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<body>
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<h1 class="title toc-ignore">Dumbbell Charts</h1>
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<h4 class="author"><em>Bob Rudis</em></h4>
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<h4 class="date"><em>2019-02-22</em></h4>
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<div id="dumbbell-charts" class="section level2">
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<h2>Dumbbell Charts</h2>
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<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" data-line-number="1"><span class="kw">library</span>(ggplot2)</a>
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<a class="sourceLine" id="cb1-2" data-line-number="2"><span class="kw">library</span>(gridExtra)</a>
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<a class="sourceLine" id="cb1-3" data-line-number="3"><span class="kw">library</span>(ggalt)</a>
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<a class="sourceLine" id="cb1-4" data-line-number="4"><span class="kw">library</span>(scales)</a></code></pre></div>
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<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" data-line-number="1">df <-<span class="st"> </span><span class="kw">data.frame</span>(<span class="dt">trt=</span>LETTERS[<span class="dv">1</span><span class="op">:</span><span class="dv">5</span>], <span class="dt">l=</span><span class="kw">c</span>(<span class="dv">20</span>, <span class="dv">40</span>, <span class="dv">10</span>, <span class="dv">30</span>, <span class="dv">50</span>), <span class="dt">r=</span><span class="kw">c</span>(<span class="dv">70</span>, <span class="dv">50</span>, <span class="dv">30</span>, <span class="dv">60</span>, <span class="dv">80</span>))</a>
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<a class="sourceLine" id="cb2-2" data-line-number="2"></a>
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<a class="sourceLine" id="cb2-3" data-line-number="3"><span class="kw">ggplot</span>(df, <span class="kw">aes</span>(<span class="dt">y=</span>trt, <span class="dt">x=</span>l, <span class="dt">xend=</span>r)) <span class="op">+</span><span class="st"> </span></a>
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<a class="sourceLine" id="cb2-4" data-line-number="4"><span class="st"> </span><span class="kw">geom_dumbbell</span>(<span class="dt">size=</span><span class="dv">3</span>, <span class="dt">color=</span><span class="st">"#e3e2e1"</span>, </a>
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<a class="sourceLine" id="cb2-5" data-line-number="5"> <span class="dt">colour_x =</span> <span class="st">"#5b8124"</span>, <span class="dt">colour_xend =</span> <span class="st">"#bad744"</span>,</a>
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<a class="sourceLine" id="cb2-6" data-line-number="6"> <span class="dt">dot_guide=</span><span class="ot">TRUE</span>, <span class="dt">dot_guide_size=</span><span class="fl">0.25</span>) <span class="op">+</span></a>
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<a class="sourceLine" id="cb2-7" data-line-number="7"><span class="st"> </span><span class="kw">labs</span>(<span class="dt">x=</span><span class="ot">NULL</span>, <span class="dt">y=</span><span class="ot">NULL</span>, <span class="dt">title=</span><span class="st">"ggplot2 geom_dumbbell with dot guide"</span>) <span class="op">+</span></a>
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<a class="sourceLine" id="cb2-8" data-line-number="8"><span class="st"> </span><span class="kw">theme_minimal</span>() <span class="op">+</span></a>
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<a class="sourceLine" id="cb2-9" data-line-number="9"><span class="st"> </span><span class="kw">theme</span>(<span class="dt">panel.grid.major.x=</span><span class="kw">element_line</span>(<span class="dt">size=</span><span class="fl">0.05</span>)) <span class="op">+</span></a>
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<a class="sourceLine" id="cb2-10" data-line-number="10"><span class="st"> </span><span class="kw">theme</span>(<span class="dt">panel.grid.major.y=</span><span class="kw">element_blank</span>())</a></code></pre></div>
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<p><img 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" 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