access to our data and I’m highlighting it in this book because it’s a unique opportunitiy to explore a dataset that emphasises diversity from the start, and by extension, provides some really interesting ways to use data science techniques to explore religion in the UK.</p>
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How can we measure religion?
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<p>Content tbd</p>
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<h1 class="unnumbered">References</h1>
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<p>Guides to geographies: https://rconsortium.github.io/censusguide/ https://ocsi.uk/2019/03/18/lsoas-leps-and-lookups-a-beginners-guide-to-statistical-geographies/</p>
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<p>An open textbook introducing data science to religious studies</p>
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return codeEl.innerText;
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}
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});
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clipboard.on('success', function(e) {
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// button target
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const button = e.trigger;
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// don't keep focus
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button.blur();
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// flash "checked"
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||||
button.classList.add('code-copy-button-checked');
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||||
var currentTitle = button.getAttribute("title");
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button.setAttribute("title", "Copied!");
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let tooltip;
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if (window.bootstrap) {
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button.setAttribute("data-bs-toggle", "tooltip");
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button.setAttribute("data-bs-placement", "left");
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button.setAttribute("data-bs-title", "Copied!");
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tooltip = new bootstrap.Tooltip(button,
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{ trigger: "manual",
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customClass: "code-copy-button-tooltip",
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offset: [0, -8]});
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tooltip.show();
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}
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setTimeout(function() {
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if (tooltip) {
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tooltip.hide();
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button.removeAttribute("data-bs-title");
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button.removeAttribute("data-bs-toggle");
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button.removeAttribute("data-bs-placement");
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}
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button.setAttribute("title", currentTitle);
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button.classList.remove('code-copy-button-checked');
|
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}, 1000);
|
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// clear code selection
|
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e.clearSelection();
|
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});
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function tippyHover(el, contentFn) {
|
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const config = {
|
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allowHTML: true,
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content: contentFn,
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maxWidth: 500,
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arrow: false,
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appendTo: function(el) {
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return el.parentElement;
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interactive: true,
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interactiveBorder: 10,
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theme: 'quarto',
|
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placement: 'bottom-start'
|
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};
|
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window.tippy(el, config);
|
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}
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const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]');
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for (var i=0; i<noterefs.length; i++) {
|
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const ref = noterefs[i];
|
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tippyHover(ref, function() {
|
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// use id or data attribute instead here
|
||||
let href = ref.getAttribute('data-footnote-href') || ref.getAttribute('href');
|
||||
try { href = new URL(href).hash; } catch {}
|
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const id = href.replace(/^#\/?/, "");
|
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const note = window.document.getElementById(id);
|
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return note.innerHTML;
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});
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}
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let selectedAnnoteEl;
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const selectorForAnnotation = ( cell, annotation) => {
|
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let cellAttr = 'data-code-cell="' + cell + '"';
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let lineAttr = 'data-code-annotation="' + annotation + '"';
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const selector = 'span[' + cellAttr + '][' + lineAttr + ']';
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return selector;
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const selectCodeLines = (annoteEl) => {
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const doc = window.document;
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const targetCell = annoteEl.getAttribute("data-target-cell");
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const annoteSpan = window.document.querySelector(selectorForAnnotation(targetCell, targetAnnotation));
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const lines = annoteSpan.getAttribute("data-code-lines").split(",");
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const lineIds = lines.map((line) => {
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return targetCell + "-" + line;
|
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})
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let top = null;
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let height = null;
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let parent = null;
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if (lineIds.length > 0) {
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//compute the position of the single el (top and bottom and make a div)
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if (top !== null && height !== null && parent !== null) {
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// cook up a div (if necessary) and position it
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let div = window.document.getElementById("code-annotation-line-highlight");
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if (div === null) {
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div = window.document.createElement("div");
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div.setAttribute("id", "code-annotation-line-highlight");
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div.style.position = 'absolute';
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parent.appendChild(div);
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}
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div.style.top = top - 2 + "px";
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div.style.height = height + 4 + "px";
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let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter");
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if (gutterDiv === null) {
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gutterDiv = window.document.createElement("div");
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gutterDiv.setAttribute("id", "code-annotation-line-highlight-gutter");
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gutterDiv.style.position = 'absolute';
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const codeCell = window.document.getElementById(targetCell);
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const gutter = codeCell.querySelector('.code-annotation-gutter');
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gutter.appendChild(gutterDiv);
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}
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gutterDiv.style.top = top - 2 + "px";
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gutterDiv.style.height = height + 4 + "px";
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}
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selectedAnnoteEl = annoteEl;
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const unselectCodeLines = () => {
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const elementsIds = ["code-annotation-line-highlight", "code-annotation-line-highlight-gutter"];
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elementsIds.forEach((elId) => {
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const div = window.document.getElementById(elId);
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if (div) {
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div.remove();
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selectedAnnoteEl = undefined;
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// Attach click handler to the DT
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const annoteDls = window.document.querySelectorAll('dt[data-target-cell]');
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for (const annoteDlNode of annoteDls) {
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annoteDlNode.addEventListener('click', (event) => {
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const clickedEl = event.target;
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if (clickedEl !== selectedAnnoteEl) {
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unselectCodeLines();
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const activeEl = window.document.querySelector('dt[data-target-cell].code-annotation-active');
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if (activeEl) {
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activeEl.classList.remove('code-annotation-active');
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}
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selectCodeLines(clickedEl);
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clickedEl.classList.add('code-annotation-active');
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} else {
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// Unselect the line
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unselectCodeLines();
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clickedEl.classList.remove('code-annotation-active');
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const findCites = (el) => {
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const parentEl = el.parentElement;
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if (parentEl) {
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const cites = parentEl.dataset.cites;
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if (cites) {
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return {
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el,
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cites: cites.split(' ')
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return findCites(el.parentElement)
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var bibliorefs = window.document.querySelectorAll('a[role="doc-biblioref"]');
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for (var i=0; i<bibliorefs.length; i++) {
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const ref = bibliorefs[i];
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const citeInfo = findCites(ref);
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if (citeInfo) {
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tippyHover(citeInfo.el, function() {
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var popup = window.document.createElement('div');
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citeInfo.cites.forEach(function(cite) {
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var citeDiv = window.document.createElement('div');
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citeDiv.classList.add('hanging-indent');
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citeDiv.classList.add('csl-entry');
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|
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</script>
|
||||
<nav class="page-navigation">
|
||||
<div class="nav-page nav-page-previous">
|
||||
<a href="./chapter_4.html" class="pagination-link">
|
||||
<i class="bi bi-arrow-left-short"></i> <span class="nav-page-text"><span class="chapter-number">5</span> <span class="chapter-title">Data scraping, corpus analysis and wordclouds</span></span>
|
||||
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|
||||
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|
||||
<div class="nav-page nav-page-next">
|
||||
<a href="./references.html" class="pagination-link">
|
||||
<span class="nav-page-text">References</span> <i class="bi bi-arrow-right-short"></i>
|
||||
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|
||||
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|
||||
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|
||||
</div> <!-- /content -->
|
||||
|
||||
|
||||
|
||||
</body></html>
|
||||
|
|
@ -44,6 +44,12 @@ Each of these measures a particular kind of dimension, and it is interesting to
|
|||
|
||||
You'll find that many surveys will only use one of these forms of question and ignore the rest. I think this is a really bad idea as religious belonging, identity, and spirituality are far too complex to work off a single form of response. We can also test out how these different attributions relate to other demographic features, like interest in politics, economic attainment, etc.
|
||||
|
||||
::: {.callout-tip}
|
||||
## So *who's* religious?
|
||||
|
||||
Content tbd
|
||||
:::
|
||||
|
||||
Let's dive into the data and see how this all works out:
|
||||
|
||||
|
||||
|
|
@ -55,7 +61,6 @@ library(RColorBrewer)
|
|||
library(hrbrthemes) # Used for ipsum theme etc.
|
||||
library(ggeasy) # used for easy_center_title() which is not strictly necessary, but tidier than theme(plot.title = element_text(hjust = 0.5))
|
||||
|
||||
|
||||
# Define colour palettes
|
||||
# TODO: confirm final colour scheme for charts and normalise across usage of different themes
|
||||
coul3 <- brewer.pal(3, "RdYlBu") # Using RdYlBu range to generate 3 colour palette: https://colorbrewer2.org/#type=diverging&scheme=RdYlBu&n=5
|
||||
|
|
@ -399,12 +404,36 @@ chart_single_result_flextable_unsorted <- function(.data, var) {
|
|||
```
|
||||
|
||||
```{r}
|
||||
religious_affiliation <- qualtrics_process_single_multiple_choice(as.factor(climate_experience_data$Q56))
|
||||
|
||||
# religious_affiliation
|
||||
# migrate haven data into separate data frame
|
||||
religious_affiliation <- as_tibble(as_factor(climate_experience_data$Q56))
|
||||
# make column names coherent and simplified
|
||||
names(religious_affiliation) <- c("response")
|
||||
# filter out NA values
|
||||
religious_affiliation <- filter(religious_affiliation, !is.na(response))
|
||||
# generate new dataframe with sums per category and sort in descending order
|
||||
religious_affiliation_sums <- religious_affiliation %>%
|
||||
dplyr::count(response, sort = TRUE) %>%
|
||||
dplyr::mutate(response = forcats::fct_rev(forcats::fct_inorder(response)))
|
||||
# add new column with percentages for each sum
|
||||
religious_affiliation_sums <- religious_affiliation_sums %>%
|
||||
dplyr::mutate(perc = scales::percent(n / sum(n), accuracy = 1, trim = FALSE))
|
||||
|
||||
|
||||
# TODO: use mutate to put "prefer not to say" at the bottom
|
||||
# Info here: https://r4ds.had.co.nz/factors.html#modifying-factor-levels
|
||||
|
||||
caption <- "Religious Affiliation"
|
||||
religious_affiliation_plot <- plot_horizontal_bar(religious_affiliation)
|
||||
|
||||
# make plot
|
||||
ggplot(religious_affiliation_sums, aes(x = n, y = response)) +
|
||||
geom_col(colour = "white") +
|
||||
## add percentage labels
|
||||
geom_text(aes(label = perc),
|
||||
## make labels left-aligned and white
|
||||
hjust = 1, nudge_x = -.5, colour = "black", size=3)
|
||||
|
||||
religious_affiliation_plot <- religious_affiliation_plot + labs(caption = caption, x = "", y = "")
|
||||
religious_affiliation_plot
|
||||
ggsave("figures/q56_religious_affiliation.png", width = 20, height = 10, units = "cm")
|
||||
|
|
@ -614,10 +643,6 @@ climate_experience_data <- climate_experience_data %>%
|
|||
)
|
||||
```
|
||||
|
||||
::: {.callout-tip}
|
||||
## How can we measure religion?
|
||||
Content tbd
|
||||
:::
|
||||
|
||||
|
||||
# References {.unnumbered}
|
||||
|
|
|
|||
Loading…
Reference in a new issue