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<body>
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<h1 class="title toc-ignore">Vertical Dodging</h1>
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<h4 class="author"><em>Jonathan Carroll</em></h4>
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<h4 class="date"><em>2019-02-22</em></h4>
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<div id="vertical-dodging" class="section level2">
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<h2>Vertical Dodging</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">df2 <-<span class="st"> </span><span class="kw">data.frame</span>(<span class="dt">trt =</span> <span class="kw">c</span>(LETTERS[<span class="dv">1</span><span class="op">:</span><span class="dv">5</span>], <span class="st">"D"</span>),</a>
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<a class="sourceLine" id="cb2-2" data-line-number="2"> <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="dv">40</span>),</a>
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<a class="sourceLine" id="cb2-3" data-line-number="3"> <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>, <span class="dv">70</span>))</a>
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<a class="sourceLine" id="cb2-4" data-line-number="4"></a>
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<a class="sourceLine" id="cb2-5" data-line-number="5"><span class="kw">ggplot</span>(df2, <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></a>
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<a class="sourceLine" id="cb2-6" data-line-number="6"><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="cb2-7" data-line-number="7"> <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="cb2-8" data-line-number="8"> <span class="dt">dot_guide=</span><span class="ot">TRUE</span>, <span class="dt">dot_guide_size=</span><span class="fl">0.25</span>,</a>
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<a class="sourceLine" id="cb2-9" data-line-number="9"> <span class="dt">position=</span><span class="kw">position_dodgev</span>(<span class="dt">height=</span><span class="fl">0.8</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">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="cb2-11" data-line-number="11"><span class="st"> </span><span class="kw">theme_minimal</span>() <span class="op">+</span></a>
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<a class="sourceLine" id="cb2-12" data-line-number="12"><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>))</a></code></pre></div>
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<p><img 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" 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