transfering old posts
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type: website
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render:
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- "*.qmd"
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- "!old_posts/"
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- "*.rmd"
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- "!old_posts/" #using for the time being to store old blog post while converting
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website:
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title: "Kyle Belanger"
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<h5 class="quarto-listing-category-title">Categories</h5><div class="quarto-listing-category category-default"><div class="category" data-category="">All <span class="quarto-category-count">(4)</span></div></div></div>
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<div class="thumbnail">
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<p><a href="./posts/2020-02-13_basic-who-TB-data/basic-exploration-of-who-tuberculosis-data.html"> <p class="card-img-top"><img src="posts\2020-02-13_basic-who-TB-data\basic-exploration-of-who-tuberculosis-data_files\figure-html\unnamed-chunk-5-1.png" class="thumbnail-image card-img"/></p> </a></p>
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<a href="./posts/2020-02-13_basic-who-TB-data/basic-exploration-of-who-tuberculosis-data.html">
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<h3 class="no-anchor listing-title">
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Basic Exploration of WHO Tuberculosis Data
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</h3>
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<div class="listing-subtitle">
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<p>Today I am going to dive into some real life data from the World Health Organization (WHO), exploring new and relapse cases of Tuberculosis. I clean up the data, and then make a few graphs to explore different variables.</p>
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</div>
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<div class="listing-description">
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Today I am going to dive into some real…
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</a>
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<div class="metadata">
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<a href="./posts/2020-02-13_basic-who-TB-data/basic-exploration-of-who-tuberculosis-data.html">
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<div class="listing-date">
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Feb 13, 2020
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</div>
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<div class="listing-author">
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Kyle Belanger
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</div>
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</a>
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</div>
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<div class="thumbnail">
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<p><a href="./posts/2020-02-10_line-graphs-and-interactivity/line-graphs-and-interactivity.html"> <p class="card-img-top"><img src="posts\2020-02-10_line-graphs-and-interactivity\flu_surveillance.png" alt="Example Line Graph" class="thumbnail-image card-img"/></p> </a></p>
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</div>
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<div class="body">
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<a href="./posts/2020-02-10_line-graphs-and-interactivity/line-graphs-and-interactivity.html">
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<h3 class="no-anchor listing-title">
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Line Graphs and Interactivity
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</h3>
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<div class="listing-subtitle">
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<p>Tableau for Healthcare Chapter 10. Static and Interactive examples</p>
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</div>
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<div class="listing-description">
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Today’s post is all about line graphs using both ggplot…
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</a>
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</div>
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<div class="metadata">
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<a href="./posts/2020-02-10_line-graphs-and-interactivity/line-graphs-and-interactivity.html">
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<div class="listing-date">
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Feb 10, 2020
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</div>
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<div class="listing-author">
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Kyle Belanger
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</div>
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</a>
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</div>
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</div>
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<div class="thumbnail">
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<p><a href="./posts/2020-01-29_facets-and-humility/facets-and-a-lesson-in-humility.html"> <p class="card-img-top"><img src="posts\2020-01-29_facets-and-humility\WHO_LIFE.png" class="thumbnail-image card-img"/></p> </a></p>
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</div>
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<div class="body">
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<a href="./posts/2020-01-29_facets-and-humility/facets-and-a-lesson-in-humility.html">
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<h3 class="no-anchor listing-title">
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Facets and a Lesson in Humility
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</h3>
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<div class="listing-subtitle">
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<p>A look at Tableau for Healthcare Chapter 8. Table Lens graph.</p>
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</div>
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<div class="listing-description">
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Todays post is a lesson in Facets, as well as humility. The task this week was to replicate the graph in Chapter 8 of Tableau for Healthcare in R. The graph in question is…
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</div>
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</a>
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</div>
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<div class="metadata">
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<a href="./posts/2020-01-29_facets-and-humility/facets-and-a-lesson-in-humility.html">
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<div class="listing-date">
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Jan 29, 2020
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</div>
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<div class="listing-author">
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Kyle Belanger
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</div>
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</a>
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</div>
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</div>
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<div class="thumbnail">
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<p><a href="./posts/2020-01-04_my-start-to-r/my-start-to-r.html"> <p class="card-img-top"><img src="posts\2020-01-04_my-start-to-r\my-start-to-r_files\figure-html\unnamed-chunk-5-1.png" class="thumbnail-image card-img"/></p> </a></p>
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<div class="quarto-title column-body">
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<h1 class="title">Facets and a Lesson in Humility</h1>
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<p class="subtitle lead"></p><p>A look at Tableau for Healthcare Chapter 8. Table Lens graph.</p><p></p>
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</div>
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<div class="quarto-title-meta-heading">Author</div>
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<p><a href="https://kyleb.rbind.io/">Kyle Belanger</a> </p>
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<p class="date">January 29, 2020</p>
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<h2 id="toc-title">Table of contents</h2>
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<ul>
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<li><a href="#load-libraries" id="toc-load-libraries" class="nav-link active" data-scroll-target="#load-libraries">Load Libraries</a></li>
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<li><a href="#import-data" id="toc-import-data" class="nav-link" data-scroll-target="#import-data">Import Data</a></li>
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<li><a href="#clean-names-and-transform" id="toc-clean-names-and-transform" class="nav-link" data-scroll-target="#clean-names-and-transform">Clean Names and Transform</a></li>
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<p>Todays post is a lesson in Facets, as well as humility. The task this week was to replicate the graph in Chapter 8 of Tableau for Healthcare in R. The graph in question is called a Table Lens (This is the name the book uses, however I did have trouble finding this name in Google searches), it is a collection of charts with a common theme, this time looking at countries in various WHO regions and some statistics associated with mortality as well as health expenditure. I say this is a lesson in humiltiy as I have read through the excellent book <a href="https://r4ds.had.co.nz/">R for Data Science</a>, and yet the idea of faceting a ggplot graph slipped my mind. This ended with hours of trying to find a package in R to line up graphs, and way more time then I care to admit spent on getting things prefect. I did find such a package called cowplots, which can be found <a href="https://wilkelab.org/cowplot/index.html">here</a>. While this is an excellent package, its use was unecessary and I reverted back to using the excellent facet feature of GGplot, which can be seen below! <img src="WHO_LIFE.png" class="img-fluid"></p>
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<section id="load-libraries" class="level1">
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<h1>Load Libraries</h1>
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<div class="cell">
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<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">library</span>(magrittr) <span class="co">#pipes</span></span>
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<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggplot2) <span class="co">#ploting </span></span>
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<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(dplyr)</span>
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<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(tidyr)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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</div>
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</section>
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<section id="import-data" class="level1">
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<h1>Import Data</h1>
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<div class="cell">
|
||||
<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>ds <span class="ot"><-</span> readxl<span class="sc">::</span><span class="fu">read_xlsx</span>(<span class="at">path =</span> <span class="st">"../2020-01-04_my-start-to-r/Tableau 10 Training Practice Data.xlsx"</span></span>
|
||||
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a> ,<span class="at">sheet =</span> <span class="st">"03 - WHO Life Expect & Mort"</span></span>
|
||||
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a> )</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||||
</div>
|
||||
</section>
|
||||
<section id="clean-names-and-transform" class="level1">
|
||||
<h1>Clean Names and Transform</h1>
|
||||
<div class="cell">
|
||||
<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>varnames <span class="ot"><-</span> <span class="fu">c</span>(<span class="st">"who_region"</span>, <span class="st">"country"</span>, <span class="st">"year"</span> , <span class="st">"sex"</span> , <span class="st">"life_expect_birth"</span> , <span class="st">"neo_mort"</span></span>
|
||||
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a> ,<span class="st">"under_five_mort"</span> , <span class="st">"health_expenditure"</span>)</span>
|
||||
<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a><span class="fu">names</span>(ds) <span class="ot"><-</span> varnames</span>
|
||||
<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a></span>
|
||||
<span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a><span class="co"># Order Countries based on Life Expectancy at Birth</span></span>
|
||||
<span id="cb3-6"><a href="#cb3-6" aria-hidden="true" tabindex="-1"></a></span>
|
||||
<span id="cb3-7"><a href="#cb3-7" aria-hidden="true" tabindex="-1"></a>ds<span class="sc">$</span>country <span class="ot"><-</span> <span class="fu">factor</span>(ds<span class="sc">$</span>country, <span class="at">levels =</span> ds<span class="sc">$</span>country[<span class="fu">order</span>(ds<span class="sc">$</span>life_expect_birth)]) </span>
|
||||
<span id="cb3-8"><a href="#cb3-8" aria-hidden="true" tabindex="-1"></a></span>
|
||||
<span id="cb3-9"><a href="#cb3-9" aria-hidden="true" tabindex="-1"></a><span class="co">#To "Long" Form</span></span>
|
||||
<span id="cb3-10"><a href="#cb3-10" aria-hidden="true" tabindex="-1"></a></span>
|
||||
<span id="cb3-11"><a href="#cb3-11" aria-hidden="true" tabindex="-1"></a>ds1 <span class="ot"><-</span> ds <span class="sc">%>%</span> <span class="fu">pivot_longer</span>(<span class="dv">5</span><span class="sc">:</span><span class="dv">8</span>)<span class="co">#select columns 5 throuh 8, leave new columns at default names</span></span>
|
||||
<span id="cb3-12"><a href="#cb3-12" aria-hidden="true" tabindex="-1"></a></span>
|
||||
<span id="cb3-13"><a href="#cb3-13" aria-hidden="true" tabindex="-1"></a><span class="co"># Set up labels for Facet, as well as function for Facet Labeller</span></span>
|
||||
<span id="cb3-14"><a href="#cb3-14" aria-hidden="true" tabindex="-1"></a></span>
|
||||
<span id="cb3-15"><a href="#cb3-15" aria-hidden="true" tabindex="-1"></a>facet_labels <span class="ot"><-</span> <span class="fu">list</span>(</span>
|
||||
<span id="cb3-16"><a href="#cb3-16" aria-hidden="true" tabindex="-1"></a><span class="st">"life_expect_birth"</span> <span class="ot">=</span> <span class="st">"Life Expectancy at Birth "</span> </span>
|
||||
<span id="cb3-17"><a href="#cb3-17" aria-hidden="true" tabindex="-1"></a>,<span class="st">"neo_mort"</span> <span class="ot">=</span> <span class="st">"Neonatal Mortality Rate"</span> </span>
|
||||
<span id="cb3-18"><a href="#cb3-18" aria-hidden="true" tabindex="-1"></a>,<span class="st">"under_five_mort"</span> <span class="ot">=</span> <span class="st">"Under-Five Mortality Rate"</span></span>
|
||||
<span id="cb3-19"><a href="#cb3-19" aria-hidden="true" tabindex="-1"></a>,<span class="st">"health_expenditure"</span> <span class="ot">=</span> <span class="st">"Health Expenditure per Capita (US$)"</span> )</span>
|
||||
<span id="cb3-20"><a href="#cb3-20" aria-hidden="true" tabindex="-1"></a></span>
|
||||
<span id="cb3-21"><a href="#cb3-21" aria-hidden="true" tabindex="-1"></a>variable_labeller <span class="ot"><-</span> <span class="cf">function</span>(variable,value){</span>
|
||||
<span id="cb3-22"><a href="#cb3-22" aria-hidden="true" tabindex="-1"></a> <span class="fu">return</span>(facet_labels[value])</span>
|
||||
<span id="cb3-23"><a href="#cb3-23" aria-hidden="true" tabindex="-1"></a>}</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||||
</div>
|
||||
</section>
|
||||
<section id="graphs" class="level1">
|
||||
<h1>Graphs</h1>
|
||||
<div class="cell">
|
||||
<div class="sourceCode cell-code" id="cb4"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>hightlight_countries <span class="ot"><-</span> (<span class="fu">c</span>(<span class="st">"Mauritania"</span>, <span class="st">"South Africa"</span>)) </span>
|
||||
<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a></span>
|
||||
<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a>g1 <span class="ot"><-</span> ds1 <span class="sc">%>%</span> <span class="fu">filter</span>(who_region <span class="sc">==</span> <span class="st">"Africa"</span>) <span class="sc">%>%</span> </span>
|
||||
<span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">name =</span> <span class="fu">factor</span>(name, <span class="at">levels =</span> <span class="fu">c</span>(<span class="st">"life_expect_birth"</span> , <span class="st">"neo_mort"</span></span>
|
||||
<span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a> ,<span class="st">"under_five_mort"</span> , <span class="st">"health_expenditure"</span>))</span>
|
||||
<span id="cb4-6"><a href="#cb4-6" aria-hidden="true" tabindex="-1"></a> ,<span class="at">highlight =</span> country <span class="sc">%in%</span> hightlight_countries) <span class="sc">%>%</span> </span>
|
||||
<span id="cb4-7"><a href="#cb4-7" aria-hidden="true" tabindex="-1"></a> <span class="fu">ggplot</span>(<span class="fu">aes</span>(<span class="at">x =</span> country, <span class="at">y =</span> value, <span class="at">fill =</span> highlight)) <span class="sc">+</span></span>
|
||||
<span id="cb4-8"><a href="#cb4-8" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_col</span>(<span class="at">show.legend =</span> <span class="cn">FALSE</span>) <span class="sc">+</span></span>
|
||||
<span id="cb4-9"><a href="#cb4-9" aria-hidden="true" tabindex="-1"></a> <span class="fu">coord_flip</span>() <span class="sc">+</span></span>
|
||||
<span id="cb4-10"><a href="#cb4-10" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span>
|
||||
<span id="cb4-11"><a href="#cb4-11" aria-hidden="true" tabindex="-1"></a> <span class="at">title =</span> <span class="st">"World Bank Life Expectancy, Neonatal & Under-Five Mortality Rates, and Health Expenditure Analysis"</span></span>
|
||||
<span id="cb4-12"><a href="#cb4-12" aria-hidden="true" tabindex="-1"></a> ,<span class="at">x =</span> <span class="cn">NULL</span></span>
|
||||
<span id="cb4-13"><a href="#cb4-13" aria-hidden="true" tabindex="-1"></a> ,<span class="at">y =</span> <span class="cn">NULL</span></span>
|
||||
<span id="cb4-14"><a href="#cb4-14" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">+</span></span>
|
||||
<span id="cb4-15"><a href="#cb4-15" aria-hidden="true" tabindex="-1"></a> <span class="fu">facet_grid</span>(<span class="sc">~</span>name, <span class="at">scales =</span> <span class="st">"free_x"</span>,<span class="at">labeller =</span> variable_labeller) <span class="sc">+</span></span>
|
||||
<span id="cb4-16"><a href="#cb4-16" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme_bw</span>() <span class="sc">+</span></span>
|
||||
<span id="cb4-17"><a href="#cb4-17" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_text</span>(<span class="fu">aes</span>(<span class="at">label =</span> <span class="fu">round</span>(value, <span class="dv">0</span>)), <span class="at">hjust =</span> <span class="dv">0</span>) <span class="sc">+</span></span>
|
||||
<span id="cb4-18"><a href="#cb4-18" aria-hidden="true" tabindex="-1"></a> <span class="fu">scale_y_continuous</span>(<span class="at">expand =</span> <span class="fu">expand_scale</span>(<span class="at">mult =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="fl">0.2</span>))) <span class="sc">+</span></span>
|
||||
<span id="cb4-19"><a href="#cb4-19" aria-hidden="true" tabindex="-1"></a> <span class="fu">scale_fill_manual</span>(<span class="at">values =</span> <span class="fu">c</span>(<span class="st">"TRUE"</span> <span class="ot">=</span> <span class="st">"#fc8d59"</span>, <span class="st">"FALSE"</span> <span class="ot">=</span> <span class="st">"#2b83ba"</span>))</span>
|
||||
<span id="cb4-20"><a href="#cb4-20" aria-hidden="true" tabindex="-1"></a>g1</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="facets-and-a-lesson-in-humility_files/figure-html/unnamed-chunk-4-1.png" class="img-fluid" width="1152"></p>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
|
||||
</section>
|
||||
|
||||
<div id="quarto-appendix" class="default"><section class="quarto-appendix-contents"><h2 class="anchored quarto-appendix-heading">Reuse</h2><div id="quarto-reuse" class="quarto-appendix-contents"><div><a rel="license" href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a></div></div></section><section class="quarto-appendix-contents"><h2 class="anchored quarto-appendix-heading">Citation</h2><div><div class="quarto-appendix-secondary-label">BibTeX citation:</div><pre class="sourceCode code-with-copy quarto-appendix-bibtex"><code class="sourceCode bibtex">@online{belanger2020,
|
||||
author = {Belanger, Kyle},
|
||||
title = {Facets and a {Lesson} in {Humility}},
|
||||
date = {2020-01-29},
|
||||
langid = {en}
|
||||
}
|
||||
</code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre><div class="quarto-appendix-secondary-label">For attribution, please cite this work as:</div><div id="ref-belanger2020" class="csl-entry quarto-appendix-citeas" role="listitem">
|
||||
Belanger, Kyle. 2020. <span>“Facets and a Lesson in Humility.”</span>
|
||||
January 29, 2020.
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<h1 class="title">Line Graphs and Interactivity</h1>
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<p class="subtitle lead"></p><p>Tableau for Healthcare Chapter 10. Static and Interactive examples</p><p></p>
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<p><a href="https://kyleb.rbind.io/">Kyle Belanger</a> </p>
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<p>Today’s post is all about line graphs using both ggplot for a static graph as well as a package called plotly for interactivity (more on this later). The example graph and data is again coming from Tableau for Healthcare, Chapter 10. <img src="flu_surveillance.png" class="img-fluid" alt="Example Line Graph"></p>
|
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<section id="load-libraries" class="level2">
|
||||
<h2 class="anchored" data-anchor-id="load-libraries">Load Libraries</h2>
|
||||
<p>As always first step is to load in our libraries, I am using quite a few here, some are a bit overkill for this example but I wanted to play around with some fun features today.</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">library</span>(magrittr) <span class="co">#pipes</span></span>
|
||||
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggplot2) <span class="co">#ploting </span></span>
|
||||
<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(dplyr) <span class="co"># data manipulation</span></span>
|
||||
<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(tidyr) <span class="co"># tidy data</span></span>
|
||||
<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(lubridate) <span class="co">#work with dates</span></span>
|
||||
<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(stringr) <span class="co"># manipulate strings</span></span>
|
||||
<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(plotly)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||||
</div>
|
||||
</section>
|
||||
<section id="import-data" class="level2">
|
||||
<h2 class="anchored" data-anchor-id="import-data">Import Data</h2>
|
||||
<p>Next lets import our data, this week we are using the sheet Flu Occurrence FY2013-2016. I am unsure if this is form a real data set or not but it is good for demonstration purposes! After importing we can glimpse at our data to understand what is contained within.</p>
|
||||
<div class="cell">
|
||||
<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>ds <span class="ot"><-</span> readxl<span class="sc">::</span><span class="fu">read_xlsx</span>(<span class="at">path =</span> <span class="st">"../2020-01-04_my-start-to-r/Tableau 10 Training Practice Data.xlsx"</span></span>
|
||||
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a> ,<span class="at">sheet =</span> <span class="st">"05 - Flu Occurrence FY2013-2016"</span></span>
|
||||
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a> )</span>
|
||||
<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a>ds <span class="sc">%>%</span> <span class="fu">glimpse</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>Rows: 48
|
||||
Columns: 4
|
||||
$ Date <dttm> 2012-10-27, 2012-11-24, …
|
||||
$ `Tests (+) for Influenza (count)` <dbl> 995, 3228, 22368, 24615, …
|
||||
$ `Total Respiratory Specimens Tested (count)` <dbl> 18986, 24757, 66683, 7561…
|
||||
$ `% Tests (+) for Influenza` <dbl> 0.05240704, 0.13038737, 0…</code></pre>
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
<section id="transform-data" class="level2">
|
||||
<h2 class="anchored" data-anchor-id="transform-data">Transform Data</h2>
|
||||
<p>I went a bit overboard today with renaming the variables. I wanted to practice writing a function and while it might not be the prettiest or the best way to do this, it worked for what I was trying to accomplish. Note the use of sapply, which lets us run the function on each column name.</p>
|
||||
<div class="cell">
|
||||
<div class="sourceCode cell-code" id="cb4"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>format_names <span class="ot"><-</span> <span class="cf">function</span>(x) {</span>
|
||||
<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a> <span class="co">#Fucntion to set all names to lower case, and strip unneeded characters</span></span>
|
||||
<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a> x <span class="ot"><-</span> <span class="fu">tolower</span>(x)</span>
|
||||
<span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a> x <span class="ot"><-</span> <span class="fu">str_replace_all</span>(x,<span class="fu">c</span>(<span class="co">#set each pattern equal to replacement</span></span>
|
||||
<span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a> <span class="st">" "</span> <span class="ot">=</span> <span class="st">"_"</span></span>
|
||||
<span id="cb4-6"><a href="#cb4-6" aria-hidden="true" tabindex="-1"></a> ,<span class="st">"</span><span class="sc">\\</span><span class="st">(</span><span class="sc">\\</span><span class="st">+</span><span class="sc">\\</span><span class="st">)"</span> <span class="ot">=</span> <span class="st">"pos"</span> <span class="co">#regualr experssion to match (+)</span></span>
|
||||
<span id="cb4-7"><a href="#cb4-7" aria-hidden="true" tabindex="-1"></a> ,<span class="st">"</span><span class="sc">\\</span><span class="st">("</span> <span class="ot">=</span> <span class="st">""</span></span>
|
||||
<span id="cb4-8"><a href="#cb4-8" aria-hidden="true" tabindex="-1"></a> ,<span class="st">"</span><span class="sc">\\</span><span class="st">)"</span> <span class="ot">=</span> <span class="st">""</span></span>
|
||||
<span id="cb4-9"><a href="#cb4-9" aria-hidden="true" tabindex="-1"></a> ,<span class="st">"</span><span class="sc">\\</span><span class="st">%"</span> <span class="ot">=</span> <span class="st">"pct"</span></span>
|
||||
<span id="cb4-10"><a href="#cb4-10" aria-hidden="true" tabindex="-1"></a> )</span>
|
||||
<span id="cb4-11"><a href="#cb4-11" aria-hidden="true" tabindex="-1"></a> ) </span>
|
||||
<span id="cb4-12"><a href="#cb4-12" aria-hidden="true" tabindex="-1"></a> }</span>
|
||||
<span id="cb4-13"><a href="#cb4-13" aria-hidden="true" tabindex="-1"></a></span>
|
||||
<span id="cb4-14"><a href="#cb4-14" aria-hidden="true" tabindex="-1"></a><span class="co">#run the format name function on all names from DS</span></span>
|
||||
<span id="cb4-15"><a href="#cb4-15" aria-hidden="true" tabindex="-1"></a><span class="fu">colnames</span>(ds) <span class="ot"><-</span> <span class="fu">sapply</span>(<span class="fu">colnames</span>(ds),format_names) </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||||
</div>
|
||||
<p>Now is were the fun really starts! For this particular data set there are a couple things we need to add to replicate the example. In the original data set the date is stored with month, day, and year; the day is irrelevant and we need to pull out the month as well as the year. For this we can use the lubridate package, first we pull out the month and set it as a factor. For this example our year actually starts in October, so we set our factor to start at October (10), and end with September (9). We then pull out the year, which presents us with a different problem. Again our year starts in October, instead of January. To solve this I have created a variable called date adjustment, in this column is our month is 10 or greater, we will place a 1, if not a 0. We then set our fiscal year to be the actual year plus the date adjustment, this allows us to have our dates in the right fiscal year. Last the percent column is currently listed as a decimal, so we will convert this to a percentage.</p>
|
||||
<div class="cell">
|
||||
<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="co"># split date time</span></span>
|
||||
<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>ds1 <span class="ot"><-</span> ds <span class="sc">%>%</span> <span class="fu">mutate</span>(</span>
|
||||
<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a> <span class="co">#create month column, then set factors and labels to start fiscal year in Oct</span></span>
|
||||
<span id="cb5-4"><a href="#cb5-4" aria-hidden="true" tabindex="-1"></a> <span class="at">month =</span> <span class="fu">month</span>(ds<span class="sc">$</span>date)</span>
|
||||
<span id="cb5-5"><a href="#cb5-5" aria-hidden="true" tabindex="-1"></a> ,<span class="at">month =</span> <span class="fu">factor</span>(month</span>
|
||||
<span id="cb5-6"><a href="#cb5-6" aria-hidden="true" tabindex="-1"></a> ,<span class="at">levels =</span> <span class="fu">c</span>(<span class="dv">10</span><span class="sc">:</span><span class="dv">12</span>, <span class="dv">1</span><span class="sc">:</span><span class="dv">9</span>)</span>
|
||||
<span id="cb5-7"><a href="#cb5-7" aria-hidden="true" tabindex="-1"></a> ,<span class="at">labels =</span> <span class="fu">c</span>(month.abb[<span class="dv">10</span><span class="sc">:</span><span class="dv">12</span>],month.abb[<span class="dv">1</span><span class="sc">:</span><span class="dv">9</span>]))</span>
|
||||
<span id="cb5-8"><a href="#cb5-8" aria-hidden="true" tabindex="-1"></a> ,<span class="at">year =</span> <span class="fu">year</span>(ds<span class="sc">$</span>date)</span>
|
||||
<span id="cb5-9"><a href="#cb5-9" aria-hidden="true" tabindex="-1"></a> ,<span class="at">date_adjustment =</span> <span class="fu">ifelse</span>(<span class="fu">month</span>(ds<span class="sc">$</span>date) <span class="sc">>=</span> <span class="dv">10</span>, <span class="dv">1</span>,<span class="dv">0</span> )</span>
|
||||
<span id="cb5-10"><a href="#cb5-10" aria-hidden="true" tabindex="-1"></a> ,<span class="at">fiscal_year =</span> <span class="fu">factor</span>(year <span class="sc">+</span> date_adjustment)</span>
|
||||
<span id="cb5-11"><a href="#cb5-11" aria-hidden="true" tabindex="-1"></a> <span class="co">#convert % Pos from decmial to pct</span></span>
|
||||
<span id="cb5-12"><a href="#cb5-12" aria-hidden="true" tabindex="-1"></a> ,<span class="at">pct_tests_pos_for_influenza =</span> <span class="fu">round</span>(pct_tests_pos_for_influenza <span class="sc">*</span> <span class="dv">100</span>, <span class="at">digits =</span> <span class="dv">0</span>)</span>
|
||||
<span id="cb5-13"><a href="#cb5-13" aria-hidden="true" tabindex="-1"></a> )</span>
|
||||
<span id="cb5-14"><a href="#cb5-14" aria-hidden="true" tabindex="-1"></a></span>
|
||||
<span id="cb5-15"><a href="#cb5-15" aria-hidden="true" tabindex="-1"></a>ds1 <span class="sc">%>%</span> <span class="fu">glimpse</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>Rows: 48
|
||||
Columns: 8
|
||||
$ date <dttm> 2012-10-27, 2012-11-24, 2012…
|
||||
$ tests_pos_for_influenza_count <dbl> 995, 3228, 22368, 24615, 1179…
|
||||
$ total_respiratory_specimens_tested_count <dbl> 18986, 24757, 66683, 75614, 5…
|
||||
$ pct_tests_pos_for_influenza <dbl> 5, 13, 34, 33, 23, 17, 11, 6,…
|
||||
$ month <fct> Oct, Nov, Dec, Jan, Feb, Mar,…
|
||||
$ year <dbl> 2012, 2012, 2012, 2013, 2013,…
|
||||
$ date_adjustment <dbl> 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,…
|
||||
$ fiscal_year <fct> 2013, 2013, 2013, 2013, 2013,…</code></pre>
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
<section id="ggplot" class="level2">
|
||||
<h2 class="anchored" data-anchor-id="ggplot">GGplot</h2>
|
||||
<p>The graph here is pretty straight forward with one exception, group! For this line graph we want ggplot to connect the lines of the same year, if we do not explicitly state this using the group mapping, ggplot will try to connect all the lines together, which of course is not at all what we want!</p>
|
||||
<div class="cell">
|
||||
<div class="sourceCode cell-code" id="cb7"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>g1 <span class="ot"><-</span> ds1 <span class="sc">%>%</span> </span>
|
||||
<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">ggplot</span>(<span class="fu">aes</span>(<span class="at">x =</span> month, <span class="at">y =</span> pct_tests_pos_for_influenza, <span class="at">color =</span> fiscal_year</span>
|
||||
<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a> ,<span class="at">group =</span> fiscal_year)) <span class="sc">+</span></span>
|
||||
<span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_line</span>() <span class="sc">+</span></span>
|
||||
<span id="cb7-5"><a href="#cb7-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span>
|
||||
<span id="cb7-6"><a href="#cb7-6" aria-hidden="true" tabindex="-1"></a> <span class="at">x =</span> <span class="cn">NULL</span></span>
|
||||
<span id="cb7-7"><a href="#cb7-7" aria-hidden="true" tabindex="-1"></a> ,<span class="at">y =</span> <span class="st">"% Tests (+) for Influenza"</span></span>
|
||||
<span id="cb7-8"><a href="#cb7-8" aria-hidden="true" tabindex="-1"></a> ,<span class="at">color =</span> <span class="cn">NULL</span></span>
|
||||
<span id="cb7-9"><a href="#cb7-9" aria-hidden="true" tabindex="-1"></a> ,<span class="at">title =</span> <span class="st">"Flu Viral Surveillance: % Respiratory Specimens Positive for Influenza </span><span class="sc">\n</span><span class="st">October - September </span><span class="sc">\n</span><span class="st">For Flu Seasons 2013 - 2016"</span></span>
|
||||
<span id="cb7-10"><a href="#cb7-10" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">+</span></span>
|
||||
<span id="cb7-11"><a href="#cb7-11" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme_classic</span>() <span class="sc">+</span></span>
|
||||
<span id="cb7-12"><a href="#cb7-12" aria-hidden="true" tabindex="-1"></a> <span class="fu">scale_y_continuous</span>(<span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">40</span>,<span class="dv">5</span>)) <span class="sc">+</span></span>
|
||||
<span id="cb7-13"><a href="#cb7-13" aria-hidden="true" tabindex="-1"></a> <span class="fu">scale_color_manual</span>(<span class="at">values =</span> <span class="fu">c</span>(<span class="st">"#a6611a"</span>,<span class="st">"#dfc27d"</span>,<span class="st">"#80cdc1"</span>,<span class="st">"#018571"</span>))</span>
|
||||
<span id="cb7-14"><a href="#cb7-14" aria-hidden="true" tabindex="-1"></a></span>
|
||||
<span id="cb7-15"><a href="#cb7-15" aria-hidden="true" tabindex="-1"></a>g1</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="line-graphs-and-interactivity_files/figure-html/unnamed-chunk-5-1.png" class="img-fluid" width="672"></p>
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
<section id="plotly" class="level2">
|
||||
<h2 class="anchored" data-anchor-id="plotly">plotly</h2>
|
||||
<p>One of the nice features of Tableau is the fact the graphs are interactive, while a good graph should speak for itself, end users love pretty things. I have been experimenting with Plotly, which has an open source package for R (as well as many other programming languages!). This example only just scratches the surface, but there will be many more to come!</p>
|
||||
<div class="cell">
|
||||
<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>g2 <span class="ot"><-</span> ds1 <span class="sc">%>%</span> </span>
|
||||
<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">plot_ly</span>(<span class="at">x =</span> <span class="sc">~</span>month, <span class="at">y =</span> <span class="sc">~</span>pct_tests_pos_for_influenza, <span class="at">type =</span> <span class="st">"scatter"</span>, <span class="at">mode =</span> <span class="st">"lines"</span> </span>
|
||||
<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a> ,<span class="at">color =</span> <span class="sc">~</span>fiscal_year</span>
|
||||
<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a> ,<span class="at">colors =</span> <span class="fu">c</span>(<span class="st">"#a6611a"</span>,<span class="st">"#dfc27d"</span>,<span class="st">"#80cdc1"</span>,<span class="st">"#018571"</span>)</span>
|
||||
<span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a> , <span class="at">hoverinfo =</span> <span class="st">'y'</span>) <span class="sc">%>%</span> </span>
|
||||
<span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">layout</span>(<span class="at">xaxis =</span> <span class="fu">list</span>(</span>
|
||||
<span id="cb8-7"><a href="#cb8-7" aria-hidden="true" tabindex="-1"></a> <span class="at">title =</span> <span class="st">""</span></span>
|
||||
<span id="cb8-8"><a href="#cb8-8" aria-hidden="true" tabindex="-1"></a> )</span>
|
||||
<span id="cb8-9"><a href="#cb8-9" aria-hidden="true" tabindex="-1"></a> ,<span class="at">yaxis =</span> <span class="fu">list</span>(</span>
|
||||
<span id="cb8-10"><a href="#cb8-10" aria-hidden="true" tabindex="-1"></a> <span class="at">title =</span> <span class="st">"% Tests (+) for Influenza"</span></span>
|
||||
<span id="cb8-11"><a href="#cb8-11" aria-hidden="true" tabindex="-1"></a> )</span>
|
||||
<span id="cb8-12"><a href="#cb8-12" aria-hidden="true" tabindex="-1"></a> ,<span class="at">title =</span> <span class="st">"Flu Viral Surveillance: % Respiratory Specimens Positive for Influenza"</span></span>
|
||||
<span id="cb8-13"><a href="#cb8-13" aria-hidden="true" tabindex="-1"></a> ,<span class="at">legend =</span> <span class="fu">list</span>(</span>
|
||||
<span id="cb8-14"><a href="#cb8-14" aria-hidden="true" tabindex="-1"></a> <span class="at">x =</span> <span class="dv">100</span></span>
|
||||
<span id="cb8-15"><a href="#cb8-15" aria-hidden="true" tabindex="-1"></a> ,<span class="at">y =</span> <span class="fl">0.5</span></span>
|
||||
<span id="cb8-16"><a href="#cb8-16" aria-hidden="true" tabindex="-1"></a> ) </span>
|
||||
<span id="cb8-17"><a href="#cb8-17" aria-hidden="true" tabindex="-1"></a> </span>
|
||||
<span id="cb8-18"><a href="#cb8-18" aria-hidden="true" tabindex="-1"></a> )</span>
|
||||
<span id="cb8-19"><a href="#cb8-19" aria-hidden="true" tabindex="-1"></a></span>
|
||||
<span id="cb8-20"><a href="#cb8-20" aria-hidden="true" tabindex="-1"></a>g2</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||||
<div class="cell-output-display">
|
||||
<div class="plotly html-widget html-fill-item-overflow-hidden html-fill-item" id="htmlwidget-eb971b11b9886c1f8351" style="width:100%;height:464px;"></div>
|
||||
<script type="application/json" data-for="htmlwidget-eb971b11b9886c1f8351">{"x":{"visdat":{"72a023bd4952":["function () ","plotlyVisDat"]},"cur_data":"72a023bd4952","attrs":{"72a023bd4952":{"x":{},"y":{},"mode":"lines","hoverinfo":"y","color":{},"colors":["#a6611a","#dfc27d","#80cdc1","#018571"],"alpha_stroke":1,"sizes":[10,100],"spans":[1,20],"type":"scatter"}},"layout":{"margin":{"b":40,"l":60,"t":25,"r":10},"xaxis":{"domain":[0,1],"automargin":true,"title":"","type":"category","categoryorder":"array","categoryarray":["Oct","Nov","Dec","Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep"]},"yaxis":{"domain":[0,1],"automargin":true,"title":"% Tests (+) for Influenza"},"title":"Flu Viral Surveillance: % Respiratory Specimens Positive for Influenza","legend":{"x":100,"y":0.5},"hovermode":"closest","showlegend":true},"source":"A","config":{"modeBarButtonsToAdd":["hoverclosest","hovercompare"],"showSendToCloud":false},"data":[{"x":["Oct","Nov","Dec","Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep"],"y":[5,13,34,33,23,17,11,6,4,3,3,3],"mode":"lines","hoverinfo":["y","y","y","y","y","y","y","y","y","y","y","y"],"type":"scatter","name":"2013","marker":{"color":"rgba(166,97,26,1)","line":{"color":"rgba(166,97,26,1)"}},"textfont":{"color":"rgba(166,97,26,1)"},"error_y":{"color":"rgba(166,97,26,1)"},"error_x":{"color":"rgba(166,97,26,1)"},"line":{"color":"rgba(166,97,26,1)"},"xaxis":"x","yaxis":"y","frame":null},{"x":["Oct","Nov","Dec","Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep"],"y":[4,8,26,27,18,12,14,10,6,4,3,2],"mode":"lines","hoverinfo":["y","y","y","y","y","y","y","y","y","y","y","y"],"type":"scatter","name":"2014","marker":{"color":"rgba(223,194,125,1)","line":{"color":"rgba(223,194,125,1)"}},"textfont":{"color":"rgba(223,194,125,1)"},"error_y":{"color":"rgba(223,194,125,1)"},"error_x":{"color":"rgba(223,194,125,1)"},"line":{"color":"rgba(223,194,125,1)"},"xaxis":"x","yaxis":"y","frame":null},{"x":["Oct","Nov","Dec","Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep"],"y":[4,13,29,23,14,12,8,4,2,2,2,2],"mode":"lines","hoverinfo":["y","y","y","y","y","y","y","y","y","y","y","y"],"type":"scatter","name":"2015","marker":{"color":"rgba(128,205,193,1)","line":{"color":"rgba(128,205,193,1)"}},"textfont":{"color":"rgba(128,205,193,1)"},"error_y":{"color":"rgba(128,205,193,1)"},"error_x":{"color":"rgba(128,205,193,1)"},"line":{"color":"rgba(128,205,193,1)"},"xaxis":"x","yaxis":"y","frame":null},{"x":["Oct","Nov","Dec","Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep"],"y":[1,1,2,6,17,20,12,5,2,1,1,2],"mode":"lines","hoverinfo":["y","y","y","y","y","y","y","y","y","y","y","y"],"type":"scatter","name":"2016","marker":{"color":"rgba(1,133,113,1)","line":{"color":"rgba(1,133,113,1)"}},"textfont":{"color":"rgba(1,133,113,1)"},"error_y":{"color":"rgba(1,133,113,1)"},"error_x":{"color":"rgba(1,133,113,1)"},"line":{"color":"rgba(1,133,113,1)"},"xaxis":"x","yaxis":"y","frame":null}],"highlight":{"on":"plotly_click","persistent":false,"dynamic":false,"selectize":false,"opacityDim":0.2,"selected":{"opacity":1},"debounce":0},"shinyEvents":["plotly_hover","plotly_click","plotly_selected","plotly_relayout","plotly_brushed","plotly_brushing","plotly_clickannotation","plotly_doubleclick","plotly_deselect","plotly_afterplot","plotly_sunburstclick"],"base_url":"https://plot.ly"},"evals":[],"jsHooks":[]}</script>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
|
||||
</section>
|
||||
|
||||
<div id="quarto-appendix" class="default"><section class="quarto-appendix-contents"><h2 class="anchored quarto-appendix-heading">Reuse</h2><div id="quarto-reuse" class="quarto-appendix-contents"><div><a rel="license" href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a></div></div></section><section class="quarto-appendix-contents"><h2 class="anchored quarto-appendix-heading">Citation</h2><div><div class="quarto-appendix-secondary-label">BibTeX citation:</div><pre class="sourceCode code-with-copy quarto-appendix-bibtex"><code class="sourceCode bibtex">@online{belanger2020,
|
||||
author = {Belanger, Kyle},
|
||||
title = {Line {Graphs} and {Interactivity}},
|
||||
date = {2020-02-10},
|
||||
langid = {en}
|
||||
}
|
||||
</code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre><div class="quarto-appendix-secondary-label">For attribution, please cite this work as:</div><div id="ref-belanger2020" class="csl-entry quarto-appendix-citeas" role="listitem">
|
||||
Belanger, Kyle. 2020. <span>“Line Graphs and Interactivity.”</span>
|
||||
February 10, 2020.
|
||||
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<h1 class="title">Basic Exploration of WHO Tuberculosis Data</h1>
|
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<p class="subtitle lead"></p><p>Today I am going to dive into some real life data from the World Health Organization (WHO), exploring new and relapse cases of Tuberculosis. I clean up the data, and then make a few graphs to explore different variables.</p><p></p>
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<p><a href="https://kyleb.rbind.io/">Kyle Belanger</a> </p>
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<p class="date">February 13, 2020</p>
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<h2 id="toc-title">Table of contents</h2>
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<ul>
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<li><a href="#tldr" id="toc-tldr" class="nav-link active" data-scroll-target="#tldr">TL:DR</a></li>
|
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<li><a href="#load-packages" id="toc-load-packages" class="nav-link" data-scroll-target="#load-packages">Load Packages</a></li>
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<li><a href="#load-in-data" id="toc-load-in-data" class="nav-link" data-scroll-target="#load-in-data">Load in Data</a></li>
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<li><a href="#transform" id="toc-transform" class="nav-link" data-scroll-target="#transform">Transform</a></li>
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<li><a href="#join-data" id="toc-join-data" class="nav-link" data-scroll-target="#join-data">Join Data</a></li>
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<li><a href="#a-different-way-to-look" id="toc-a-different-way-to-look" class="nav-link" data-scroll-target="#a-different-way-to-look">A different way to look</a>
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<li><a href="#last-exploration" id="toc-last-exploration" class="nav-link" data-scroll-target="#last-exploration">Last Exploration</a></li>
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<li><a href="#next-steps" id="toc-next-steps" class="nav-link" data-scroll-target="#next-steps">Next Steps</a></li>
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<section id="tldr" class="level1">
|
||||
<h1>TL:DR</h1>
|
||||
<p>Today I am going to dive into some real life data from the World Health Organization (WHO), exploring new and relapse cases of Tuberculosis. I clean up the data, and then make a few graphs to explore different variables.</p>
|
||||
</section>
|
||||
<section id="load-packages" class="level1">
|
||||
<h1>Load Packages</h1>
|
||||
<p>Since I am going to use quite a few packages in the tidyverse I am going to load them all in at once instead of individually.</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">library</span>(tidyverse)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
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</div>
|
||||
</section>
|
||||
<section id="load-in-data" class="level1">
|
||||
<h1>Load in Data</h1>
|
||||
<p>We are using the WHO data set which contains tuberculosis (TB) cases broken down by year, this data set is contained in the Tidyr package, however its only recent up to 2014. For a little added fun I have downloaded the latest data from the WHO website, <a href="https://www.who.int/tb/country/data/download/en/">Found here</a>. For some added fun I have also included GDP per Capita data from World bank <a href="https://data.worldbank.org/indicator/NY.GDP.PCAP.CD">Found here</a></p>
|
||||
<div class="cell">
|
||||
<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>who_raw <span class="ot"><-</span> <span class="fu">read.csv</span>(<span class="st">"TB_notifications_2020-02-11.csv"</span>)</span>
|
||||
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a></span>
|
||||
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a><span class="co">#GPD file contains 4 rows of instrusctions above the actually data, we can tell</span></span>
|
||||
<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a><span class="co">#read.csv to skip these using the skip command</span></span>
|
||||
<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a>gpd_raw <span class="ot"><-</span> <span class="fu">read.csv</span>(<span class="st">"API_NY.GDP.PCAP.CD_DS2_en_csv_v2_713080.csv"</span>,</span>
|
||||
<span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a> <span class="at">skip =</span> <span class="dv">4</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||||
</div>
|
||||
</section>
|
||||
<section id="transform" class="level1">
|
||||
<h1>Transform</h1>
|
||||
<p>This data set is very ugly looking! The first 3 columns are all country Identifiers, with column four indicating the WHO region. This is redundant and can be dropped down to one Identifier and Region. As we can see there are quite a few Variable columns that are in fact values and not true Variables. When reading the data dictionary for this data set, WHO has changed their reporting over the years, so for our purposes we can strip a lot of the extra data out. Lets try and look at three types of TB, Extrapulmonary, Lab Diagnosed, and Clinician Diagnosed. As well as try and look at the breakdowns by Age and Sex of new and relapse case (post 2012) Lots of Cleaning to do, lets get to it!</p>
|
||||
<div class="cell">
|
||||
<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>who1 <span class="ot"><-</span> who_raw <span class="sc">%>%</span> </span>
|
||||
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a> <span class="co">#lets drop some columns not needed for our exploration, what each column means can be found in the CSV Data dictionary file</span></span>
|
||||
<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(<span class="sc">-</span>iso2</span>
|
||||
<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a> ,<span class="sc">-</span>iso_numeric</span>
|
||||
<span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a> ,<span class="sc">-</span>(rdx_data_available<span class="sc">:</span>hiv_reg_new2)</span>
|
||||
<span id="cb3-6"><a href="#cb3-6" aria-hidden="true" tabindex="-1"></a> ,<span class="sc">-</span>(new_sp<span class="sc">:</span>rel_in_agesex_flg)</span>
|
||||
<span id="cb3-7"><a href="#cb3-7" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">%>%</span></span>
|
||||
<span id="cb3-8"><a href="#cb3-8" aria-hidden="true" tabindex="-1"></a> <span class="co">#Lets just look at new date</span></span>
|
||||
<span id="cb3-9"><a href="#cb3-9" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(year <span class="sc">>=</span> <span class="dv">2013</span>) <span class="sc">%>%</span> </span>
|
||||
<span id="cb3-10"><a href="#cb3-10" aria-hidden="true" tabindex="-1"></a> <span class="co">#Move the values that are currently stored as variables to observations</span></span>
|
||||
<span id="cb3-11"><a href="#cb3-11" aria-hidden="true" tabindex="-1"></a> <span class="fu">pivot_longer</span>(<span class="at">cols =</span> newrel_m04<span class="sc">:</span>newrel_sexunkageunk</span>
|
||||
<span id="cb3-12"><a href="#cb3-12" aria-hidden="true" tabindex="-1"></a> ,<span class="at">names_to =</span> <span class="st">"key"</span></span>
|
||||
<span id="cb3-13"><a href="#cb3-13" aria-hidden="true" tabindex="-1"></a> ,<span class="at">values_to =</span> <span class="st">"values"</span></span>
|
||||
<span id="cb3-14"><a href="#cb3-14" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">%>%</span> </span>
|
||||
<span id="cb3-15"><a href="#cb3-15" aria-hidden="true" tabindex="-1"></a> <span class="fu">separate</span>(<span class="at">col =</span> key</span>
|
||||
<span id="cb3-16"><a href="#cb3-16" aria-hidden="true" tabindex="-1"></a> ,<span class="at">into =</span> <span class="fu">c</span>(<span class="st">"new"</span>,<span class="st">"sexage"</span>)</span>
|
||||
<span id="cb3-17"><a href="#cb3-17" aria-hidden="true" tabindex="-1"></a> ,<span class="at">sep =</span> <span class="st">"_"</span></span>
|
||||
<span id="cb3-18"><a href="#cb3-18" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">%>%</span> </span>
|
||||
<span id="cb3-19"><a href="#cb3-19" aria-hidden="true" tabindex="-1"></a> <span class="co">#the data set contains male, female and unknown</span></span>
|
||||
<span id="cb3-20"><a href="#cb3-20" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate_if</span>(is.character</span>
|
||||
<span id="cb3-21"><a href="#cb3-21" aria-hidden="true" tabindex="-1"></a> ,str_replace_all</span>
|
||||
<span id="cb3-22"><a href="#cb3-22" aria-hidden="true" tabindex="-1"></a> ,<span class="at">pattern =</span> <span class="st">"sexunk"</span></span>
|
||||
<span id="cb3-23"><a href="#cb3-23" aria-hidden="true" tabindex="-1"></a> , <span class="at">replacement =</span> <span class="st">"u"</span></span>
|
||||
<span id="cb3-24"><a href="#cb3-24" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">%>%</span> </span>
|
||||
<span id="cb3-25"><a href="#cb3-25" aria-hidden="true" tabindex="-1"></a> <span class="fu">separate</span>(<span class="at">col =</span> sexage</span>
|
||||
<span id="cb3-26"><a href="#cb3-26" aria-hidden="true" tabindex="-1"></a> ,<span class="at">into =</span> <span class="fu">c</span>(<span class="st">"sex"</span>,<span class="st">"age"</span>)</span>
|
||||
<span id="cb3-27"><a href="#cb3-27" aria-hidden="true" tabindex="-1"></a> ,<span class="at">sep =</span> <span class="dv">1</span>) <span class="sc">%>%</span> </span>
|
||||
<span id="cb3-28"><a href="#cb3-28" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">age_start =</span> <span class="fu">case_when</span>(</span>
|
||||
<span id="cb3-29"><a href="#cb3-29" aria-hidden="true" tabindex="-1"></a> <span class="fu">str_detect</span>(age, <span class="st">"65"</span>) <span class="sc">~</span> <span class="st">"65"</span></span>
|
||||
<span id="cb3-30"><a href="#cb3-30" aria-hidden="true" tabindex="-1"></a> ,(<span class="fu">str_length</span>(age) <span class="sc">==</span> <span class="dv">2</span>) <span class="sc">~</span> <span class="fu">str_match</span>(age, <span class="st">"</span><span class="sc">\\</span><span class="st">S"</span>)</span>
|
||||
<span id="cb3-31"><a href="#cb3-31" aria-hidden="true" tabindex="-1"></a> ,(<span class="fu">str_length</span>(age) <span class="sc">==</span> <span class="dv">3</span>) <span class="sc">~</span> <span class="fu">str_match</span>(age, <span class="st">"</span><span class="sc">\\</span><span class="st">S"</span>)</span>
|
||||
<span id="cb3-32"><a href="#cb3-32" aria-hidden="true" tabindex="-1"></a> ,(<span class="fu">str_length</span>(age) <span class="sc">==</span> <span class="dv">4</span>) <span class="sc">~</span> <span class="fu">str_match</span>(age, <span class="st">"</span><span class="sc">\\</span><span class="st">S</span><span class="sc">\\</span><span class="st">S"</span>)</span>
|
||||
<span id="cb3-33"><a href="#cb3-33" aria-hidden="true" tabindex="-1"></a> </span>
|
||||
<span id="cb3-34"><a href="#cb3-34" aria-hidden="true" tabindex="-1"></a> ,<span class="cn">TRUE</span> <span class="sc">~</span> <span class="st">""</span></span>
|
||||
<span id="cb3-35"><a href="#cb3-35" aria-hidden="true" tabindex="-1"></a> )</span>
|
||||
<span id="cb3-36"><a href="#cb3-36" aria-hidden="true" tabindex="-1"></a> ,<span class="at">age_end =</span> <span class="fu">case_when</span>(</span>
|
||||
<span id="cb3-37"><a href="#cb3-37" aria-hidden="true" tabindex="-1"></a> <span class="fu">str_detect</span>(age, <span class="st">"65"</span>) <span class="sc">~</span> <span class="st">"& Over"</span></span>
|
||||
<span id="cb3-38"><a href="#cb3-38" aria-hidden="true" tabindex="-1"></a> ,(<span class="fu">str_length</span>(age) <span class="sc">==</span> <span class="dv">2</span>) <span class="sc">~</span> <span class="fu">str_match</span>(age, <span class="st">"</span><span class="sc">\\</span><span class="st">S$"</span>)</span>
|
||||
<span id="cb3-39"><a href="#cb3-39" aria-hidden="true" tabindex="-1"></a> ,(<span class="fu">str_length</span>(age) <span class="sc">==</span> <span class="dv">3</span>) <span class="sc">~</span> <span class="fu">str_match</span>(age, <span class="st">"</span><span class="sc">\\</span><span class="st">S</span><span class="sc">\\</span><span class="st">S$"</span>)</span>
|
||||
<span id="cb3-40"><a href="#cb3-40" aria-hidden="true" tabindex="-1"></a> ,(<span class="fu">str_length</span>(age) <span class="sc">==</span> <span class="dv">4</span>) <span class="sc">~</span> <span class="fu">str_match</span>(age, <span class="st">"</span><span class="sc">\\</span><span class="st">S</span><span class="sc">\\</span><span class="st">S$"</span>)</span>
|
||||
<span id="cb3-41"><a href="#cb3-41" aria-hidden="true" tabindex="-1"></a> ,<span class="cn">TRUE</span> <span class="sc">~</span> <span class="st">""</span></span>
|
||||
<span id="cb3-42"><a href="#cb3-42" aria-hidden="true" tabindex="-1"></a> ))</span>
|
||||
<span id="cb3-43"><a href="#cb3-43" aria-hidden="true" tabindex="-1"></a></span>
|
||||
<span id="cb3-44"><a href="#cb3-44" aria-hidden="true" tabindex="-1"></a><span class="co">#overall WHO data is now cleaned and tidy. </span></span>
|
||||
<span id="cb3-45"><a href="#cb3-45" aria-hidden="true" tabindex="-1"></a> </span>
|
||||
<span id="cb3-46"><a href="#cb3-46" aria-hidden="true" tabindex="-1"></a><span class="co"># Lets tidy up the GPD data so we can match it to our WHO data set</span></span>
|
||||
<span id="cb3-47"><a href="#cb3-47" aria-hidden="true" tabindex="-1"></a>gdp1 <span class="ot"><-</span> gpd_raw <span class="sc">%>%</span> </span>
|
||||
<span id="cb3-48"><a href="#cb3-48" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(<span class="sc">-</span>(Indicator.Name<span class="sc">:</span>X2012)</span>
|
||||
<span id="cb3-49"><a href="#cb3-49" aria-hidden="true" tabindex="-1"></a> ,<span class="sc">-</span>X2019</span>
|
||||
<span id="cb3-50"><a href="#cb3-50" aria-hidden="true" tabindex="-1"></a> ,<span class="sc">-</span>X) <span class="sc">%>%</span> </span>
|
||||
<span id="cb3-51"><a href="#cb3-51" aria-hidden="true" tabindex="-1"></a> <span class="fu">pivot_longer</span>(<span class="at">cols =</span> X2013<span class="sc">:</span>X2018</span>
|
||||
<span id="cb3-52"><a href="#cb3-52" aria-hidden="true" tabindex="-1"></a> ,<span class="at">names_to =</span> <span class="st">"year"</span> </span>
|
||||
<span id="cb3-53"><a href="#cb3-53" aria-hidden="true" tabindex="-1"></a> ,<span class="at">values_to =</span> <span class="st">"gdp"</span>) <span class="sc">%>%</span> </span>
|
||||
<span id="cb3-54"><a href="#cb3-54" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate_if</span>(is.character</span>
|
||||
<span id="cb3-55"><a href="#cb3-55" aria-hidden="true" tabindex="-1"></a> ,str_remove_all</span>
|
||||
<span id="cb3-56"><a href="#cb3-56" aria-hidden="true" tabindex="-1"></a> ,<span class="at">pattern =</span> <span class="st">"X(?=</span><span class="sc">\\</span><span class="st">d*)"</span>) <span class="co"># regex to check for an X followed by a digit</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||||
</div>
|
||||
</section>
|
||||
<section id="join-data" class="level1">
|
||||
<h1>Join Data</h1>
|
||||
<p>Lets combine the data sets so we can later visual TB Cases based on a countries GDP per capita.</p>
|
||||
<div class="cell">
|
||||
<div class="sourceCode cell-code" id="cb4"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>who_combined <span class="ot"><-</span> who1 <span class="sc">%>%</span> </span>
|
||||
<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">rename</span>(<span class="at">Country.Code =</span> iso3) <span class="sc">%>%</span> </span>
|
||||
<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">year =</span> <span class="fu">as.character</span>(year)) <span class="sc">%>%</span> </span>
|
||||
<span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">left_join</span>(<span class="at">y =</span> gdp1) <span class="sc">%>%</span> </span>
|
||||
<span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(<span class="sc">-</span>Country.Name)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||||
</div>
|
||||
</section>
|
||||
<section id="analyze" class="level1">
|
||||
<h1>Analyze</h1>
|
||||
<p>Lets first explore 2018 and see if GDP has any affect on the amount of TB cases in a particular country.</p>
|
||||
<div class="cell">
|
||||
<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>g1 <span class="ot"><-</span> who_combined <span class="sc">%>%</span> </span>
|
||||
<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(<span class="fu">str_detect</span>(age,<span class="st">"014|15plus|u"</span>),year <span class="sc">==</span> <span class="dv">2018</span>) <span class="sc">%>%</span> </span>
|
||||
<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">group_by</span>(country) <span class="sc">%>%</span> </span>
|
||||
<span id="cb5-4"><a href="#cb5-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">summarise</span>(<span class="at">sum_tb_cases =</span> (<span class="fu">sum</span>(values,<span class="at">na.rm =</span> <span class="cn">TRUE</span>)<span class="sc">/</span><span class="dv">10000</span>)</span>
|
||||
<span id="cb5-5"><a href="#cb5-5" aria-hidden="true" tabindex="-1"></a> ,<span class="at">gdp =</span> <span class="fu">first</span>(gdp)<span class="sc">/</span><span class="dv">1000</span></span>
|
||||
<span id="cb5-6"><a href="#cb5-6" aria-hidden="true" tabindex="-1"></a> ,<span class="at">who_region =</span> <span class="fu">first</span>(g_whoregion)) <span class="sc">%>%</span> </span>
|
||||
<span id="cb5-7"><a href="#cb5-7" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(</span>
|
||||
<span id="cb5-8"><a href="#cb5-8" aria-hidden="true" tabindex="-1"></a> <span class="at">label =</span> <span class="fu">ifelse</span>((sum_tb_cases<span class="sc">></span><span class="dv">50</span>), <span class="at">yes =</span> <span class="fu">as.character</span>(country),<span class="at">no =</span> <span class="st">""</span>)) <span class="sc">%>%</span> </span>
|
||||
<span id="cb5-9"><a href="#cb5-9" aria-hidden="true" tabindex="-1"></a> <span class="fu">ggplot</span>(<span class="fu">aes</span>(<span class="at">x =</span> gdp, <span class="at">y =</span> sum_tb_cases )) <span class="sc">+</span></span>
|
||||
<span id="cb5-10"><a href="#cb5-10" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_point</span>(<span class="fu">aes</span>(<span class="at">color =</span> who_region)) <span class="sc">+</span></span>
|
||||
<span id="cb5-11"><a href="#cb5-11" aria-hidden="true" tabindex="-1"></a> ggrepel<span class="sc">::</span><span class="fu">geom_text_repel</span>(<span class="fu">aes</span>(<span class="at">x =</span> gdp, <span class="at">y =</span> sum_tb_cases, <span class="at">label =</span> label)) <span class="sc">+</span></span>
|
||||
<span id="cb5-12"><a href="#cb5-12" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span>
|
||||
<span id="cb5-13"><a href="#cb5-13" aria-hidden="true" tabindex="-1"></a> <span class="at">title =</span> <span class="st">"Total TB Cases by Country compared to Gross Domestic Product (GDP)"</span></span>
|
||||
<span id="cb5-14"><a href="#cb5-14" aria-hidden="true" tabindex="-1"></a> ,<span class="at">x =</span> <span class="st">"GDP (per 1,000USD)"</span></span>
|
||||
<span id="cb5-15"><a href="#cb5-15" aria-hidden="true" tabindex="-1"></a> ,<span class="at">y =</span> <span class="st">"Total TB Case (per 10,000 cases)"</span></span>
|
||||
<span id="cb5-16"><a href="#cb5-16" aria-hidden="true" tabindex="-1"></a> ,<span class="at">color =</span> <span class="st">"WHO Region"</span></span>
|
||||
<span id="cb5-17"><a href="#cb5-17" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">+</span></span>
|
||||
<span id="cb5-18"><a href="#cb5-18" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme_bw</span>() </span>
|
||||
<span id="cb5-19"><a href="#cb5-19" aria-hidden="true" tabindex="-1"></a>g1</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="basic-exploration-of-who-tuberculosis-data_files/figure-html/unnamed-chunk-5-1.png" class="img-fluid" width="672"></p>
|
||||
</div>
|
||||
</div>
|
||||
<section id="subset" class="level3">
|
||||
<h3 class="anchored" data-anchor-id="subset">Subset</h3>
|
||||
<p>Lets subset the above data to remove some of the outliers.</p>
|
||||
<div class="cell">
|
||||
<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>g2 <span class="ot"><-</span> who_combined <span class="sc">%>%</span> </span>
|
||||
<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(<span class="fu">str_detect</span>(age,<span class="st">"014|15plus|u"</span>),year <span class="sc">==</span> <span class="dv">2018</span>) <span class="sc">%>%</span> </span>
|
||||
<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">group_by</span>(country) <span class="sc">%>%</span> </span>
|
||||
<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">summarise</span>(<span class="at">sum_tb_cases =</span> (<span class="fu">sum</span>(values,<span class="at">na.rm =</span> <span class="cn">TRUE</span>)<span class="sc">/</span><span class="dv">10000</span>)</span>
|
||||
<span id="cb6-5"><a href="#cb6-5" aria-hidden="true" tabindex="-1"></a> ,<span class="at">gdp =</span> <span class="fu">first</span>(gdp)<span class="sc">/</span><span class="dv">1000</span></span>
|
||||
<span id="cb6-6"><a href="#cb6-6" aria-hidden="true" tabindex="-1"></a> ,<span class="at">who_region =</span> <span class="fu">first</span>(g_whoregion)) <span class="sc">%>%</span> </span>
|
||||
<span id="cb6-7"><a href="#cb6-7" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(</span>
|
||||
<span id="cb6-8"><a href="#cb6-8" aria-hidden="true" tabindex="-1"></a> <span class="at">label =</span> <span class="fu">ifelse</span>((sum_tb_cases<span class="sc">></span><span class="dv">50</span>), <span class="at">yes =</span> <span class="fu">as.character</span>(country),<span class="at">no =</span> <span class="st">""</span>)) <span class="sc">%>%</span> </span>
|
||||
<span id="cb6-9"><a href="#cb6-9" aria-hidden="true" tabindex="-1"></a> <span class="fu">ggplot</span>(<span class="fu">aes</span>(<span class="at">x =</span> gdp, <span class="at">y =</span> sum_tb_cases )) <span class="sc">+</span></span>
|
||||
<span id="cb6-10"><a href="#cb6-10" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_point</span>(<span class="fu">aes</span>(<span class="at">color =</span> who_region)) <span class="sc">+</span></span>
|
||||
<span id="cb6-11"><a href="#cb6-11" aria-hidden="true" tabindex="-1"></a> ggrepel<span class="sc">::</span><span class="fu">geom_text_repel</span>(<span class="fu">aes</span>(<span class="at">x =</span> gdp, <span class="at">y =</span> sum_tb_cases, <span class="at">label =</span> label)) <span class="sc">+</span></span>
|
||||
<span id="cb6-12"><a href="#cb6-12" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span>
|
||||
<span id="cb6-13"><a href="#cb6-13" aria-hidden="true" tabindex="-1"></a> <span class="at">title =</span> <span class="st">"Total TB Cases by Country compared to Gross Domestic Product (GDP)"</span></span>
|
||||
<span id="cb6-14"><a href="#cb6-14" aria-hidden="true" tabindex="-1"></a> ,<span class="at">x =</span> <span class="st">"GDP (per 1,000USD)"</span></span>
|
||||
<span id="cb6-15"><a href="#cb6-15" aria-hidden="true" tabindex="-1"></a> ,<span class="at">y =</span> <span class="st">"Total TB Case (per 10,000 cases)"</span></span>
|
||||
<span id="cb6-16"><a href="#cb6-16" aria-hidden="true" tabindex="-1"></a> ,<span class="at">color =</span> <span class="st">"WHO Region"</span></span>
|
||||
<span id="cb6-17"><a href="#cb6-17" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">+</span></span>
|
||||
<span id="cb6-18"><a href="#cb6-18" aria-hidden="true" tabindex="-1"></a> <span class="fu">xlim</span>(<span class="dv">0</span>,<span class="dv">50</span>) <span class="sc">+</span></span>
|
||||
<span id="cb6-19"><a href="#cb6-19" aria-hidden="true" tabindex="-1"></a> <span class="fu">ylim</span>(<span class="dv">0</span>,<span class="dv">50</span>) <span class="sc">+</span></span>
|
||||
<span id="cb6-20"><a href="#cb6-20" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme_bw</span>() </span>
|
||||
<span id="cb6-21"><a href="#cb6-21" aria-hidden="true" tabindex="-1"></a></span>
|
||||
<span id="cb6-22"><a href="#cb6-22" aria-hidden="true" tabindex="-1"></a>g2</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="basic-exploration-of-who-tuberculosis-data_files/figure-html/unnamed-chunk-6-1.png" class="img-fluid" width="672"></p>
|
||||
</div>
|
||||
</div>
|
||||
<p>We can see in the graph above there seems to be a small correlation between lower GDP and amount of TB cases.</p>
|
||||
</section>
|
||||
<section id="a-different-way-to-look" class="level2">
|
||||
<h2 class="anchored" data-anchor-id="a-different-way-to-look">A different way to look</h2>
|
||||
<p>Could there be any correlation between a countries population and the amount of TB cases? Maybe its just as simple as having more people means more people to get sick? Lets bring in another data set, again from World Bank <a href="https://data.worldbank.org/indicator/SP.POP.TOTL">Found Here</a>, this contains total population data by country.</p>
|
||||
<div class="cell">
|
||||
<div class="sourceCode cell-code" id="cb7"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>pop_raw <span class="ot"><-</span> <span class="fu">read.csv</span>(<span class="st">"API_SP.POP.TOTL_DS2_en_csv_v2_713131.csv"</span></span>
|
||||
<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a> ,<span class="at">skip =</span> <span class="dv">4</span>)</span>
|
||||
<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a><span class="co">#If this looks famialer its because it is, the data set looks very simalar to the GDP data</span></span>
|
||||
<span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a><span class="co">#In the future this could be moved to a function to allow cleaning much easier</span></span>
|
||||
<span id="cb7-5"><a href="#cb7-5" aria-hidden="true" tabindex="-1"></a>pop1 <span class="ot"><-</span> pop_raw <span class="sc">%>%</span> </span>
|
||||
<span id="cb7-6"><a href="#cb7-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(<span class="sc">-</span>(Indicator.Name<span class="sc">:</span>X2012)</span>
|
||||
<span id="cb7-7"><a href="#cb7-7" aria-hidden="true" tabindex="-1"></a> ,<span class="sc">-</span>X2019</span>
|
||||
<span id="cb7-8"><a href="#cb7-8" aria-hidden="true" tabindex="-1"></a> ,<span class="sc">-</span>X) <span class="sc">%>%</span> </span>
|
||||
<span id="cb7-9"><a href="#cb7-9" aria-hidden="true" tabindex="-1"></a> <span class="fu">pivot_longer</span>(<span class="at">cols =</span> X2013<span class="sc">:</span>X2018</span>
|
||||
<span id="cb7-10"><a href="#cb7-10" aria-hidden="true" tabindex="-1"></a> ,<span class="at">names_to =</span> <span class="st">"year"</span> </span>
|
||||
<span id="cb7-11"><a href="#cb7-11" aria-hidden="true" tabindex="-1"></a> ,<span class="at">values_to =</span> <span class="st">"population"</span>) <span class="sc">%>%</span> </span>
|
||||
<span id="cb7-12"><a href="#cb7-12" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate_if</span>(is.character</span>
|
||||
<span id="cb7-13"><a href="#cb7-13" aria-hidden="true" tabindex="-1"></a> ,str_remove_all</span>
|
||||
<span id="cb7-14"><a href="#cb7-14" aria-hidden="true" tabindex="-1"></a> ,<span class="at">pattern =</span> <span class="st">"X(?=</span><span class="sc">\\</span><span class="st">d*)"</span>)</span>
|
||||
<span id="cb7-15"><a href="#cb7-15" aria-hidden="true" tabindex="-1"></a></span>
|
||||
<span id="cb7-16"><a href="#cb7-16" aria-hidden="true" tabindex="-1"></a><span class="co">#now lets combine this into are overall data set</span></span>
|
||||
<span id="cb7-17"><a href="#cb7-17" aria-hidden="true" tabindex="-1"></a></span>
|
||||
<span id="cb7-18"><a href="#cb7-18" aria-hidden="true" tabindex="-1"></a>who_combined <span class="ot"><-</span> who_combined <span class="sc">%>%</span> </span>
|
||||
<span id="cb7-19"><a href="#cb7-19" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">year =</span> <span class="fu">as.character</span>(year)) <span class="sc">%>%</span> </span>
|
||||
<span id="cb7-20"><a href="#cb7-20" aria-hidden="true" tabindex="-1"></a> <span class="fu">left_join</span>(<span class="at">y =</span> pop1) <span class="sc">%>%</span> </span>
|
||||
<span id="cb7-21"><a href="#cb7-21" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(<span class="sc">-</span>Country.Name)</span>
|
||||
<span id="cb7-22"><a href="#cb7-22" aria-hidden="true" tabindex="-1"></a></span>
|
||||
<span id="cb7-23"><a href="#cb7-23" aria-hidden="true" tabindex="-1"></a><span class="co">#now lets Graph again</span></span>
|
||||
<span id="cb7-24"><a href="#cb7-24" aria-hidden="true" tabindex="-1"></a></span>
|
||||
<span id="cb7-25"><a href="#cb7-25" aria-hidden="true" tabindex="-1"></a>g3 <span class="ot"><-</span> who_combined <span class="sc">%>%</span> </span>
|
||||
<span id="cb7-26"><a href="#cb7-26" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(<span class="fu">str_detect</span>(age,<span class="st">"014|15plus|u"</span>),year <span class="sc">==</span> <span class="dv">2018</span>) <span class="sc">%>%</span> </span>
|
||||
<span id="cb7-27"><a href="#cb7-27" aria-hidden="true" tabindex="-1"></a> <span class="fu">group_by</span>(country) <span class="sc">%>%</span> </span>
|
||||
<span id="cb7-28"><a href="#cb7-28" aria-hidden="true" tabindex="-1"></a> <span class="fu">summarise</span>(<span class="at">sum_tb_cases =</span> (<span class="fu">sum</span>(values,<span class="at">na.rm =</span> <span class="cn">TRUE</span>)<span class="sc">/</span><span class="dv">10000</span>)</span>
|
||||
<span id="cb7-29"><a href="#cb7-29" aria-hidden="true" tabindex="-1"></a> ,<span class="at">population =</span> <span class="fu">first</span>(population)<span class="sc">/</span><span class="dv">1000000</span></span>
|
||||
<span id="cb7-30"><a href="#cb7-30" aria-hidden="true" tabindex="-1"></a> ,<span class="at">who_region =</span> <span class="fu">first</span>(g_whoregion)) <span class="sc">%>%</span> </span>
|
||||
<span id="cb7-31"><a href="#cb7-31" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(</span>
|
||||
<span id="cb7-32"><a href="#cb7-32" aria-hidden="true" tabindex="-1"></a> <span class="at">label =</span> <span class="fu">ifelse</span>((population<span class="sc">></span><span class="dv">250</span>), <span class="at">yes =</span> <span class="fu">as.character</span>(country),<span class="at">no =</span> <span class="st">""</span>)) <span class="sc">%>%</span></span>
|
||||
<span id="cb7-33"><a href="#cb7-33" aria-hidden="true" tabindex="-1"></a> <span class="fu">ggplot</span>(<span class="fu">aes</span>(<span class="at">x =</span> population, <span class="at">y =</span> sum_tb_cases )) <span class="sc">+</span></span>
|
||||
<span id="cb7-34"><a href="#cb7-34" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_point</span>(<span class="fu">aes</span>(<span class="at">color =</span> who_region)) <span class="sc">+</span></span>
|
||||
<span id="cb7-35"><a href="#cb7-35" aria-hidden="true" tabindex="-1"></a> ggrepel<span class="sc">::</span><span class="fu">geom_text_repel</span>(<span class="fu">aes</span>(<span class="at">x =</span> population, <span class="at">y =</span> sum_tb_cases, <span class="at">label =</span> label)) <span class="sc">+</span></span>
|
||||
<span id="cb7-36"><a href="#cb7-36" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span>
|
||||
<span id="cb7-37"><a href="#cb7-37" aria-hidden="true" tabindex="-1"></a> <span class="at">title =</span> <span class="st">"Total TB Cases by Country compared to Gross Domestic Product (GDP)"</span></span>
|
||||
<span id="cb7-38"><a href="#cb7-38" aria-hidden="true" tabindex="-1"></a> ,<span class="at">x =</span> <span class="st">"Population (in Millions)"</span></span>
|
||||
<span id="cb7-39"><a href="#cb7-39" aria-hidden="true" tabindex="-1"></a> ,<span class="at">y =</span> <span class="st">"Total TB Case (per 10,000 cases)"</span></span>
|
||||
<span id="cb7-40"><a href="#cb7-40" aria-hidden="true" tabindex="-1"></a> ,<span class="at">color =</span> <span class="st">"WHO Region"</span></span>
|
||||
<span id="cb7-41"><a href="#cb7-41" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">+</span></span>
|
||||
<span id="cb7-42"><a href="#cb7-42" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme_bw</span>() </span>
|
||||
<span id="cb7-43"><a href="#cb7-43" aria-hidden="true" tabindex="-1"></a></span>
|
||||
<span id="cb7-44"><a href="#cb7-44" aria-hidden="true" tabindex="-1"></a> g3 </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="basic-exploration-of-who-tuberculosis-data_files/figure-html/unnamed-chunk-7-1.png" class="img-fluid" width="672"></p>
|
||||
</div>
|
||||
</div>
|
||||
<section id="further-exploration" class="level3">
|
||||
<h3 class="anchored" data-anchor-id="further-exploration">Further Exploration</h3>
|
||||
<p>Maybe we are on to something, the more people, the more likely they are to get sick! However India seems to have a very large number of cases so lets break these cases down further by age group for 2018.</p>
|
||||
<div class="cell">
|
||||
<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>g4 <span class="ot"><-</span> who_combined <span class="sc">%>%</span> </span>
|
||||
<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(year <span class="sc">==</span> <span class="dv">2018</span></span>
|
||||
<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a> ,country <span class="sc">==</span> <span class="st">"India"</span></span>
|
||||
<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a> ,<span class="sc">!</span>(<span class="fu">str_detect</span>(age,<span class="st">"15plus|ageunk|u|014"</span>))</span>
|
||||
<span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a> ,(<span class="fu">str_detect</span>(sex,<span class="st">"m|f"</span>))</span>
|
||||
<span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">%>%</span> </span>
|
||||
<span id="cb8-7"><a href="#cb8-7" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">age_range =</span> glue<span class="sc">::</span><span class="fu">glue</span>(<span class="st">"{age_start} -- {age_end}"</span>)) <span class="sc">%>%</span> </span>
|
||||
<span id="cb8-8"><a href="#cb8-8" aria-hidden="true" tabindex="-1"></a> <span class="fu">ggplot</span>(<span class="fu">aes</span>(<span class="at">x =</span> <span class="fu">reorder</span>(age_range, <span class="fu">as.numeric</span>(age_start)), <span class="at">y =</span> (values<span class="sc">/</span><span class="dv">1000</span>), <span class="at">fill =</span> sex)) <span class="sc">+</span></span>
|
||||
<span id="cb8-9"><a href="#cb8-9" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_col</span>(<span class="at">position =</span> <span class="st">"dodge"</span>) <span class="sc">+</span></span>
|
||||
<span id="cb8-10"><a href="#cb8-10" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span>
|
||||
<span id="cb8-11"><a href="#cb8-11" aria-hidden="true" tabindex="-1"></a> <span class="at">title =</span> <span class="st">"TB Case in India by age and gender 2018"</span></span>
|
||||
<span id="cb8-12"><a href="#cb8-12" aria-hidden="true" tabindex="-1"></a> ,<span class="at">x =</span> <span class="cn">NULL</span></span>
|
||||
<span id="cb8-13"><a href="#cb8-13" aria-hidden="true" tabindex="-1"></a> ,<span class="at">y =</span> <span class="st">"Total Cases (per 1000)"</span></span>
|
||||
<span id="cb8-14"><a href="#cb8-14" aria-hidden="true" tabindex="-1"></a> ,<span class="at">fill =</span> <span class="st">"Gender"</span>) <span class="sc">+</span></span>
|
||||
<span id="cb8-15"><a href="#cb8-15" aria-hidden="true" tabindex="-1"></a> <span class="fu">scale_fill_manual</span>(<span class="at">labels =</span> <span class="fu">c</span>(<span class="st">"Female"</span>,<span class="st">"Male"</span>), <span class="at">values =</span> <span class="fu">c</span>(<span class="st">"#e9a3c9"</span>,<span class="st">"#67a9cf"</span>) )</span>
|
||||
<span id="cb8-16"><a href="#cb8-16" aria-hidden="true" tabindex="-1"></a> </span>
|
||||
<span id="cb8-17"><a href="#cb8-17" aria-hidden="true" tabindex="-1"></a>g4</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="basic-exploration-of-who-tuberculosis-data_files/figure-html/unnamed-chunk-8-1.png" class="img-fluid" width="672"></p>
|
||||
</div>
|
||||
</div>
|
||||
<p>There seems to be a huge spike in cases after adolescences. Females have a sharp decline the older they get, where as male case stay elevated with a slight decrease at 55.</p>
|
||||
</section>
|
||||
</section>
|
||||
<section id="last-exploration" class="level2">
|
||||
<h2 class="anchored" data-anchor-id="last-exploration">Last Exploration</h2>
|
||||
<p>Lets look at overall cases in India, going back to 1980 and see if there as been any trends. To get these numbers we will go back to our raw data and strip everything out expect the total count</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>g5 <span class="ot"><-</span> who_raw <span class="sc">%>%</span> </span>
|
||||
<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(country <span class="sc">==</span> <span class="st">"India"</span>) <span class="sc">%>%</span> </span>
|
||||
<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(year, c_newinc) <span class="sc">%>%</span> </span>
|
||||
<span id="cb9-4"><a href="#cb9-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">ggplot</span>(<span class="fu">aes</span>(<span class="at">x =</span> year, <span class="at">y =</span> c_newinc<span class="sc">/</span><span class="dv">1000000</span>)) <span class="sc">+</span></span>
|
||||
<span id="cb9-5"><a href="#cb9-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_line</span>() <span class="sc">+</span></span>
|
||||
<span id="cb9-6"><a href="#cb9-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_point</span>() <span class="sc">+</span></span>
|
||||
<span id="cb9-7"><a href="#cb9-7" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span>
|
||||
<span id="cb9-8"><a href="#cb9-8" aria-hidden="true" tabindex="-1"></a> <span class="at">title =</span> <span class="st">"New and Relapse Tuberculosis Cases In India </span><span class="sc">\n</span><span class="st">1980 -- 2018"</span></span>
|
||||
<span id="cb9-9"><a href="#cb9-9" aria-hidden="true" tabindex="-1"></a> ,<span class="at">x =</span> <span class="cn">NULL</span></span>
|
||||
<span id="cb9-10"><a href="#cb9-10" aria-hidden="true" tabindex="-1"></a> ,<span class="at">y =</span> <span class="st">"Total Cases (in millions)"</span>) <span class="sc">+</span></span>
|
||||
<span id="cb9-11"><a href="#cb9-11" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme_bw</span>() <span class="sc">+</span></span>
|
||||
<span id="cb9-12"><a href="#cb9-12" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme</span>(<span class="at">plot.title =</span> <span class="fu">element_text</span>(<span class="at">hjust =</span> <span class="fl">0.5</span>)) <span class="sc">+</span> <span class="co">#center title </span></span>
|
||||
<span id="cb9-13"><a href="#cb9-13" aria-hidden="true" tabindex="-1"></a> <span class="fu">scale_x_continuous</span>(<span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">1980</span>,<span class="dv">2020</span>,<span class="dv">5</span>)) <span class="sc">+</span></span>
|
||||
<span id="cb9-14"><a href="#cb9-14" aria-hidden="true" tabindex="-1"></a> <span class="fu">scale_y_continuous</span>(<span class="at">breaks =</span> scales<span class="sc">::</span><span class="fu">pretty_breaks</span>(<span class="at">n=</span><span class="dv">10</span>)) <span class="co">#different way to add tick marks</span></span>
|
||||
<span id="cb9-15"><a href="#cb9-15" aria-hidden="true" tabindex="-1"></a>g5</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="basic-exploration-of-who-tuberculosis-data_files/figure-html/unnamed-chunk-9-1.png" class="img-fluid" width="672"></p>
|
||||
</div>
|
||||
</div>
|
||||
<p>Cases were steadily rising from 1980 to 1990, then suddenly feel off. Starting in the early 2010s there was a sharp increase and the amount of new and relapse cases just keep growing.</p>
|
||||
</section>
|
||||
</section>
|
||||
<section id="next-steps" class="level1">
|
||||
<h1>Next Steps</h1>
|
||||
<p>While no other country has the amount of cases that India does, the sudden spike in cases at adolescences asks the question do other countries follow this same trend? We can also see the sudden spike in the 2010s, again is this just based in India or do we see this trend in other countries. There is much more exploration we can do with this data set at a later time!</p>
|
||||
|
||||
|
||||
</section>
|
||||
|
||||
<div id="quarto-appendix" class="default"><section class="quarto-appendix-contents"><h2 class="anchored quarto-appendix-heading">Reuse</h2><div id="quarto-reuse" class="quarto-appendix-contents"><div><a rel="license" href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a></div></div></section><section class="quarto-appendix-contents"><h2 class="anchored quarto-appendix-heading">Citation</h2><div><div class="quarto-appendix-secondary-label">BibTeX citation:</div><pre class="sourceCode code-with-copy quarto-appendix-bibtex"><code class="sourceCode bibtex">@online{belanger2020,
|
||||
author = {Belanger, Kyle},
|
||||
title = {Basic {Exploration} of {WHO} {Tuberculosis} {Data}},
|
||||
date = {2020-02-13},
|
||||
langid = {en}
|
||||
}
|
||||
</code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre><div class="quarto-appendix-secondary-label">For attribution, please cite this work as:</div><div id="ref-belanger2020" class="csl-entry quarto-appendix-citeas" role="listitem">
|
||||
Belanger, Kyle. 2020. <span>“Basic Exploration of WHO Tuberculosis
|
||||
Data.”</span> February 13, 2020.
|
||||
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@ -39,7 +39,7 @@
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||||
"text": "My Start to R\n\n\nA short introduction to my blog, and R journey.\n\n\n\n\n\n\n\n\n\nJan 24, 2020\n\n\nKyle Belanger\n\n\n\n\n\n\nNo matching items"
|
||||
"text": "Basic Exploration of WHO Tuberculosis Data\n\n\nToday I am going to dive into some real life data from the World Health Organization (WHO), exploring new and relapse cases of Tuberculosis. I clean up the data, and then make a few graphs to explore different variables.\n\n\n\n\n\n\n\n\n\nFeb 13, 2020\n\n\nKyle Belanger\n\n\n\n\n\n\n \n\n\n\n\nLine Graphs and Interactivity\n\n\nTableau for Healthcare Chapter 10. Static and Interactive examples\n\n\n\n\n\n\n\n\n\nFeb 10, 2020\n\n\nKyle Belanger\n\n\n\n\n\n\n \n\n\n\n\nFacets and a Lesson in Humility\n\n\nA look at Tableau for Healthcare Chapter 8. Table Lens graph.\n\n\n\n\n\n\n\n\n\nJan 29, 2020\n\n\nKyle Belanger\n\n\n\n\n\n\n \n\n\n\n\nMy Start to R\n\n\nA short introduction to my blog, and R journey.\n\n\n\n\n\n\n\n\n\nJan 24, 2020\n\n\nKyle Belanger\n\n\n\n\n\n\nNo matching items"
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@ -54,5 +54,75 @@
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"title": "My Start to R",
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"section": "",
|
||||
"text": "Today starts my attempt at sharing my R journey with the world! I have been learning R off and on now since late 2019, I have begun to take it much more serious as I work through my Data Analytics class at UCF. My love for all things numbers and graphs has really blossomed, and I am choosing to share that love with anyone who cares to read. I will not claim to be the best at R, or any programming for that matter, but these are my attempts. Each post in this serious will be replicated a graph created in Tableau from the book Tableau for Healthcare. Todays graph is a simple horizontal bar chart, in transferring to both a new blog site and computer I have unfortunately lost the original bar graph, but trust me the one I created looks just like it.\n\nLoad Libraries\n\nlibrary(tidyr)\nlibrary(magrittr)\nlibrary(ggplot2)\nlibrary(stringr)\nlibrary(dplyr)\n\n\n\nImport Data\n\nds <- readxl::read_excel(\n path = \"Tableau 10 Training Practice Data.xlsx\" \n ,sheet = \"02 - Patient Falls-Single Hosp\"\n )\n\n\n\nClean Data Names\n\n#should make reusable forumla at later time\nnames(ds) <- tolower(names(ds))\nnames(ds) <- str_replace_all(names(ds),\" \", \"_\")\n\n\n\nConvert Data to ‘Long Form’\n\nds1 <- ds %>% \n gather(\"patient_falls_no_injury_rate\" , \"patient_falls_with_injury_rate\"\n ,key = \"injury\" \n ,value = \"rate\" ) %>% \n mutate(injury = (injury == \"patient_falls_with_injury_rate\"))\n\n\n\nGraph 5.1\n\nb1 <- ds %>% \n ggplot(mapping = aes(x = reorder(type_of_care,total_patient_falls_rate ) , y = total_patient_falls_rate)) +\n geom_col(fill = \"#2b83ba\") + \n coord_flip() +\n scale_y_continuous(breaks = NULL) +\n theme(axis.ticks = element_blank()) +\n labs(title = \"Rate of Patient Falls (per 1,000 Pateint Days)\\nby Type of Care for FY2017\"\n ,x = NULL\n ,y = NULL\n ) +\n theme_classic() +\n geom_text(aes(label = format(total_patient_falls_rate, digits = 2)), nudge_y = -.25, color = \"white\")\n \nb1\n\n\n\n\n\n\n\n\nCitationBibTeX citation:@online{belanger2020,\n author = {Belanger, Kyle},\n title = {My {Start} to {R}},\n date = {2020-01-24},\n langid = {en}\n}\nFor attribution, please cite this work as:\nBelanger, Kyle. 2020. “My Start to R.” January 24, 2020."
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|
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|
||||
"title": "Basic Exploration of WHO Tuberculosis Data",
|
||||
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|
||||
"text": "Today I am going to dive into some real life data from the World Health Organization (WHO), exploring new and relapse cases of Tuberculosis. I clean up the data, and then make a few graphs to explore different variables."
|
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|
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|
||||
"title": "Basic Exploration of WHO Tuberculosis Data",
|
||||
"section": "A different way to look",
|
||||
"text": "A different way to look\nCould there be any correlation between a countries population and the amount of TB cases? Maybe its just as simple as having more people means more people to get sick? Lets bring in another data set, again from World Bank Found Here, this contains total population data by country.\n\npop_raw <- read.csv(\"API_SP.POP.TOTL_DS2_en_csv_v2_713131.csv\"\n ,skip = 4)\n#If this looks famialer its because it is, the data set looks very simalar to the GDP data\n#In the future this could be moved to a function to allow cleaning much easier\npop1 <- pop_raw %>% \n select(-(Indicator.Name:X2012)\n ,-X2019\n ,-X) %>% \n pivot_longer(cols = X2013:X2018\n ,names_to = \"year\" \n ,values_to = \"population\") %>% \n mutate_if(is.character\n ,str_remove_all\n ,pattern = \"X(?=\\\\d*)\")\n\n#now lets combine this into are overall data set\n\nwho_combined <- who_combined %>% \n mutate(year = as.character(year)) %>% \n left_join(y = pop1) %>% \n select(-Country.Name)\n\n#now lets Graph again\n\ng3 <- who_combined %>% \n filter(str_detect(age,\"014|15plus|u\"),year == 2018) %>% \n group_by(country) %>% \n summarise(sum_tb_cases = (sum(values,na.rm = TRUE)/10000)\n ,population = first(population)/1000000\n ,who_region = first(g_whoregion)) %>% \n mutate(\n label = ifelse((population>250), yes = as.character(country),no = \"\")) %>%\n ggplot(aes(x = population, y = sum_tb_cases )) +\n geom_point(aes(color = who_region)) +\n ggrepel::geom_text_repel(aes(x = population, y = sum_tb_cases, label = label)) +\n labs(\n title = \"Total TB Cases by Country compared to Gross Domestic Product (GDP)\"\n ,x = \"Population (in Millions)\"\n ,y = \"Total TB Case (per 10,000 cases)\"\n ,color = \"WHO Region\"\n ) +\n theme_bw() \n\n g3 \n\n\n\n\n\nFurther Exploration\nMaybe we are on to something, the more people, the more likely they are to get sick! However India seems to have a very large number of cases so lets break these cases down further by age group for 2018.\n\ng4 <- who_combined %>% \n filter(year == 2018\n ,country == \"India\"\n ,!(str_detect(age,\"15plus|ageunk|u|014\"))\n ,(str_detect(sex,\"m|f\"))\n ) %>% \n mutate(age_range = glue::glue(\"{age_start} -- {age_end}\")) %>% \n ggplot(aes(x = reorder(age_range, as.numeric(age_start)), y = (values/1000), fill = sex)) +\n geom_col(position = \"dodge\") +\n labs(\n title = \"TB Case in India by age and gender 2018\"\n ,x = NULL\n ,y = \"Total Cases (per 1000)\"\n ,fill = \"Gender\") +\n scale_fill_manual(labels = c(\"Female\",\"Male\"), values = c(\"#e9a3c9\",\"#67a9cf\") )\n \ng4\n\n\n\n\nThere seems to be a huge spike in cases after adolescences. Females have a sharp decline the older they get, where as male case stay elevated with a slight decrease at 55."
|
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|
||||
"title": "Basic Exploration of WHO Tuberculosis Data",
|
||||
"section": "Last Exploration",
|
||||
"text": "Last Exploration\nLets look at overall cases in India, going back to 1980 and see if there as been any trends. To get these numbers we will go back to our raw data and strip everything out expect the total count\n\ng5 <- who_raw %>% \n filter(country == \"India\") %>% \n select(year, c_newinc) %>% \n ggplot(aes(x = year, y = c_newinc/1000000)) +\n geom_line() +\n geom_point() +\n labs(\n title = \"New and Relapse Tuberculosis Cases In India \\n1980 -- 2018\"\n ,x = NULL\n ,y = \"Total Cases (in millions)\") +\n theme_bw() +\n theme(plot.title = element_text(hjust = 0.5)) + #center title \n scale_x_continuous(breaks = seq(1980,2020,5)) +\n scale_y_continuous(breaks = scales::pretty_breaks(n=10)) #different way to add tick marks\ng5\n\n\n\n\nCases were steadily rising from 1980 to 1990, then suddenly feel off. Starting in the early 2010s there was a sharp increase and the amount of new and relapse cases just keep growing."
|
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|
||||
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|
||||
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|
||||
"text": "Todays post is a lesson in Facets, as well as humility. The task this week was to replicate the graph in Chapter 8 of Tableau for Healthcare in R. The graph in question is called a Table Lens (This is the name the book uses, however I did have trouble finding this name in Google searches), it is a collection of charts with a common theme, this time looking at countries in various WHO regions and some statistics associated with mortality as well as health expenditure. I say this is a lesson in humiltiy as I have read through the excellent book R for Data Science, and yet the idea of faceting a ggplot graph slipped my mind. This ended with hours of trying to find a package in R to line up graphs, and way more time then I care to admit spent on getting things prefect. I did find such a package called cowplots, which can be found here. While this is an excellent package, its use was unecessary and I reverted back to using the excellent facet feature of GGplot, which can be seen below! \n\nLoad Libraries\n\nlibrary(magrittr) #pipes\nlibrary(ggplot2) #ploting \nlibrary(dplyr)\nlibrary(tidyr)\n\n\n\nImport Data\n\nds <- readxl::read_xlsx(path = \"../2020-01-04_my-start-to-r/Tableau 10 Training Practice Data.xlsx\"\n ,sheet = \"03 - WHO Life Expect & Mort\"\n )\n\n\n\nClean Names and Transform\n\nvarnames <- c(\"who_region\", \"country\", \"year\" , \"sex\" , \"life_expect_birth\" , \"neo_mort\"\n ,\"under_five_mort\" , \"health_expenditure\")\nnames(ds) <- varnames\n\n# Order Countries based on Life Expectancy at Birth\n\nds$country <- factor(ds$country, levels = ds$country[order(ds$life_expect_birth)]) \n\n#To \"Long\" Form\n\nds1 <- ds %>% pivot_longer(5:8)#select columns 5 throuh 8, leave new columns at default names\n\n# Set up labels for Facet, as well as function for Facet Labeller\n\nfacet_labels <- list(\n\"life_expect_birth\" = \"Life Expectancy at Birth \" \n,\"neo_mort\" = \"Neonatal Mortality Rate\" \n,\"under_five_mort\" = \"Under-Five Mortality Rate\"\n,\"health_expenditure\" = \"Health Expenditure per Capita (US$)\" )\n\nvariable_labeller <- function(variable,value){\n return(facet_labels[value])\n}\n\n\n\nGraphs\n\nhightlight_countries <- (c(\"Mauritania\", \"South Africa\")) \n\ng1 <- ds1 %>% filter(who_region == \"Africa\") %>% \n mutate(name = factor(name, levels = c(\"life_expect_birth\" , \"neo_mort\"\n ,\"under_five_mort\" , \"health_expenditure\"))\n ,highlight = country %in% hightlight_countries) %>% \n ggplot(aes(x = country, y = value, fill = highlight)) +\n geom_col(show.legend = FALSE) +\n coord_flip() +\n labs(\n title = \"World Bank Life Expectancy, Neonatal & Under-Five Mortality Rates, and Health Expenditure Analysis\"\n ,x = NULL\n ,y = NULL\n ) +\n facet_grid(~name, scales = \"free_x\",labeller = variable_labeller) +\n theme_bw() +\n geom_text(aes(label = round(value, 0)), hjust = 0) +\n scale_y_continuous(expand = expand_scale(mult = c(0,0.2))) +\n scale_fill_manual(values = c(\"TRUE\" = \"#fc8d59\", \"FALSE\" = \"#2b83ba\"))\ng1\n\n\n\n\n\n\n\n\nReusehttps://creativecommons.org/licenses/by/4.0/CitationBibTeX citation:@online{belanger2020,\n author = {Belanger, Kyle},\n title = {Facets and a {Lesson} in {Humility}},\n date = {2020-01-29},\n langid = {en}\n}\nFor attribution, please cite this work as:\nBelanger, Kyle. 2020. “Facets and a Lesson in Humility.”\nJanuary 29, 2020."
|
||||
},
|
||||
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|
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|
||||
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|
||||
"title": "Line Graphs and Interactivity",
|
||||
"section": "",
|
||||
"text": "Today’s post is all about line graphs using both ggplot for a static graph as well as a package called plotly for interactivity (more on this later). The example graph and data is again coming from Tableau for Healthcare, Chapter 10."
|
||||
},
|
||||
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|
||||
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|
||||
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|
||||
"title": "Line Graphs and Interactivity",
|
||||
"section": "Load Libraries",
|
||||
"text": "Load Libraries\nAs always first step is to load in our libraries, I am using quite a few here, some are a bit overkill for this example but I wanted to play around with some fun features today.\n\nlibrary(magrittr) #pipes\nlibrary(ggplot2) #ploting \nlibrary(dplyr) # data manipulation\nlibrary(tidyr) # tidy data\nlibrary(lubridate) #work with dates\nlibrary(stringr) # manipulate strings\nlibrary(plotly)"
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"title": "Line Graphs and Interactivity",
|
||||
"section": "Import Data",
|
||||
"text": "Import Data\nNext lets import our data, this week we are using the sheet Flu Occurrence FY2013-2016. I am unsure if this is form a real data set or not but it is good for demonstration purposes! After importing we can glimpse at our data to understand what is contained within.\n\nds <- readxl::read_xlsx(path = \"../2020-01-04_my-start-to-r/Tableau 10 Training Practice Data.xlsx\"\n ,sheet = \"05 - Flu Occurrence FY2013-2016\"\n )\nds %>% glimpse()\n\nRows: 48\nColumns: 4\n$ Date <dttm> 2012-10-27, 2012-11-24, …\n$ `Tests (+) for Influenza (count)` <dbl> 995, 3228, 22368, 24615, …\n$ `Total Respiratory Specimens Tested (count)` <dbl> 18986, 24757, 66683, 7561…\n$ `% Tests (+) for Influenza` <dbl> 0.05240704, 0.13038737, 0…"
|
||||
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|
||||
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|
||||
"objectID": "posts/2020-02-10_line-graphs-and-interactivity/line-graphs-and-interactivity.html#transform-data",
|
||||
"href": "posts/2020-02-10_line-graphs-and-interactivity/line-graphs-and-interactivity.html#transform-data",
|
||||
"title": "Line Graphs and Interactivity",
|
||||
"section": "Transform Data",
|
||||
"text": "Transform Data\nI went a bit overboard today with renaming the variables. I wanted to practice writing a function and while it might not be the prettiest or the best way to do this, it worked for what I was trying to accomplish. Note the use of sapply, which lets us run the function on each column name.\n\nformat_names <- function(x) {\n #Fucntion to set all names to lower case, and strip unneeded characters\n x <- tolower(x)\n x <- str_replace_all(x,c(#set each pattern equal to replacement\n \" \" = \"_\"\n ,\"\\\\(\\\\+\\\\)\" = \"pos\" #regualr experssion to match (+)\n ,\"\\\\(\" = \"\"\n ,\"\\\\)\" = \"\"\n ,\"\\\\%\" = \"pct\"\n )\n ) \n }\n\n#run the format name function on all names from DS\ncolnames(ds) <- sapply(colnames(ds),format_names) \n\nNow is were the fun really starts! For this particular data set there are a couple things we need to add to replicate the example. In the original data set the date is stored with month, day, and year; the day is irrelevant and we need to pull out the month as well as the year. For this we can use the lubridate package, first we pull out the month and set it as a factor. For this example our year actually starts in October, so we set our factor to start at October (10), and end with September (9). We then pull out the year, which presents us with a different problem. Again our year starts in October, instead of January. To solve this I have created a variable called date adjustment, in this column is our month is 10 or greater, we will place a 1, if not a 0. We then set our fiscal year to be the actual year plus the date adjustment, this allows us to have our dates in the right fiscal year. Last the percent column is currently listed as a decimal, so we will convert this to a percentage.\n\n# split date time\nds1 <- ds %>% mutate(\n #create month column, then set factors and labels to start fiscal year in Oct\n month = month(ds$date)\n ,month = factor(month\n ,levels = c(10:12, 1:9)\n ,labels = c(month.abb[10:12],month.abb[1:9]))\n ,year = year(ds$date)\n ,date_adjustment = ifelse(month(ds$date) >= 10, 1,0 )\n ,fiscal_year = factor(year + date_adjustment)\n #convert % Pos from decmial to pct\n ,pct_tests_pos_for_influenza = round(pct_tests_pos_for_influenza * 100, digits = 0)\n )\n\nds1 %>% glimpse()\n\nRows: 48\nColumns: 8\n$ date <dttm> 2012-10-27, 2012-11-24, 2012…\n$ tests_pos_for_influenza_count <dbl> 995, 3228, 22368, 24615, 1179…\n$ total_respiratory_specimens_tested_count <dbl> 18986, 24757, 66683, 75614, 5…\n$ pct_tests_pos_for_influenza <dbl> 5, 13, 34, 33, 23, 17, 11, 6,…\n$ month <fct> Oct, Nov, Dec, Jan, Feb, Mar,…\n$ year <dbl> 2012, 2012, 2012, 2013, 2013,…\n$ date_adjustment <dbl> 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,…\n$ fiscal_year <fct> 2013, 2013, 2013, 2013, 2013,…"
|
||||
},
|
||||
{
|
||||
"objectID": "posts/2020-02-10_line-graphs-and-interactivity/line-graphs-and-interactivity.html#ggplot",
|
||||
"href": "posts/2020-02-10_line-graphs-and-interactivity/line-graphs-and-interactivity.html#ggplot",
|
||||
"title": "Line Graphs and Interactivity",
|
||||
"section": "GGplot",
|
||||
"text": "GGplot\nThe graph here is pretty straight forward with one exception, group! For this line graph we want ggplot to connect the lines of the same year, if we do not explicitly state this using the group mapping, ggplot will try to connect all the lines together, which of course is not at all what we want!\n\ng1 <- ds1 %>% \n ggplot(aes(x = month, y = pct_tests_pos_for_influenza, color = fiscal_year\n ,group = fiscal_year)) +\n geom_line() +\n labs(\n x = NULL\n ,y = \"% Tests (+) for Influenza\"\n ,color = NULL\n ,title = \"Flu Viral Surveillance: % Respiratory Specimens Positive for Influenza \\nOctober - September \\nFor Flu Seasons 2013 - 2016\"\n ) +\n theme_classic() +\n scale_y_continuous(breaks = seq(0,40,5)) +\n scale_color_manual(values = c(\"#a6611a\",\"#dfc27d\",\"#80cdc1\",\"#018571\"))\n\ng1"
|
||||
},
|
||||
{
|
||||
"objectID": "posts/2020-02-10_line-graphs-and-interactivity/line-graphs-and-interactivity.html#plotly",
|
||||
"href": "posts/2020-02-10_line-graphs-and-interactivity/line-graphs-and-interactivity.html#plotly",
|
||||
"title": "Line Graphs and Interactivity",
|
||||
"section": "plotly",
|
||||
"text": "plotly\nOne of the nice features of Tableau is the fact the graphs are interactive, while a good graph should speak for itself, end users love pretty things. I have been experimenting with Plotly, which has an open source package for R (as well as many other programming languages!). This example only just scratches the surface, but there will be many more to come!\n\ng2 <- ds1 %>% \n plot_ly(x = ~month, y = ~pct_tests_pos_for_influenza, type = \"scatter\", mode = \"lines\" \n ,color = ~fiscal_year\n ,colors = c(\"#a6611a\",\"#dfc27d\",\"#80cdc1\",\"#018571\")\n , hoverinfo = 'y') %>% \n layout(xaxis = list(\n title = \"\"\n )\n ,yaxis = list(\n title = \"% Tests (+) for Influenza\"\n )\n ,title = \"Flu Viral Surveillance: % Respiratory Specimens Positive for Influenza\"\n ,legend = list(\n x = 100\n ,y = 0.5\n ) \n \n )\n\ng2"
|
||||
}
|
||||
]
|
||||
1
_site/site_libs/crosstalk-1.2.0/css/crosstalk.min.css
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|
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|
|||
.container-fluid.crosstalk-bscols{margin-left:-30px;margin-right:-30px;white-space:normal}body>.container-fluid.crosstalk-bscols{margin-left:auto;margin-right:auto}.crosstalk-input-checkboxgroup .crosstalk-options-group .crosstalk-options-column{display:inline-block;padding-right:12px;vertical-align:top}@media only screen and (max-width: 480px){.crosstalk-input-checkboxgroup .crosstalk-options-group .crosstalk-options-column{display:block;padding-right:inherit}}.crosstalk-input{margin-bottom:15px}.crosstalk-input .control-label{margin-bottom:0;vertical-align:middle}.crosstalk-input input[type="checkbox"]{margin:4px 0 0;margin-top:1px;line-height:normal}.crosstalk-input .checkbox{position:relative;display:block;margin-top:10px;margin-bottom:10px}.crosstalk-input .checkbox>label{padding-left:20px;margin-bottom:0;font-weight:400;cursor:pointer}.crosstalk-input .checkbox input[type="checkbox"],.crosstalk-input .checkbox-inline input[type="checkbox"]{position:absolute;margin-top:2px;margin-left:-20px}.crosstalk-input .checkbox+.checkbox{margin-top:-5px}.crosstalk-input .checkbox-inline{position:relative;display:inline-block;padding-left:20px;margin-bottom:0;font-weight:400;vertical-align:middle;cursor:pointer}.crosstalk-input .checkbox-inline+.checkbox-inline{margin-top:0;margin-left:10px}
|
||||
1474
_site/site_libs/crosstalk-1.2.0/js/crosstalk.js
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|
|
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|
|||
/* Adjust margins outwards, so column contents line up with the edges of the
|
||||
parent of container-fluid. */
|
||||
.container-fluid.crosstalk-bscols {
|
||||
margin-left: -30px;
|
||||
margin-right: -30px;
|
||||
white-space: normal;
|
||||
}
|
||||
|
||||
/* But don't adjust the margins outwards if we're directly under the body,
|
||||
i.e. we were the top-level of something at the console. */
|
||||
body > .container-fluid.crosstalk-bscols {
|
||||
margin-left: auto;
|
||||
margin-right: auto;
|
||||
}
|
||||
|
||||
.crosstalk-input-checkboxgroup .crosstalk-options-group .crosstalk-options-column {
|
||||
display: inline-block;
|
||||
padding-right: 12px;
|
||||
vertical-align: top;
|
||||
}
|
||||
|
||||
@media only screen and (max-width:480px) {
|
||||
.crosstalk-input-checkboxgroup .crosstalk-options-group .crosstalk-options-column {
|
||||
display: block;
|
||||
padding-right: inherit;
|
||||
}
|
||||
}
|
||||
|
||||
/* Relevant BS3 styles to make filter_checkbox() look reasonable without Bootstrap */
|
||||
.crosstalk-input {
|
||||
margin-bottom: 15px; /* a la .form-group */
|
||||
.control-label {
|
||||
margin-bottom: 0;
|
||||
vertical-align: middle;
|
||||
}
|
||||
input[type="checkbox"] {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
.checkbox {
|
||||
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|
||||
display: block;
|
||||
margin-top: 10px;
|
||||
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|
||||
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|
||||
.checkbox > label{
|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
margin-left: 10px;
|
||||
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|
||||
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|
||||
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_site/site_libs/htmlwidgets-1.6.2/htmlwidgets.js
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|
|
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|
|||
(function() {
|
||||
// If window.HTMLWidgets is already defined, then use it; otherwise create a
|
||||
// new object. This allows preceding code to set options that affect the
|
||||
// initialization process (though none currently exist).
|
||||
window.HTMLWidgets = window.HTMLWidgets || {};
|
||||
|
||||
// See if we're running in a viewer pane. If not, we're in a web browser.
|
||||
var viewerMode = window.HTMLWidgets.viewerMode =
|
||||
/\bviewer_pane=1\b/.test(window.location);
|
||||
|
||||
// See if we're running in Shiny mode. If not, it's a static document.
|
||||
// Note that static widgets can appear in both Shiny and static modes, but
|
||||
// obviously, Shiny widgets can only appear in Shiny apps/documents.
|
||||
var shinyMode = window.HTMLWidgets.shinyMode =
|
||||
typeof(window.Shiny) !== "undefined" && !!window.Shiny.outputBindings;
|
||||
|
||||
// We can't count on jQuery being available, so we implement our own
|
||||
// version if necessary.
|
||||
function querySelectorAll(scope, selector) {
|
||||
if (typeof(jQuery) !== "undefined" && scope instanceof jQuery) {
|
||||
return scope.find(selector);
|
||||
}
|
||||
if (scope.querySelectorAll) {
|
||||
return scope.querySelectorAll(selector);
|
||||
}
|
||||
}
|
||||
|
||||
function asArray(value) {
|
||||
if (value === null)
|
||||
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|
||||
if ($.isArray(value))
|
||||
return value;
|
||||
return [value];
|
||||
}
|
||||
|
||||
// Implement jQuery's extend
|
||||
function extend(target /*, ... */) {
|
||||
if (arguments.length == 1) {
|
||||
return target;
|
||||
}
|
||||
for (var i = 1; i < arguments.length; i++) {
|
||||
var source = arguments[i];
|
||||
for (var prop in source) {
|
||||
if (source.hasOwnProperty(prop)) {
|
||||
target[prop] = source[prop];
|
||||
}
|
||||
}
|
||||
}
|
||||
return target;
|
||||
}
|
||||
|
||||
// IE8 doesn't support Array.forEach.
|
||||
function forEach(values, callback, thisArg) {
|
||||
if (values.forEach) {
|
||||
values.forEach(callback, thisArg);
|
||||
} else {
|
||||
for (var i = 0; i < values.length; i++) {
|
||||
callback.call(thisArg, values[i], i, values);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Replaces the specified method with the return value of funcSource.
|
||||
//
|
||||
// Note that funcSource should not BE the new method, it should be a function
|
||||
// that RETURNS the new method. funcSource receives a single argument that is
|
||||
// the overridden method, it can be called from the new method. The overridden
|
||||
// method can be called like a regular function, it has the target permanently
|
||||
// bound to it so "this" will work correctly.
|
||||
function overrideMethod(target, methodName, funcSource) {
|
||||
var superFunc = target[methodName] || function() {};
|
||||
var superFuncBound = function() {
|
||||
return superFunc.apply(target, arguments);
|
||||
};
|
||||
target[methodName] = funcSource(superFuncBound);
|
||||
}
|
||||
|
||||
// Add a method to delegator that, when invoked, calls
|
||||
// delegatee.methodName. If there is no such method on
|
||||
// the delegatee, but there was one on delegator before
|
||||
// delegateMethod was called, then the original version
|
||||
// is invoked instead.
|
||||
// For example:
|
||||
//
|
||||
// var a = {
|
||||
// method1: function() { console.log('a1'); }
|
||||
// method2: function() { console.log('a2'); }
|
||||
// };
|
||||
// var b = {
|
||||
// method1: function() { console.log('b1'); }
|
||||
// };
|
||||
// delegateMethod(a, b, "method1");
|
||||
// delegateMethod(a, b, "method2");
|
||||
// a.method1();
|
||||
// a.method2();
|
||||
//
|
||||
// The output would be "b1", "a2".
|
||||
function delegateMethod(delegator, delegatee, methodName) {
|
||||
var inherited = delegator[methodName];
|
||||
delegator[methodName] = function() {
|
||||
var target = delegatee;
|
||||
var method = delegatee[methodName];
|
||||
|
||||
// The method doesn't exist on the delegatee. Instead,
|
||||
// call the method on the delegator, if it exists.
|
||||
if (!method) {
|
||||
target = delegator;
|
||||
method = inherited;
|
||||
}
|
||||
|
||||
if (method) {
|
||||
return method.apply(target, arguments);
|
||||
}
|
||||
};
|
||||
}
|
||||
|
||||
// Implement a vague facsimilie of jQuery's data method
|
||||
function elementData(el, name, value) {
|
||||
if (arguments.length == 2) {
|
||||
return el["htmlwidget_data_" + name];
|
||||
} else if (arguments.length == 3) {
|
||||
el["htmlwidget_data_" + name] = value;
|
||||
return el;
|
||||
} else {
|
||||
throw new Error("Wrong number of arguments for elementData: " +
|
||||
arguments.length);
|
||||
}
|
||||
}
|
||||
|
||||
// http://stackoverflow.com/questions/3446170/escape-string-for-use-in-javascript-regex
|
||||
function escapeRegExp(str) {
|
||||
return str.replace(/[\-\[\]\/\{\}\(\)\*\+\?\.\\\^\$\|]/g, "\\$&");
|
||||
}
|
||||
|
||||
function hasClass(el, className) {
|
||||
var re = new RegExp("\\b" + escapeRegExp(className) + "\\b");
|
||||
return re.test(el.className);
|
||||
}
|
||||
|
||||
// elements - array (or array-like object) of HTML elements
|
||||
// className - class name to test for
|
||||
// include - if true, only return elements with given className;
|
||||
// if false, only return elements *without* given className
|
||||
function filterByClass(elements, className, include) {
|
||||
var results = [];
|
||||
for (var i = 0; i < elements.length; i++) {
|
||||
if (hasClass(elements[i], className) == include)
|
||||
results.push(elements[i]);
|
||||
}
|
||||
return results;
|
||||
}
|
||||
|
||||
function on(obj, eventName, func) {
|
||||
if (obj.addEventListener) {
|
||||
obj.addEventListener(eventName, func, false);
|
||||
} else if (obj.attachEvent) {
|
||||
obj.attachEvent(eventName, func);
|
||||
}
|
||||
}
|
||||
|
||||
function off(obj, eventName, func) {
|
||||
if (obj.removeEventListener)
|
||||
obj.removeEventListener(eventName, func, false);
|
||||
else if (obj.detachEvent) {
|
||||
obj.detachEvent(eventName, func);
|
||||
}
|
||||
}
|
||||
|
||||
// Translate array of values to top/right/bottom/left, as usual with
|
||||
// the "padding" CSS property
|
||||
// https://developer.mozilla.org/en-US/docs/Web/CSS/padding
|
||||
function unpackPadding(value) {
|
||||
if (typeof(value) === "number")
|
||||
value = [value];
|
||||
if (value.length === 1) {
|
||||
return {top: value[0], right: value[0], bottom: value[0], left: value[0]};
|
||||
}
|
||||
if (value.length === 2) {
|
||||
return {top: value[0], right: value[1], bottom: value[0], left: value[1]};
|
||||
}
|
||||
if (value.length === 3) {
|
||||
return {top: value[0], right: value[1], bottom: value[2], left: value[1]};
|
||||
}
|
||||
if (value.length === 4) {
|
||||
return {top: value[0], right: value[1], bottom: value[2], left: value[3]};
|
||||
}
|
||||
}
|
||||
|
||||
// Convert an unpacked padding object to a CSS value
|
||||
function paddingToCss(paddingObj) {
|
||||
return paddingObj.top + "px " + paddingObj.right + "px " + paddingObj.bottom + "px " + paddingObj.left + "px";
|
||||
}
|
||||
|
||||
// Makes a number suitable for CSS
|
||||
function px(x) {
|
||||
if (typeof(x) === "number")
|
||||
return x + "px";
|
||||
else
|
||||
return x;
|
||||
}
|
||||
|
||||
// Retrieves runtime widget sizing information for an element.
|
||||
// The return value is either null, or an object with fill, padding,
|
||||
// defaultWidth, defaultHeight fields.
|
||||
function sizingPolicy(el) {
|
||||
var sizingEl = document.querySelector("script[data-for='" + el.id + "'][type='application/htmlwidget-sizing']");
|
||||
if (!sizingEl)
|
||||
return null;
|
||||
var sp = JSON.parse(sizingEl.textContent || sizingEl.text || "{}");
|
||||
if (viewerMode) {
|
||||
return sp.viewer;
|
||||
} else {
|
||||
return sp.browser;
|
||||
}
|
||||
}
|
||||
|
||||
// @param tasks Array of strings (or falsy value, in which case no-op).
|
||||
// Each element must be a valid JavaScript expression that yields a
|
||||
// function. Or, can be an array of objects with "code" and "data"
|
||||
// properties; in this case, the "code" property should be a string
|
||||
// of JS that's an expr that yields a function, and "data" should be
|
||||
// an object that will be added as an additional argument when that
|
||||
// function is called.
|
||||
// @param target The object that will be "this" for each function
|
||||
// execution.
|
||||
// @param args Array of arguments to be passed to the functions. (The
|
||||
// same arguments will be passed to all functions.)
|
||||
function evalAndRun(tasks, target, args) {
|
||||
if (tasks) {
|
||||
forEach(tasks, function(task) {
|
||||
var theseArgs = args;
|
||||
if (typeof(task) === "object") {
|
||||
theseArgs = theseArgs.concat([task.data]);
|
||||
task = task.code;
|
||||
}
|
||||
var taskFunc = tryEval(task);
|
||||
if (typeof(taskFunc) !== "function") {
|
||||
throw new Error("Task must be a function! Source:\n" + task);
|
||||
}
|
||||
taskFunc.apply(target, theseArgs);
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// Attempt eval() both with and without enclosing in parentheses.
|
||||
// Note that enclosing coerces a function declaration into
|
||||
// an expression that eval() can parse
|
||||
// (otherwise, a SyntaxError is thrown)
|
||||
function tryEval(code) {
|
||||
var result = null;
|
||||
try {
|
||||
result = eval("(" + code + ")");
|
||||
} catch(error) {
|
||||
if (!(error instanceof SyntaxError)) {
|
||||
throw error;
|
||||
}
|
||||
try {
|
||||
result = eval(code);
|
||||
} catch(e) {
|
||||
if (e instanceof SyntaxError) {
|
||||
throw error;
|
||||
} else {
|
||||
throw e;
|
||||
}
|
||||
}
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
function initSizing(el) {
|
||||
var sizing = sizingPolicy(el);
|
||||
if (!sizing)
|
||||
return;
|
||||
|
||||
var cel = document.getElementById("htmlwidget_container");
|
||||
if (!cel)
|
||||
return;
|
||||
|
||||
if (typeof(sizing.padding) !== "undefined") {
|
||||
document.body.style.margin = "0";
|
||||
document.body.style.padding = paddingToCss(unpackPadding(sizing.padding));
|
||||
}
|
||||
|
||||
if (sizing.fill) {
|
||||
document.body.style.overflow = "hidden";
|
||||
document.body.style.width = "100%";
|
||||
document.body.style.height = "100%";
|
||||
document.documentElement.style.width = "100%";
|
||||
document.documentElement.style.height = "100%";
|
||||
cel.style.position = "absolute";
|
||||
var pad = unpackPadding(sizing.padding);
|
||||
cel.style.top = pad.top + "px";
|
||||
cel.style.right = pad.right + "px";
|
||||
cel.style.bottom = pad.bottom + "px";
|
||||
cel.style.left = pad.left + "px";
|
||||
el.style.width = "100%";
|
||||
el.style.height = "100%";
|
||||
|
||||
return {
|
||||
getWidth: function() { return cel.getBoundingClientRect().width; },
|
||||
getHeight: function() { return cel.getBoundingClientRect().height; }
|
||||
};
|
||||
|
||||
} else {
|
||||
el.style.width = px(sizing.width);
|
||||
el.style.height = px(sizing.height);
|
||||
|
||||
return {
|
||||
getWidth: function() { return cel.getBoundingClientRect().width; },
|
||||
getHeight: function() { return cel.getBoundingClientRect().height; }
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
// Default implementations for methods
|
||||
var defaults = {
|
||||
find: function(scope) {
|
||||
return querySelectorAll(scope, "." + this.name);
|
||||
},
|
||||
renderError: function(el, err) {
|
||||
var $el = $(el);
|
||||
|
||||
this.clearError(el);
|
||||
|
||||
// Add all these error classes, as Shiny does
|
||||
var errClass = "shiny-output-error";
|
||||
if (err.type !== null) {
|
||||
// use the classes of the error condition as CSS class names
|
||||
errClass = errClass + " " + $.map(asArray(err.type), function(type) {
|
||||
return errClass + "-" + type;
|
||||
}).join(" ");
|
||||
}
|
||||
errClass = errClass + " htmlwidgets-error";
|
||||
|
||||
// Is el inline or block? If inline or inline-block, just display:none it
|
||||
// and add an inline error.
|
||||
var display = $el.css("display");
|
||||
$el.data("restore-display-mode", display);
|
||||
|
||||
if (display === "inline" || display === "inline-block") {
|
||||
$el.hide();
|
||||
if (err.message !== "") {
|
||||
var errorSpan = $("<span>").addClass(errClass);
|
||||
errorSpan.text(err.message);
|
||||
$el.after(errorSpan);
|
||||
}
|
||||
} else if (display === "block") {
|
||||
// If block, add an error just after the el, set visibility:none on the
|
||||
// el, and position the error to be on top of the el.
|
||||
// Mark it with a unique ID and CSS class so we can remove it later.
|
||||
$el.css("visibility", "hidden");
|
||||
if (err.message !== "") {
|
||||
var errorDiv = $("<div>").addClass(errClass).css("position", "absolute")
|
||||
.css("top", el.offsetTop)
|
||||
.css("left", el.offsetLeft)
|
||||
// setting width can push out the page size, forcing otherwise
|
||||
// unnecessary scrollbars to appear and making it impossible for
|
||||
// the element to shrink; so use max-width instead
|
||||
.css("maxWidth", el.offsetWidth)
|
||||
.css("height", el.offsetHeight);
|
||||
errorDiv.text(err.message);
|
||||
$el.after(errorDiv);
|
||||
|
||||
// Really dumb way to keep the size/position of the error in sync with
|
||||
// the parent element as the window is resized or whatever.
|
||||
var intId = setInterval(function() {
|
||||
if (!errorDiv[0].parentElement) {
|
||||
clearInterval(intId);
|
||||
return;
|
||||
}
|
||||
errorDiv
|
||||
.css("top", el.offsetTop)
|
||||
.css("left", el.offsetLeft)
|
||||
.css("maxWidth", el.offsetWidth)
|
||||
.css("height", el.offsetHeight);
|
||||
}, 500);
|
||||
}
|
||||
}
|
||||
},
|
||||
clearError: function(el) {
|
||||
var $el = $(el);
|
||||
var display = $el.data("restore-display-mode");
|
||||
$el.data("restore-display-mode", null);
|
||||
|
||||
if (display === "inline" || display === "inline-block") {
|
||||
if (display)
|
||||
$el.css("display", display);
|
||||
$(el.nextSibling).filter(".htmlwidgets-error").remove();
|
||||
} else if (display === "block"){
|
||||
$el.css("visibility", "inherit");
|
||||
$(el.nextSibling).filter(".htmlwidgets-error").remove();
|
||||
}
|
||||
},
|
||||
sizing: {}
|
||||
};
|
||||
|
||||
// Called by widget bindings to register a new type of widget. The definition
|
||||
// object can contain the following properties:
|
||||
// - name (required) - A string indicating the binding name, which will be
|
||||
// used by default as the CSS classname to look for.
|
||||
// - initialize (optional) - A function(el) that will be called once per
|
||||
// widget element; if a value is returned, it will be passed as the third
|
||||
// value to renderValue.
|
||||
// - renderValue (required) - A function(el, data, initValue) that will be
|
||||
// called with data. Static contexts will cause this to be called once per
|
||||
// element; Shiny apps will cause this to be called multiple times per
|
||||
// element, as the data changes.
|
||||
window.HTMLWidgets.widget = function(definition) {
|
||||
if (!definition.name) {
|
||||
throw new Error("Widget must have a name");
|
||||
}
|
||||
if (!definition.type) {
|
||||
throw new Error("Widget must have a type");
|
||||
}
|
||||
// Currently we only support output widgets
|
||||
if (definition.type !== "output") {
|
||||
throw new Error("Unrecognized widget type '" + definition.type + "'");
|
||||
}
|
||||
// TODO: Verify that .name is a valid CSS classname
|
||||
|
||||
// Support new-style instance-bound definitions. Old-style class-bound
|
||||
// definitions have one widget "object" per widget per type/class of
|
||||
// widget; the renderValue and resize methods on such widget objects
|
||||
// take el and instance arguments, because the widget object can't
|
||||
// store them. New-style instance-bound definitions have one widget
|
||||
// object per widget instance; the definition that's passed in doesn't
|
||||
// provide renderValue or resize methods at all, just the single method
|
||||
// factory(el, width, height)
|
||||
// which returns an object that has renderValue(x) and resize(w, h).
|
||||
// This enables a far more natural programming style for the widget
|
||||
// author, who can store per-instance state using either OO-style
|
||||
// instance fields or functional-style closure variables (I guess this
|
||||
// is in contrast to what can only be called C-style pseudo-OO which is
|
||||
// what we required before).
|
||||
if (definition.factory) {
|
||||
definition = createLegacyDefinitionAdapter(definition);
|
||||
}
|
||||
|
||||
if (!definition.renderValue) {
|
||||
throw new Error("Widget must have a renderValue function");
|
||||
}
|
||||
|
||||
// For static rendering (non-Shiny), use a simple widget registration
|
||||
// scheme. We also use this scheme for Shiny apps/documents that also
|
||||
// contain static widgets.
|
||||
window.HTMLWidgets.widgets = window.HTMLWidgets.widgets || [];
|
||||
// Merge defaults into the definition; don't mutate the original definition.
|
||||
var staticBinding = extend({}, defaults, definition);
|
||||
overrideMethod(staticBinding, "find", function(superfunc) {
|
||||
return function(scope) {
|
||||
var results = superfunc(scope);
|
||||
// Filter out Shiny outputs, we only want the static kind
|
||||
return filterByClass(results, "html-widget-output", false);
|
||||
};
|
||||
});
|
||||
window.HTMLWidgets.widgets.push(staticBinding);
|
||||
|
||||
if (shinyMode) {
|
||||
// Shiny is running. Register the definition with an output binding.
|
||||
// The definition itself will not be the output binding, instead
|
||||
// we will make an output binding object that delegates to the
|
||||
// definition. This is because we foolishly used the same method
|
||||
// name (renderValue) for htmlwidgets definition and Shiny bindings
|
||||
// but they actually have quite different semantics (the Shiny
|
||||
// bindings receive data that includes lots of metadata that it
|
||||
// strips off before calling htmlwidgets renderValue). We can't
|
||||
// just ignore the difference because in some widgets it's helpful
|
||||
// to call this.renderValue() from inside of resize(), and if
|
||||
// we're not delegating, then that call will go to the Shiny
|
||||
// version instead of the htmlwidgets version.
|
||||
|
||||
// Merge defaults with definition, without mutating either.
|
||||
var bindingDef = extend({}, defaults, definition);
|
||||
|
||||
// This object will be our actual Shiny binding.
|
||||
var shinyBinding = new Shiny.OutputBinding();
|
||||
|
||||
// With a few exceptions, we'll want to simply use the bindingDef's
|
||||
// version of methods if they are available, otherwise fall back to
|
||||
// Shiny's defaults. NOTE: If Shiny's output bindings gain additional
|
||||
// methods in the future, and we want them to be overrideable by
|
||||
// HTMLWidget binding definitions, then we'll need to add them to this
|
||||
// list.
|
||||
delegateMethod(shinyBinding, bindingDef, "getId");
|
||||
delegateMethod(shinyBinding, bindingDef, "onValueChange");
|
||||
delegateMethod(shinyBinding, bindingDef, "onValueError");
|
||||
delegateMethod(shinyBinding, bindingDef, "renderError");
|
||||
delegateMethod(shinyBinding, bindingDef, "clearError");
|
||||
delegateMethod(shinyBinding, bindingDef, "showProgress");
|
||||
|
||||
// The find, renderValue, and resize are handled differently, because we
|
||||
// want to actually decorate the behavior of the bindingDef methods.
|
||||
|
||||
shinyBinding.find = function(scope) {
|
||||
var results = bindingDef.find(scope);
|
||||
|
||||
// Only return elements that are Shiny outputs, not static ones
|
||||
var dynamicResults = results.filter(".html-widget-output");
|
||||
|
||||
// It's possible that whatever caused Shiny to think there might be
|
||||
// new dynamic outputs, also caused there to be new static outputs.
|
||||
// Since there might be lots of different htmlwidgets bindings, we
|
||||
// schedule execution for later--no need to staticRender multiple
|
||||
// times.
|
||||
if (results.length !== dynamicResults.length)
|
||||
scheduleStaticRender();
|
||||
|
||||
return dynamicResults;
|
||||
};
|
||||
|
||||
// Wrap renderValue to handle initialization, which unfortunately isn't
|
||||
// supported natively by Shiny at the time of this writing.
|
||||
|
||||
shinyBinding.renderValue = function(el, data) {
|
||||
Shiny.renderDependencies(data.deps);
|
||||
// Resolve strings marked as javascript literals to objects
|
||||
if (!(data.evals instanceof Array)) data.evals = [data.evals];
|
||||
for (var i = 0; data.evals && i < data.evals.length; i++) {
|
||||
window.HTMLWidgets.evaluateStringMember(data.x, data.evals[i]);
|
||||
}
|
||||
if (!bindingDef.renderOnNullValue) {
|
||||
if (data.x === null) {
|
||||
el.style.visibility = "hidden";
|
||||
return;
|
||||
} else {
|
||||
el.style.visibility = "inherit";
|
||||
}
|
||||
}
|
||||
if (!elementData(el, "initialized")) {
|
||||
initSizing(el);
|
||||
|
||||
elementData(el, "initialized", true);
|
||||
if (bindingDef.initialize) {
|
||||
var rect = el.getBoundingClientRect();
|
||||
var result = bindingDef.initialize(el, rect.width, rect.height);
|
||||
elementData(el, "init_result", result);
|
||||
}
|
||||
}
|
||||
bindingDef.renderValue(el, data.x, elementData(el, "init_result"));
|
||||
evalAndRun(data.jsHooks.render, elementData(el, "init_result"), [el, data.x]);
|
||||
};
|
||||
|
||||
// Only override resize if bindingDef implements it
|
||||
if (bindingDef.resize) {
|
||||
shinyBinding.resize = function(el, width, height) {
|
||||
// Shiny can call resize before initialize/renderValue have been
|
||||
// called, which doesn't make sense for widgets.
|
||||
if (elementData(el, "initialized")) {
|
||||
bindingDef.resize(el, width, height, elementData(el, "init_result"));
|
||||
}
|
||||
};
|
||||
}
|
||||
|
||||
Shiny.outputBindings.register(shinyBinding, bindingDef.name);
|
||||
}
|
||||
};
|
||||
|
||||
var scheduleStaticRenderTimerId = null;
|
||||
function scheduleStaticRender() {
|
||||
if (!scheduleStaticRenderTimerId) {
|
||||
scheduleStaticRenderTimerId = setTimeout(function() {
|
||||
scheduleStaticRenderTimerId = null;
|
||||
window.HTMLWidgets.staticRender();
|
||||
}, 1);
|
||||
}
|
||||
}
|
||||
|
||||
// Render static widgets after the document finishes loading
|
||||
// Statically render all elements that are of this widget's class
|
||||
window.HTMLWidgets.staticRender = function() {
|
||||
var bindings = window.HTMLWidgets.widgets || [];
|
||||
forEach(bindings, function(binding) {
|
||||
var matches = binding.find(document.documentElement);
|
||||
forEach(matches, function(el) {
|
||||
var sizeObj = initSizing(el, binding);
|
||||
|
||||
var getSize = function(el) {
|
||||
if (sizeObj) {
|
||||
return {w: sizeObj.getWidth(), h: sizeObj.getHeight()}
|
||||
} else {
|
||||
var rect = el.getBoundingClientRect();
|
||||
return {w: rect.width, h: rect.height}
|
||||
}
|
||||
};
|
||||
|
||||
if (hasClass(el, "html-widget-static-bound"))
|
||||
return;
|
||||
el.className = el.className + " html-widget-static-bound";
|
||||
|
||||
var initResult;
|
||||
if (binding.initialize) {
|
||||
var size = getSize(el);
|
||||
initResult = binding.initialize(el, size.w, size.h);
|
||||
elementData(el, "init_result", initResult);
|
||||
}
|
||||
|
||||
if (binding.resize) {
|
||||
var lastSize = getSize(el);
|
||||
var resizeHandler = function(e) {
|
||||
var size = getSize(el);
|
||||
if (size.w === 0 && size.h === 0)
|
||||
return;
|
||||
if (size.w === lastSize.w && size.h === lastSize.h)
|
||||
return;
|
||||
lastSize = size;
|
||||
binding.resize(el, size.w, size.h, initResult);
|
||||
};
|
||||
|
||||
on(window, "resize", resizeHandler);
|
||||
|
||||
// This is needed for cases where we're running in a Shiny
|
||||
// app, but the widget itself is not a Shiny output, but
|
||||
// rather a simple static widget. One example of this is
|
||||
// an rmarkdown document that has runtime:shiny and widget
|
||||
// that isn't in a render function. Shiny only knows to
|
||||
// call resize handlers for Shiny outputs, not for static
|
||||
// widgets, so we do it ourselves.
|
||||
if (window.jQuery) {
|
||||
window.jQuery(document).on(
|
||||
"shown.htmlwidgets shown.bs.tab.htmlwidgets shown.bs.collapse.htmlwidgets",
|
||||
resizeHandler
|
||||
);
|
||||
window.jQuery(document).on(
|
||||
"hidden.htmlwidgets hidden.bs.tab.htmlwidgets hidden.bs.collapse.htmlwidgets",
|
||||
resizeHandler
|
||||
);
|
||||
}
|
||||
|
||||
// This is needed for the specific case of ioslides, which
|
||||
// flips slides between display:none and display:block.
|
||||
// Ideally we would not have to have ioslide-specific code
|
||||
// here, but rather have ioslides raise a generic event,
|
||||
// but the rmarkdown package just went to CRAN so the
|
||||
// window to getting that fixed may be long.
|
||||
if (window.addEventListener) {
|
||||
// It's OK to limit this to window.addEventListener
|
||||
// browsers because ioslides itself only supports
|
||||
// such browsers.
|
||||
on(document, "slideenter", resizeHandler);
|
||||
on(document, "slideleave", resizeHandler);
|
||||
}
|
||||
}
|
||||
|
||||
var scriptData = document.querySelector("script[data-for='" + el.id + "'][type='application/json']");
|
||||
if (scriptData) {
|
||||
var data = JSON.parse(scriptData.textContent || scriptData.text);
|
||||
// Resolve strings marked as javascript literals to objects
|
||||
if (!(data.evals instanceof Array)) data.evals = [data.evals];
|
||||
for (var k = 0; data.evals && k < data.evals.length; k++) {
|
||||
window.HTMLWidgets.evaluateStringMember(data.x, data.evals[k]);
|
||||
}
|
||||
binding.renderValue(el, data.x, initResult);
|
||||
evalAndRun(data.jsHooks.render, initResult, [el, data.x]);
|
||||
}
|
||||
});
|
||||
});
|
||||
|
||||
invokePostRenderHandlers();
|
||||
}
|
||||
|
||||
|
||||
function has_jQuery3() {
|
||||
if (!window.jQuery) {
|
||||
return false;
|
||||
}
|
||||
var $version = window.jQuery.fn.jquery;
|
||||
var $major_version = parseInt($version.split(".")[0]);
|
||||
return $major_version >= 3;
|
||||
}
|
||||
|
||||
/*
|
||||
/ Shiny 1.4 bumped jQuery from 1.x to 3.x which means jQuery's
|
||||
/ on-ready handler (i.e., $(fn)) is now asyncronous (i.e., it now
|
||||
/ really means $(setTimeout(fn)).
|
||||
/ https://jquery.com/upgrade-guide/3.0/#breaking-change-document-ready-handlers-are-now-asynchronous
|
||||
/
|
||||
/ Since Shiny uses $() to schedule initShiny, shiny>=1.4 calls initShiny
|
||||
/ one tick later than it did before, which means staticRender() is
|
||||
/ called renderValue() earlier than (advanced) widget authors might be expecting.
|
||||
/ https://github.com/rstudio/shiny/issues/2630
|
||||
/
|
||||
/ For a concrete example, leaflet has some methods (e.g., updateBounds)
|
||||
/ which reference Shiny methods registered in initShiny (e.g., setInputValue).
|
||||
/ Since leaflet is privy to this life-cycle, it knows to use setTimeout() to
|
||||
/ delay execution of those methods (until Shiny methods are ready)
|
||||
/ https://github.com/rstudio/leaflet/blob/18ec981/javascript/src/index.js#L266-L268
|
||||
/
|
||||
/ Ideally widget authors wouldn't need to use this setTimeout() hack that
|
||||
/ leaflet uses to call Shiny methods on a staticRender(). In the long run,
|
||||
/ the logic initShiny should be broken up so that method registration happens
|
||||
/ right away, but binding happens later.
|
||||
*/
|
||||
function maybeStaticRenderLater() {
|
||||
if (shinyMode && has_jQuery3()) {
|
||||
window.jQuery(window.HTMLWidgets.staticRender);
|
||||
} else {
|
||||
window.HTMLWidgets.staticRender();
|
||||
}
|
||||
}
|
||||
|
||||
if (document.addEventListener) {
|
||||
document.addEventListener("DOMContentLoaded", function() {
|
||||
document.removeEventListener("DOMContentLoaded", arguments.callee, false);
|
||||
maybeStaticRenderLater();
|
||||
}, false);
|
||||
} else if (document.attachEvent) {
|
||||
document.attachEvent("onreadystatechange", function() {
|
||||
if (document.readyState === "complete") {
|
||||
document.detachEvent("onreadystatechange", arguments.callee);
|
||||
maybeStaticRenderLater();
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
|
||||
window.HTMLWidgets.getAttachmentUrl = function(depname, key) {
|
||||
// If no key, default to the first item
|
||||
if (typeof(key) === "undefined")
|
||||
key = 1;
|
||||
|
||||
var link = document.getElementById(depname + "-" + key + "-attachment");
|
||||
if (!link) {
|
||||
throw new Error("Attachment " + depname + "/" + key + " not found in document");
|
||||
}
|
||||
return link.getAttribute("href");
|
||||
};
|
||||
|
||||
window.HTMLWidgets.dataframeToD3 = function(df) {
|
||||
var names = [];
|
||||
var length;
|
||||
for (var name in df) {
|
||||
if (df.hasOwnProperty(name))
|
||||
names.push(name);
|
||||
if (typeof(df[name]) !== "object" || typeof(df[name].length) === "undefined") {
|
||||
throw new Error("All fields must be arrays");
|
||||
} else if (typeof(length) !== "undefined" && length !== df[name].length) {
|
||||
throw new Error("All fields must be arrays of the same length");
|
||||
}
|
||||
length = df[name].length;
|
||||
}
|
||||
var results = [];
|
||||
var item;
|
||||
for (var row = 0; row < length; row++) {
|
||||
item = {};
|
||||
for (var col = 0; col < names.length; col++) {
|
||||
item[names[col]] = df[names[col]][row];
|
||||
}
|
||||
results.push(item);
|
||||
}
|
||||
return results;
|
||||
};
|
||||
|
||||
window.HTMLWidgets.transposeArray2D = function(array) {
|
||||
if (array.length === 0) return array;
|
||||
var newArray = array[0].map(function(col, i) {
|
||||
return array.map(function(row) {
|
||||
return row[i]
|
||||
})
|
||||
});
|
||||
return newArray;
|
||||
};
|
||||
// Split value at splitChar, but allow splitChar to be escaped
|
||||
// using escapeChar. Any other characters escaped by escapeChar
|
||||
// will be included as usual (including escapeChar itself).
|
||||
function splitWithEscape(value, splitChar, escapeChar) {
|
||||
var results = [];
|
||||
var escapeMode = false;
|
||||
var currentResult = "";
|
||||
for (var pos = 0; pos < value.length; pos++) {
|
||||
if (!escapeMode) {
|
||||
if (value[pos] === splitChar) {
|
||||
results.push(currentResult);
|
||||
currentResult = "";
|
||||
} else if (value[pos] === escapeChar) {
|
||||
escapeMode = true;
|
||||
} else {
|
||||
currentResult += value[pos];
|
||||
}
|
||||
} else {
|
||||
currentResult += value[pos];
|
||||
escapeMode = false;
|
||||
}
|
||||
}
|
||||
if (currentResult !== "") {
|
||||
results.push(currentResult);
|
||||
}
|
||||
return results;
|
||||
}
|
||||
// Function authored by Yihui/JJ Allaire
|
||||
window.HTMLWidgets.evaluateStringMember = function(o, member) {
|
||||
var parts = splitWithEscape(member, '.', '\\');
|
||||
for (var i = 0, l = parts.length; i < l; i++) {
|
||||
var part = parts[i];
|
||||
// part may be a character or 'numeric' member name
|
||||
if (o !== null && typeof o === "object" && part in o) {
|
||||
if (i == (l - 1)) { // if we are at the end of the line then evalulate
|
||||
if (typeof o[part] === "string")
|
||||
o[part] = tryEval(o[part]);
|
||||
} else { // otherwise continue to next embedded object
|
||||
o = o[part];
|
||||
}
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
// Retrieve the HTMLWidget instance (i.e. the return value of an
|
||||
// HTMLWidget binding's initialize() or factory() function)
|
||||
// associated with an element, or null if none.
|
||||
window.HTMLWidgets.getInstance = function(el) {
|
||||
return elementData(el, "init_result");
|
||||
};
|
||||
|
||||
// Finds the first element in the scope that matches the selector,
|
||||
// and returns the HTMLWidget instance (i.e. the return value of
|
||||
// an HTMLWidget binding's initialize() or factory() function)
|
||||
// associated with that element, if any. If no element matches the
|
||||
// selector, or the first matching element has no HTMLWidget
|
||||
// instance associated with it, then null is returned.
|
||||
//
|
||||
// The scope argument is optional, and defaults to window.document.
|
||||
window.HTMLWidgets.find = function(scope, selector) {
|
||||
if (arguments.length == 1) {
|
||||
selector = scope;
|
||||
scope = document;
|
||||
}
|
||||
|
||||
var el = scope.querySelector(selector);
|
||||
if (el === null) {
|
||||
return null;
|
||||
} else {
|
||||
return window.HTMLWidgets.getInstance(el);
|
||||
}
|
||||
};
|
||||
|
||||
// Finds all elements in the scope that match the selector, and
|
||||
// returns the HTMLWidget instances (i.e. the return values of
|
||||
// an HTMLWidget binding's initialize() or factory() function)
|
||||
// associated with the elements, in an array. If elements that
|
||||
// match the selector don't have an associated HTMLWidget
|
||||
// instance, the returned array will contain nulls.
|
||||
//
|
||||
// The scope argument is optional, and defaults to window.document.
|
||||
window.HTMLWidgets.findAll = function(scope, selector) {
|
||||
if (arguments.length == 1) {
|
||||
selector = scope;
|
||||
scope = document;
|
||||
}
|
||||
|
||||
var nodes = scope.querySelectorAll(selector);
|
||||
var results = [];
|
||||
for (var i = 0; i < nodes.length; i++) {
|
||||
results.push(window.HTMLWidgets.getInstance(nodes[i]));
|
||||
}
|
||||
return results;
|
||||
};
|
||||
|
||||
var postRenderHandlers = [];
|
||||
function invokePostRenderHandlers() {
|
||||
while (postRenderHandlers.length) {
|
||||
var handler = postRenderHandlers.shift();
|
||||
if (handler) {
|
||||
handler();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Register the given callback function to be invoked after the
|
||||
// next time static widgets are rendered.
|
||||
window.HTMLWidgets.addPostRenderHandler = function(callback) {
|
||||
postRenderHandlers.push(callback);
|
||||
};
|
||||
|
||||
// Takes a new-style instance-bound definition, and returns an
|
||||
// old-style class-bound definition. This saves us from having
|
||||
// to rewrite all the logic in this file to accomodate both
|
||||
// types of definitions.
|
||||
function createLegacyDefinitionAdapter(defn) {
|
||||
var result = {
|
||||
name: defn.name,
|
||||
type: defn.type,
|
||||
initialize: function(el, width, height) {
|
||||
return defn.factory(el, width, height);
|
||||
},
|
||||
renderValue: function(el, x, instance) {
|
||||
return instance.renderValue(x);
|
||||
},
|
||||
resize: function(el, width, height, instance) {
|
||||
return instance.resize(width, height);
|
||||
}
|
||||
};
|
||||
|
||||
if (defn.find)
|
||||
result.find = defn.find;
|
||||
if (defn.renderError)
|
||||
result.renderError = defn.renderError;
|
||||
if (defn.clearError)
|
||||
result.clearError = defn.clearError;
|
||||
|
||||
return result;
|
||||
}
|
||||
})();
|
||||
357
_site/site_libs/jquery-3.5.1/jquery-AUTHORS.txt
Normal file
357
_site/site_libs/jquery-3.5.1/jquery-AUTHORS.txt
Normal file
|
|
@ -0,0 +1,357 @@
|
|||
Authors ordered by first contribution.
|
||||
|
||||
John Resig <jeresig@gmail.com>
|
||||
Gilles van den Hoven <gilles0181@gmail.com>
|
||||
Michael Geary <mike@geary.com>
|
||||
Stefan Petre <stefan.petre@gmail.com>
|
||||
Yehuda Katz <wycats@gmail.com>
|
||||
Corey Jewett <cj@syntheticplayground.com>
|
||||
Klaus Hartl <klaus.hartl@gmail.com>
|
||||
Franck Marcia <franck.marcia@gmail.com>
|
||||
Jörn Zaefferer <joern.zaefferer@gmail.com>
|
||||
Paul Bakaus <paul.bakaus@gmail.com>
|
||||
Brandon Aaron <brandon.aaron@gmail.com>
|
||||
Mike Alsup <malsup@gmail.com>
|
||||
Dave Methvin <dave.methvin@gmail.com>
|
||||
Ed Engelhardt <edengelhardt@gmail.com>
|
||||
Sean Catchpole <littlecooldude@gmail.com>
|
||||
Paul Mclanahan <pmclanahan@gmail.com>
|
||||
David Serduke <davidserduke@gmail.com>
|
||||
Richard D. Worth <rdworth@gmail.com>
|
||||
Scott González <scott.gonzalez@gmail.com>
|
||||
Ariel Flesler <aflesler@gmail.com>
|
||||
Cheah Chu Yeow <chuyeow@gmail.com>
|
||||
Andrew Chalkley <andrew@chalkley.org>
|
||||
Fabio Buffoni <fabio.buffoni@bitmaster.it>
|
||||
Stefan Bauckmeier <stefan@bauckmeier.de>
|
||||
Jon Evans <jon@springyweb.com>
|
||||
TJ Holowaychuk <tj@vision-media.ca>
|
||||
Riccardo De Agostini <rdeago@gmail.com>
|
||||
Michael Bensoussan <mickey@seesmic.com>
|
||||
Louis-Rémi Babé <lrbabe@gmail.com>
|
||||
Robert Katić <robert.katic@gmail.com>
|
||||
Damian Janowski <damian.janowski@gmail.com>
|
||||
Anton Kovalyov <anton@kovalyov.net>
|
||||
Dušan B. Jovanovic <dbjdbj@gmail.com>
|
||||
Earle Castledine <mrspeaker@gmail.com>
|
||||
Rich Dougherty <rich@rd.gen.nz>
|
||||
Kim Dalsgaard <kim@kimdalsgaard.com>
|
||||
Andrea Giammarchi <andrea.giammarchi@gmail.com>
|
||||
Fabian Jakobs <fabian.jakobs@web.de>
|
||||
Mark Gibson <jollytoad@gmail.com>
|
||||
Karl Swedberg <kswedberg@gmail.com>
|
||||
Justin Meyer <justinbmeyer@gmail.com>
|
||||
Ben Alman <cowboy@rj3.net>
|
||||
James Padolsey <cla@padolsey.net>
|
||||
David Petersen <public@petersendidit.com>
|
||||
Batiste Bieler <batiste.bieler@gmail.com>
|
||||
Jake Archibald <jake.archibald@bbc.co.uk>
|
||||
Alexander Farkas <info@corrupt-system.de>
|
||||
Filipe Fortes <filipe@fortes.com>
|
||||
Rick Waldron <waldron.rick@gmail.com>
|
||||
Neeraj Singh <neerajdotname@gmail.com>
|
||||
Paul Irish <paul.irish@gmail.com>
|
||||
Iraê Carvalho <irae@irae.pro.br>
|
||||
Matt Curry <matt@pseudocoder.com>
|
||||
Michael Monteleone <michael@michaelmonteleone.net>
|
||||
Noah Sloan <noah.sloan@gmail.com>
|
||||
Tom Viner <github@viner.tv>
|
||||
J. Ryan Stinnett <jryans@gmail.com>
|
||||
Douglas Neiner <doug@dougneiner.com>
|
||||
Adam J. Sontag <ajpiano@ajpiano.com>
|
||||
Heungsub Lee <h@subl.ee>
|
||||
Dave Reed <dareed@microsoft.com>
|
||||
Carl Fürstenberg <azatoth@gmail.com>
|
||||
Jacob Wright <jacwright@gmail.com>
|
||||
Ralph Whitbeck <ralph.whitbeck@gmail.com>
|
||||
unknown <Igen005@.upcorp.ad.uprr.com>
|
||||
temp01 <temp01irc@gmail.com>
|
||||
Colin Snover <github.com@zetafleet.com>
|
||||
Jared Grippe <jared@deadlyicon.com>
|
||||
Ryan W Tenney <ryan@10e.us>
|
||||
Alex Sexton <AlexSexton@gmail.com>
|
||||
Pinhook <contact@pinhooklabs.com>
|
||||
Ron Otten <r.j.g.otten@gmail.com>
|
||||
Jephte Clain <Jephte.Clain@univ-reunion.fr>
|
||||
Anton Matzneller <obhvsbypqghgc@gmail.com>
|
||||
Dan Heberden <danheberden@gmail.com>
|
||||
Henri Wiechers <hwiechers@gmail.com>
|
||||
Russell Holbrook <russell.holbrook@patch.com>
|
||||
Julian Aubourg <aubourg.julian@gmail.com>
|
||||
Gianni Alessandro Chiappetta <gianni@runlevel6.org>
|
||||
Scott Jehl <scottjehl@gmail.com>
|
||||
James Burke <jrburke@gmail.com>
|
||||
Jonas Pfenniger <jonas@pfenniger.name>
|
||||
Xavi Ramirez <xavi.rmz@gmail.com>
|
||||
Sylvester Keil <sylvester@keil.or.at>
|
||||
Brandon Sterne <bsterne@mozilla.com>
|
||||
Mathias Bynens <mathias@qiwi.be>
|
||||
Lee Carpenter <elcarpie@gmail.com>
|
||||
Timmy Willison <4timmywil@gmail.com>
|
||||
Corey Frang <gnarf37@gmail.com>
|
||||
Digitalxero <digitalxero>
|
||||
David Murdoch <david@davidmurdoch.com>
|
||||
Josh Varner <josh.varner@gmail.com>
|
||||
Charles McNulty <cmcnulty@kznf.com>
|
||||
Jordan Boesch <jboesch26@gmail.com>
|
||||
Jess Thrysoee <jess@thrysoee.dk>
|
||||
Michael Murray <m@murz.net>
|
||||
Alexis Abril <me@alexisabril.com>
|
||||
Rob Morgan <robbym@gmail.com>
|
||||
John Firebaugh <john_firebaugh@bigfix.com>
|
||||
Sam Bisbee <sam@sbisbee.com>
|
||||
Gilmore Davidson <gilmoreorless@gmail.com>
|
||||
Brian Brennan <me@brianlovesthings.com>
|
||||
Xavier Montillet <xavierm02.net@gmail.com>
|
||||
Daniel Pihlstrom <sciolist.se@gmail.com>
|
||||
Sahab Yazdani <sahab.yazdani+github@gmail.com>
|
||||
avaly <github-com@agachi.name>
|
||||
Scott Hughes <hi@scott-hughes.me>
|
||||
Mike Sherov <mike.sherov@gmail.com>
|
||||
Greg Hazel <ghazel@gmail.com>
|
||||
Schalk Neethling <schalk@ossreleasefeed.com>
|
||||
Denis Knauf <Denis.Knauf@gmail.com>
|
||||
Timo Tijhof <krinklemail@gmail.com>
|
||||
Steen Nielsen <swinedk@gmail.com>
|
||||
Anton Ryzhov <anton@ryzhov.me>
|
||||
Shi Chuan <shichuanr@gmail.com>
|
||||
Matt Mueller <mattmuelle@gmail.com>
|
||||
Berker Peksag <berker.peksag@gmail.com>
|
||||
Toby Brain <tobyb@freshview.com>
|
||||
Justin <drakefjustin@gmail.com>
|
||||
Daniel Herman <daniel.c.herman@gmail.com>
|
||||
Oleg Gaidarenko <markelog@gmail.com>
|
||||
Rock Hymas <rock@fogcreek.com>
|
||||
Richard Gibson <richard.gibson@gmail.com>
|
||||
Rafaël Blais Masson <rafbmasson@gmail.com>
|
||||
cmc3cn <59194618@qq.com>
|
||||
Joe Presbrey <presbrey@gmail.com>
|
||||
Sindre Sorhus <sindresorhus@gmail.com>
|
||||
Arne de Bree <arne@bukkie.nl>
|
||||
Vladislav Zarakovsky <vlad.zar@gmail.com>
|
||||
Andrew E Monat <amonat@gmail.com>
|
||||
Oskari <admin@o-programs.com>
|
||||
Joao Henrique de Andrade Bruni <joaohbruni@yahoo.com.br>
|
||||
tsinha <tsinha@Anthonys-MacBook-Pro.local>
|
||||
Dominik D. Geyer <dominik.geyer@gmail.com>
|
||||
Matt Farmer <matt@frmr.me>
|
||||
Trey Hunner <treyhunner@gmail.com>
|
||||
Jason Moon <jmoon@socialcast.com>
|
||||
Jeffery To <jeffery.to@gmail.com>
|
||||
Kris Borchers <kris.borchers@gmail.com>
|
||||
Vladimir Zhuravlev <private.face@gmail.com>
|
||||
Jacob Thornton <jacobthornton@gmail.com>
|
||||
Chad Killingsworth <chadkillingsworth@missouristate.edu>
|
||||
Vitya Muhachev <vic99999@yandex.ru>
|
||||
Nowres Rafid <nowres.rafed@gmail.com>
|
||||
David Benjamin <davidben@mit.edu>
|
||||
Alan Plum <github@ap.apsq.de>
|
||||
Uri Gilad <antishok@gmail.com>
|
||||
Chris Faulkner <thefaulkner@gmail.com>
|
||||
Marcel Greter <marcel.greter@ocbnet.ch>
|
||||
Elijah Manor <elijah.manor@gmail.com>
|
||||
Daniel Chatfield <chatfielddaniel@gmail.com>
|
||||
Daniel Gálvez <dgalvez@editablething.com>
|
||||
Nikita Govorov <nikita.govorov@gmail.com>
|
||||
Wesley Walser <waw325@gmail.com>
|
||||
Mike Pennisi <mike@mikepennisi.com>
|
||||
Matthias Jäggli <matthias.jaeggli@gmail.com>
|
||||
Devin Cooper <cooper.semantics@gmail.com>
|
||||
Markus Staab <markus.staab@redaxo.de>
|
||||
Dave Riddle <david@joyvuu.com>
|
||||
Callum Macrae <callum@lynxphp.com>
|
||||
Jonathan Sampson <jjdsampson@gmail.com>
|
||||
Benjamin Truyman <bentruyman@gmail.com>
|
||||
Jay Merrifield <fracmak@gmail.com>
|
||||
James Huston <james@jameshuston.net>
|
||||
Sai Lung Wong <sai.wong@huffingtonpost.com>
|
||||
Erick Ruiz de Chávez <erickrdch@gmail.com>
|
||||
David Bonner <dbonner@cogolabs.com>
|
||||
Allen J Schmidt Jr <cobrasoft@gmail.com>
|
||||
Akintayo Akinwunmi <aakinwunmi@judge.com>
|
||||
MORGAN <morgan@morgangraphics.com>
|
||||
Ismail Khair <ismail.khair@gmail.com>
|
||||
Carl Danley <carldanley@gmail.com>
|
||||
Mike Petrovich <michael.c.petrovich@gmail.com>
|
||||
Greg Lavallee <greglavallee@wapolabs.com>
|
||||
Tom H Fuertes <TomFuertes@gmail.com>
|
||||
Roland Eckl <eckl.roland@googlemail.com>
|
||||
Yiming He <yiminghe@gmail.com>
|
||||
David Fox <dfoxinator@gmail.com>
|
||||
Bennett Sorbo <bsorbo@gmail.com>
|
||||
Paul Ramos <paul.b.ramos@gmail.com>
|
||||
Rod Vagg <rod@vagg.org>
|
||||
Sebastian Burkhard <sebi.burkhard@gmail.com>
|
||||
Zachary Adam Kaplan <razic@viralkitty.com>
|
||||
Adam Coulombe <me@adam.co>
|
||||
nanto_vi <nanto@moon.email.ne.jp>
|
||||
nanto <nanto@moon.email.ne.jp>
|
||||
Danil Somsikov <danilasomsikov@gmail.com>
|
||||
Ryunosuke SATO <tricknotes.rs@gmail.com>
|
||||
Diego Tres <diegotres@gmail.com>
|
||||
Jean Boussier <jean.boussier@gmail.com>
|
||||
Andrew Plummer <plummer.andrew@gmail.com>
|
||||
Mark Raddatz <mraddatz@gmail.com>
|
||||
Pascal Borreli <pascal@borreli.com>
|
||||
Isaac Z. Schlueter <i@izs.me>
|
||||
Karl Sieburg <ksieburg@yahoo.com>
|
||||
Nguyen Phuc Lam <ruado1987@gmail.com>
|
||||
Dmitry Gusev <dmitry.gusev@gmail.com>
|
||||
Steven Benner <admin@stevenbenner.com>
|
||||
Li Xudong <istonelee@gmail.com>
|
||||
Michał Gołębiowski-Owczarek <m.goleb@gmail.com>
|
||||
Renato Oliveira dos Santos <ros3@cin.ufpe.br>
|
||||
Frederic Junod <frederic.junod@camptocamp.com>
|
||||
Tom H Fuertes <tomfuertes@gmail.com>
|
||||
Mitch Foley <mitch@thefoley.net>
|
||||
ros3cin <ros3@cin.ufpe.br>
|
||||
Kyle Robinson Young <kyle@dontkry.com>
|
||||
John Paul <john@johnkpaul.com>
|
||||
Jason Bedard <jason+jquery@jbedard.ca>
|
||||
Chris Talkington <chris@talkingtontech.com>
|
||||
Eddie Monge <eddie@eddiemonge.com>
|
||||
Terry Jones <terry@jon.es>
|
||||
Jason Merino <jasonmerino@gmail.com>
|
||||
Dan Burzo <danburzo@gmail.com>
|
||||
Jeremy Dunck <jdunck@gmail.com>
|
||||
Chris Price <price.c@gmail.com>
|
||||
Guy Bedford <guybedford@gmail.com>
|
||||
njhamann <njhamann@gmail.com>
|
||||
Goare Mao <mygoare@gmail.com>
|
||||
Amey Sakhadeo <me@ameyms.com>
|
||||
Mike Sidorov <mikes.ekb@gmail.com>
|
||||
Anthony Ryan <anthonyryan1@gmail.com>
|
||||
Lihan Li <frankieteardrop@gmail.com>
|
||||
George Kats <katsgeorgeek@gmail.com>
|
||||
Dongseok Paeng <dongseok83.paeng@lge.com>
|
||||
Ronny Springer <springer.ronny@gmail.com>
|
||||
Ilya Kantor <iliakan@gmail.com>
|
||||
Marian Sollmann <marian.sollmann@cargomedia.ch>
|
||||
Chris Antaki <ChrisAntaki@gmail.com>
|
||||
David Hong <d.hong@me.com>
|
||||
Jakob Stoeck <jakob@pokermania.de>
|
||||
Christopher Jones <chris@cjqed.com>
|
||||
Forbes Lindesay <forbes@lindesay.co.uk>
|
||||
S. Andrew Sheppard <andrew@wq.io>
|
||||
Leonardo Balter <leonardo.balter@gmail.com>
|
||||
Rodrigo Rosenfeld Rosas <rr.rosas@gmail.com>
|
||||
Daniel Husar <dano.husar@gmail.com>
|
||||
Philip Jägenstedt <philip@foolip.org>
|
||||
John Hoven <hovenj@gmail.com>
|
||||
Roman Reiß <me@silverwind.io>
|
||||
Benjy Cui <benjytrys@gmail.com>
|
||||
Christian Kosmowski <ksmwsk@gmail.com>
|
||||
David Corbacho <davidcorbacho@gmail.com>
|
||||
Liang Peng <poppinlp@gmail.com>
|
||||
TJ VanToll <tj.vantoll@gmail.com>
|
||||
Aurelio De Rosa <aurelioderosa@gmail.com>
|
||||
Senya Pugach <upisfree@outlook.com>
|
||||
Dan Hart <danhart@notonthehighstreet.com>
|
||||
Nazar Mokrynskyi <nazar@mokrynskyi.com>
|
||||
Benjamin Tan <demoneaux@gmail.com>
|
||||
Amit Merchant <bullredeyes@gmail.com>
|
||||
Jason Bedard <jason+github@jbedard.ca>
|
||||
Veaceslav Grimalschi <grimalschi@yandex.ru>
|
||||
Richard McDaniel <rm0026@uah.edu>
|
||||
Arthur Verschaeve <contact@arthurverschaeve.be>
|
||||
Shivaji Varma <contact@shivajivarma.com>
|
||||
Ben Toews <mastahyeti@gmail.com>
|
||||
Bin Xin <rhyzix@gmail.com>
|
||||
Neftaly Hernandez <neftaly.hernandez@gmail.com>
|
||||
T.J. Crowder <tj.crowder@farsightsoftware.com>
|
||||
Nicolas HENRY <icewil@gmail.com>
|
||||
Frederic Hemberger <mail@frederic-hemberger.de>
|
||||
Victor Homyakov <vkhomyackov@gmail.com>
|
||||
Aditya Raghavan <araghavan3@gmail.com>
|
||||
Anne-Gaelle Colom <coloma@westminster.ac.uk>
|
||||
Leonardo Braga <leonardo.braga@gmail.com>
|
||||
George Mauer <gmauer@gmail.com>
|
||||
Stephen Edgar <stephen@netweb.com.au>
|
||||
Thomas Tortorini <thomastortorini@gmail.com>
|
||||
Jörn Wagner <joern.wagner@explicatis.com>
|
||||
Jon Hester <jon.d.hester@gmail.com>
|
||||
Colin Frick <colin@bash.li>
|
||||
Winston Howes <winstonhowes@gmail.com>
|
||||
Alexander O'Mara <me@alexomara.com>
|
||||
Chris Rebert <github@rebertia.com>
|
||||
Bastian Buchholz <buchholz.bastian@googlemail.com>
|
||||
Mu Haibao <mhbseal@163.com>
|
||||
Calvin Metcalf <calvin.metcalf@gmail.com>
|
||||
Arthur Stolyar <nekr.fabula@gmail.com>
|
||||
Gabriel Schulhof <gabriel.schulhof@intel.com>
|
||||
Gilad Peleg <giladp007@gmail.com>
|
||||
Julian Alexander Murillo <julian.alexander.murillo@gmail.com>
|
||||
Kevin Kirsche <Kev.Kirsche+GitHub@gmail.com>
|
||||
Martin Naumann <martin@geekonaut.de>
|
||||
Yongwoo Jeon <yongwoo.jeon@navercorp.com>
|
||||
John-David Dalton <john.david.dalton@gmail.com>
|
||||
Marek Lewandowski <m.lewandowski@cksource.com>
|
||||
Bruno Pérel <brunoperel@gmail.com>
|
||||
Daniel Nill <daniellnill@gmail.com>
|
||||
Reed Loden <reed@reedloden.com>
|
||||
Sean Henderson <seanh.za@gmail.com>
|
||||
Gary Ye <garysye@gmail.com>
|
||||
Richard Kraaijenhagen <stdin+git@riichard.com>
|
||||
Connor Atherton <c.liam.atherton@gmail.com>
|
||||
Christian Grete <webmaster@christiangrete.com>
|
||||
Tom von Clef <thomas.vonclef@gmail.com>
|
||||
Liza Ramo <liza.h.ramo@gmail.com>
|
||||
Joelle Fleurantin <joasqueeniebee@gmail.com>
|
||||
Steve Mao <maochenyan@gmail.com>
|
||||
Jon Dufresne <jon.dufresne@gmail.com>
|
||||
Jae Sung Park <alberto.park@gmail.com>
|
||||
Josh Soref <apache@soref.com>
|
||||
Saptak Sengupta <saptak013@gmail.com>
|
||||
Henry Wong <henryw4k@gmail.com>
|
||||
Jun Sun <klsforever@gmail.com>
|
||||
Martijn W. van der Lee <martijn@vanderlee.com>
|
||||
Devin Wilson <dwilson6.github@gmail.com>
|
||||
Damian Senn <jquery@topaxi.codes>
|
||||
Zack Hall <zackhall@outlook.com>
|
||||
Vitaliy Terziev <vitaliyterziev@gmail.com>
|
||||
Todor Prikumov <tono_pr@abv.bg>
|
||||
Bernhard M. Wiedemann <jquerybmw@lsmod.de>
|
||||
Jha Naman <createnaman@gmail.com>
|
||||
Alexander Lisianoi <all3fox@gmail.com>
|
||||
William Robinet <william.robinet@conostix.com>
|
||||
Joe Trumbull <trumbull.j@gmail.com>
|
||||
Alexander K <xpyro@ya.ru>
|
||||
Ralin Chimev <ralin.chimev@gmail.com>
|
||||
Felipe Sateler <fsateler@gmail.com>
|
||||
Christophe Tafani-Dereeper <christophetd@hotmail.fr>
|
||||
Manoj Kumar <nithmanoj@gmail.com>
|
||||
David Broder-Rodgers <broder93@gmail.com>
|
||||
Alex Louden <alex@louden.com>
|
||||
Alex Padilla <alexonezero@outlook.com>
|
||||
karan-96 <karanbatra96@gmail.com>
|
||||
南漂一卒 <shiy007@qq.com>
|
||||
Erik Lax <erik@datahack.se>
|
||||
Boom Lee <teabyii@gmail.com>
|
||||
Andreas Solleder <asol@num42.de>
|
||||
Pierre Spring <pierre@nelm.io>
|
||||
Shashanka Nataraj <shashankan.10@gmail.com>
|
||||
CDAGaming <cstack2011@yahoo.com>
|
||||
Matan Kotler-Berkowitz <205matan@gmail.com>
|
||||
Jordan Beland <jordan.beland@gmail.com>
|
||||
Henry Zhu <hi@henryzoo.com>
|
||||
Nilton Cesar <niltoncms@gmail.com>
|
||||
basil.belokon <basil.belokon@gmail.com>
|
||||
Andrey Meshkov <ay.meshkov@gmail.com>
|
||||
tmybr11 <tomas.perone@gmail.com>
|
||||
Luis Emilio Velasco Sanchez <emibloque@gmail.com>
|
||||
Ed S <ejsanders@gmail.com>
|
||||
Bert Zhang <enbo@users.noreply.github.com>
|
||||
Sébastien Règne <regseb@users.noreply.github.com>
|
||||
wartmanm <3869625+wartmanm@users.noreply.github.com>
|
||||
Siddharth Dungarwal <sd5869@gmail.com>
|
||||
abnud1 <ahmad13932013@hotmail.com>
|
||||
Andrei Fangli <andrei_fangli@outlook.com>
|
||||
Marja Hölttä <marja.holtta@gmail.com>
|
||||
buddh4 <mail@jharrer.de>
|
||||
Hoang <dangkyokhoang@gmail.com>
|
||||
Wonseop Kim <wonseop.kim@samsung.com>
|
||||
Pat O'Callaghan <patocallaghan@gmail.com>
|
||||
JuanMa Ruiz <ruizjuanma@gmail.com>
|
||||
Ahmed.S.ElAfifi <ahmed.s.elafifi@gmail.com>
|
||||
Sean Robinson <sean.robinson@scottsdalecc.edu>
|
||||
Christian Oliff <christianoliff@pm.me>
|
||||
10872
_site/site_libs/jquery-3.5.1/jquery.js
vendored
Normal file
10872
_site/site_libs/jquery-3.5.1/jquery.js
vendored
Normal file
File diff suppressed because it is too large
Load diff
2
_site/site_libs/jquery-3.5.1/jquery.min.js
vendored
Normal file
2
_site/site_libs/jquery-3.5.1/jquery.min.js
vendored
Normal file
File diff suppressed because one or more lines are too long
1
_site/site_libs/jquery-3.5.1/jquery.min.map
Normal file
1
_site/site_libs/jquery-3.5.1/jquery.min.map
Normal file
File diff suppressed because one or more lines are too long
941
_site/site_libs/plotly-binding-4.10.2/plotly.js
Normal file
941
_site/site_libs/plotly-binding-4.10.2/plotly.js
Normal file
|
|
@ -0,0 +1,941 @@
|
|||
|
||||
HTMLWidgets.widget({
|
||||
name: "plotly",
|
||||
type: "output",
|
||||
|
||||
initialize: function(el, width, height) {
|
||||
return {};
|
||||
},
|
||||
|
||||
resize: function(el, width, height, instance) {
|
||||
if (instance.autosize) {
|
||||
var width = instance.width || width;
|
||||
var height = instance.height || height;
|
||||
Plotly.relayout(el.id, {width: width, height: height});
|
||||
}
|
||||
},
|
||||
|
||||
renderValue: function(el, x, instance) {
|
||||
|
||||
// Plotly.relayout() mutates the plot input object, so make sure to
|
||||
// keep a reference to the user-supplied width/height *before*
|
||||
// we call Plotly.plot();
|
||||
var lay = x.layout || {};
|
||||
instance.width = lay.width;
|
||||
instance.height = lay.height;
|
||||
instance.autosize = lay.autosize || true;
|
||||
|
||||
/*
|
||||
/ 'inform the world' about highlighting options this is so other
|
||||
/ crosstalk libraries have a chance to respond to special settings
|
||||
/ such as persistent selection.
|
||||
/ AFAIK, leaflet is the only library with such intergration
|
||||
/ https://github.com/rstudio/leaflet/pull/346/files#diff-ad0c2d51ce5fdf8c90c7395b102f4265R154
|
||||
*/
|
||||
var ctConfig = crosstalk.var('plotlyCrosstalkOpts').set(x.highlight);
|
||||
|
||||
if (typeof(window) !== "undefined") {
|
||||
// make sure plots don't get created outside the network (for on-prem)
|
||||
window.PLOTLYENV = window.PLOTLYENV || {};
|
||||
window.PLOTLYENV.BASE_URL = x.base_url;
|
||||
|
||||
// Enable persistent selection when shift key is down
|
||||
// https://stackoverflow.com/questions/1828613/check-if-a-key-is-down
|
||||
var persistOnShift = function(e) {
|
||||
if (!e) window.event;
|
||||
if (e.shiftKey) {
|
||||
x.highlight.persistent = true;
|
||||
x.highlight.persistentShift = true;
|
||||
} else {
|
||||
x.highlight.persistent = false;
|
||||
x.highlight.persistentShift = false;
|
||||
}
|
||||
};
|
||||
|
||||
// Only relevant if we haven't forced persistent mode at command line
|
||||
if (!x.highlight.persistent) {
|
||||
window.onmousemove = persistOnShift;
|
||||
}
|
||||
}
|
||||
|
||||
var graphDiv = document.getElementById(el.id);
|
||||
|
||||
// TODO: move the control panel injection strategy inside here...
|
||||
HTMLWidgets.addPostRenderHandler(function() {
|
||||
|
||||
// lower the z-index of the modebar to prevent it from highjacking hover
|
||||
// (TODO: do this via CSS?)
|
||||
// https://github.com/ropensci/plotly/issues/956
|
||||
// https://www.w3schools.com/jsref/prop_style_zindex.asp
|
||||
var modebars = document.querySelectorAll(".js-plotly-plot .plotly .modebar");
|
||||
for (var i = 0; i < modebars.length; i++) {
|
||||
modebars[i].style.zIndex = 1;
|
||||
}
|
||||
});
|
||||
|
||||
// inject a "control panel" holding selectize/dynamic color widget(s)
|
||||
if ((x.selectize || x.highlight.dynamic) && !instance.plotly) {
|
||||
var flex = document.createElement("div");
|
||||
flex.class = "plotly-crosstalk-control-panel";
|
||||
flex.style = "display: flex; flex-wrap: wrap";
|
||||
|
||||
// inject the colourpicker HTML container into the flexbox
|
||||
if (x.highlight.dynamic) {
|
||||
var pickerDiv = document.createElement("div");
|
||||
|
||||
var pickerInput = document.createElement("input");
|
||||
pickerInput.id = el.id + "-colourpicker";
|
||||
pickerInput.placeholder = "asdasd";
|
||||
|
||||
var pickerLabel = document.createElement("label");
|
||||
pickerLabel.for = pickerInput.id;
|
||||
pickerLabel.innerHTML = "Brush color ";
|
||||
|
||||
pickerDiv.appendChild(pickerLabel);
|
||||
pickerDiv.appendChild(pickerInput);
|
||||
flex.appendChild(pickerDiv);
|
||||
}
|
||||
|
||||
// inject selectize HTML containers (one for every crosstalk group)
|
||||
if (x.selectize) {
|
||||
var ids = Object.keys(x.selectize);
|
||||
|
||||
for (var i = 0; i < ids.length; i++) {
|
||||
var container = document.createElement("div");
|
||||
container.id = ids[i];
|
||||
container.style = "width: 80%; height: 10%";
|
||||
container.class = "form-group crosstalk-input-plotly-highlight";
|
||||
|
||||
var label = document.createElement("label");
|
||||
label.for = ids[i];
|
||||
label.innerHTML = x.selectize[ids[i]].group;
|
||||
label.class = "control-label";
|
||||
|
||||
var selectDiv = document.createElement("div");
|
||||
var select = document.createElement("select");
|
||||
select.multiple = true;
|
||||
|
||||
selectDiv.appendChild(select);
|
||||
container.appendChild(label);
|
||||
container.appendChild(selectDiv);
|
||||
flex.appendChild(container);
|
||||
}
|
||||
}
|
||||
|
||||
// finally, insert the flexbox inside the htmlwidget container,
|
||||
// but before the plotly graph div
|
||||
graphDiv.parentElement.insertBefore(flex, graphDiv);
|
||||
|
||||
if (x.highlight.dynamic) {
|
||||
var picker = $("#" + pickerInput.id);
|
||||
var colors = x.highlight.color || [];
|
||||
// TODO: let users specify options?
|
||||
var opts = {
|
||||
value: colors[0],
|
||||
showColour: "both",
|
||||
palette: "limited",
|
||||
allowedCols: colors.join(" "),
|
||||
width: "20%",
|
||||
height: "10%"
|
||||
};
|
||||
picker.colourpicker({changeDelay: 0});
|
||||
picker.colourpicker("settings", opts);
|
||||
picker.colourpicker("value", opts.value);
|
||||
// inform crosstalk about a change in the current selection colour
|
||||
var grps = x.highlight.ctGroups || [];
|
||||
for (var i = 0; i < grps.length; i++) {
|
||||
crosstalk.group(grps[i]).var('plotlySelectionColour')
|
||||
.set(picker.colourpicker('value'));
|
||||
}
|
||||
picker.on("change", function() {
|
||||
for (var i = 0; i < grps.length; i++) {
|
||||
crosstalk.group(grps[i]).var('plotlySelectionColour')
|
||||
.set(picker.colourpicker('value'));
|
||||
}
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// if no plot exists yet, create one with a particular configuration
|
||||
if (!instance.plotly) {
|
||||
|
||||
var plot = Plotly.newPlot(graphDiv, x);
|
||||
instance.plotly = true;
|
||||
|
||||
} else if (x.layout.transition) {
|
||||
|
||||
var plot = Plotly.react(graphDiv, x);
|
||||
|
||||
} else {
|
||||
|
||||
// this is essentially equivalent to Plotly.newPlot(), but avoids creating
|
||||
// a new webgl context
|
||||
// https://github.com/plotly/plotly.js/blob/2b24f9def901831e61282076cf3f835598d56f0e/src/plot_api/plot_api.js#L531-L532
|
||||
|
||||
// TODO: restore crosstalk selections?
|
||||
Plotly.purge(graphDiv);
|
||||
// TODO: why is this necessary to get crosstalk working?
|
||||
graphDiv.data = undefined;
|
||||
graphDiv.layout = undefined;
|
||||
var plot = Plotly.newPlot(graphDiv, x);
|
||||
}
|
||||
|
||||
// Trigger plotly.js calls defined via `plotlyProxy()`
|
||||
plot.then(function() {
|
||||
if (HTMLWidgets.shinyMode) {
|
||||
Shiny.addCustomMessageHandler("plotly-calls", function(msg) {
|
||||
var gd = document.getElementById(msg.id);
|
||||
if (!gd) {
|
||||
throw new Error("Couldn't find plotly graph with id: " + msg.id);
|
||||
}
|
||||
// This isn't an official plotly.js method, but it's the only current way to
|
||||
// change just the configuration of a plot
|
||||
// https://community.plot.ly/t/update-config-function/9057
|
||||
if (msg.method == "reconfig") {
|
||||
Plotly.react(gd, gd.data, gd.layout, msg.args);
|
||||
return;
|
||||
}
|
||||
if (!Plotly[msg.method]) {
|
||||
throw new Error("Unknown method " + msg.method);
|
||||
}
|
||||
var args = [gd].concat(msg.args);
|
||||
Plotly[msg.method].apply(null, args);
|
||||
});
|
||||
}
|
||||
|
||||
// plotly's mapbox API doesn't currently support setting bounding boxes
|
||||
// https://www.mapbox.com/mapbox-gl-js/example/fitbounds/
|
||||
// so we do this manually...
|
||||
// TODO: make sure this triggers on a redraw and relayout as well as on initial draw
|
||||
var mapboxIDs = graphDiv._fullLayout._subplots.mapbox || [];
|
||||
for (var i = 0; i < mapboxIDs.length; i++) {
|
||||
var id = mapboxIDs[i];
|
||||
var mapOpts = x.layout[id] || {};
|
||||
var args = mapOpts._fitBounds || {};
|
||||
if (!args) {
|
||||
continue;
|
||||
}
|
||||
var mapObj = graphDiv._fullLayout[id]._subplot.map;
|
||||
mapObj.fitBounds(args.bounds, args.options);
|
||||
}
|
||||
|
||||
});
|
||||
|
||||
// Attach attributes (e.g., "key", "z") to plotly event data
|
||||
function eventDataWithKey(eventData) {
|
||||
if (eventData === undefined || !eventData.hasOwnProperty("points")) {
|
||||
return null;
|
||||
}
|
||||
return eventData.points.map(function(pt) {
|
||||
var obj = {
|
||||
curveNumber: pt.curveNumber,
|
||||
pointNumber: pt.pointNumber,
|
||||
x: pt.x,
|
||||
y: pt.y
|
||||
};
|
||||
|
||||
// If 'z' is reported with the event data, then use it!
|
||||
if (pt.hasOwnProperty("z")) {
|
||||
obj.z = pt.z;
|
||||
}
|
||||
|
||||
if (pt.hasOwnProperty("customdata")) {
|
||||
obj.customdata = pt.customdata;
|
||||
}
|
||||
|
||||
/*
|
||||
TL;DR: (I think) we have to select the graph div (again) to attach keys...
|
||||
|
||||
Why? Remember that crosstalk will dynamically add/delete traces
|
||||
(see traceManager.prototype.updateSelection() below)
|
||||
For this reason, we can't simply grab keys from x.data (like we did previously)
|
||||
Moreover, we can't use _fullData, since that doesn't include
|
||||
unofficial attributes. It's true that click/hover events fire with
|
||||
pt.data, but drag events don't...
|
||||
*/
|
||||
var gd = document.getElementById(el.id);
|
||||
var trace = gd.data[pt.curveNumber];
|
||||
|
||||
if (!trace._isSimpleKey) {
|
||||
var attrsToAttach = ["key"];
|
||||
} else {
|
||||
// simple keys fire the whole key
|
||||
obj.key = trace.key;
|
||||
var attrsToAttach = [];
|
||||
}
|
||||
|
||||
for (var i = 0; i < attrsToAttach.length; i++) {
|
||||
var attr = trace[attrsToAttach[i]];
|
||||
if (Array.isArray(attr)) {
|
||||
if (typeof pt.pointNumber === "number") {
|
||||
obj[attrsToAttach[i]] = attr[pt.pointNumber];
|
||||
} else if (Array.isArray(pt.pointNumber)) {
|
||||
obj[attrsToAttach[i]] = attr[pt.pointNumber[0]][pt.pointNumber[1]];
|
||||
} else if (Array.isArray(pt.pointNumbers)) {
|
||||
obj[attrsToAttach[i]] = pt.pointNumbers.map(function(idx) { return attr[idx]; });
|
||||
}
|
||||
}
|
||||
}
|
||||
return obj;
|
||||
});
|
||||
}
|
||||
|
||||
|
||||
var legendEventData = function(d) {
|
||||
// if legendgroup is not relevant just return the trace
|
||||
var trace = d.data[d.curveNumber];
|
||||
if (!trace.legendgroup) return trace;
|
||||
|
||||
// if legendgroup was specified, return all traces that match the group
|
||||
var legendgrps = d.data.map(function(trace){ return trace.legendgroup; });
|
||||
var traces = [];
|
||||
for (i = 0; i < legendgrps.length; i++) {
|
||||
if (legendgrps[i] == trace.legendgroup) {
|
||||
traces.push(d.data[i]);
|
||||
}
|
||||
}
|
||||
|
||||
return traces;
|
||||
};
|
||||
|
||||
|
||||
// send user input event data to shiny
|
||||
if (HTMLWidgets.shinyMode && Shiny.setInputValue) {
|
||||
|
||||
// Some events clear other input values
|
||||
// TODO: always register these?
|
||||
var eventClearMap = {
|
||||
plotly_deselect: ["plotly_selected", "plotly_selecting", "plotly_brushed", "plotly_brushing", "plotly_click"],
|
||||
plotly_unhover: ["plotly_hover"],
|
||||
plotly_doubleclick: ["plotly_click"]
|
||||
};
|
||||
|
||||
Object.keys(eventClearMap).map(function(evt) {
|
||||
graphDiv.on(evt, function() {
|
||||
var inputsToClear = eventClearMap[evt];
|
||||
inputsToClear.map(function(input) {
|
||||
Shiny.setInputValue(input + "-" + x.source, null, {priority: "event"});
|
||||
});
|
||||
});
|
||||
});
|
||||
|
||||
var eventDataFunctionMap = {
|
||||
plotly_click: eventDataWithKey,
|
||||
plotly_sunburstclick: eventDataWithKey,
|
||||
plotly_hover: eventDataWithKey,
|
||||
plotly_unhover: eventDataWithKey,
|
||||
// If 'plotly_selected' has already been fired, and you click
|
||||
// on the plot afterwards, this event fires `undefined`?!?
|
||||
// That might be considered a plotly.js bug, but it doesn't make
|
||||
// sense for this input change to occur if `d` is falsy because,
|
||||
// even in the empty selection case, `d` is truthy (an object),
|
||||
// and the 'plotly_deselect' event will reset this input
|
||||
plotly_selected: function(d) { if (d) { return eventDataWithKey(d); } },
|
||||
plotly_selecting: function(d) { if (d) { return eventDataWithKey(d); } },
|
||||
plotly_brushed: function(d) {
|
||||
if (d) { return d.range ? d.range : d.lassoPoints; }
|
||||
},
|
||||
plotly_brushing: function(d) {
|
||||
if (d) { return d.range ? d.range : d.lassoPoints; }
|
||||
},
|
||||
plotly_legendclick: legendEventData,
|
||||
plotly_legenddoubleclick: legendEventData,
|
||||
plotly_clickannotation: function(d) { return d.fullAnnotation }
|
||||
};
|
||||
|
||||
var registerShinyValue = function(event) {
|
||||
var eventDataPreProcessor = eventDataFunctionMap[event] || function(d) { return d ? d : el.id };
|
||||
// some events are unique to the R package
|
||||
var plotlyJSevent = (event == "plotly_brushed") ? "plotly_selected" : (event == "plotly_brushing") ? "plotly_selecting" : event;
|
||||
// register the event
|
||||
graphDiv.on(plotlyJSevent, function(d) {
|
||||
Shiny.setInputValue(
|
||||
event + "-" + x.source,
|
||||
JSON.stringify(eventDataPreProcessor(d)),
|
||||
{priority: "event"}
|
||||
);
|
||||
});
|
||||
}
|
||||
|
||||
var shinyEvents = x.shinyEvents || [];
|
||||
shinyEvents.map(registerShinyValue);
|
||||
}
|
||||
|
||||
// Given an array of {curveNumber: x, pointNumber: y} objects,
|
||||
// return a hash of {
|
||||
// set1: {value: [key1, key2, ...], _isSimpleKey: false},
|
||||
// set2: {value: [key3, key4, ...], _isSimpleKey: false}
|
||||
// }
|
||||
function pointsToKeys(points) {
|
||||
var keysBySet = {};
|
||||
for (var i = 0; i < points.length; i++) {
|
||||
|
||||
var trace = graphDiv.data[points[i].curveNumber];
|
||||
if (!trace.key || !trace.set) {
|
||||
continue;
|
||||
}
|
||||
|
||||
// set defaults for this keySet
|
||||
// note that we don't track the nested property (yet) since we always
|
||||
// emit the union -- http://cpsievert.github.io/talks/20161212b/#21
|
||||
keysBySet[trace.set] = keysBySet[trace.set] || {
|
||||
value: [],
|
||||
_isSimpleKey: trace._isSimpleKey
|
||||
};
|
||||
|
||||
// Use pointNumber by default, but aggregated traces should emit pointNumbers
|
||||
var ptNum = points[i].pointNumber;
|
||||
var hasPtNum = typeof ptNum === "number";
|
||||
var ptNum = hasPtNum ? ptNum : points[i].pointNumbers;
|
||||
|
||||
// selecting a point of a "simple" trace means: select the
|
||||
// entire key attached to this trace, which is useful for,
|
||||
// say clicking on a fitted line to select corresponding observations
|
||||
var key = trace._isSimpleKey ? trace.key : Array.isArray(ptNum) ? ptNum.map(function(idx) { return trace.key[idx]; }) : trace.key[ptNum];
|
||||
// http://stackoverflow.com/questions/10865025/merge-flatten-an-array-of-arrays-in-javascript
|
||||
var keyFlat = trace._isNestedKey ? [].concat.apply([], key) : key;
|
||||
|
||||
// TODO: better to only add new values?
|
||||
keysBySet[trace.set].value = keysBySet[trace.set].value.concat(keyFlat);
|
||||
}
|
||||
|
||||
return keysBySet;
|
||||
}
|
||||
|
||||
|
||||
x.highlight.color = x.highlight.color || [];
|
||||
// make sure highlight color is an array
|
||||
if (!Array.isArray(x.highlight.color)) {
|
||||
x.highlight.color = [x.highlight.color];
|
||||
}
|
||||
|
||||
var traceManager = new TraceManager(graphDiv, x.highlight);
|
||||
|
||||
// Gather all *unique* sets.
|
||||
var allSets = [];
|
||||
for (var curveIdx = 0; curveIdx < x.data.length; curveIdx++) {
|
||||
var newSet = x.data[curveIdx].set;
|
||||
if (newSet) {
|
||||
if (allSets.indexOf(newSet) === -1) {
|
||||
allSets.push(newSet);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// register event listeners for all sets
|
||||
for (var i = 0; i < allSets.length; i++) {
|
||||
|
||||
var set = allSets[i];
|
||||
var selection = new crosstalk.SelectionHandle(set);
|
||||
var filter = new crosstalk.FilterHandle(set);
|
||||
|
||||
var filterChange = function(e) {
|
||||
removeBrush(el);
|
||||
traceManager.updateFilter(set, e.value);
|
||||
};
|
||||
filter.on("change", filterChange);
|
||||
|
||||
|
||||
var selectionChange = function(e) {
|
||||
|
||||
// Workaround for 'plotly_selected' now firing previously selected
|
||||
// points (in addition to new ones) when holding shift key. In our case,
|
||||
// we just want the new keys
|
||||
if (x.highlight.on === "plotly_selected" && x.highlight.persistentShift) {
|
||||
// https://stackoverflow.com/questions/1187518/how-to-get-the-difference-between-two-arrays-in-javascript
|
||||
Array.prototype.diff = function(a) {
|
||||
return this.filter(function(i) {return a.indexOf(i) < 0;});
|
||||
};
|
||||
e.value = e.value.diff(e.oldValue);
|
||||
}
|
||||
|
||||
// array of "event objects" tracking the selection history
|
||||
// this is used to avoid adding redundant selections
|
||||
var selectionHistory = crosstalk.var("plotlySelectionHistory").get() || [];
|
||||
|
||||
// Construct an event object "defining" the current event.
|
||||
var event = {
|
||||
receiverID: traceManager.gd.id,
|
||||
plotlySelectionColour: crosstalk.group(set).var("plotlySelectionColour").get()
|
||||
};
|
||||
event[set] = e.value;
|
||||
// TODO: is there a smarter way to check object equality?
|
||||
if (selectionHistory.length > 0) {
|
||||
var ev = JSON.stringify(event);
|
||||
for (var i = 0; i < selectionHistory.length; i++) {
|
||||
var sel = JSON.stringify(selectionHistory[i]);
|
||||
if (sel == ev) {
|
||||
return;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// accumulate history for persistent selection
|
||||
if (!x.highlight.persistent) {
|
||||
selectionHistory = [event];
|
||||
} else {
|
||||
selectionHistory.push(event);
|
||||
}
|
||||
crosstalk.var("plotlySelectionHistory").set(selectionHistory);
|
||||
|
||||
// do the actual updating of traces, frames, and the selectize widget
|
||||
traceManager.updateSelection(set, e.value);
|
||||
// https://github.com/selectize/selectize.js/blob/master/docs/api.md#methods_items
|
||||
if (x.selectize) {
|
||||
if (!x.highlight.persistent || e.value === null) {
|
||||
selectize.clear(true);
|
||||
}
|
||||
selectize.addItems(e.value, true);
|
||||
selectize.close();
|
||||
}
|
||||
}
|
||||
selection.on("change", selectionChange);
|
||||
|
||||
// Set a crosstalk variable selection value, triggering an update
|
||||
var turnOn = function(e) {
|
||||
if (e) {
|
||||
var selectedKeys = pointsToKeys(e.points);
|
||||
// Keys are group names, values are array of selected keys from group.
|
||||
for (var set in selectedKeys) {
|
||||
if (selectedKeys.hasOwnProperty(set)) {
|
||||
selection.set(selectedKeys[set].value, {sender: el});
|
||||
}
|
||||
}
|
||||
}
|
||||
};
|
||||
if (x.highlight.debounce > 0) {
|
||||
turnOn = debounce(turnOn, x.highlight.debounce);
|
||||
}
|
||||
graphDiv.on(x.highlight.on, turnOn);
|
||||
|
||||
graphDiv.on(x.highlight.off, function turnOff(e) {
|
||||
// remove any visual clues
|
||||
removeBrush(el);
|
||||
// remove any selection history
|
||||
crosstalk.var("plotlySelectionHistory").set(null);
|
||||
// trigger the actual removal of selection traces
|
||||
selection.set(null, {sender: el});
|
||||
});
|
||||
|
||||
// register a callback for selectize so that there is bi-directional
|
||||
// communication between the widget and direct manipulation events
|
||||
if (x.selectize) {
|
||||
var selectizeID = Object.keys(x.selectize)[i];
|
||||
var options = x.selectize[selectizeID];
|
||||
var first = [{value: "", label: "(All)"}];
|
||||
var opts = $.extend({
|
||||
options: first.concat(options.items),
|
||||
searchField: "label",
|
||||
valueField: "value",
|
||||
labelField: "label",
|
||||
maxItems: 50
|
||||
},
|
||||
options
|
||||
);
|
||||
var select = $("#" + selectizeID).find("select")[0];
|
||||
var selectize = $(select).selectize(opts)[0].selectize;
|
||||
// NOTE: this callback is triggered when *directly* altering
|
||||
// dropdown items
|
||||
selectize.on("change", function() {
|
||||
var currentItems = traceManager.groupSelections[set] || [];
|
||||
if (!x.highlight.persistent) {
|
||||
removeBrush(el);
|
||||
for (var i = 0; i < currentItems.length; i++) {
|
||||
selectize.removeItem(currentItems[i], true);
|
||||
}
|
||||
}
|
||||
var newItems = selectize.items.filter(function(idx) {
|
||||
return currentItems.indexOf(idx) < 0;
|
||||
});
|
||||
if (newItems.length > 0) {
|
||||
traceManager.updateSelection(set, newItems);
|
||||
} else {
|
||||
// Item has been removed...
|
||||
// TODO: this logic won't work for dynamically changing palette
|
||||
traceManager.updateSelection(set, null);
|
||||
traceManager.updateSelection(set, selectize.items);
|
||||
}
|
||||
});
|
||||
}
|
||||
} // end of selectionChange
|
||||
|
||||
} // end of renderValue
|
||||
}); // end of widget definition
|
||||
|
||||
/**
|
||||
* @param graphDiv The Plotly graph div
|
||||
* @param highlight An object with options for updating selection(s)
|
||||
*/
|
||||
function TraceManager(graphDiv, highlight) {
|
||||
// The Plotly graph div
|
||||
this.gd = graphDiv;
|
||||
|
||||
// Preserve the original data.
|
||||
// TODO: try using Lib.extendFlat() as done in
|
||||
// https://github.com/plotly/plotly.js/pull/1136
|
||||
this.origData = JSON.parse(JSON.stringify(graphDiv.data));
|
||||
|
||||
// avoid doing this over and over
|
||||
this.origOpacity = [];
|
||||
for (var i = 0; i < this.origData.length; i++) {
|
||||
this.origOpacity[i] = this.origData[i].opacity === 0 ? 0 : (this.origData[i].opacity || 1);
|
||||
}
|
||||
|
||||
// key: group name, value: null or array of keys representing the
|
||||
// most recently received selection for that group.
|
||||
this.groupSelections = {};
|
||||
|
||||
// selection parameters (e.g., transient versus persistent selection)
|
||||
this.highlight = highlight;
|
||||
}
|
||||
|
||||
TraceManager.prototype.close = function() {
|
||||
// TODO: Unhook all event handlers
|
||||
};
|
||||
|
||||
TraceManager.prototype.updateFilter = function(group, keys) {
|
||||
|
||||
if (typeof(keys) === "undefined" || keys === null) {
|
||||
|
||||
this.gd.data = JSON.parse(JSON.stringify(this.origData));
|
||||
|
||||
} else {
|
||||
|
||||
var traces = [];
|
||||
for (var i = 0; i < this.origData.length; i++) {
|
||||
var trace = this.origData[i];
|
||||
if (!trace.key || trace.set !== group) {
|
||||
continue;
|
||||
}
|
||||
var matchFunc = getMatchFunc(trace);
|
||||
var matches = matchFunc(trace.key, keys);
|
||||
|
||||
if (matches.length > 0) {
|
||||
if (!trace._isSimpleKey) {
|
||||
// subsetArrayAttrs doesn't mutate trace (it makes a modified clone)
|
||||
trace = subsetArrayAttrs(trace, matches);
|
||||
}
|
||||
traces.push(trace);
|
||||
}
|
||||
}
|
||||
this.gd.data = traces;
|
||||
}
|
||||
|
||||
Plotly.redraw(this.gd);
|
||||
|
||||
// NOTE: we purposely do _not_ restore selection(s), since on filter,
|
||||
// axis likely will update, changing the pixel -> data mapping, leading
|
||||
// to a likely mismatch in the brush outline and highlighted marks
|
||||
|
||||
};
|
||||
|
||||
TraceManager.prototype.updateSelection = function(group, keys) {
|
||||
|
||||
if (keys !== null && !Array.isArray(keys)) {
|
||||
throw new Error("Invalid keys argument; null or array expected");
|
||||
}
|
||||
|
||||
// if selection has been cleared, or if this is transient
|
||||
// selection, delete the "selection traces"
|
||||
var nNewTraces = this.gd.data.length - this.origData.length;
|
||||
if (keys === null || !this.highlight.persistent && nNewTraces > 0) {
|
||||
var tracesToRemove = [];
|
||||
for (var i = 0; i < this.gd.data.length; i++) {
|
||||
if (this.gd.data[i]._isCrosstalkTrace) tracesToRemove.push(i);
|
||||
}
|
||||
Plotly.deleteTraces(this.gd, tracesToRemove);
|
||||
this.groupSelections[group] = keys;
|
||||
} else {
|
||||
// add to the groupSelection, rather than overwriting it
|
||||
// TODO: can this be removed?
|
||||
this.groupSelections[group] = this.groupSelections[group] || [];
|
||||
for (var i = 0; i < keys.length; i++) {
|
||||
var k = keys[i];
|
||||
if (this.groupSelections[group].indexOf(k) < 0) {
|
||||
this.groupSelections[group].push(k);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (keys === null) {
|
||||
|
||||
Plotly.restyle(this.gd, {"opacity": this.origOpacity});
|
||||
|
||||
} else if (keys.length >= 1) {
|
||||
|
||||
// placeholder for new "selection traces"
|
||||
var traces = [];
|
||||
// this variable is set in R/highlight.R
|
||||
var selectionColour = crosstalk.group(group).var("plotlySelectionColour").get() ||
|
||||
this.highlight.color[0];
|
||||
|
||||
for (var i = 0; i < this.origData.length; i++) {
|
||||
// TODO: try using Lib.extendFlat() as done in
|
||||
// https://github.com/plotly/plotly.js/pull/1136
|
||||
var trace = JSON.parse(JSON.stringify(this.gd.data[i]));
|
||||
if (!trace.key || trace.set !== group) {
|
||||
continue;
|
||||
}
|
||||
// Get sorted array of matching indices in trace.key
|
||||
var matchFunc = getMatchFunc(trace);
|
||||
var matches = matchFunc(trace.key, keys);
|
||||
|
||||
if (matches.length > 0) {
|
||||
// If this is a "simple" key, that means select the entire trace
|
||||
if (!trace._isSimpleKey) {
|
||||
trace = subsetArrayAttrs(trace, matches);
|
||||
}
|
||||
// reach into the full trace object so we can properly reflect the
|
||||
// selection attributes in every view
|
||||
var d = this.gd._fullData[i];
|
||||
|
||||
/*
|
||||
/ Recursively inherit selection attributes from various sources,
|
||||
/ in order of preference:
|
||||
/ (1) official plotly.js selected attribute
|
||||
/ (2) highlight(selected = attrs_selected(...))
|
||||
*/
|
||||
// TODO: it would be neat to have a dropdown to dynamically specify these!
|
||||
$.extend(true, trace, this.highlight.selected);
|
||||
|
||||
// if it is defined, override color with the "dynamic brush color""
|
||||
if (d.marker) {
|
||||
trace.marker = trace.marker || {};
|
||||
trace.marker.color = selectionColour || trace.marker.color || d.marker.color;
|
||||
}
|
||||
if (d.line) {
|
||||
trace.line = trace.line || {};
|
||||
trace.line.color = selectionColour || trace.line.color || d.line.color;
|
||||
}
|
||||
if (d.textfont) {
|
||||
trace.textfont = trace.textfont || {};
|
||||
trace.textfont.color = selectionColour || trace.textfont.color || d.textfont.color;
|
||||
}
|
||||
if (d.fillcolor) {
|
||||
// TODO: should selectionColour inherit alpha from the existing fillcolor?
|
||||
trace.fillcolor = selectionColour || trace.fillcolor || d.fillcolor;
|
||||
}
|
||||
// attach a sensible name/legendgroup
|
||||
trace.name = trace.name || keys.join("<br />");
|
||||
trace.legendgroup = trace.legendgroup || keys.join("<br />");
|
||||
|
||||
// keep track of mapping between this new trace and the trace it targets
|
||||
// (necessary for updating frames to reflect the selection traces)
|
||||
trace._originalIndex = i;
|
||||
trace._newIndex = this.gd._fullData.length + traces.length;
|
||||
trace._isCrosstalkTrace = true;
|
||||
traces.push(trace);
|
||||
}
|
||||
}
|
||||
|
||||
if (traces.length > 0) {
|
||||
|
||||
Plotly.addTraces(this.gd, traces).then(function(gd) {
|
||||
// incrementally add selection traces to frames
|
||||
// (this is heavily inspired by Plotly.Plots.modifyFrames()
|
||||
// in src/plots/plots.js)
|
||||
var _hash = gd._transitionData._frameHash;
|
||||
var _frames = gd._transitionData._frames || [];
|
||||
|
||||
for (var i = 0; i < _frames.length; i++) {
|
||||
|
||||
// add to _frames[i].traces *if* this frame references selected trace(s)
|
||||
var newIndices = [];
|
||||
for (var j = 0; j < traces.length; j++) {
|
||||
var tr = traces[j];
|
||||
if (_frames[i].traces.indexOf(tr._originalIndex) > -1) {
|
||||
newIndices.push(tr._newIndex);
|
||||
_frames[i].traces.push(tr._newIndex);
|
||||
}
|
||||
}
|
||||
|
||||
// nothing to do...
|
||||
if (newIndices.length === 0) {
|
||||
continue;
|
||||
}
|
||||
|
||||
var ctr = 0;
|
||||
var nFrameTraces = _frames[i].data.length;
|
||||
|
||||
for (var j = 0; j < nFrameTraces; j++) {
|
||||
var frameTrace = _frames[i].data[j];
|
||||
if (!frameTrace.key || frameTrace.set !== group) {
|
||||
continue;
|
||||
}
|
||||
|
||||
var matchFunc = getMatchFunc(frameTrace);
|
||||
var matches = matchFunc(frameTrace.key, keys);
|
||||
|
||||
if (matches.length > 0) {
|
||||
if (!trace._isSimpleKey) {
|
||||
frameTrace = subsetArrayAttrs(frameTrace, matches);
|
||||
}
|
||||
var d = gd._fullData[newIndices[ctr]];
|
||||
if (d.marker) {
|
||||
frameTrace.marker = d.marker;
|
||||
}
|
||||
if (d.line) {
|
||||
frameTrace.line = d.line;
|
||||
}
|
||||
if (d.textfont) {
|
||||
frameTrace.textfont = d.textfont;
|
||||
}
|
||||
ctr = ctr + 1;
|
||||
_frames[i].data.push(frameTrace);
|
||||
}
|
||||
}
|
||||
|
||||
// update gd._transitionData._frameHash
|
||||
_hash[_frames[i].name] = _frames[i];
|
||||
}
|
||||
|
||||
});
|
||||
|
||||
// dim traces that have a set matching the set of selection sets
|
||||
var tracesToDim = [],
|
||||
opacities = [],
|
||||
sets = Object.keys(this.groupSelections),
|
||||
n = this.origData.length;
|
||||
|
||||
for (var i = 0; i < n; i++) {
|
||||
var opacity = this.origOpacity[i] || 1;
|
||||
// have we already dimmed this trace? Or is this even worth doing?
|
||||
if (opacity !== this.gd._fullData[i].opacity || this.highlight.opacityDim === 1) {
|
||||
continue;
|
||||
}
|
||||
// is this set an element of the set of selection sets?
|
||||
var matches = findMatches(sets, [this.gd.data[i].set]);
|
||||
if (matches.length) {
|
||||
tracesToDim.push(i);
|
||||
opacities.push(opacity * this.highlight.opacityDim);
|
||||
}
|
||||
}
|
||||
|
||||
if (tracesToDim.length > 0) {
|
||||
Plotly.restyle(this.gd, {"opacity": opacities}, tracesToDim);
|
||||
// turn off the selected/unselected API
|
||||
Plotly.restyle(this.gd, {"selectedpoints": null});
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
}
|
||||
};
|
||||
|
||||
/*
|
||||
Note: in all of these match functions, we assume needleSet (i.e. the selected keys)
|
||||
is a 1D (or flat) array. The real difference is the meaning of haystack.
|
||||
findMatches() does the usual thing you'd expect for
|
||||
linked brushing on a scatterplot matrix. findSimpleMatches() returns a match iff
|
||||
haystack is a subset of the needleSet. findNestedMatches() returns
|
||||
*/
|
||||
|
||||
function getMatchFunc(trace) {
|
||||
return (trace._isNestedKey) ? findNestedMatches :
|
||||
(trace._isSimpleKey) ? findSimpleMatches : findMatches;
|
||||
}
|
||||
|
||||
// find matches for "flat" keys
|
||||
function findMatches(haystack, needleSet) {
|
||||
var matches = [];
|
||||
haystack.forEach(function(obj, i) {
|
||||
if (obj === null || needleSet.indexOf(obj) >= 0) {
|
||||
matches.push(i);
|
||||
}
|
||||
});
|
||||
return matches;
|
||||
}
|
||||
|
||||
// find matches for "simple" keys
|
||||
function findSimpleMatches(haystack, needleSet) {
|
||||
var match = haystack.every(function(val) {
|
||||
return val === null || needleSet.indexOf(val) >= 0;
|
||||
});
|
||||
// yes, this doesn't make much sense other than conforming
|
||||
// to the output type of the other match functions
|
||||
return (match) ? [0] : []
|
||||
}
|
||||
|
||||
// find matches for a "nested" haystack (2D arrays)
|
||||
function findNestedMatches(haystack, needleSet) {
|
||||
var matches = [];
|
||||
for (var i = 0; i < haystack.length; i++) {
|
||||
var hay = haystack[i];
|
||||
var match = hay.every(function(val) {
|
||||
return val === null || needleSet.indexOf(val) >= 0;
|
||||
});
|
||||
if (match) {
|
||||
matches.push(i);
|
||||
}
|
||||
}
|
||||
return matches;
|
||||
}
|
||||
|
||||
function isPlainObject(obj) {
|
||||
return (
|
||||
Object.prototype.toString.call(obj) === '[object Object]' &&
|
||||
Object.getPrototypeOf(obj) === Object.prototype
|
||||
);
|
||||
}
|
||||
|
||||
function subsetArrayAttrs(obj, indices) {
|
||||
var newObj = {};
|
||||
Object.keys(obj).forEach(function(k) {
|
||||
var val = obj[k];
|
||||
|
||||
if (k.charAt(0) === "_") {
|
||||
newObj[k] = val;
|
||||
} else if (k === "transforms" && Array.isArray(val)) {
|
||||
newObj[k] = val.map(function(transform) {
|
||||
return subsetArrayAttrs(transform, indices);
|
||||
});
|
||||
} else if (k === "colorscale" && Array.isArray(val)) {
|
||||
newObj[k] = val;
|
||||
} else if (isPlainObject(val)) {
|
||||
newObj[k] = subsetArrayAttrs(val, indices);
|
||||
} else if (Array.isArray(val)) {
|
||||
newObj[k] = subsetArray(val, indices);
|
||||
} else {
|
||||
newObj[k] = val;
|
||||
}
|
||||
});
|
||||
return newObj;
|
||||
}
|
||||
|
||||
function subsetArray(arr, indices) {
|
||||
var result = [];
|
||||
for (var i = 0; i < indices.length; i++) {
|
||||
result.push(arr[indices[i]]);
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
// Convenience function for removing plotly's brush
|
||||
function removeBrush(el) {
|
||||
var outlines = el.querySelectorAll(".select-outline");
|
||||
for (var i = 0; i < outlines.length; i++) {
|
||||
outlines[i].remove();
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
// https://davidwalsh.name/javascript-debounce-function
|
||||
|
||||
// Returns a function, that, as long as it continues to be invoked, will not
|
||||
// be triggered. The function will be called after it stops being called for
|
||||
// N milliseconds. If `immediate` is passed, trigger the function on the
|
||||
// leading edge, instead of the trailing.
|
||||
function debounce(func, wait, immediate) {
|
||||
var timeout;
|
||||
return function() {
|
||||
var context = this, args = arguments;
|
||||
var later = function() {
|
||||
timeout = null;
|
||||
if (!immediate) func.apply(context, args);
|
||||
};
|
||||
var callNow = immediate && !timeout;
|
||||
clearTimeout(timeout);
|
||||
timeout = setTimeout(later, wait);
|
||||
if (callNow) func.apply(context, args);
|
||||
};
|
||||
};
|
||||
|
|
@ -0,0 +1,9 @@
|
|||
/*
|
||||
just here so that plotly works
|
||||
correctly with ioslides.
|
||||
see https://github.com/ropensci/plotly/issues/463
|
||||
*/
|
||||
|
||||
slide:not(.current) .plotly.html-widget{
|
||||
display: none;
|
||||
}
|
||||
69
_site/site_libs/plotly-main-2.11.1/plotly-latest.min.js
vendored
Normal file
69
_site/site_libs/plotly-main-2.11.1/plotly-latest.min.js
vendored
Normal file
File diff suppressed because one or more lines are too long
1
_site/site_libs/typedarray-0.1/typedarray.min.js
vendored
Normal file
1
_site/site_libs/typedarray-0.1/typedarray.min.js
vendored
Normal file
File diff suppressed because one or more lines are too long
BIN
posts/2020-01-29_facets-and-humility/WHO_LIFE.png
Normal file
BIN
posts/2020-01-29_facets-and-humility/WHO_LIFE.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 170 KiB |
|
|
@ -0,0 +1,85 @@
|
|||
---
|
||||
title: "Facets and a Lesson in Humility"
|
||||
subtitle: |
|
||||
A look at Tableau for Healthcare Chapter 8. Table Lens graph.
|
||||
date: 01-29-2020
|
||||
|
||||
---
|
||||
|
||||
Todays post is a lesson in Facets, as well as humility. The task this week was to replicate the graph in Chapter 8 of Tableau for Healthcare in R. The graph in question is called a Table Lens (This is the name the book uses, however I did have trouble finding this name in Google searches), it is a collection of charts with a common theme, this time looking at countries in various WHO regions and some statistics associated with mortality as well as health expenditure. I say this is a lesson in humiltiy as I have read through the excellent book [R for Data Science](https://r4ds.had.co.nz/), and yet the idea of faceting a ggplot graph slipped my mind. This ended with hours of trying to find a package in R to line up graphs, and way more time then I care to admit spent on getting things prefect. I did find such a package called cowplots, which can be found [here](https://wilkelab.org/cowplot/index.html). While this is an excellent package, its use was unecessary and I reverted back to using the excellent facet feature of GGplot, which can be seen below! 
|
||||
|
||||
# Load Libraries
|
||||
|
||||
```{r}
|
||||
library(magrittr) #pipes
|
||||
library(ggplot2) #ploting
|
||||
library(dplyr)
|
||||
library(tidyr)
|
||||
|
||||
```
|
||||
|
||||
# Import Data
|
||||
|
||||
```{r}
|
||||
ds <- readxl::read_xlsx(path = "../2020-01-04_my-start-to-r/Tableau 10 Training Practice Data.xlsx"
|
||||
,sheet = "03 - WHO Life Expect & Mort"
|
||||
)
|
||||
```
|
||||
|
||||
|
||||
# Clean Names and Transform
|
||||
|
||||
```{r}
|
||||
varnames <- c("who_region", "country", "year" , "sex" , "life_expect_birth" , "neo_mort"
|
||||
,"under_five_mort" , "health_expenditure")
|
||||
names(ds) <- varnames
|
||||
|
||||
# Order Countries based on Life Expectancy at Birth
|
||||
|
||||
ds$country <- factor(ds$country, levels = ds$country[order(ds$life_expect_birth)])
|
||||
|
||||
#To "Long" Form
|
||||
|
||||
ds1 <- ds %>% pivot_longer(5:8)#select columns 5 throuh 8, leave new columns at default names
|
||||
|
||||
# Set up labels for Facet, as well as function for Facet Labeller
|
||||
|
||||
facet_labels <- list(
|
||||
"life_expect_birth" = "Life Expectancy at Birth "
|
||||
,"neo_mort" = "Neonatal Mortality Rate"
|
||||
,"under_five_mort" = "Under-Five Mortality Rate"
|
||||
,"health_expenditure" = "Health Expenditure per Capita (US$)" )
|
||||
|
||||
variable_labeller <- function(variable,value){
|
||||
return(facet_labels[value])
|
||||
}
|
||||
|
||||
|
||||
```
|
||||
|
||||
|
||||
# Graphs
|
||||
|
||||
```{r fig.height=7, fig.width=12}
|
||||
|
||||
hightlight_countries <- (c("Mauritania", "South Africa"))
|
||||
|
||||
g1 <- ds1 %>% filter(who_region == "Africa") %>%
|
||||
mutate(name = factor(name, levels = c("life_expect_birth" , "neo_mort"
|
||||
,"under_five_mort" , "health_expenditure"))
|
||||
,highlight = country %in% hightlight_countries) %>%
|
||||
ggplot(aes(x = country, y = value, fill = highlight)) +
|
||||
geom_col(show.legend = FALSE) +
|
||||
coord_flip() +
|
||||
labs(
|
||||
title = "World Bank Life Expectancy, Neonatal & Under-Five Mortality Rates, and Health Expenditure Analysis"
|
||||
,x = NULL
|
||||
,y = NULL
|
||||
) +
|
||||
facet_grid(~name, scales = "free_x",labeller = variable_labeller) +
|
||||
theme_bw() +
|
||||
geom_text(aes(label = round(value, 0)), hjust = 0) +
|
||||
scale_y_continuous(expand = expand_scale(mult = c(0,0.2))) +
|
||||
scale_fill_manual(values = c("TRUE" = "#fc8d59", "FALSE" = "#2b83ba"))
|
||||
g1
|
||||
```
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 165 KiB |
|
|
@ -0,0 +1,136 @@
|
|||
---
|
||||
title: "Line Graphs and Interactivity"
|
||||
subtitle: |
|
||||
Tableau for Healthcare Chapter 10. Static and Interactive examples
|
||||
date: 02-10-2020
|
||||
---
|
||||
|
||||
Today's post is all about line graphs using both ggplot for a static graph as well as a package called plotly for interactivity (more on this later). The example graph and data is again coming from Tableau for Healthcare, Chapter 10. 
|
||||
|
||||
## Load Libraries
|
||||
|
||||
As always first step is to load in our libraries, I am using quite a few here, some are a bit overkill for this example but I wanted to play around with some fun features today.
|
||||
|
||||
```{r}
|
||||
library(magrittr) #pipes
|
||||
library(ggplot2) #ploting
|
||||
library(dplyr) # data manipulation
|
||||
library(tidyr) # tidy data
|
||||
library(lubridate) #work with dates
|
||||
library(stringr) # manipulate strings
|
||||
library(plotly)
|
||||
```
|
||||
|
||||
## Import Data
|
||||
|
||||
Next lets import our data, this week we are using the sheet Flu Occurrence FY2013-2016. I am unsure if this is form a real data set or not but it is good for demonstration purposes! After importing we can glimpse at our data to understand what is contained within.
|
||||
|
||||
```{r}
|
||||
|
||||
ds <- readxl::read_xlsx(path = "../2020-01-04_my-start-to-r/Tableau 10 Training Practice Data.xlsx"
|
||||
,sheet = "05 - Flu Occurrence FY2013-2016"
|
||||
)
|
||||
ds %>% glimpse()
|
||||
```
|
||||
|
||||
## Transform Data
|
||||
|
||||
I went a bit overboard today with renaming the variables. I wanted to practice writing a function and while it might not be the prettiest or the best way to do this, it worked for what I was trying to accomplish. Note the use of sapply, which lets us run the function on each column name.
|
||||
|
||||
```{r}
|
||||
|
||||
|
||||
format_names <- function(x) {
|
||||
#Fucntion to set all names to lower case, and strip unneeded characters
|
||||
x <- tolower(x)
|
||||
x <- str_replace_all(x,c(#set each pattern equal to replacement
|
||||
" " = "_"
|
||||
,"\\(\\+\\)" = "pos" #regualr experssion to match (+)
|
||||
,"\\(" = ""
|
||||
,"\\)" = ""
|
||||
,"\\%" = "pct"
|
||||
)
|
||||
)
|
||||
}
|
||||
|
||||
#run the format name function on all names from DS
|
||||
colnames(ds) <- sapply(colnames(ds),format_names)
|
||||
```
|
||||
|
||||
Now is were the fun really starts! For this particular data set there are a couple things we need to add to replicate the example. In the original data set the date is stored with month, day, and year; the day is irrelevant and we need to pull out the month as well as the year. For this we can use the lubridate package, first we pull out the month and set it as a factor. For this example our year actually starts in October, so we set our factor to start at October (10), and end with September (9). We then pull out the year, which presents us with a different problem. Again our year starts in October, instead of January. To solve this I have created a variable called date adjustment, in this column is our month is 10 or greater, we will place a 1, if not a 0. We then set our fiscal year to be the actual year plus the date adjustment, this allows us to have our dates in the right fiscal year. Last the percent column is currently listed as a decimal, so we will convert this to a percentage.
|
||||
|
||||
|
||||
``` {r}
|
||||
# split date time
|
||||
ds1 <- ds %>% mutate(
|
||||
#create month column, then set factors and labels to start fiscal year in Oct
|
||||
month = month(ds$date)
|
||||
,month = factor(month
|
||||
,levels = c(10:12, 1:9)
|
||||
,labels = c(month.abb[10:12],month.abb[1:9]))
|
||||
,year = year(ds$date)
|
||||
,date_adjustment = ifelse(month(ds$date) >= 10, 1,0 )
|
||||
,fiscal_year = factor(year + date_adjustment)
|
||||
#convert % Pos from decmial to pct
|
||||
,pct_tests_pos_for_influenza = round(pct_tests_pos_for_influenza * 100, digits = 0)
|
||||
)
|
||||
|
||||
ds1 %>% glimpse()
|
||||
|
||||
```
|
||||
|
||||
## GGplot
|
||||
|
||||
The graph here is pretty straight forward with one exception, group! For this line graph we want ggplot to connect the lines of the same year, if we do not explicitly state this using the group mapping, ggplot will try to connect all the lines together, which of course is not at all what we want!
|
||||
|
||||
```{r}
|
||||
|
||||
|
||||
g1 <- ds1 %>%
|
||||
ggplot(aes(x = month, y = pct_tests_pos_for_influenza, color = fiscal_year
|
||||
,group = fiscal_year)) +
|
||||
geom_line() +
|
||||
labs(
|
||||
x = NULL
|
||||
,y = "% Tests (+) for Influenza"
|
||||
,color = NULL
|
||||
,title = "Flu Viral Surveillance: % Respiratory Specimens Positive for Influenza \nOctober - September \nFor Flu Seasons 2013 - 2016"
|
||||
) +
|
||||
theme_classic() +
|
||||
scale_y_continuous(breaks = seq(0,40,5)) +
|
||||
scale_color_manual(values = c("#a6611a","#dfc27d","#80cdc1","#018571"))
|
||||
|
||||
g1
|
||||
```
|
||||
|
||||
## plotly
|
||||
|
||||
One of the nice features of Tableau is the fact the graphs are interactive, while a good graph should speak for itself, end users love pretty things. I have been experimenting with Plotly, which has an open source package for R (as well as many other programming languages!). This example only just scratches the surface, but there will be many more to come!
|
||||
|
||||
```{r}
|
||||
|
||||
|
||||
|
||||
g2 <- ds1 %>%
|
||||
plot_ly(x = ~month, y = ~pct_tests_pos_for_influenza, type = "scatter", mode = "lines"
|
||||
,color = ~fiscal_year
|
||||
,colors = c("#a6611a","#dfc27d","#80cdc1","#018571")
|
||||
, hoverinfo = 'y') %>%
|
||||
layout(xaxis = list(
|
||||
title = ""
|
||||
)
|
||||
,yaxis = list(
|
||||
title = "% Tests (+) for Influenza"
|
||||
)
|
||||
,title = "Flu Viral Surveillance: % Respiratory Specimens Positive for Influenza"
|
||||
,legend = list(
|
||||
x = 100
|
||||
,y = 0.5
|
||||
)
|
||||
|
||||
)
|
||||
|
||||
g2
|
||||
```
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,269 @@
|
|||
"Data Source","World Development Indicators",
|
||||
|
||||
"Last Updated Date","2019-12-20",
|
||||
|
||||
"Country Name","Country Code","Indicator Name","Indicator Code","1960","1961","1962","1963","1964","1965","1966","1967","1968","1969","1970","1971","1972","1973","1974","1975","1976","1977","1978","1979","1980","1981","1982","1983","1984","1985","1986","1987","1988","1989","1990","1991","1992","1993","1994","1995","1996","1997","1998","1999","2000","2001","2002","2003","2004","2005","2006","2007","2008","2009","2010","2011","2012","2013","2014","2015","2016","2017","2018","2019",
|
||||
"Aruba","ABW","GDP per capita (current US$)","NY.GDP.PCAP.CD","","","","","","","","","","","","","","","","","","","","","","","","","","","6472.50202920407","7885.79654466735","9764.78997879329","11392.4558105764","12307.3117378314","13496.0033851411","14046.5039965619","14936.827039276","16241.0463247117","16439.3563609282","16586.0684357542","17927.7496352086","19078.3431907515","19356.2033894901","20620.7006259175","20669.0319688645","20436.8871286309","20833.7616116694","22569.9749851801","23300.0395575696","24045.2724833547","25835.1326676284","27084.7036903653","24630.4537141023","23512.6025956397","24985.9932813737","24713.6980451285","25025.0995629083","25533.5697803984","25796.3802506382","25239.6004109932","25630.2664922069","","",
|
||||
"Afghanistan","AFG","GDP per capita (current US$)","NY.GDP.PCAP.CD","59.7731938409853","59.8608738790779","58.4580149495439","78.7063875407802","82.0952307131832","101.108304853377","137.594352053111","160.898588872438","129.108323102596","129.329712876621","156.518939442982","159.567578521888","135.31730831433","143.144649500081","173.653764639169","186.510897140201","197.445507551145","224.224797281134","247.354106347038","275.738197619262","272.655285650232","264.111317453061","","","","","","","","","","","","","","","","","","","","","179.426494467624","190.684008786064","211.381969507195","242.031379173351","263.733734678911","359.693480030525","364.660465388571","438.076034406941","543.303041863931","591.16234645088","641.872033785411","637.16504385598","613.856332882312","578.466352941708","547.228110150363","556.302138508508","520.896602719135","",
|
||||
"Angola","AGO","GDP per capita (current US$)","NY.GDP.PCAP.CD","","","","","","","","","","","","","","","","","","","","","710.981648140027","642.383857952257","619.96135753111","623.440584831564","637.715230700475","758.237576171151","685.270085316704","756.261853027412","792.303120218644","890.554136459008","947.704182085371","865.69272959239","656.361755960006","441.200673252825","328.673294707808","397.179450769472","522.643807265256","514.295223223424","423.59366023208","387.784316047502","556.836318086553","527.333528536691","872.494491592841","982.960899291112","1255.56404471491","1902.42234554625","2599.56646397608","3121.99563726236","4080.94140992346","3122.78076649385","3587.88379824396","4615.46802807906","5100.09580809767","5254.88233799616","5408.41049555432","4166.97968386501","3506.07288506966","4095.81294155857","3432.38573600893","",
|
||||
"Albania","ALB","GDP per capita (current US$)","NY.GDP.PCAP.CD","","","","","","","","","","","","","","","","","","","","","","","","","639.48473584821","639.865909445734","693.873474633887","674.793383069492","652.774321396566","697.995596576519","617.230435515505","336.586994504629","200.852219772323","367.279225077581","586.416339644261","750.604449178826","1009.97766752106","717.380567391734","813.790263555599","1033.2416930932","1126.68331762741","1281.65939343074","1425.12484888368","1846.11881331912","2373.57984371994","2673.78728253113","2972.74326462176","3595.03716321514","4370.54008654749","4114.14015001395","4094.36211924475","4437.17806741261","4247.61427862374","4413.0817427964","4578.66672031333","3952.82945832578","4124.10890718622","4532.89016212124","5268.84850355919","",
|
||||
"Andorra","AND","GDP per capita (current US$)","NY.GDP.PCAP.CD","","","","","","","","","","","3238.55684977329","3498.1736515232","4217.17358114253","5342.16856044208","6319.73903384837","7169.10100559556","7152.37513361051","7751.37021575891","9129.7061851808","11820.8493928325","12377.4116456877","10372.232834745","9610.26630831575","8022.65478128161","7728.90669456943","7774.39382903478","10361.8159820255","12616.167565929","14304.3569645785","15166.437850754","18878.5059692988","19532.5401504525","20547.7117897897","16516.4710272204","16234.8090102012","18461.0648581512","19017.1745902241","18353.0597218473","18894.5214963227","19261.7105038886","21936.5301014708","22228.8464928922","24741.4935704562","32776.4422698769","38503.4796144857","41282.0201219785","43747.6918489433","48582.2208830236","47785.089272664","43338.8667579064","39736.3540626699","41100.7299382214","38392.9439012206","40626.7516320228","42300.3341276669","36039.6534962289","37224.1089162924","39134.3933706717","42029.7627372989","",
|
||||
"Arab World","ARB","GDP per capita (current US$)","NY.GDP.PCAP.CD","","","","","","","","","223.938947785634","239.147036355855","261.94766214987","294.987379758969","342.443640866306","421.757000067229","775.699966965721","834.15817981439","1004.84677077155","1120.74550130302","1191.3185333356","1560.74583018969","2049.45250982749","2047.23177355938","1861.54946956017","1696.01460667464","1669.27583438721","1603.94198676464","1486.94344479601","1540.41237314671","1472.85553683274","1502.31642265252","2006.12708300312","1925.42055393079","2024.03061679983","1993.74197559378","1986.17340340063","2069.44431589776","2232.79367678474","2316.06851371354","2186.37068077205","2329.86453679684","2603.67170971375","2507.93677128845","2474.35737815448","2734.14946514457","3132.30394981358","3763.03404594664","4356.68841474906","4960.64958863061","6143.56120827107","5182.72658022931","5946.25857899362","6890.10551399639","7504.53252479256","7552.72030955995","7498.93831698475","6458.87015899263","6213.7851565211","6279.46742538325","6608.80697772202","",
|
||||
"United Arab Emirates","ARE","GDP per capita (current US$)","NY.GDP.PCAP.CD","","","","","","","","","","","","","","","","26847.7943802772","30118.1377833851","33823.3196498568","28456.7374380931","33512.741325999","42764.4566639901","44987.2097966579","40026.1662502465","34843.1028997611","32309.8077436844","29720.9194734633","23467.9766691997","23726.1523330469","22295.1461115987","24028.1928780116","27729.4664216648","26612.3355369691","26420.8727534823","25596.6922747864","25847.9261239252","27222.0358561647","28975.0229000791","29512.6637440248","26899.601766231","28470.8378632746","33291.419366353","31280.7842785952","31567.4737120596","33499.0937513705","36333.2498445266","39365.4338996517","41907.405655968","41809.516382842","44498.9342301633","32024.1816031187","33893.303514039","39194.6766208456","40976.4997112455","42412.6302780364","43751.8388860721","38663.3838066363","38141.8467585492","39811.634701763","43004.9533584848","",
|
||||
"Argentina","ARG","GDP per capita (current US$)","NY.GDP.PCAP.CD","","","1155.89017025099","850.30473690206","1173.23821277123","1279.11343214531","1272.80297767396","1062.54355190601","1141.0804807929","1329.05865921566","1322.59080368944","1372.3743432739","1408.86520848881","2097.02257902742","2844.86364753884","2027.33712384746","1948.22468587272","2129.70828089903","2146.36448309866","2520.92163373012","2758.83521210768","2776.32199028147","2927.89756021605","3553.37775166279","2659.70860023312","2926.126969514","3613.62170927636","3562.87560174521","3985.19121035849","2383.86769311311","4333.48297312146","5735.36032735223","6823.5376160468","6969.11808781005","7483.13968170844","7408.70823818765","7721.35454273963","8213.12719994479","8289.50572933118","7774.73620280001","7708.10099605414","7208.3715665121","2593.40415042466","3349.8061244115","4277.72135064389","5109.85132522621","5919.01203707753","7245.44831728945","9020.87309807194","8225.13717626412","10385.9644319555","12848.8641969705","13082.664325572","13080.2547323367","12334.7982453893","13789.060424772","12790.2424732447","14591.8633810541","11683.9496216363","",
|
||||
"Armenia","ARM","GDP per capita (current US$)","NY.GDP.PCAP.CD","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","637.854659491342","590.121047267559","369.632616583198","357.203277745679","400.515238080735","456.375048663809","504.058559449916","523.283068040748","609.172437516102","597.432514707894","622.742139063498","694.42322774468","783.240698647042","930.127321851799","1191.91901957402","1643.7530293611","2158.14369710179","3139.27749894333","4010.85724255226","2994.34254448509","3218.37270660563","3525.80474671153","3681.8574560431","3838.18580148382","3986.23162376713","3607.29669672271","3591.82927553023","3914.5012684128","4212.07094272937","",
|
||||
"American Samoa","ASM","GDP per capita (current US$)","NY.GDP.PCAP.CD","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","8700.06770480704","8856.54745899435","8578.94472277609","8444.98169974145","8391.56106721708","8909.44915617236","9792.66680581646","11961.2582255703","10271.2245225485","10294.3022651052","11568.7930012395","11505.3937142139","11507.2323493037","11843.3311832581","11696.9555623329","10823.4448040273","11466.6907058505","",
|
||||
"Antigua and Barbuda","ATG","GDP per capita (current US$)","NY.GDP.PCAP.CD","","","","","","","","","","","","","","","","","","1246.75032905458","1416.5861996499","1760.717634441","2124.48115520353","2392.26107515282","2660.60115717135","2947.93553600212","3372.49289962607","3899.32872843063","4703.17940118439","5463.24127633961","6453.47700017448","7076.42221194876","7348.21294282384","7601.84849106528","7744.27491660046","8136.16883983425","8770.75163074508","8406.59299171022","9030.97529861385","9492.03825604026","9940.19163925206","10260.5850219076","10920.8426880891","10370.6711296076","10404.4362134662","10789.950959787","11446.6384702767","12547.6130399103","13989.7156660433","15607.0898690088","16024.3455512142","14113.0772600466","13049.2570545735","12746.2050243357","13272.4413293826","12909.7441775006","13501.5809223367","14286.0931598293","15197.6174551735","15383.4151884998","16726.9808079479","",
|
||||
"Australia","AUS","GDP per capita (current US$)","NY.GDP.PCAP.CD","1807.78571021206","1874.73210577126","1851.84185074588","1964.1504696359","2128.06835509202","2277.55839760436","2340.43868460617","2576.2845085275","2720.08260948799","2986.94950744786","3299.84320663058","3489.95229368358","3943.7876390397","4763.6283896816","6473.16515297326","6994.20992688909","7476.74874774519","7765.07054434927","8241.99727587296","9281.52335605458","10194.3187014782","11833.7432115058","12766.5222521717","11518.6685192068","12431.9458488308","11437.6644609658","11364.2394626146","11624.5395871355","14254.5575749384","17798.3142378324","18211.2745901556","18821.4773892062","18569.8127525565","17634.2563519674","18046.0202439789","20319.6306283956","21861.32550987","23468.5968306374","21318.9641694318","20533.035061907","21679.2478424147","19490.8611097303","20082.4832672749","23447.0310006717","30430.6764374443","33999.2428575835","36044.9228108485","40960.0544948199","49601.6567082178","42772.3591664498","52022.1255961876","62517.8337471503","68012.1479005934","68150.1070413215","62510.7911705641","56755.7217124249","49971.131456129","54066.4712686117","57373.6866841281","",
|
||||
"Austria","AUT","GDP per capita (current US$)","NY.GDP.PCAP.CD","935.460426850415","1031.8150043291","1087.8342434189","1167.00053244585","1269.41258289256","1374.53213986075","1486.96860600566","1569.66718289967","1677.67352804272","1825.38612552124","2058.76907922607","2380.97844333246","2924.04887110057","3890.72245647634","4630.7572070671","5285.62072414121","5678.38663782273","6810.6277286986","8205.46892702823","9793.76530724628","10869.5464236099","9385.24901832146","9410.34722457985","9537.40743056414","8991.06507601017","9172.09676880976","13083.0726978243","16392.7695665813","17578.6189397397","17468.9461918554","21680.9896863626","22410.9117362704","24880.164070125","24081.5278077646","25646.7006591684","30325.8495818396","29809.0767730821","26705.4785993891","27361.8751106437","27174.2971559775","24564.4582948404","24537.5142629883","26401.7454564357","32222.8972411655","36821.5214680093","38403.1338770715","40635.2818159724","46855.7717452095","51708.7657541758","47963.1794023217","46858.0432733717","51374.9584066934","48567.69528642","50716.7087062864","51717.4959405515","44178.0473777432","45237.8050921543","47431.630607607","51461.9547804677","",
|
||||
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Can't render this file because it has a wrong number of fields in line 5.
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@ -0,0 +1,269 @@
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||||
"Uganda","UGA","Population, total","SP.POP.TOTL","6767095","6983818","7215796","7461634","7719104","7986066","8263439","8550443","8841153","9127857","9405600","9671860","9929636","10186455","10453037","10737403","11042974","11368615","11712530","12071145","12442334","12825082","13221991","13638729","14082875","14559355","15070302","15612754","16180124","16763042","17354392","17953531","18561674","19175988","19793541","20413152","21032821","21655398","22290780","22952410","23650172","24388968","25167257","25980552","26821297","27684585","28571475","29486338","30431736","31411096","32428167","33476919","34559168","35695246","36912148","38225453","39647506","41162465","42723139","",
|
||||
"Ukraine","UKR","Population, total","SP.POP.TOTL","42664652","43206345","43752230","44288608","44796964","45264548","45684979","46063228","46411810","46749283","47088857","47435010","47783005","48125842","48452626","48755665","49032729","49287127","49523314","49747989","49965874","50221000","50384000","50564000","50754000","50917000","51097000","51293000","51521000","51773000","51892000","52000470","52150266","52179210","51921041","51512299","51057189","50594105","50143939","49673350","49175848","48683865","48202500","47812950","47451600","47105150","46787750","46509350","46258200","46053300","45870700","45706100","45593300","45489600","45271947","45154029","45004645","44831135","44622516","",
|
||||
"Upper middle income","UMC","Population, total","SP.POP.TOTL","1184555662","1189517219","1206934313","1235735480","1264214938","1293671190","1326076088","1357707469","1390259908","1424467769","1459613363","1495392316","1529438212","1562594826","1594423792","1624083079","1652300299","1679100999","1706009390","1733303601","1760396042","1788251122","1818380977","1848610954","1878028567","1908228254","1939929947","1973093324","2006397732","2038575917","2069481368","2098733828","2125919794","2151996102","2177563609","2202547627","2227015777","2251187823","2274454564","2296375670","2317310149","2337064426","2355936797","2374520060","2392790614","2410930308","2428657785","2445871770","2463294188","2481349075","2499410493","2518197739","2537625754","2557649560","2577855859","2597757681","2617804661","2637690770","2655635719","",
|
||||
"Uruguay","URY","Population, total","SP.POP.TOTL","2538643","2571681","2603882","2635126","2665388","2694536","2722875","2750086","2774759","2795042","2809793","2818281","2821461","2822117","2824095","2830176","2841372","2856958","2875728","2895688","2915389","2934613","2953852","2973036","2992246","3011519","3030649","3049581","3068705","3088595","3109601","3131660","3154464","3177745","3201151","3224281","3247381","3270164","3291053","3308012","3319736","3325473","3326040","3323668","3321476","3321803","3325401","3331749","3340221","3349676","3359275","3368934","3378974","3389439","3400434","3412009","3424132","3436646","3449299","",
|
||||
"United States","USA","Population, total","SP.POP.TOTL","180671000","183691000","186538000","189242000","191889000","194303000","196560000","198712000","200706000","202677000","205052000","207661000","209896000","211909000","213854000","215973000","218035000","220239000","222585000","225055000","227225000","229466000","231664000","233792000","235825000","237924000","240133000","242289000","244499000","246819000","249623000","252981000","256514000","259919000","263126000","266278000","269394000","272657000","275854000","279040000","282162411","284968955","287625193","290107933","292805298","295516599","298379912","301231207","304093966","306771529","309326085","311580009","313874218","316057727","318386421","320742673","323071342","325147121","327167434","",
|
||||
"Uzbekistan","UZB","Population, total","SP.POP.TOTL","8526300","8813616","9113620","9428906","9762816","10116870","10493436","10889509","11294672","11694847","12080317","12446443","12796980","13140797","13491117","13857478","14242769","14643875","15057224","15476922","15898757","16321696","16747426","17177664","17615040","18061284","18515578","18976417","19443887","19918119","20510000","20952000","21449000","21942000","22377000","22785000","23225000","23667000","24051000","24311650","24650400","24964450","25271850","25567650","25864350","26167000","26488250","26868000","27302800","27767400","28562400","29339400","29774500","30243200","30757700","31298900","31847900","32388600","32955400","",
|
||||
"St. Vincent and the Grenadines","VCT","Population, total","SP.POP.TOTL","80977","82169","83237","84198","85111","86011","86903","87777","88662","89568","90509","91491","92513","93571","94614","95662","96692","97701","98697","99649","100566","101437","102256","103032","103779","104506","105217","105906","106532","107071","107484","107776","107950","108033","108053","108035","107976","107895","107801","107758","107784","107896","108097","108326","108512","108614","108603","108518","108393","108287","108255","108316","108435","108622","108861","109148","109459","109827","110210","",
|
||||
"Venezuela, RB","VEN","Population, total","SP.POP.TOTL","8141841","8439261","8742777","9052635","9369096","9692278","10022592","10359745","10702291","11048262","11396393","11745945","12097694","12453718","12816955","13189509","13572208","13964379","14364727","14771271","15182611","15597886","16017573","16443134","16876703","17319520","17772001","18232730","18698847","19166471","19632665","20096317","20557683","21016901","21474549","21931084","22385650","22837743","23288564","23739841","24192446","24646472","25100408","25551624","25996594","26432447","26850194","27247610","27635832","28031009","28439940","28888369","29362449","29783571","30045134","30081829","29846179","29390409","28870195","",
|
||||
"British Virgin Islands","VGB","Population, total","SP.POP.TOTL","8048","8167","8310","8476","8646","8830","9025","9222","9421","9626","9827","10017","10208","10392","10562","10699","10821","10928","11045","11218","11478","11826","12249","12752","13322","13956","14649","15407","16154","16862","17489","18006","18438","18786","19074","19313","19502","19665","19821","20026","20311","20675","21129","21672","22334","23107","24023","25047","26097","27039","27794","28319","28650","28847","28989","29152","29355","29577","29802","",
|
||||
"Virgin Islands (U.S.)","VIR","Population, total","SP.POP.TOTL","32500","34300","35000","39800","40800","43500","46200","49100","55700","60300","63476","70937","76319","84121","89941","94484","96166","93203","95929","96183","99636","99853","100068","100348","100600","100760","100842","100901","100952","101041","103963","104807","105712","106578","107318","107818","108095","108357","108537","108599","108642","108549","108510","108506","108467","108454","108371","108339","108399","108405","108358","108292","108191","108044","107884","107710","107510","107268","106977","",
|
||||
"Vietnam","VNM","Population, total","SP.POP.TOTL","32670039","33666110","34683407","35721217","36779999","37858951","38958048","40072948","41193601","42307146","43404793","44484035","45548479","46603525","47657561","48718189","49785282","50861162","51959015","53095408","54281846","55522803","56814306","58148384","59512619","60896721","62293856","63701972","65120439","66550234","67988862","69436954","70883481","72300308","73651218","74910461","76068743","77133214","78115710","79035871","79910412","80742499","81534407","82301656","83062821","83832661","84617540","85419591","86243413","87092252","87967651","88871561","89802487","90753472","91714595","92677076","93638724","94596642","95540395","",
|
||||
"Vanuatu","VUT","Population, total","SP.POP.TOTL","63689","65705","67794","69946","72115","74270","76395","78499","80657","82927","85377","88010","90809","93747","96774","99859","103006","106200","109405","112549","115597","118541","121395","124209","127049","129984","132995","136079","139315","142794","146573","150716","155170","159744","164129","168158","171722","174921","177987","181265","184972","189219","193920","198959","204127","209282","214370","219472","224704","230247","236211","242653","249499","256635","263888","271130","278330","285510","292680","",
|
||||
"World","WLD","Population, total","SP.POP.TOTL","3032019978","3073077563","3126066253","3191186048","3256700083","3323623700","3393699205","3463147267","3533536526","3608235815","3683676306","3761307048","3837726171","3913217944","3989385034","4063806523","4136393107","4208770941","4282341460","4357793599","4434021975","4512268962","4593454253","4675367633","4756998073","4840155168","4925801334","5013576387","5102293348","5190965222","5281340078","5369210095","5453393960","5538448726","5622575421","5707533023","5790454220","5873071768","5954810550","6035284135","6115108363","6194460444","6273526441","6352677699","6432374971","6512602867","6593623202","6675130418","6757887172","6840591577","6922947261","7004011262","7086993625","7170961674","7255653881","7340548192","7426103221","7510990456","7594270356","",
|
||||
"Samoa","WSM","Population, total","SP.POP.TOTL","108629","112105","115776","119559","123342","127054","130673","134177","137487","140500","143149","145413","147296","148864","150198","151359","152367","153224","153987","154739","155525","156407","157372","158352","159248","159990","160544","160965","161376","161940","162803","163997","165490","167119","168694","170054","171165","172068","172839","173609","174454","175392","176407","177484","178590","179727","180876","182046","183263","184556","185949","187469","189088","190717","192221","193513","194535","195352","196130","",
|
||||
"Kosovo","XKX","Population, total","SP.POP.TOTL","947000","966000","994000","1022000","1050000","1078000","1106000","1135000","1163000","1191000","1219000","1247000","1278000","1308000","1339000","1369000","1400000","1430000","1460000","1491000","1521000","1552000","1582000","1614000","1647000","1682000","1717000","1753000","1791000","1827000","1862000","1898000","1932000","1965000","1997000","2029000","2059000","2086000","1966000","1762000","1700000","1701154","1702310","1703466","1704622","1705780","1719536","1733404","1747383","1761474","1775680","1791000","1805200","1824100","1821800","1801800","1816200","1830700","1845300","",
|
||||
"Yemen, Rep.","YEM","Population, total","SP.POP.TOTL","5315355","5393036","5473671","5556766","5641597","5727751","5816247","5907874","6001852","6097035","6193384","6290365","6390574","6500816","6629999","6784695","6967941","7178675","7414158","7669694","7941898","8231910","8541605","8869370","9213084","9572175","9941109","10322043","10730862","11189177","11709993","12302124","12954155","13634076","14297613","14913315","15469274","15975668","16450310","16921149","17409072","17918373","18443691","18985000","19540098","20107409","20687646","21282515","21892146","22516460","23154855","23807588","24473178","25147109","25823485","26497889","27168210","27834821","28498687","",
|
||||
"South Africa","ZAF","Population, total","SP.POP.TOTL","17099840","17524533","17965725","18423161","18896307","19384841","19888250","20406864","20942145","21496075","22069776","22665271","23281508","23913099","24552540","25195187","25836888","26480913","27138965","27827320","28556769","29333103","30150448","30993758","31841593","32678874","33495953","34297727","35100909","35930050","36800509","37718950","38672607","39633750","40564059","41435758","42241011","42987461","43682260","44338543","44967708","45571274","46150913","46719196","47291610","47880601","48489459","49119759","49779471","50477011","51216964","52004172","52834005","53689236","54545991","55386367","56203654","57000451","57779622","",
|
||||
"Zambia","ZMB","Population, total","SP.POP.TOTL","3070776","3164329","3260650","3360104","3463213","3570464","3681955","3797873","3918872","4045740","4179067","4319224","4466174","4619546","4778724","4943283","5112823","5287548","5468262","5656139","5851825","6055366","6265864","6481916","6701540","6923149","7146969","7372837","7598275","7820205","8036845","8246656","8451347","8656486","8869740","9096607","9339733","9597609","9866476","10140561","10415944","10692193","10971698","11256743","11550642","11856247","12173514","12502958","12848530","13215139","13605984","14023193","14465121","14926504","15399753","15879361","16363507","16853688","17351822","",
|
||||
"Zimbabwe","ZWE","Population, total","SP.POP.TOTL","3776681","3905034","4039201","4178726","4322861","4471177","4623351","4779827","4941906","5111337","5289303","5476982","5673911","5877726","6085074","6293875","6502569","6712827","6929664","7160023","7408624","7675591","7958241","8254747","8562249","8877489","9200149","9527203","9849125","10153852","10432421","10680995","10900502","11092766","11261744","11410714","11541217","11653242","11747072","11822719","11881477","11923914","11954290","11982224","12019912","12076699","12155491","12255922","12379549","12526968","12697723","12894316","13115131","13350356","13586681","13814629","14030390","14236745","14439018","",
|
||||
|
Can't render this file because it has a wrong number of fields in line 5.
|
8287
posts/2020-02-13_basic-who-TB-data/TB_notifications_2020-02-11.csv
Normal file
8287
posts/2020-02-13_basic-who-TB-data/TB_notifications_2020-02-11.csv
Normal file
File diff suppressed because it is too large
Load diff
|
|
@ -0,0 +1,278 @@
|
|||
---
|
||||
title: "Basic Exploration of WHO Tuberculosis Data"
|
||||
subtitle: |
|
||||
Today I am going to dive into some real life data from the World Health Organization (WHO), exploring new and relapse cases of Tuberculosis. I clean up the data, and then make a few graphs to explore different variables.
|
||||
date: 02-13-2020
|
||||
---
|
||||
|
||||
# TL:DR
|
||||
Today I am going to dive into some real life data from the World Health Organization (WHO), exploring new and relapse cases of Tuberculosis. I clean up the data, and then make a few graphs to explore different variables.
|
||||
|
||||
|
||||
# Load Packages
|
||||
|
||||
Since I am going to use quite a few packages in the tidyverse I am going to load them all in at once instead of individually.
|
||||
|
||||
```{r}
|
||||
library(tidyverse)
|
||||
|
||||
```
|
||||
|
||||
# Load in Data
|
||||
We are using the WHO data set which contains tuberculosis (TB) cases broken down by year, this data set is contained in the Tidyr package, however its only recent up to 2014. For a little added fun I have downloaded the latest data from the WHO website, [Found here](https://www.who.int/tb/country/data/download/en/). For some added fun I have also included GDP per Capita data from World bank [Found here](https://data.worldbank.org/indicator/NY.GDP.PCAP.CD)
|
||||
|
||||
```{r}
|
||||
who_raw <- read.csv("TB_notifications_2020-02-11.csv")
|
||||
|
||||
#GPD file contains 4 rows of instrusctions above the actually data, we can tell
|
||||
#read.csv to skip these using the skip command
|
||||
gpd_raw <- read.csv("API_NY.GDP.PCAP.CD_DS2_en_csv_v2_713080.csv",
|
||||
skip = 4)
|
||||
|
||||
|
||||
```
|
||||
|
||||
# Transform
|
||||
|
||||
This data set is very ugly looking! The first 3 columns are all country Identifiers, with column four indicating the WHO region. This is redundant and can be dropped down to one Identifier and Region. As we can see there are quite a few Variable columns that are in fact values and not true Variables. When reading the data dictionary for this data set, WHO has changed their reporting over the years, so for our purposes we can strip a lot of the extra data out. Lets try and look at three types of TB, Extrapulmonary, Lab Diagnosed, and Clinician Diagnosed. As well as try and look at the breakdowns by Age and Sex of new and relapse case (post 2012) Lots of Cleaning to do, lets get to it!
|
||||
|
||||
```{r}
|
||||
|
||||
who1 <- who_raw %>%
|
||||
#lets drop some columns not needed for our exploration, what each column means can be found in the CSV Data dictionary file
|
||||
select(-iso2
|
||||
,-iso_numeric
|
||||
,-(rdx_data_available:hiv_reg_new2)
|
||||
,-(new_sp:rel_in_agesex_flg)
|
||||
) %>%
|
||||
#Lets just look at new date
|
||||
filter(year >= 2013) %>%
|
||||
#Move the values that are currently stored as variables to observations
|
||||
pivot_longer(cols = newrel_m04:newrel_sexunkageunk
|
||||
,names_to = "key"
|
||||
,values_to = "values"
|
||||
) %>%
|
||||
separate(col = key
|
||||
,into = c("new","sexage")
|
||||
,sep = "_"
|
||||
) %>%
|
||||
#the data set contains male, female and unknown
|
||||
mutate_if(is.character
|
||||
,str_replace_all
|
||||
,pattern = "sexunk"
|
||||
, replacement = "u"
|
||||
) %>%
|
||||
separate(col = sexage
|
||||
,into = c("sex","age")
|
||||
,sep = 1) %>%
|
||||
mutate(age_start = case_when(
|
||||
str_detect(age, "65") ~ "65"
|
||||
,(str_length(age) == 2) ~ str_match(age, "\\S")
|
||||
,(str_length(age) == 3) ~ str_match(age, "\\S")
|
||||
,(str_length(age) == 4) ~ str_match(age, "\\S\\S")
|
||||
|
||||
,TRUE ~ ""
|
||||
)
|
||||
,age_end = case_when(
|
||||
str_detect(age, "65") ~ "& Over"
|
||||
,(str_length(age) == 2) ~ str_match(age, "\\S$")
|
||||
,(str_length(age) == 3) ~ str_match(age, "\\S\\S$")
|
||||
,(str_length(age) == 4) ~ str_match(age, "\\S\\S$")
|
||||
,TRUE ~ ""
|
||||
))
|
||||
|
||||
#overall WHO data is now cleaned and tidy.
|
||||
|
||||
# Lets tidy up the GPD data so we can match it to our WHO data set
|
||||
gdp1 <- gpd_raw %>%
|
||||
select(-(Indicator.Name:X2012)
|
||||
,-X2019
|
||||
,-X) %>%
|
||||
pivot_longer(cols = X2013:X2018
|
||||
,names_to = "year"
|
||||
,values_to = "gdp") %>%
|
||||
mutate_if(is.character
|
||||
,str_remove_all
|
||||
,pattern = "X(?=\\d*)") # regex to check for an X followed by a digit
|
||||
|
||||
```
|
||||
|
||||
# Join Data
|
||||
Lets combine the data sets so we can later visual TB Cases based on a countries GDP per capita.
|
||||
|
||||
```{r}
|
||||
who_combined <- who1 %>%
|
||||
rename(Country.Code = iso3) %>%
|
||||
mutate(year = as.character(year)) %>%
|
||||
left_join(y = gdp1) %>%
|
||||
select(-Country.Name)
|
||||
|
||||
```
|
||||
|
||||
|
||||
|
||||
# Analyze
|
||||
|
||||
Lets first explore 2018 and see if GDP has any affect on the amount of TB cases in a particular country.
|
||||
|
||||
```{r}
|
||||
g1 <- who_combined %>%
|
||||
filter(str_detect(age,"014|15plus|u"),year == 2018) %>%
|
||||
group_by(country) %>%
|
||||
summarise(sum_tb_cases = (sum(values,na.rm = TRUE)/10000)
|
||||
,gdp = first(gdp)/1000
|
||||
,who_region = first(g_whoregion)) %>%
|
||||
mutate(
|
||||
label = ifelse((sum_tb_cases>50), yes = as.character(country),no = "")) %>%
|
||||
ggplot(aes(x = gdp, y = sum_tb_cases )) +
|
||||
geom_point(aes(color = who_region)) +
|
||||
ggrepel::geom_text_repel(aes(x = gdp, y = sum_tb_cases, label = label)) +
|
||||
labs(
|
||||
title = "Total TB Cases by Country compared to Gross Domestic Product (GDP)"
|
||||
,x = "GDP (per 1,000USD)"
|
||||
,y = "Total TB Case (per 10,000 cases)"
|
||||
,color = "WHO Region"
|
||||
) +
|
||||
theme_bw()
|
||||
g1
|
||||
```
|
||||
|
||||
### Subset
|
||||
|
||||
Lets subset the above data to remove some of the outliers.
|
||||
|
||||
```{r}
|
||||
g2 <- who_combined %>%
|
||||
filter(str_detect(age,"014|15plus|u"),year == 2018) %>%
|
||||
group_by(country) %>%
|
||||
summarise(sum_tb_cases = (sum(values,na.rm = TRUE)/10000)
|
||||
,gdp = first(gdp)/1000
|
||||
,who_region = first(g_whoregion)) %>%
|
||||
mutate(
|
||||
label = ifelse((sum_tb_cases>50), yes = as.character(country),no = "")) %>%
|
||||
ggplot(aes(x = gdp, y = sum_tb_cases )) +
|
||||
geom_point(aes(color = who_region)) +
|
||||
ggrepel::geom_text_repel(aes(x = gdp, y = sum_tb_cases, label = label)) +
|
||||
labs(
|
||||
title = "Total TB Cases by Country compared to Gross Domestic Product (GDP)"
|
||||
,x = "GDP (per 1,000USD)"
|
||||
,y = "Total TB Case (per 10,000 cases)"
|
||||
,color = "WHO Region"
|
||||
) +
|
||||
xlim(0,50) +
|
||||
ylim(0,50) +
|
||||
theme_bw()
|
||||
|
||||
g2
|
||||
```
|
||||
|
||||
We can see in the graph above there seems to be a small correlation between lower GDP and amount of TB cases.
|
||||
|
||||
## A different way to look
|
||||
|
||||
Could there be any correlation between a countries population and the amount of TB cases? Maybe its just as simple as having more people means more people to get sick? Lets bring in another data set, again from World Bank [Found Here](https://data.worldbank.org/indicator/SP.POP.TOTL), this contains total population data by country.
|
||||
|
||||
```{r}
|
||||
pop_raw <- read.csv("API_SP.POP.TOTL_DS2_en_csv_v2_713131.csv"
|
||||
,skip = 4)
|
||||
#If this looks famialer its because it is, the data set looks very simalar to the GDP data
|
||||
#In the future this could be moved to a function to allow cleaning much easier
|
||||
pop1 <- pop_raw %>%
|
||||
select(-(Indicator.Name:X2012)
|
||||
,-X2019
|
||||
,-X) %>%
|
||||
pivot_longer(cols = X2013:X2018
|
||||
,names_to = "year"
|
||||
,values_to = "population") %>%
|
||||
mutate_if(is.character
|
||||
,str_remove_all
|
||||
,pattern = "X(?=\\d*)")
|
||||
|
||||
#now lets combine this into are overall data set
|
||||
|
||||
who_combined <- who_combined %>%
|
||||
mutate(year = as.character(year)) %>%
|
||||
left_join(y = pop1) %>%
|
||||
select(-Country.Name)
|
||||
|
||||
#now lets Graph again
|
||||
|
||||
g3 <- who_combined %>%
|
||||
filter(str_detect(age,"014|15plus|u"),year == 2018) %>%
|
||||
group_by(country) %>%
|
||||
summarise(sum_tb_cases = (sum(values,na.rm = TRUE)/10000)
|
||||
,population = first(population)/1000000
|
||||
,who_region = first(g_whoregion)) %>%
|
||||
mutate(
|
||||
label = ifelse((population>250), yes = as.character(country),no = "")) %>%
|
||||
ggplot(aes(x = population, y = sum_tb_cases )) +
|
||||
geom_point(aes(color = who_region)) +
|
||||
ggrepel::geom_text_repel(aes(x = population, y = sum_tb_cases, label = label)) +
|
||||
labs(
|
||||
title = "Total TB Cases by Country compared to Gross Domestic Product (GDP)"
|
||||
,x = "Population (in Millions)"
|
||||
,y = "Total TB Case (per 10,000 cases)"
|
||||
,color = "WHO Region"
|
||||
) +
|
||||
theme_bw()
|
||||
|
||||
g3
|
||||
|
||||
```
|
||||
|
||||
### Further Exploration
|
||||
|
||||
Maybe we are on to something, the more people, the more likely they are to get sick! However India seems to have a very large number of cases so lets break these cases down further by age group for 2018.
|
||||
|
||||
```{r}
|
||||
|
||||
g4 <- who_combined %>%
|
||||
filter(year == 2018
|
||||
,country == "India"
|
||||
,!(str_detect(age,"15plus|ageunk|u|014"))
|
||||
,(str_detect(sex,"m|f"))
|
||||
) %>%
|
||||
mutate(age_range = glue::glue("{age_start} -- {age_end}")) %>%
|
||||
ggplot(aes(x = reorder(age_range, as.numeric(age_start)), y = (values/1000), fill = sex)) +
|
||||
geom_col(position = "dodge") +
|
||||
labs(
|
||||
title = "TB Case in India by age and gender 2018"
|
||||
,x = NULL
|
||||
,y = "Total Cases (per 1000)"
|
||||
,fill = "Gender") +
|
||||
scale_fill_manual(labels = c("Female","Male"), values = c("#e9a3c9","#67a9cf") )
|
||||
|
||||
g4
|
||||
```
|
||||
|
||||
There seems to be a huge spike in cases after adolescences. Females have a sharp decline the older they get, where as male case stay elevated with a slight decrease at 55.
|
||||
|
||||
## Last Exploration
|
||||
Lets look at overall cases in India, going back to 1980 and see if there as been any trends. To get these numbers we will go back to our raw data and strip everything out expect the total count
|
||||
|
||||
```{r}
|
||||
g5 <- who_raw %>%
|
||||
filter(country == "India") %>%
|
||||
select(year, c_newinc) %>%
|
||||
ggplot(aes(x = year, y = c_newinc/1000000)) +
|
||||
geom_line() +
|
||||
geom_point() +
|
||||
labs(
|
||||
title = "New and Relapse Tuberculosis Cases In India \n1980 -- 2018"
|
||||
,x = NULL
|
||||
,y = "Total Cases (in millions)") +
|
||||
theme_bw() +
|
||||
theme(plot.title = element_text(hjust = 0.5)) + #center title
|
||||
scale_x_continuous(breaks = seq(1980,2020,5)) +
|
||||
scale_y_continuous(breaks = scales::pretty_breaks(n=10)) #different way to add tick marks
|
||||
g5
|
||||
|
||||
```
|
||||
|
||||
Cases were steadily rising from 1980 to 1990, then suddenly feel off. Starting in the early 2010s there was a sharp increase and the amount of new and relapse cases just keep growing.
|
||||
|
||||
# Next Steps
|
||||
|
||||
While no other country has the amount of cases that India does, the sudden spike in cases at adolescences asks the question do other countries follow this same trend? We can also see the sudden spike in the 2010s, again is this just based in India or do we see this trend in other countries. There is much more exploration we can do with this data set at a later time!
|
||||
|
||||
|
||||
|
|
@ -27,3 +27,5 @@ execute:
|
|||
|
||||
# this will enable blog wide code folding which I do not want at this time
|
||||
# code-fold: true
|
||||
|
||||
license: "CC BY"
|
||||
Loading…
Reference in a new issue