503 lines
564 KiB
HTML
503 lines
564 KiB
HTML
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<meta name="viewport" content="width=device-width, initial-scale=1" />
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<meta name="author" content="牧小熊" />
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<meta name="date" content="2021-07-15" />
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<title>Task04_Visualization.knit</title>
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// v0.0.1
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// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
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<body>
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<section class="page-header">
|
||
<h1 class="title toc-ignore project-name"><div class="line-block">DataWhale 组队学习 R语言数据分析<br />
|
||
Task04 数据可视化</div></h1>
|
||
<h4 class="author project-author">牧小熊</h4>
|
||
<h4 class="date project-date">2021-07-15</h4>
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</section>
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" style="width:60.0%" /></p>
|
||
<div id="ggplot2包介绍" class="section level2">
|
||
<h2>0.ggplot2包介绍</h2>
|
||
<p>ggplot2包由Hadley Wickham编写，提供了一种基于Wilkinson所述图形语法的图形系统。ggplot2包的目标是提供一个全面的、基于语法的、连贯一致的图形生成系统，允许用户创建新颖的、有创新性的数据可视化图形。</p>
|
||
<p>总的来说有以下几点：</p>
|
||
<ul>
|
||
<li>ggplot2的核心理念是将绘图与数据分离，数据相关的绘图与数据无关的绘图分离</li>
|
||
<li>ggplot2保有命令式作图的调整函数，使其更具灵活性</li>
|
||
<li>ggplot2将常见的统计变换融入到了绘图中。</li>
|
||
<li>ggplot2是按图层作图</li>
|
||
</ul>
|
||
<p>ggplot2图像的三个基本构成：数据、图形属性映射、几何对象</p>
|
||
<p>按照ggplot2的绘图理念，Plot(图)= data(数据集)+ Aesthetics(美学映射)+ Geometry(几何对象)。</p>
|
||
<p>例如：</p>
|
||
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1"></a><span class="co"># ggplot(data,aes(x=x,y=y))+geom_point()</span></span></code></pre></div>
|
||
<ul>
|
||
<li>数据：用于绘制图形的数据</li>
|
||
<li>映射：aes()函数是ggplot2中的映射函数, 所谓的映射即为数据集中的数据关联到相应的图形属性过程中一种对应关系, 图形的颜色，形状，分组等都可以通过通过数据集中的变量映射。</li>
|
||
<li>几何对象：我们在图中实际看到的图形元素，如点、线、多边形等。</li>
|
||
</ul>
|
||
<p>ggplot2绘图代码如同数据公式一般，只需要套相应的公式即可绘制出丰富的图形，后续的讲解也会按照此方法。</p>
|
||
<p>ggplot2参考链接：</p>
|
||
<ul>
|
||
<li><a href="https://ggplot2.tidyverse.org/reference/" class="uri">https://ggplot2.tidyverse.org/reference/</a></li>
|
||
<li><a href="https://ggplot2-book.org/" class="uri">https://ggplot2-book.org/</a></li>
|
||
</ul>
|
||
<p>ggplot2的安装方法</p>
|
||
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1"></a><span class="co"># install.packages('ggplot2')</span></span></code></pre></div>
|
||
</div>
|
||
<div id="环境配置" class="section level2">
|
||
<h2>1.环境配置</h2>
|
||
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1"></a><span class="kw">library</span>(ggplot2) <span class="co">#画图工具ggplot2</span></span>
|
||
<span id="cb3-2"><a href="#cb3-2"></a><span class="kw">library</span>(ggpubr) <span class="co">#将多个图形拼接</span></span>
|
||
<span id="cb3-3"><a href="#cb3-3"></a><span class="kw">library</span>(plyr) <span class="co">#数据处理包</span></span></code></pre></div>
|
||
<p>在本讲中会用到ggpubr中的ggrrange这个多图拼接工具，详细使用方法参见： <a href="http://www.sthda.com/english/articles/24-ggpubr-publication-ready-plots/81-ggplot2-easy-way-to-mix-multiple-graphs-on-the-same-page/" class="uri">http://www.sthda.com/english/articles/24-ggpubr-publication-ready-plots/81-ggplot2-easy-way-to-mix-multiple-graphs-on-the-same-page/</a></p>
|
||
</div>
|
||
<div id="案例数据" class="section level2">
|
||
<h2>案例数据</h2>
|
||
<p>本节内容将会使用到两个数据集</p>
|
||
<p><strong>1.1h1n1流感问卷数据集</strong></p>
|
||
<p>h1n1流感问卷数据集是关于h1n1流感问卷调查的一个数据，属于外部数据 数据集包含26,707个受访者数据，共有32个特征+1个标签（是否接种h1n1疫苗）</p>
|
||
<p>读取相关的数据集</p>
|
||
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1"></a>h1n1_data <-<span class="st"> </span><span class="kw">read.csv</span>(<span class="st">"./datasets/h1n1_flu.csv"</span>, <span class="dt">header =</span> <span class="ot">TRUE</span>)</span></code></pre></div>
|
||
<p><strong>1.2波士顿房价数据集</strong></p>
|
||
<p>波士顿房价数据集属于R语言自带数据集，也可以通过外部读取</p>
|
||
<p>读取相关的数据集</p>
|
||
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1"></a>boston_data <-<span class="st"> </span><span class="kw">read.csv</span>(<span class="st">"./datasets/BostonHousing.csv"</span>, <span class="dt">header =</span> <span class="ot">TRUE</span>)</span></code></pre></div>
|
||
</div>
|
||
<div id="散点图" class="section level2">
|
||
<h2>2.散点图</h2>
|
||
<p>散点图是指在数理统计回归分析中，数据点在直角坐标系平面上的分布图，散点图表示因变量随自变量而变化的大致趋势，由此趋势可以选择合适的函数进行经验分布的拟合，进而找到变量之间的函数关系。</p>
|
||
<p>散点图的优势：</p>
|
||
<ul>
|
||
<li>数据用图表来展示，显然比较直观，在工作汇报等场合能起到事半功倍的效果，让听者更容易接受，理解你所处理的数据。</li>
|
||
<li>散点图更偏向于研究型图表，能让我们发现变量之间隐藏的关系为我们决策作出重要的引导作用。</li>
|
||
<li>散点图核心的价值在于发现变量之间的关系，包括线性与非线性之间的关系。</li>
|
||
</ul>
|
||
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1"></a><span class="co"># 读取数据</span></span>
|
||
<span id="cb6-2"><a href="#cb6-2"></a>boston_data <-<span class="st"> </span><span class="kw">read.csv</span>(<span class="st">"./datasets/BostonHousing.csv"</span>, <span class="dt">header =</span> <span class="ot">TRUE</span>)</span>
|
||
<span id="cb6-3"><a href="#cb6-3"></a><span class="co"># 绘制简单的散点图 x轴选择的是lstat ,y轴选择的是medv</span></span>
|
||
<span id="cb6-4"><a href="#cb6-4"></a><span class="kw">ggplot</span>(<span class="dt">data =</span> boston_data, <span class="kw">aes</span>(<span class="dt">x =</span> lstat, <span class="dt">y =</span> medv)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_point</span>()</span></code></pre></div>
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<p><img 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" /><!-- --></p>
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<p>上图选择的是lstat为x轴，medv为y轴绘制的散点图，x轴表示弱势群体人口所占比例，y轴表示房屋的平均价格，通过图上的数据可以看到，弱势人群的比例增加会影响房价，这2个变量呈现一定的负相关。</p>
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<p>ggplot2可以修改散点图的性状和大小,R语言中存储了一些相关的形状 <img 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" style="width:50.0%" /></p>
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<p>size参数修改点的大小，color参数修改点的颜色</p>
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<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1"></a><span class="co"># 使用第17号形状</span></span>
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<span id="cb7-2"><a href="#cb7-2"></a>p1 <-<span class="st"> </span><span class="kw">ggplot</span>(<span class="dt">data =</span> boston_data, <span class="kw">aes</span>(<span class="dt">x =</span> lstat, <span class="dt">y =</span> medv)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_point</span>(<span class="dt">shape =</span> <span class="dv">17</span>)</span>
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<span id="cb7-3"><a href="#cb7-3"></a><span class="co"># size参数修改点的大小，color参数修改点的颜色</span></span>
|
||
<span id="cb7-4"><a href="#cb7-4"></a>p2 <-<span class="st"> </span><span class="kw">ggplot</span>(<span class="dt">data =</span> boston_data, <span class="kw">aes</span>(<span class="dt">x =</span> lstat, <span class="dt">y =</span> medv)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_point</span>(<span class="dt">size =</span> <span class="dv">3</span>,</span>
|
||
<span id="cb7-5"><a href="#cb7-5"></a> <span class="dt">color =</span> <span class="st">"red"</span>)</span>
|
||
<span id="cb7-6"><a href="#cb7-6"></a><span class="kw">ggarrange</span>(p1, p2, <span class="dt">nrow =</span> <span class="dv">1</span>)</span></code></pre></div>
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||
<p><img src="data:image/png;base64,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" /><!-- --></p>
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<p>可将数据集的其它属性映射到散点图的颜色属性中</p>
|
||
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1"></a>p3 <-<span class="st"> </span><span class="kw">ggplot</span>(<span class="dt">data =</span> boston_data, <span class="kw">aes</span>(<span class="dt">x =</span> lstat, <span class="dt">y =</span> medv, <span class="dt">colour =</span> <span class="kw">factor</span>(rad))) <span class="op">+</span></span>
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<span id="cb8-2"><a href="#cb8-2"></a><span class="st"> </span><span class="kw">geom_point</span>()</span>
|
||
<span id="cb8-3"><a href="#cb8-3"></a>p4 <-<span class="st"> </span><span class="kw">ggplot</span>(<span class="dt">data =</span> boston_data, <span class="kw">aes</span>(<span class="dt">x =</span> lstat, <span class="dt">y =</span> medv, <span class="dt">colour =</span> rad)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_point</span>()</span>
|
||
<span id="cb8-4"><a href="#cb8-4"></a><span class="kw">ggarrange</span>(p3, p4, <span class="dt">nrow =</span> <span class="dv">1</span>)</span></code></pre></div>
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<p><img 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" /><!-- --></p>
|
||
<p>ggplot2关于散点图的相关做法有很详细的介绍，相关参考链接：<a href="https://ggplot2.tidyverse.org/reference/geom_point.html" class="uri">https://ggplot2.tidyverse.org/reference/geom_point.html</a></p>
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||
</div>
|
||
<div id="直方图" class="section level2">
|
||
<h2>3.直方图</h2>
|
||
<p>直方图是一种统计报告图，由一系列高度不等的纵向条纹或线段表示数据分布的情况。 一般用横轴表示数据类型，纵轴表示分布情况。 直方图可以很好的查看数据的分布情况，是常用的数据可视化展示图形。</p>
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||
<p>我们对rad变量进行直方图分析</p>
|
||
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1"></a><span class="kw">ggplot</span>(<span class="dt">data =</span> boston_data, <span class="kw">aes</span>(<span class="dt">x =</span> rad)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_histogram</span>()</span></code></pre></div>
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||
<p><img src="data:image/png;base64,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" /><!-- --></p>
|
||
<p>可以看到ggplot2可以自动对数据进行直方图的统计</p>
|
||
<p>我们给直方图填充颜色，同时改变直方图类型color表示直方图的边框，fill表示直方图中的填充颜色，ggplot2支持RGB颜色表的配色方案，linetype表示直方图线的类型</p>
|
||
<p>RGB颜色表可以参考：<a href="http://www.mgzxzs.com/sytool/se.htm" class="uri">http://www.mgzxzs.com/sytool/se.htm</a></p>
|
||
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1"></a>p5 <-<span class="st"> </span><span class="kw">ggplot</span>(<span class="dt">data =</span> boston_data, <span class="kw">aes</span>(<span class="dt">x =</span> rad)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_histogram</span>(<span class="dt">color =</span> <span class="st">"black"</span>,</span>
|
||
<span id="cb10-2"><a href="#cb10-2"></a> <span class="dt">fill =</span> <span class="st">"#69b3a2"</span>)</span>
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||
<span id="cb10-3"><a href="#cb10-3"></a>p6 <-<span class="st"> </span><span class="kw">ggplot</span>(<span class="dt">data =</span> boston_data, <span class="kw">aes</span>(<span class="dt">x =</span> rad)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_histogram</span>(<span class="dt">color =</span> <span class="st">"black"</span>,</span>
|
||
<span id="cb10-4"><a href="#cb10-4"></a> <span class="dt">fill =</span> <span class="st">"#69b3a2"</span>, <span class="dt">linetype =</span> <span class="st">"dashed"</span>)</span>
|
||
<span id="cb10-5"><a href="#cb10-5"></a><span class="kw">ggarrange</span>(p5, p6, <span class="dt">nrow =</span> <span class="dv">1</span>)</span></code></pre></div>
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<p><img src="data:image/png;base64,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" /><!-- --></p>
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<p>ggplot2也支持在直方图上添加平均线和密度图</p>
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<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1"></a>p7 <-<span class="st"> </span>p5 <span class="op">+</span><span class="st"> </span><span class="kw">geom_vline</span>(<span class="kw">aes</span>(<span class="dt">xintercept =</span> <span class="kw">mean</span>(rad)), <span class="dt">color =</span> <span class="st">"blue"</span>, <span class="dt">linetype =</span> <span class="st">"dashed"</span>,</span>
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<span id="cb11-2"><a href="#cb11-2"></a> <span class="dt">size =</span> <span class="dv">1</span>)</span>
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<span id="cb11-3"><a href="#cb11-3"></a>p8 <-<span class="st"> </span><span class="kw">ggplot</span>(<span class="dt">data =</span> boston_data, <span class="kw">aes</span>(<span class="dt">x =</span> rad)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_histogram</span>(<span class="dt">color =</span> <span class="st">"black"</span>,</span>
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<span id="cb11-4"><a href="#cb11-4"></a> <span class="dt">fill =</span> <span class="st">"#69b3a2"</span>, <span class="kw">aes</span>(<span class="dt">y =</span> ..density..)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_density</span>(<span class="dt">alpha =</span> <span class="fl">0.2</span>, <span class="dt">fill =</span> <span class="st">"#FF6666"</span>)</span>
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<span id="cb11-5"><a href="#cb11-5"></a><span class="kw">ggarrange</span>(p7, p8, <span class="dt">nrow =</span> <span class="dv">1</span>)</span></code></pre></div>
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||
<p><img src="data:image/png;base64,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" /><!-- --></p>
|
||
<p>ggplot2关于直方图的相关做法有很详细的介绍，相关参考链接：<a href="https://ggplot2.tidyverse.org/reference/geom_histogram.html" class="uri">https://ggplot2.tidyverse.org/reference/geom_histogram.html</a></p>
|
||
</div>
|
||
<div id="柱状图" class="section level2">
|
||
<h2>4.柱状图</h2>
|
||
<p>柱状图是一种常用的数据可视化图形，根据翻译的不同，柱状图又叫长条图、柱状统计图、条状图、棒形图 柱状图图用来比较两个或以上的价值（不同时间或者不同条件），只有一个变量，通常利用于较小的数据集分析。长条图亦可横向排列，或用多维方式表达。需要注意的是柱状图与直方图是不同的数据可视化方法，不要弄混淆了。</p>
|
||
<p>对h1n1数据集中填写人的受教育情况进行可视化展示,使用pylr包中的count对edcation进行计数统计</p>
|
||
<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1"></a>data <-<span class="st"> </span><span class="kw">count</span>(h1n1_data[<span class="st">"race"</span>])</span>
|
||
<span id="cb12-2"><a href="#cb12-2"></a>p <-<span class="st"> </span><span class="kw">ggplot</span>(data, <span class="kw">aes</span>(<span class="dt">x =</span> race, <span class="dt">y =</span> freq)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_bar</span>(<span class="dt">stat =</span> <span class="st">"identity"</span>)</span>
|
||
<span id="cb12-3"><a href="#cb12-3"></a><span class="co"># 也可以进行水平放置</span></span>
|
||
<span id="cb12-4"><a href="#cb12-4"></a>p1 <-<span class="st"> </span>p <span class="op">+</span><span class="st"> </span><span class="kw">coord_flip</span>()</span>
|
||
<span id="cb12-5"><a href="#cb12-5"></a><span class="kw">ggarrange</span>(p, p1)</span></code></pre></div>
|
||
<p><img src="data:image/png;base64,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" /><!-- --></p>
|
||
<p>可以看到左边的柱状图文字有点挡住了，我们把文字旋转45°</p>
|
||
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1"></a>data <-<span class="st"> </span><span class="kw">count</span>(h1n1_data[<span class="st">"race"</span>])</span>
|
||
<span id="cb13-2"><a href="#cb13-2"></a><span class="kw">ggplot</span>(data, <span class="kw">aes</span>(<span class="dt">x =</span> race, <span class="dt">y =</span> freq)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_bar</span>(<span class="dt">stat =</span> <span class="st">"identity"</span>) <span class="op">+</span><span class="st"> </span><span class="kw">theme</span>(<span class="dt">axis.text.x =</span> <span class="kw">element_text</span>(<span class="dt">angle =</span> <span class="dv">45</span>,</span>
|
||
<span id="cb13-3"><a href="#cb13-3"></a> <span class="dt">hjust =</span> <span class="dv">1</span>))</span></code></pre></div>
|
||
<p><img src="data:image/png;base64,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" /><!-- --></p>
|
||
<p>对柱状图的样式进行修改</p>
|
||
<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1"></a><span class="co"># 更改条的宽度和颜色： 更改条的宽度</span></span>
|
||
<span id="cb14-2"><a href="#cb14-2"></a>p2 <-<span class="st"> </span><span class="kw">ggplot</span>(data, <span class="kw">aes</span>(<span class="dt">x =</span> race, <span class="dt">y =</span> freq)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_bar</span>(<span class="dt">stat =</span> <span class="st">"identity"</span>, <span class="dt">width =</span> <span class="fl">0.5</span>) <span class="op">+</span></span>
|
||
<span id="cb14-3"><a href="#cb14-3"></a><span class="st"> </span><span class="kw">theme</span>(<span class="dt">axis.text.x =</span> <span class="kw">element_text</span>(<span class="dt">angle =</span> <span class="dv">45</span>, <span class="dt">hjust =</span> <span class="dv">1</span>))</span>
|
||
<span id="cb14-4"><a href="#cb14-4"></a><span class="co"># 改变颜色</span></span>
|
||
<span id="cb14-5"><a href="#cb14-5"></a>p3 <-<span class="st"> </span><span class="kw">ggplot</span>(data, <span class="kw">aes</span>(<span class="dt">x =</span> race, <span class="dt">y =</span> freq)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_bar</span>(<span class="dt">stat =</span> <span class="st">"identity"</span>, <span class="dt">color =</span> <span class="st">"blue"</span>,</span>
|
||
<span id="cb14-6"><a href="#cb14-6"></a> <span class="dt">fill =</span> <span class="st">"white"</span>) <span class="op">+</span><span class="st"> </span><span class="kw">theme</span>(<span class="dt">axis.text.x =</span> <span class="kw">element_text</span>(<span class="dt">angle =</span> <span class="dv">45</span>, <span class="dt">hjust =</span> <span class="dv">1</span>))</span>
|
||
<span id="cb14-7"><a href="#cb14-7"></a><span class="co"># 最小主题+蓝色填充颜色</span></span>
|
||
<span id="cb14-8"><a href="#cb14-8"></a>p4 <-<span class="st"> </span><span class="kw">ggplot</span>(data, <span class="kw">aes</span>(<span class="dt">x =</span> race, <span class="dt">y =</span> freq)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_bar</span>(<span class="dt">stat =</span> <span class="st">"identity"</span>, <span class="dt">fill =</span> <span class="st">"steelblue"</span>) <span class="op">+</span></span>
|
||
<span id="cb14-9"><a href="#cb14-9"></a><span class="st"> </span><span class="kw">theme_minimal</span>() <span class="op">+</span><span class="st"> </span><span class="kw">theme</span>(<span class="dt">axis.text.x =</span> <span class="kw">element_text</span>(<span class="dt">angle =</span> <span class="dv">45</span>, <span class="dt">hjust =</span> <span class="dv">1</span>))</span>
|
||
<span id="cb14-10"><a href="#cb14-10"></a><span class="co"># 选择要显示的项目</span></span>
|
||
<span id="cb14-11"><a href="#cb14-11"></a>p5 <-<span class="st"> </span>p <span class="op">+</span><span class="st"> </span><span class="kw">scale_x_discrete</span>(<span class="dt">limits =</span> <span class="kw">c</span>(<span class="st">"White"</span>, <span class="st">"Black"</span>)) <span class="op">+</span><span class="st"> </span><span class="kw">theme</span>(<span class="dt">axis.text.x =</span> <span class="kw">element_text</span>(<span class="dt">angle =</span> <span class="dv">45</span>,</span>
|
||
<span id="cb14-12"><a href="#cb14-12"></a> <span class="dt">hjust =</span> <span class="dv">1</span>))</span>
|
||
<span id="cb14-13"><a href="#cb14-13"></a><span class="kw">ggarrange</span>(p2, p3, p4, p5)</span></code></pre></div>
|
||
<p><img src="data:image/png;base64,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" /><!-- --></p>
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||
<p>对柱状图进行标签显示</p>
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||
<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1"></a>p6 <-<span class="st"> </span><span class="kw">ggplot</span>(<span class="dt">data =</span> data, <span class="kw">aes</span>(<span class="dt">x =</span> race, <span class="dt">y =</span> freq)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_bar</span>(<span class="dt">stat =</span> <span class="st">"identity"</span>,</span>
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||
<span id="cb15-2"><a href="#cb15-2"></a> <span class="dt">fill =</span> <span class="st">"steelblue"</span>) <span class="op">+</span><span class="st"> </span><span class="kw">geom_text</span>(<span class="kw">aes</span>(<span class="dt">label =</span> freq), <span class="dt">vjust =</span> <span class="fl">-0.3</span>, <span class="dt">size =</span> <span class="fl">3.5</span>) <span class="op">+</span></span>
|
||
<span id="cb15-3"><a href="#cb15-3"></a><span class="st"> </span><span class="kw">theme_minimal</span>() <span class="op">+</span><span class="st"> </span><span class="kw">theme</span>(<span class="dt">axis.text.x =</span> <span class="kw">element_text</span>(<span class="dt">angle =</span> <span class="dv">45</span>, <span class="dt">hjust =</span> <span class="dv">1</span>))</span>
|
||
<span id="cb15-4"><a href="#cb15-4"></a><span class="co"># 条形内部标签</span></span>
|
||
<span id="cb15-5"><a href="#cb15-5"></a>p7 <-<span class="st"> </span><span class="kw">ggplot</span>(<span class="dt">data =</span> data, <span class="kw">aes</span>(<span class="dt">x =</span> race, <span class="dt">y =</span> freq)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_bar</span>(<span class="dt">stat =</span> <span class="st">"identity"</span>,</span>
|
||
<span id="cb15-6"><a href="#cb15-6"></a> <span class="dt">fill =</span> <span class="st">"steelblue"</span>) <span class="op">+</span><span class="st"> </span><span class="kw">geom_text</span>(<span class="kw">aes</span>(<span class="dt">label =</span> freq), <span class="dt">vjust =</span> <span class="fl">1.6</span>, <span class="dt">color =</span> <span class="st">"white"</span>,</span>
|
||
<span id="cb15-7"><a href="#cb15-7"></a> <span class="dt">size =</span> <span class="fl">3.5</span>) <span class="op">+</span><span class="st"> </span><span class="kw">theme_minimal</span>() <span class="op">+</span><span class="st"> </span><span class="kw">theme</span>(<span class="dt">axis.text.x =</span> <span class="kw">element_text</span>(<span class="dt">angle =</span> <span class="dv">45</span>,</span>
|
||
<span id="cb15-8"><a href="#cb15-8"></a> <span class="dt">hjust =</span> <span class="dv">1</span>))</span>
|
||
<span id="cb15-9"><a href="#cb15-9"></a><span class="kw">ggarrange</span>(p6, p7, <span class="dt">nrow =</span> <span class="dv">1</span>)</span></code></pre></div>
|
||
<p><img src="data:image/png;base64,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" /><!-- --></p>
|
||
<p>如果觉得柱状图的顺序不是你想要的，可以对柱状图的顺序进行修改</p>
|
||
<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1"></a>data <-<span class="st"> </span><span class="kw">within</span>(data, {</span>
|
||
<span id="cb16-2"><a href="#cb16-2"></a> race <-<span class="st"> </span><span class="kw">factor</span>(race, <span class="dt">levels =</span> <span class="kw">c</span>(<span class="st">"White"</span>, <span class="st">"Black"</span>, <span class="st">"Hispanic"</span>, <span class="st">"Other or Multiple"</span>))</span>
|
||
<span id="cb16-3"><a href="#cb16-3"></a>})</span>
|
||
<span id="cb16-4"><a href="#cb16-4"></a><span class="kw">ggplot</span>(data, <span class="kw">aes</span>(<span class="dt">x =</span> race, <span class="dt">y =</span> freq)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_bar</span>(<span class="dt">stat =</span> <span class="st">"identity"</span>, <span class="dt">fill =</span> <span class="st">"steelblue"</span>) <span class="op">+</span></span>
|
||
<span id="cb16-5"><a href="#cb16-5"></a><span class="st"> </span><span class="kw">theme</span>(<span class="dt">axis.text.x =</span> <span class="kw">element_text</span>(<span class="dt">angle =</span> <span class="dv">45</span>, <span class="dt">hjust =</span> <span class="dv">1</span>))</span></code></pre></div>
|
||
<p><img src="data:image/png;base64,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" /><!-- --></p>
|
||
<p>ggplot2关于柱状图的相关做法有很详细的介绍，相关参考链接： <a href="https://ggplot2.tidyverse.org/reference/geom_bar.html" class="uri">https://ggplot2.tidyverse.org/reference/geom_bar.html</a></p>
|
||
</div>
|
||
<div id="饼状图" class="section level2">
|
||
<h2>5.饼状图</h2>
|
||
<p>饼状图作为常用的数据可视化图形之一，广泛的使用在各个领域，能够很清楚展示数据的所占的百分比。 ggplot2并没有类似于geom_pie()这样的函数实现饼图的绘制，但ggplot2有一个理念，就是通过极坐标变换绘制饼图</p>
|
||
<p>饼图在ggplot2中就是通过极坐标变换获得，在绘制饼图之前需要绘制堆叠的条形图，通过将条形图进行极坐标变换后，就能实现饼图绘制了。</p>
|
||
<p>对h1n1问卷表中race数据进行数据展示</p>
|
||
<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1"></a>data <-<span class="st"> </span><span class="kw">count</span>(h1n1_data[<span class="st">"race"</span>])</span>
|
||
<span id="cb17-2"><a href="#cb17-2"></a><span class="kw">ggplot</span>(<span class="dt">data =</span> data, <span class="kw">aes</span>(<span class="dt">x =</span> <span class="st">""</span>, <span class="dt">y =</span> freq, <span class="dt">fill =</span> race)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_bar</span>(<span class="dt">stat =</span> <span class="st">"identity"</span>)</span></code></pre></div>
|
||
<p><img src="data:image/png;base64,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" /><!-- --></p>
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<p>堆叠的条形图绘制完后，接下来就需要进行极坐标变换了，ggplot2中coord_polar()函数可以非常方便的实现极坐标变换。</p>
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<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1"></a><span class="kw">ggplot</span>(<span class="dt">data =</span> data, <span class="kw">aes</span>(<span class="dt">x =</span> <span class="st">""</span>, <span class="dt">y =</span> freq, <span class="dt">fill =</span> race)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_bar</span>(<span class="dt">stat =</span> <span class="st">"identity"</span>) <span class="op">+</span></span>
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<span id="cb18-2"><a href="#cb18-2"></a><span class="st"> </span><span class="kw">coord_polar</span>(<span class="dt">theta =</span> <span class="st">"y"</span>)</span></code></pre></div>
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<p><img src="data:image/png;base64,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" /><!-- --></p>
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<p>看起来像饼图了，但是饼图周围还有多余的数字，如何清除呢？ 这里的标签其实就是坐标轴的标签，可以通过labs()函数将其清除。</p>
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<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1"></a><span class="kw">ggplot</span>(<span class="dt">data =</span> data, <span class="kw">aes</span>(<span class="dt">x =</span> <span class="st">""</span>, <span class="dt">y =</span> freq, <span class="dt">fill =</span> race)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_bar</span>(<span class="dt">stat =</span> <span class="st">"identity"</span>) <span class="op">+</span></span>
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<span id="cb19-2"><a href="#cb19-2"></a><span class="st"> </span><span class="kw">coord_polar</span>(<span class="dt">theta =</span> <span class="st">"y"</span>) <span class="op">+</span><span class="st"> </span><span class="kw">labs</span>(<span class="dt">x =</span> <span class="st">""</span>, <span class="dt">y =</span> <span class="st">""</span>, <span class="dt">title =</span> <span class="st">""</span>) <span class="op">+</span><span class="st"> </span><span class="kw">theme</span>(<span class="dt">axis.text =</span> <span class="kw">element_blank</span>())</span></code></pre></div>
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||
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAMAAABNUi8GAAAAulBMVEUAAAAAADoAAGYAOmYAOpAAZrYAv8QzMzM6AAA6ADo6AGY6OgA6OmY6OpA6ZpA6ZrY6kJA6kNtmAABmADpmAGZmOgBmOjpmOpBmZgBmZjpmZmZmZrZmtv98rgCQOgCQOjqQOmaQZgCQZpCQkDqQkGaQtpCQ27aQ2/+2ZgC2Zjq2tma225C2/7a2/9u2///HfP/bkDrbkGbb/7bb/9vb///r6+vy8vL4dm3/tmb/25D/29v//7b//9v///8m5IK6AAAACXBIWXMAAA7DAAAOwwHHb6hkAAAYFklEQVR4nO3dC38T23nFYUPAh9DQExOSNKmBtGlqTk9juQ0kdo2+/9eqZkaydZnLvqy91/turf8vOWCsy8w7j0cWaMsXa6UMd8HeAKXmElBlOgFVphNQZToBVaYTUGU6AVWmE1BlOgFVphNQZToBVaYTUGU6AVWmE1BlOgFVphNQZToBVaYTUGU6AVWmE1BlOgFVphNQZToBVaYTUGU6AVWmE1BlOgFVphNQZToBVaYTUGU6AVWmywX6TRkPwoSXgLYehAkvAW09CBNeAtp6ECa8BLT1IEx4CWjrQZjwEtDWgzDhJaCtB2HCS0BbD8KEl4C2HoQJLwFtPQgTXgLaehAmvAS09SBMeAlo60GY8BLQ1oMw4SWgrQdhwktAWw/ChJeAth6ECS8BbT0IE14C2noQJrwEtPUgTHgJ6PwRZG9cfpkHmN1ZAx3bheMP3e9l5gFmd45AZzd+/A8d727mAWZ3TkCDtnnhs/52O/MAszsXoMHbGngpP7ueTYTbOQCN2s6IS/rY/Wwi3JoHGruNkZe2P4J8I9SaBpqygfF7ZHwM2US4NQs0dePS9sjwKLKJcGsTaMaGpe+R0WkAkDBrEGjeVmXtkcWBAJAwaw1o9ibl7pG5kSCUEGsKKGJ7ALdgayoAJMzaAQraGMyNGBoMAAmzVoDCtgR2O1ZGA2HCqw2gwA3B7ZGR4SCUEGsBaLcVBoEaIQphwss/0O02WHuI394af0AQJrzcA10f/Yq6PVT0EUGY8HIOdG8DQJuC3yPykCBMeLkGenD3ZoGSiUKY8HIMdGE1UUT/2Gu1V+YG7m8bb1AQJrzcAj2966Rt+cdxq/EKbG+lIEx4OQU6dseR23JCcxYogClpWBAmvHwCHb/b0I2ZoBkANFMpZVoQJrw8Ap261+WNmaUZCjTDKGNeECa8/AGduc+ZrQmgGQE03Wj9iUGY8HIHdO4epz4XrDMCaLLS2iODMOHlDOj8HY5+MkZnLNAko5WHBmHCyxXQxbs7+nykzSSgKUarjg3ChJcnoMt3tn+JFJ1pQBOUVpwbhAkvP0CD7mt3mUSdGUAjjdabHIQJLzdAo95ZKd1nDtBIotEjSAvChJcToMF31F0ug2cm0Aii61pCIUx4+QAa895fWTyzgQYTXdd6mIcw4eUCaPi9ZOpEAA0jil0GMHtPvnMANPhO8nVigAYYfVoHUH6AECa87AOtyhMFdIkofCHAzF35zjzQsHsA6QQCnTVaYCXA9H35zjjQwDvA+UQCnSR6uFOFhwhhwss20Oo8wUAniB7vVtEpQpjwMg006NahPOFAx4ie7lbJMUKY8DIMNOjGP378aBzoidCx3So4SAgTXnaBBvK0D/SYaN56leggTHiZBRpwyx+3mQd6QHRqx0qNEsKEl1WgET49AH0WmrRgJScIE15Ggcb4dAH0iejMnpUZJoQJL5tAl2/2434ugA5E45es5AZhwssi0IBb/fixlNCCQFeLBEvME8KEl0Gg0TzdAF2tFvetxJvruc4e0MWbPOEJFVoW6PJL8eAThTDhZQ5okk8/QOsLhTDhZQ1omk+g0NJAw18rCgrChJcxoKk+cULLA10kih0qhAkvW0CXbm6Spy+gVYVCmPAyBTTDJ0xoFaA1hUKY8DIEdPHGZn36AlpRKIQJLztA83jChFYCWk8ohAkvM0CzfYKE1gJaTSiECS8rQAE+nQENX/iZOVnfGQGK8IkRWhFoHaEQJrxsAF24mTCe/oBWEQphwssEUJBPiNCqQGsIhTDhZQEozKc/oAtEEeOFMOFlAOj8bUTwdAkU+PMWJ8brOwNAZ4vyiRBaHeisUMB8IUx48YHO3kSkT5dACwuFMOFFBwr16RNoWaEQJrzYQLE+AUIZQIsKhTDhRQYKfIIEEkoBWlIohAkvLlC8T6dACwqFMOFlGGiSz2yhJKDlhEKY8KICLeHTK9A5oQKaXtbo5j6Z6jNXKA1oKaEQJryIQAv5zBTKA1pIKIQJL6NAc3y6BVpGKIQJLx7QYj7zhDKBFhEKYcKLBrSgT79AA39yTdygfWcRaK7PLKFWgSYLhTDhxQJa1GeOUC7QAkIhTHiRgBb26RcoXiiECS9zQCE+M4SygcK/DYUw4cUBWvoE2ibQNKEQJrysAQX5TBfK9gk/hUKY8KIAreDTMVCwUAgTXgygNXwmC2Xr7EoZ3fRVfEcBOnU1pE/PQKeFCmhs8QPrhzx+vctLAR2aHV7kuH1HAjr6ZqCXXWyhbJrbZocXN27f1Qe6Pvr1qctLC0LZMrctji983r6rDvT5GkdXvrxEC/UMFCcUwoQXEejRrC9tCGXD3BU0wKCB+6420MMr7F3/8hIv1DNQmFAIE15coHsfXxoRynb5XOgIlybuu8pATy+/vYnLo2hC2Sz3Ch+igE6W6/PbQPTYJ4qoa6AYoRAmvAwA7f54BChJKBvlfgJaGejkpcd8Yoi6BgoRCmHCywTQCZ8UoWyThwloTaDRPhlC2SSPyhcKYcKrItAEnwihAuo660ABRF0DzRcKYcKrHtBEn/lCfQOdECqggVUAWlco2+NJkeMU0L1++OGHCj6zifoGmnsKRUkhVe0MOnHJIJ81hbI5nhY3UAE9LNNnKNA8ob6BZp5CIUx4eQGaJbRJoKFCIUx4VQKa7TOPqG+geadQCBNejoDWEcrGOFbkUAV0r4o+c4QKqNdcAc0g6hpo1mM8hAmvKkBhPtOF+gaaIxTChJc3oMWFsimOJ6Cp5QBN8VlcKJviROlCIUx41QAK9ZlMVEBd5hFoUaFsiVNN4BPQhdJ9ZgBNE9oc0M30BXSpdKAZPhOJugZ6LHQ7++UDgFBCzCvQYkLZDic7Ujc/XAHdxfKZJLQVoPtjXzwCECa8/AItJJTtcLpncAHjFdBtqUDzfaYQ9Q/0ZOICOl+iTwzQEkLZDKeb+OkTS4cAoYQYCSjGZ7xQz0BX46MU0NnSgKJ8xhP1DDRiwAK6K8knEmik0PaALgmFMOHlH2icUM9A006hECa8CgOt4BMslI1wrqghC2ifBaBxRB0DTTqFQpjwagNoDFEBdVVZoNV84oSyDc4WNWYB7TIDNFyoY6App1AIE17tAAUJZROcT0Bjs+MTJJRNcCEBjcwS0GChrQGdFQphwqs+0HI+w4kKqJsaAxoo1C/Q+Mf48eP28PbPFy9vH95cXFxcrdffP11cvF4Pv768zSSBrSTQ2o/wEKFsgEuhgL7ZgHx8f71e3728/f7pdf/77tf13auvmSagNQc0Vygb4FIxo54FurH5fx3Fh1/ebP7X/+F9d/bs1dqpPaBhRN0CjT6Fjh+3rcn7zUP8i5v77cP63UXfVaYJaC0CzRLK9rcYEOjj+xc33e+egNp6dO8rCJTmM0iogG7Onx3I+xfPD/EvbjI54KsOtB+yBaJegcYKHT9uA9DuBPrmxU335Gj4/0asMaUcoKvyRgV0Gej6y+Y70P/sn74//zWTLZ/Vge5Pmiv0vIG6qRzQ+RPoNntCKeaiihm3gEYCPR03j6iAeogOdIgh1CvQyMd4CBNeRoCuChmNF1qNWXoCGlGczzmgBKItAZ0UCmHCyxLQoYpCnQKNO4VCmPCyB3SFNhontCwtTAIaXhzQ8GNQR+j5Av37eJkaClQKaM4JdFsNoj6BRgkdP25/H32GKKBxMYQWAYVOQIMrCnSoqFABFdBcoCuM0UChSEfFEtDgooBmHJJiQgVUQBFAhyoIBfCpkICGVhfoKs9oiFDENpZPQEOL8QkBiicqoAIKBTpUSihyG8sVMfTx47YAtH9Hh4vr9cPbw7dx+P659uvt3QJdJRoV0L4loB3MbsX8WQHFHycQUQEdBbrR2P3anU2vuzXKFy9uNn/Ur6yrl2+gqySjAhp4Bn33dfPr429uug+/f7pe37/62+ebL3Xf18E90FWC0TmhpTYSW3Gg/fegr75uH+IfP9wOv/v++Z8qv+9IC0D7soQK6OhD/Kfr/tcv3VvebU6n6+6Pfv37uu8+0gzQVZxRAV0Guv7SAe3eTGzzy+4MenOnh/iMUokK6AjQzXefW5mb5/Pd96APb//6+abyE/mKQKsdvSyh1bYyq+JA9/4e9O7i4he/vX56Fn/yd6NlaxHoKtCoY6AR/5Q0ftz0L0lcoKsgo2NCK29lagIaWLjP2kBXAUYFVECZQFeLRk+FUrYyvvCxjx83ATUCtC+M6HkBPe9VncaArmaNCqiA8oGuZoweCGVvZWDZQE/+ubdLQOnNCRVQAbXQvFD21gUmoIE5BLoaMfoslL1pgQloYOFA2Yf0qFGhAmquswU6TlRArXXGQIcOiQqotc4e6GrPaCeUvTGBCWhg4YNiH9K5noUKaN/wkrqn1ylPVv6ldwL6XC+UvRGB1QKaySM/AT3oUkD7Ds6g992apPXjhz/3PyZxWIX88O7fdi9o7l/JnMloOgH1WUWgjx9u13evNw5ffb1/uVuF/PDmqvthyU/rkYutpCsDdCz2IW2r0kD7JR/dYs5hYfy6e+OG690bi2xXIQ9+C38foDOoz0oD3X+I32jdPIb3Tr9c71YhPwN9V3QdsoD6rCbQTZvH8A7o5gy6twpZZ1A1WUWg3TeYHdD3r7ffde7eVWz49LAeuRjSekAlFFlFoMNj+uYh/g/9s/VhFfIz0GaexQsossJAR9o+V6qdgPpMQAMTUE71gZISUJ8JaGCjg9LT+OJlA9WqTgEtmYAGJqCcsoGOLm8VUAVKQAMTUE4CGpiAchLQwASUk4AGFgFUQoEJaGACyil86uPHTUAFtGgCGpiAcioO9K7/KR8BK4+7Ht683vz3y8vdBY9fypzxelEBdVnE1MeP2xLQu1df14/vr0KB/vKfNxf/3dtDoHuftwZUT+MLVxpot5Kzc/Xf7y9e/vz2X3Y/7nhzinz41W/7E+XBRw9v/3Czfvjj3kqQ4Yq327XK29c1v0xgKqAuKw10WEc8/LDO7Qrj9ZerbvXxw5vr/hJfhmXHw0cPb//rev0/fzlaqtT/HMV+rXL/uvz+6tEJqMvCfSYCHSj1P6xzi657wfLTTz3uT7HDj0rsPnp4+/OP3//08xjQ6+2Pnd9ePRqYgLqsDtDtGXQL9P3madOLZ5K3W3nDR3/99//91+PFnrs19dufSdtfPRqYgLqsNNDd96C3e0A/PMlcn55Bb3/6j6sJoNszaMLZs0tAPRY+8+Rn8d06zudn8dtvIrfLjrsOPuoWJ7/YPZDfvdx/iH9aqzxcIRpYTaASiqo80O4dw7qn7t8/dU/G994kbAd0+yz+CehW8t3Fxa8/DMvl+2fxw1rljNXJAuqxCkAx5S8FFVCPRfgU0Aih7OPaTG6A5iegDhsdvICOFgVUQjE9ucsAet6rOgW0aIf4BHQuASU0IjAa6OgNnz1QCYU0ynDySIx29kB1Ci1YxBlBQAW0ehHzFtBIoBIKSEAjEtD6CWhEk0D1GF8sAY1IQOsX5VNABbRyEdNOBtr9wK777uV2968PXlGXqSWh2kAlNLsaQO+uNv//Y/efK8jq9vTKAdUptFA1gD68+/r981+6/9w8DKuOh5XEt4mLh9MTUG/FDDsZ6OOH28ff/+1P3Uqi559rnLF4OL3qQCU0sypAuzPnj91yzB/XB+vgUhcPpyeg3or0mfgs/u767np9f3V3dQQ0cfFwegWB6jG+RFEng3Sg969/ulk//PjTzRHQqmfPrvpAJTSrSkAff/fu6+aB/le3h0BTFw+nJ6DOqgT0+6f+DRVfr/eAfv/UP4uv+ghfFKge4/FF+9S/JAlozQQ0MgGtWtSgBXS9AFRC0cX7FFABrRgO6Lmv6pwbHfso+y3B5wRQNzGASmhqAhqdgNZMQKNbAKrHeGQpPgU0AaiEpiWg8QloxQQ0viWgEooryaeACmitBDQhAa1W1IAFdNsiUAlFleZTQNOASmh0ApqSgNYqbrwCum0ZqIRiSvQpoAJapbjhCuiudKASGpWAphUAVKdQRKk+BTQZqIRGFDlaAd0loHVK9img6ULZB91RcYMV0OdygEpocOk+BVRCyxc5VgF9TkBrlOFTQCW0eJFDFdC9BLR8WT4FVEJLJ6AZ5QKV0MXyfApoplD24befgOYUDFRCE8v0KaACWrTYcQroYeFAJTSpXJ8Cmg1UQmeKHqaAHhYBVKfQhLJ9Cmg+UAmdLHqUAnpUDFCdQmMD+BRQAFAJnUhA6wKV0Liixzh6Yd8JqOGixzh6Yd/VBTo12rWEjhQ5xKlL+64y0PHhrnUKHSlqhDMT950BoMONsDnYK2KEsxP3XW2gp+Pd/QHbg7WCB7g0cN+xge7dAluErUIHuDxw31UHejjg/Q/YJEwVNr6gefuuPtC9ER9dnY3CUsvDCx6375hAT67MVmGn5eGFj9t3BKDbIY9cl83CTAuji5u272hAR6/JhmGkhdFFTtt3DKCbMU9ckS3DRnODSxi27yhA9W/ys8WPbXbWvjMGVELRJ1ABTUpCJ0sZ2uyofUcCKqFTpYxsftK+swf0zIWmTGx+0r5jAZXQ8ZIGNj9o39GASuhYSeNamLPveEAl9LSkYS2N2XcCaqi0YS2N2XdEoBJ6VNqoFqfsOyZQCT0obVDLQ/YdFaiE7pU2poAZ+84s0HMTmjilgBn7jgtUQnfNTCL3JOA7MlAJHVqYRNaEfccGKqFdc5PIfpDynWmg5yJ0bhT5j1G+owNdOAJsOzWaGwXgDOA7PtCzFzo3CsQJwHcGgJ650LlRQL7+fWcB6FkLnRsF5svfdyaAnrHQuVGAvvp9ZwPoQmxF5RoDdfKbvCBMeBkBunA7bEeFSppF9Gh9ZwTo4lFhWyrR/CxgX/u+swL0DIXOzwL3pe87M0DPTuj8LIBf+b6zA/S8hGbOIiIIE16GgC4fFbYqXLmTiAjChJcloOcjNHcOMUGY8DIF9EyEZk8hKggTXraATr1v6F5sXfktzgAzyqeb850xoCGHhw0sL8AA4oIw4WUOaNtCEbsfGYQJL3tAQw4R21lqkJ2PDMKEl0Gg7QqF7HpsECa8LAJtVShkx6ODMOFlEmiTQjG7HR+ECS+bQIMOFVtcVKidjg/ChJdRoAF/IfrND1HcHicEYcLLKtDA8wmbXlC43U0JwoSXXaCBh4ytbzHkzqYEYcLLMNAmhEJ3NSkIE16WgYYeNjbCmbA7mhSECS/TQIOfOLAdTgTey7QgTHjZBur6JArexdQgTHhZBxp++Ngejwrev9IjhDDhZR5ozBFko3wqYu8SJhIXhAkv+0CjDiJb5lCRXUsNwoSXB6BRD4NsnFE8a4wPwoSXC6CeTqJldiojCBNeToCGn2y6y/ngWWl2ECa8vAANPqDDpazrrMZTQCsWdGd7F7JJM3xXMEGY8PIENOS0c3QJozxrjg3ChJcroAGH9vTz1nTW5SmgtZu/x/HPGtJZ9dF9uD/fuQM6fwKa+pwVnbVPn98ElND0vdZ6J3H8lpcLwoSXR6DTEMsvZAq8k/TNgwdhwssn0KlTUdHXlcTfSdA2lw7ChJdToOOHO3prImkm3svk9lYJwoSXW6Bjh7zSP26nXIf5lew6x0BPDrvBV65sr8D9Onada6BHh94oUPpXseucAz04/CaB0kcEYcLLPdBnAvVeHxRxUf6AIEx4NQC0Pwbfav4tY+A92RgPAAmzJoB+q4wh7HV/RmaDUEKsFaBVQQTck53JQJjwagdoRRSL92NpLhAmvFoCWg3Gwr3YmgqECa+2gNb6XnT6PuzNJN8ItdaAfqtiZOKlKibnkU2EW4NAuwpv2ult251FNhFujQLtKrh1Rzdseg4AJMwaBvqt3Hlt70bNDyGbCLe2gXYV2cp1uZtGl02EW/tAu+BbuvaBsyubCLfzANoF3FpXO55NhNv5AO0CbLG7nc4mwu28gHYlbrnbHc48wOzOD+iugJ1oYkczDzC78wW611nunJMEtPUgTHgJaOtBmPAS0NaDMOEloK0HYcJLQFsPwoSXgLYehAkvAW09CBNeAtp6ECa8BLT1IEx4CWjrQZjwEtDWgzDhJaCtB2HCS0BbD8KEl4C2HoQJLwFtPQgTXgLaehAmvAS09SBMeAlo60GY8BLQ1oMw4SWgrQdhwisXqFJFE1BlOgFVphNQZToBVaYTUGU6AVWmE1BlOgFVphNQZToBVaYTUGU6AVWmE1BlOgFVphNQZToBVaYTUGU6AVWmE1BlOgFVphNQZToBVaYTUGU6AVWmE1BlOgFVphNQZToBVaYTUGW6/wcp5qmZKfY72QAAAABJRU5ErkJggg==" /><!-- --></p>
|
||
<p>接下来就是显示各个所占的比例 第一种方法，将百分比直接显示在图例中，这种方式适合分类较多的情况。</p>
|
||
<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1"></a>label_value <-<span class="st"> </span><span class="kw">paste</span>(<span class="st">"("</span>, <span class="kw">round</span>(data<span class="op">$</span>freq<span class="op">/</span><span class="kw">sum</span>(data<span class="op">$</span>freq) <span class="op">*</span><span class="st"> </span><span class="dv">100</span>, <span class="dv">1</span>), <span class="st">"%)"</span>, <span class="dt">sep =</span> <span class="st">""</span>)</span>
|
||
<span id="cb20-2"><a href="#cb20-2"></a>label_value</span>
|
||
<span id="cb20-3"><a href="#cb20-3"></a><span class="co">## [1] "(7.9%)" "(6.6%)" "(6%)" "(79.5%)"</span></span></code></pre></div>
|
||
<p>将计算的百分比和race匹配</p>
|
||
<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1"></a>label <-<span class="st"> </span><span class="kw">paste</span>(data<span class="op">$</span>race, label_value, <span class="dt">sep =</span> <span class="st">""</span>)</span>
|
||
<span id="cb21-2"><a href="#cb21-2"></a>label</span>
|
||
<span id="cb21-3"><a href="#cb21-3"></a><span class="co">## [1] "Black(7.9%)" "Hispanic(6.6%)" "Other or Multiple(6%)"</span></span>
|
||
<span id="cb21-4"><a href="#cb21-4"></a><span class="co">## [4] "White(79.5%)"</span></span></code></pre></div>
|
||
<p>接下来就是将这些百分比标签放到图例中</p>
|
||
<div class="sourceCode" id="cb22"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1"></a><span class="kw">ggplot</span>(<span class="dt">data =</span> data, <span class="kw">aes</span>(<span class="dt">x =</span> <span class="st">""</span>, <span class="dt">y =</span> freq, <span class="dt">fill =</span> race)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_bar</span>(<span class="dt">stat =</span> <span class="st">"identity"</span>) <span class="op">+</span></span>
|
||
<span id="cb22-2"><a href="#cb22-2"></a><span class="st"> </span><span class="kw">coord_polar</span>(<span class="dt">theta =</span> <span class="st">"y"</span>) <span class="op">+</span><span class="st"> </span><span class="kw">labs</span>(<span class="dt">x =</span> <span class="st">""</span>, <span class="dt">y =</span> <span class="st">""</span>, <span class="dt">title =</span> <span class="st">""</span>) <span class="op">+</span><span class="st"> </span><span class="kw">theme</span>(<span class="dt">axis.text =</span> <span class="kw">element_blank</span>()) <span class="op">+</span></span>
|
||
<span id="cb22-3"><a href="#cb22-3"></a><span class="st"> </span><span class="kw">scale_fill_discrete</span>(<span class="dt">labels =</span> label)</span></code></pre></div>
|
||
<p><img src="data:image/png;base64,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" /><!-- --></p>
|
||
<p>看起来就很不错~</p>
|
||
<p>第二种方法，直接将百分比放到各自的饼区中。</p>
|
||
<p>首先是去掉饼图中的图例</p>
|
||
<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1"></a><span class="kw">ggplot</span>(<span class="dt">data =</span> data, <span class="kw">aes</span>(<span class="dt">x =</span> <span class="st">""</span>, <span class="dt">y =</span> freq, <span class="dt">fill =</span> race)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_bar</span>(<span class="dt">stat =</span> <span class="st">"identity"</span>) <span class="op">+</span></span>
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||
<span id="cb23-2"><a href="#cb23-2"></a><span class="st"> </span><span class="kw">coord_polar</span>(<span class="dt">theta =</span> <span class="st">"y"</span>) <span class="op">+</span><span class="st"> </span><span class="kw">labs</span>(<span class="dt">x =</span> <span class="st">""</span>, <span class="dt">y =</span> <span class="st">""</span>, <span class="dt">title =</span> <span class="st">""</span>) <span class="op">+</span><span class="st"> </span><span class="kw">theme</span>(<span class="dt">axis.text =</span> <span class="kw">element_blank</span>()) <span class="op">+</span></span>
|
||
<span id="cb23-3"><a href="#cb23-3"></a><span class="st"> </span><span class="kw">theme</span>(<span class="dt">legend.position =</span> <span class="st">"none"</span>)</span></code></pre></div>
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||
<p><img src="data:image/png;base64,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" /><!-- --></p>
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<p>将标签放置在饼图中</p>
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<div class="sourceCode" id="cb24"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1"></a><span class="kw">ggplot</span>(<span class="dt">data =</span> data, <span class="kw">aes</span>(<span class="dt">x =</span> <span class="st">""</span>, <span class="dt">y =</span> freq, <span class="dt">fill =</span> race)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_bar</span>(<span class="dt">stat =</span> <span class="st">"identity"</span>,</span>
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||
<span id="cb24-2"><a href="#cb24-2"></a> <span class="dt">width =</span> <span class="dv">1</span>) <span class="op">+</span><span class="st"> </span><span class="kw">coord_polar</span>(<span class="dt">theta =</span> <span class="st">"y"</span>) <span class="op">+</span><span class="st"> </span><span class="kw">labs</span>(<span class="dt">x =</span> <span class="st">""</span>, <span class="dt">y =</span> <span class="st">""</span>, <span class="dt">title =</span> <span class="st">""</span>) <span class="op">+</span><span class="st"> </span><span class="kw">theme</span>(<span class="dt">axis.text =</span> <span class="kw">element_blank</span>(),</span>
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||
<span id="cb24-3"><a href="#cb24-3"></a> <span class="dt">legend.position =</span> <span class="st">"none"</span>) <span class="op">+</span><span class="st"> </span><span class="kw">geom_text</span>(<span class="kw">aes</span>(<span class="dt">label =</span> label), <span class="dt">size =</span> <span class="dv">3</span>, <span class="dt">position =</span> <span class="kw">position_stack</span>(<span class="dt">vjust =</span> <span class="fl">0.5</span>))</span></code></pre></div>
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||
<p><img src="data:image/png;base64,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" /><!-- --></p>
|
||
</div>
|
||
<div id="折线图" class="section level2">
|
||
<h2>6.折线图</h2>
|
||
<p>折线图作为反映数据变化的趋势是常用的数据可视化图形之一，在ggplot2中通过geom_line()这个函数进行绘制。</p>
|
||
<p>对波士顿房价中rad进行可视化展示，使用pylr包中的count对edcation进行计数统计</p>
|
||
<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1"></a>data <-<span class="st"> </span><span class="kw">count</span>(boston_data[<span class="st">"rad"</span>])</span>
|
||
<span id="cb25-2"><a href="#cb25-2"></a>data</span>
|
||
<span id="cb25-3"><a href="#cb25-3"></a><span class="co">## rad freq</span></span>
|
||
<span id="cb25-4"><a href="#cb25-4"></a><span class="co">## 1 1 20</span></span>
|
||
<span id="cb25-5"><a href="#cb25-5"></a><span class="co">## 2 2 24</span></span>
|
||
<span id="cb25-6"><a href="#cb25-6"></a><span class="co">## 3 3 38</span></span>
|
||
<span id="cb25-7"><a href="#cb25-7"></a><span class="co">## 4 4 110</span></span>
|
||
<span id="cb25-8"><a href="#cb25-8"></a><span class="co">## 5 5 115</span></span>
|
||
<span id="cb25-9"><a href="#cb25-9"></a><span class="co">## 6 6 26</span></span>
|
||
<span id="cb25-10"><a href="#cb25-10"></a><span class="co">## 7 7 17</span></span>
|
||
<span id="cb25-11"><a href="#cb25-11"></a><span class="co">## 8 8 24</span></span>
|
||
<span id="cb25-12"><a href="#cb25-12"></a><span class="co">## 9 24 132</span></span></code></pre></div>
|
||
<p>把rad为24的数据去除掉</p>
|
||
<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1"></a>data <-<span class="st"> </span>data[<span class="dv">1</span><span class="op">:</span><span class="dv">8</span>, ]</span>
|
||
<span id="cb26-2"><a href="#cb26-2"></a><span class="kw">ggplot</span>(data, <span class="kw">aes</span>(<span class="dt">x =</span> rad, <span class="dt">y =</span> freq)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_line</span>()</span></code></pre></div>
|
||
<p><img src="data:image/png;base64,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" /><!-- --></p>
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<p>有时候我们需要在折线图上显示对应x轴的点数据，从而可以更加清晰的辨别原始数据,这特别适合数据比较稀疏的情况</p>
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<div class="sourceCode" id="cb27"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1"></a><span class="kw">ggplot</span>(data, <span class="kw">aes</span>(<span class="dt">x =</span> rad, <span class="dt">y =</span> freq)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_line</span>() <span class="op">+</span><span class="st"> </span><span class="kw">geom_point</span>(<span class="dt">size =</span> <span class="dv">4</span>)</span></code></pre></div>
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<p><img src="data:image/png;base64,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" /><!-- --></p>
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<p>我们调整横坐标的显示刻度</p>
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<div class="sourceCode" id="cb28"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb28-1"><a href="#cb28-1"></a><span class="kw">ggplot</span>(data, <span class="kw">aes</span>(<span class="dt">x =</span> rad, <span class="dt">y =</span> freq)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_line</span>() <span class="op">+</span><span class="st"> </span><span class="kw">geom_point</span>(<span class="dt">size =</span> <span class="dv">4</span>) <span class="op">+</span><span class="st"> </span><span class="kw">scale_x_continuous</span>(<span class="dt">breaks =</span> <span class="kw">c</span>(<span class="dv">1</span><span class="op">:</span><span class="dv">8</span>))</span></code></pre></div>
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<p><img src="data:image/png;base64,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" /><!-- --></p>
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<p>也可以修改线的类型和颜色</p>
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<div class="sourceCode" id="cb29"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb29-1"><a href="#cb29-1"></a><span class="kw">ggplot</span>(data, <span class="kw">aes</span>(<span class="dt">x =</span> rad, <span class="dt">y =</span> freq)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_line</span>(<span class="dt">linetype =</span> <span class="st">"dashed"</span>, <span class="dt">color =</span> <span class="st">"red"</span>) <span class="op">+</span></span>
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<span id="cb29-2"><a href="#cb29-2"></a><span class="st"> </span><span class="kw">geom_point</span>(<span class="dt">size =</span> <span class="dv">4</span>) <span class="op">+</span><span class="st"> </span><span class="kw">scale_x_continuous</span>(<span class="dt">breaks =</span> <span class="kw">c</span>(<span class="dv">1</span><span class="op">:</span><span class="dv">8</span>))</span></code></pre></div>
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||
<p><img src="data:image/png;base64,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" /><!-- --></p>
|
||
<p>ggplt2关于折线图的相关做法的参考链接： <a href="https://ggplot2.tidyverse.org/reference/geom_abline.html" class="uri">https://ggplot2.tidyverse.org/reference/geom_abline.html</a></p>
|
||
</div>
|
||
<div id="ggplot2扩展包主题" class="section level2">
|
||
<h2>7.ggplot2扩展包主题</h2>
|
||
<p>R语言中的ggplot2包里面的风格固定，在需要特殊的图形时，需要更改甚至自定义设置主题。 ggplot2内置了8种风格的主题</p>
|
||
<table>
|
||
<thead>
|
||
<tr class="header">
|
||
<th>主题函数</th>
|
||
<th>效果</th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr class="odd">
|
||
<td>theme_bw()</td>
|
||
<td>网格白色主题</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>theme_classic()</td>
|
||
<td>经典主题</td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td>theme_dark()</td>
|
||
<td>暗色主题，可用于对比</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>theme_gray()</td>
|
||
<td>默认主题</td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td>theme_light()</td>
|
||
<td>浅色坐标带网格</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>theme_linedraw()</td>
|
||
<td>黑色网格线</td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td>theme_minimal()</td>
|
||
<td>极简主题</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>theme_void()</td>
|
||
<td>空白主题</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
<p>我们来试一试不同的主题</p>
|
||
<div class="sourceCode" id="cb30"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1"></a>p <-<span class="st"> </span><span class="kw">ggplot</span>(<span class="dt">data =</span> boston_data, <span class="kw">aes</span>(<span class="dt">x =</span> lstat, <span class="dt">y =</span> medv, <span class="dt">colour =</span> rad)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_point</span>()</span>
|
||
<span id="cb30-2"><a href="#cb30-2"></a>p1 <-<span class="st"> </span>p <span class="op">+</span><span class="st"> </span><span class="kw">theme_bw</span>() <span class="op">+</span><span class="st"> </span><span class="kw">labs</span>(<span class="dt">title =</span> <span class="st">"网格白色主题"</span>) <span class="op">+</span><span class="st"> </span><span class="kw">theme</span>(<span class="dt">legend.position =</span> <span class="st">"none"</span>)</span>
|
||
<span id="cb30-3"><a href="#cb30-3"></a>p2 <-<span class="st"> </span>p <span class="op">+</span><span class="st"> </span><span class="kw">theme_classic</span>() <span class="op">+</span><span class="st"> </span><span class="kw">labs</span>(<span class="dt">title =</span> <span class="st">"经典主题"</span>) <span class="op">+</span><span class="st"> </span><span class="kw">theme</span>(<span class="dt">legend.position =</span> <span class="st">"none"</span>)</span>
|
||
<span id="cb30-4"><a href="#cb30-4"></a>p3 <-<span class="st"> </span>p <span class="op">+</span><span class="st"> </span><span class="kw">theme_dark</span>() <span class="op">+</span><span class="st"> </span><span class="kw">labs</span>(<span class="dt">title =</span> <span class="st">"暗色主题"</span>) <span class="op">+</span><span class="st"> </span><span class="kw">theme</span>(<span class="dt">legend.position =</span> <span class="st">"none"</span>)</span>
|
||
<span id="cb30-5"><a href="#cb30-5"></a>p4 <-<span class="st"> </span>p <span class="op">+</span><span class="st"> </span><span class="kw">theme_gray</span>() <span class="op">+</span><span class="st"> </span><span class="kw">labs</span>(<span class="dt">title =</span> <span class="st">"默认主题"</span>) <span class="op">+</span><span class="st"> </span><span class="kw">theme</span>(<span class="dt">legend.position =</span> <span class="st">"none"</span>)</span>
|
||
<span id="cb30-6"><a href="#cb30-6"></a>p5 <-<span class="st"> </span>p <span class="op">+</span><span class="st"> </span><span class="kw">theme_light</span>() <span class="op">+</span><span class="st"> </span><span class="kw">labs</span>(<span class="dt">title =</span> <span class="st">"浅色坐标带网格"</span>) <span class="op">+</span><span class="st"> </span><span class="kw">theme</span>(<span class="dt">legend.position =</span> <span class="st">"none"</span>)</span>
|
||
<span id="cb30-7"><a href="#cb30-7"></a>p6 <-<span class="st"> </span>p <span class="op">+</span><span class="st"> </span><span class="kw">theme_linedraw</span>() <span class="op">+</span><span class="st"> </span><span class="kw">labs</span>(<span class="dt">title =</span> <span class="st">"黑色网格线"</span>) <span class="op">+</span><span class="st"> </span><span class="kw">theme</span>(<span class="dt">legend.position =</span> <span class="st">"none"</span>)</span>
|
||
<span id="cb30-8"><a href="#cb30-8"></a>p7 <-<span class="st"> </span>p <span class="op">+</span><span class="st"> </span><span class="kw">theme_minimal</span>() <span class="op">+</span><span class="st"> </span><span class="kw">labs</span>(<span class="dt">title =</span> <span class="st">"极简主题"</span>) <span class="op">+</span><span class="st"> </span><span class="kw">theme</span>(<span class="dt">legend.position =</span> <span class="st">"none"</span>)</span>
|
||
<span id="cb30-9"><a href="#cb30-9"></a>p8 <-<span class="st"> </span>p <span class="op">+</span><span class="st"> </span><span class="kw">theme_void</span>() <span class="op">+</span><span class="st"> </span><span class="kw">labs</span>(<span class="dt">title =</span> <span class="st">"空白主题"</span>) <span class="op">+</span><span class="st"> </span><span class="kw">theme</span>(<span class="dt">legend.position =</span> <span class="st">"none"</span>)</span>
|
||
<span id="cb30-10"><a href="#cb30-10"></a></span>
|
||
<span id="cb30-11"><a href="#cb30-11"></a><span class="kw">ggarrange</span>(p1, p2, p3, p4, p5, p6, p7, p8, <span class="dt">ncol =</span> <span class="dv">4</span>, <span class="dt">nrow =</span> <span class="dv">2</span>, <span class="dt">heights =</span> <span class="fl">1.2</span>)</span></code></pre></div>
|
||
<p><img 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bJDc82mzqhPww7tU66THZqrVtS/HUCrrMrjyQFsj9DoSWm7/qBRn7CzSMRnTUV9ws4ihM9U6taW4qM+YWcRwmcqdYW72725v59iEbIeVDd5QZ8n/e5Ul4QPVrG7HeETAhrxuZ0InxBQwueDeMfXBzTqEwJK+LyId/zmgBJCgCpN1qRR3lQjomV5SXA/F4etPvlI8Im0NbYIUN+nKiX5mBlsawBKCQLq+1SlJB8zg20hQF9NGf2rn/953ieMQa1P6VQ0JpKAjh4OKwGN+oQxaMQnkO9zpaI+YQxK+aQBHe3SMoBGfcIYlPJJAzrapWUAffnz/3j67Mc/fDrvE+Iis3t5q4/j8W4W0LE0oHGf8LIiPq0In+sU9wnZkT4fkE8S0LE0oHGfkB3p84J8koCOpQH98PQZr3F4G+9uJzX5vLm5UUZ1dwwiBi0NaIJP9NyFfVqVBjTBJ2KH8EnGoKUBTfCJ2CF8kjHo3oAeDrJPNfep35xm1QEBOrr3fUtAIz6tCJ+rlAko4dOuOiBAx3PJGDQTUMKnXXVAgI7nsjHoK57VB79QoQA9qQKdvfGyytZuyWsei9aDRn1SgM74BFuWbqaL+6QABT4tn4cD7ldSth406pMCFPi0fPKcCRJaox405BMDetBfT0UAFVeYPJdaDxryiQEN+gwcZbWiPjGgns8ZQGNILqsHDX45hwD1fM4AGkOyYjWTkHR6skbBirqARoUuK+xz0fFLCbHj+QQr6gIaF2TH8wlWNAUoL36MKhIWD5J5QeIYVF5h8lxxQMM+d5WDkusTENrEd/Fark9AaNWK+nefuf0CXcF+Vydw38/nuydPntzBEK4ioHGfoD9ozGdFxX2C/qCn04PvM5G6lYDGfYL+oKfTxfeZSN1KQMM1YUKgP6jsJqAqbM/nJ1yie8vlIqaOlyIiAE3wCXszzflkTPqMHWuxEnyC/qCzPp8/lz6LdAel+oMm+DTaPD1D3yQRgt3tTrJJFhoVdWJ3yjN2WvGbJEII0CyfBZXgE3a3i/qslYMm+ITd7aI+k/PM/Bw0E1BRYSudcssLAfUQiQGa4BMDmuNzXtkoJ/jEgEZ8JiLpbhkFNMEnBjTiMxFJ7yUbj0GDVbVCKAYdj6K6wTo9LQFUtJzNb0rFoHGfKAbN8jkr32dUcZ8oBo35TANU+MwCNMEnikFjPtMAFT4zAc0pJE137IlrVIQid+M4+TRNMnq6JKA5haRcn7NaAGhOIWkiK+JTbqWnSwKaU0iayIr4lFvp6ZKA/viH5NHtuGSTLDTKC3W2fCfutniq+HRBQBN8Op1FMnzOKh/QBJ/OqznsU/EpCS0IaIJP59Uc9qn4lISWBDSnkKRumHFqw2bjMxHQ/Bg0p5CU7XNe2RloTiFJgRX0mQZofgyaU0hSYAV9pgG6IAbNKSShGz/FyWIwvjMWv6MJgGaX4nMKSdk+CyqnkIQAnfeZAmh2KT6nkIQAnfeZAuiSivpfpReSmHp2lNEb3cEF+ZR3/hyMQb25hBg06tMBNMNnScV9OoDGfcZj0HxAE3w6gMZ9xmPQBYDqQSZSC0nKp3BqjEpjxuhRh24XMZCXuV589Wx+lnzFR33iGDTHJ0M+VynBJ341J/uU1IH+eCvrQaM+8as52aekDvTH27gtHhg9KqM6a7dGVR+2C7sDPTG3bYvP8cmQz9rCKKX6lNTdgR6j27bFp/qU1N2BHqM7Acqbvk7mQdJOb7VPcedbADTFZwuARn02AmjU596AqjsvvNychLsnxunIf9WxDUBzfO4J6EOqz50BvaT63B1Q/aSAspx9lCaj0xYW0GIxaIK87nbJPlm5GDRBXne7sM9pCwtosRg0RRilmM9pCwtovRj0/Zf3949zA9hy2bwclOVkg5cKRuTPtfMBuyr2ByV84svK8VlRhE+MUtyn/Fn52wiSKwElfGKU4j7lz8rfRpBcCSgfOer9717MDWDLZm48d3o0nV2Y/hXXi9mFsbKAUj4TAJ3xaXeJnjhTlM8EQLHPB/1rs8/NLqUBpXwmAIp9XvSvzTKzS3FAv+fu5gYPE6KNymKcnhFGR07ddAGmvbAkoJTPFEBpnwz5LCnKZwqglE9O3eTTtGuWBJTymQIo5ZNTN/k07ZrFY1A0DB8cwBY9F1O8DGtrkVH+w80iGpGvAFM/lqq0GNT16ceg/E+CT9XoqXwmnTtHrk+nN/FRdktO8Cl6MRuf6zosJ/h0Vm+YnmFA+Qhn9AC2UpJzWScmOgtIh8CnNMs/O4ZGj4ULSZ5P8rKSfKIELU2o55PM61J8PiBAj4ULSZ5PMq9L8Xlx0rNsDvrD779ijB7AVtkW/4oWhVHrTMgAyqoA6vukAU3xyZDPhJNnyPdJA5rgUwH6UAVQ3ycNaIJPBeilCqDvv3zkbudjUIWSDUmMU1Ulpo2epk3FMr114VK855O8rCSfDPmMnzxDhE8SpQSf06Zimd66cCne80milOBz2lQs01sXBVT6ZOQAttq2+PfoOB2PoOHLAsqlNg8Uks68BwxQws/QED7nAY35ZMjnvM7ZPUson/OAhn1yQLnU5oFC0rRRJqCUz3lAwz45oFxq80AhiadnJqBv7rkeA/WgM4CeZOf/swlIjhxQdEv1+Uddia9l8NCKA0r5TAKU9EmjN7qD4ng+46J8JgFK+OSbnsH4TPo4o67EB3ziYZyW+UwClPDJNz2D8Zn0cUZdiQ/4dIYZU1rXkmQrN7nRJ4fDgfs8nWzDlzR6dF/j+Bff1gFKib6sFJ9Y3i/TzfpcJvplHPfpvsZl3aj+NaV1gJIiX8Zxn+5rXP1akfC5IaDqbShGPjndTP8csdOjeG+DO8rryPjsxoCm+GQBn0BVAVXV70GfKAdVPrcGVFUzhtMT5qDK5/aASgmjN+Jf51EyUltKn6IewgF0QQxKKXhZi31CFeFzDlCpBJ8WUOtzbQxKigZUKsGnBdT6XB2DUpoD9Ib/0LU1+iTxxsvYzr35FQEt6rOA5gC94T/IneDTAVTGoGPoh5EXGp0B9Ib/IHeCTwdQGYOOoR9G1icuA6iosmXcKc/V5x8k8O60s172VA9Q6ZMV8llAM4AKnw/P4z71jhpQLpWNbgGo8HlJSE+9owaUS2WjWwLKVEvCYcYmKsWb2e0BTffJQj4LKAzoQ8wn3NHMbg/oJeYT7mhmdwSUj4YSyJbkjpO7mSLyZoDGfbKQzwJKA3TGJ95R+NwX0BmfeEfhc2tAZWwHAJW/eq9mdL0YuL8XebvNnaePSpwxUcEYlKX7ZCGfBRSMQQGgcz7tjsqnIpQ+anFAZQwKAJ3zaXdUPhWh9FErAMrF8x7ZZ+CsymnKp3IKcyAEaP0e9Ug5PhGgFRQqxfM8MuqTArR+j3o0y/PIqE8K0A0++XDqh1SNobZ1BkZHJbOjvfFexlQX0CyfLOSzgAIoybd4zCcBqJuBVgZUvsVjPglA3Qy0DKCXWYWN6iYxuz3wOY7yh3K5jk5v0Y0B1T7t9oBP4VMtLtOTJNBxU7h6HvNptxc+5V/hE3cxzewPmqHK6Vk6B2XaoOodYmORo/GJezNpowe9DjutBKj1yUI+wX4Rn8sUzUEftE/VO8TxKbbEvZmMT70OD9W00GgsB71onxcyPXXeA3Y0PvU6nDPpExeOQbmOpsUQa9Rb2XvLUXBvvD6MOl6dGLS8z2WKxaCSMTHl+9RbWQY5si6g+jDqeAuNBl7GZ1VOF6e6XHyfeivLIEfWBVQfRh1Pn7hkUyfsOmLvuynNidttp5iO9pRPdePFBnxKTVRo6mQ1fC5TsKkTdh15IH0qbCWgOirVPuXOYgOQIy80GgAUdR25kD4VthJQHZVqn3JnsQHIkfWJKwDK7B0+Ox+qaB74Bjp8FjvaWGQ7QPN9soDPZUoB9MGS6Pl80AsNn7K7neYT9BapDujFkuj5vOiFhk/Z3c6WPbYEVIg0etaLLw6gzn5goiKgCT6ZA+i8z2VKAtSC5vnUi587gDr7bQOoBc3zqRczB1Bnvy0BZaRRY9jceO8j5HPtGNRR0CdDPt39nIkFSgf0IejTAup9hHyuHYM6c+H0NIB6HyGfN4tBzZxx6vnUd14VQ7atqPeU7LO8MgB9/hDy+SAIVcWlbSvqvbmQz4sgVBWXNquoVw2INKAM+ASTYht+33luTx6VPGOi6MtSPl0l+1xkJSgaJdHQOQfoA+1TbMP5nHxuBaho6JwD9EL7FNtwPnl6bgSo7oJBvuJtPKdbGOGdJ7tg1AJU+8Ra7rOA5r+L9wC1KNqpo0so0VWkHqCyq8jMK97GnbolFBJKdBXZGlDrCN355gBd4bOA0gEFPiGhzQEKfEJCmwMU3nJvQUOArvFZQMmAQjS9BQ0BCtH0FuwFKBGD6pvr3nh7582mu8ageT4XWQkqNQbVELqAWkLNprvGoBpCF1BLqNm0SAwqRpaIfhdPzJmba24xagITmyoMvMMsANTzmfrc+T5R06deUOgTOd9naineQGhQfA59ik0Vrt5hFgDq+UwtxRsIDYooPcWmClfvMAsA/f7+19+xwPigMUBhzuQBCii44PufDajvMw9Q6NMDFM6s5NT3mQcozEE9QO2MWLcGUN9nHqAwB/UAtTNi3RpAv/3iX6YnKT42EzXn3maGnCJcvcaYXEAJn8mRi+cTF5uImdQjJ/lMrgdFOMqVCFC02m61CFDCZ3I9KMJRrkSAotV2q4U5qDAYGh80JNcpXOJch2MUKfkVHxofNKQQoQ6/KwElfGYM7AmceEtQdqq7kq4YH9TzmXGNhdMzDmh8fFB6DjpizLzUscezfnOuyUEJn7mAWp8s3We+PJ+5TZ02GH2Ol5zNzMPqHJTwmdvUaYNRhpeczcylfA7K8po68U01txk+QnhT8jgLclCWVzmR7nN1WcnzmdPUieEzOAI8nU3XARofH3RmDsNncAR4OpuuBHRZDGrmYHZkFxi7YBv6OMmALotBjWifLOQzX57PrLZ4KiNVC8w7HmyzEtBlMaiZg7fZLjDpCbZZC2h8fNDwHLBlZy7ozoMbfz6fFwIaHx80LNInC/nMl+czH1CddUoG9cxzVB8KAD27P4y8zGc+oDrrlAzqGVtkcgA9uz+MrE9cqR6UkWV6KlwWPTHMjZeW4WHq1oM6yvOZf/zF9aAPZJkevt4toMonyF4XAbqoHvRClump4rHoMWIAlemZD6inVYCS5TmmOg6CDbfubrfU57JzIK0C9IEEVPkEG27d3c4FlKy/uSifYMO9AWXwxpv7br6PtGTsDGiyz2XnQFoD6AOuHdXQ6u84LcE7A3rBtaMaWv0dpyV4R0BRGdn0ZJWrYNdrG4Oa1ZsCmueTsaWxqNFCQFFZ3tiSq6BPG4Oa1QuNLgMUleWNLbkK+rQxqFmtT7wRoCDLscbUCtD12uxp57YFNMsnW5+TLgUUlIOsT7UC+DR72rmFRhcCCspB1qdaAXyaPe2cPnEDgAKZPVsE1PPJ2gQUqGlAgdoD1FbNE/e9IUBZyCdrB1BbNe/z2RKgtmre53NfQEGwdsS/k+Tf9R1jUNcnXkHguI7P5YCCn1A44t9JcuhUe573iUFBUOn+TpJDp9rzvFcMSqw8w5fn7KZbA+pr1mcBLQfUX3k+k+1I23/VGVmJXvKzm+oT7weovd/ufW8L0HmfBVQQUMulw2djgFouHT7bAFTdZATo+s4inlYDah3BjLM4oqsBVTAiQNd3FvG1FlAFIwK0QGcRT2sB1XcbA0rEoHNnTNRaQGlz5V/yawHVVGJAiRh0Z0A1lRhQIgZdDehlnaAlNLWuw7KngoDOZKZllNFheb4Xs50mli7qsFxcy9NzvxwUqOkcNLp0jQrmoEBN56Az2ZW7Up94vxgUrms5Bo0vXaGCMShc13IMCte1F4Pmb7p7Kb6qSlYzpW260OhaQLM31SfugO6qDmgHtGl1QDugTasDWhrQ7bUoPX8xBLR4ZWjdIpvhx2/xymVptvRsdX2uy0ELrfyYEjRP3WdMHdCMld1n2ZUd0MIru8+yKysA2tW1rTqgXU2rA9rVtDqgXU2rA9rVtHIARYP1OPJH87Grvry/f5xZ+f09H206eOR8dZ8fkc8MQPGg5e4JvVHNtfgYk+9/94JcyS9vWhg6cr66z4/JZwageMBIJGpUc3MN3MG3j7N7TwsDR16g7vNj8pkBKB5y1z9dYINp6ezK6REKHjlb3efH5DMDUDxoOWF0dgM+FurMyvdffvEifORsdZ8fk88tctAffv9VYO/QU7ZI3efH5LNMDEqNam5XffkY3DsQpyxS9/kx+cwqxX81X+byRzXHPumVKo8PHjlb3efH5LN+Peibe67H2ZVTLNJEvV332aTP3pLU1bQ6oF1NqwPa1bQ6oF1NqwPa1bQ6oF1NqwPa1ZwH1jYAACAASURBVLTqAfrul1+b6f/8d0ZOt6Dus6wK+9wEUOgZTjeh7rOsCvvsgHafZXVVgL77bBiGZ/zfz+X053K6JXWfZVXYZ1VAxVPz7rNn/O+Hp88Ye/Wzb1p84rvPgirssy6gv/pGT7L/+jOz5ltS91lWhX3WfcW/HIZPmY4/3k5Z/SdNJmj3WVCFfdYuJH14Osjs/cPTT75u9Yln3Wc5FfZZvxQ/BSF88u3Pp7z+baNPPFf3WUaFfVYF9O3PvhF/eaD8lj9In30ipptS91lWhX3WzUFl+MGmmORTHpd88k+Ty5ciPGlH3WdZFfbZ2+K7mlYHtKtpdUC7mlYHtKtpdUC7mlYHtKtpdUC7mlYHtKtpdUC7mlYHtKtpdUC7mlYHtKtpdUC7mlYHtKtpdUC7mlYHtKtpdUC7mtZSQAe4J3WQYXZGLxmCZx/mDrxC0cN5Jx3UvzWfY/fYA7W4lgHyzmxy5kTln17fM7ArdU0DXC5mpOwScIgBChxzTeJ4R/TuecJJiYstrsFaAb4Yto5dgQ3WGovzWOvMaVpwFv8uuoZJQIkdzF4+E+osK9JhwP/4PhNOSmZmpTV4/wz2z+we698wg3cb4kcsc+YcLQfUvYtOjhoClHk5qO9odVIM5hwzPhNOamCpmV+YE0JIgQufIz21ztPgzZDnqXDmxS5TtyffmwPeyF4G+T4gAAW3Rr9JFhjEZxjsWTyf8ZMaOCvSqRIHXip5Nvdx0oHB8jMzkDI4iciHt9yZs5R9GvetN/jBnpd3mWuau67BbA7yO3SEfHlRhJcbxE5KRiDlBV/sxoaXVmjah3r5mYm/AUALnTldywEFNxr9w8B9N3koPpkX/UBS0C5rU8FLaPjqTDipQblejmGegcE7/RwmZtG66gXvRJudOUMLTjM4OTxx480VmJc8OhmaG8B/kGl9mFWKABo76cDqv85AUGcfHJtRIfd6BxWNlMYkBmi9M6d4ytsFZPID8Y+TDZlg0N8CH20wfw3iSxOCiDywxfhJ4QNVLQOFp9VnG+wf6kqQ3zXndg7rJ1mtM2d4XLgHeL6Jq8AHBoB6ac7s0eDbdkDbZnukXBAxaOSkOAKppARAvSspiclMXrnBmdOUexrwcM0ewiFhINJ8AP/pFYAVPbn69Tqf0CkntZdYkU94brMo9IpnTMfEJSjJA7TkmdO0NAfFTzxxOHRgexvAPghQSAs+/rKUGLAonxknHfxF5UQBav/Arez0AHZcf3Z/eoszp2kpoEQFI552AHWuSwOCsmN0uJWAzhoHR0846WCLTbUzUXvORED91F94dmJ6izOnaSGg5raxlEuE999ODeieUNuuAdTLtIkcNHJS+Kav+E4bnDMMLPKKH0qGHFmAFj1zmhYBatq91BL0LiVervqmD+6B/En8tl0Rgw7Of57PhJMO4CprhqHYp4OJn544kZef179NTkxU68xZLjfZmXwnkICiTHVFDgry+blDZJ60Zg7K8IOsV0R9V3ASOsfWuede5+zqSlYHtKtpdUC7mlYHtKtpdUC7mlYHtKtpdUC7mlYHtKtpdUC7mlYmoP+2vZZd1vZa5PMSmg3NLd8UaZzEzlJ3d3zGLJ/+lcvn995CuYDi2dfJc4s3XQhoaDY0t3jTqwSU8zmetcScXa7B3ZfQDmgHtAO6ZtMOaOFNoa4c0Df39/e//o798Pv73/xFLWoS0FdTBPizb9iHp8PP/6wWNQkokZ49Bo0plK7fPvJ/f/rjI3vzW7WI42KeOn1B5iLR3tsB+vIZ//fHPzxjr/RPkg/pPrcDlEjPS7rPKoC2r0C6/vSnF/zPD3//HXv/19/JZf8G0lO+E9ACuPtmgP74j1/zPx/+1zfs3a++kcuGdJ+bAUql5yXd50aA2ly0DQXSdXoV3d8/svd/+xf2w9/xtOWVPq9fOwmKFrwurzig06t9GJ6xd//jz+zD/+SsymofxydasEs1k5eeky6Xi+MTLbiU14y54/Eo/to4tBEF0vX9717wp/7734AEbTEHfffLr3ku+vbnGlDWZg5KpWcrOejx7u5OEHpNgAp9+4ieeC8GVSm6awzK9fKZzUEZEYPO+9y2FO+kpxeDzvusCugdF2NXCagbgyqJdwK+nD2rmV4+c2PQeZ97VjM56WnZIXxuUIrXMoBeUwzK30U//fN3P/3xK1yKFzryl0IbgPJ3+4//55sf//A5LsXP+twJUCo9DTuUz10AbU2RetAvXjC6HvQoLqkJQHk96CdfM7oelPK5Yz2om54WUMLnhoCyVvlc3JIkE/Q1eiG02JIkfQ7IZ4stSdLnBfncEtBmtbipUwKKVrYIqPTZRksSIcuOBHRmpT/XAaUFcOEvhSsAVPi8AkCFz/0A/ehe8R3QjwrQ6ywkEeqv+LnZ637Ff3SATsH8VQCqfDYPqPK5E6Dq7LHNdtEyQEV1yBUAqn22Dqj2uQ+g6uyRrXbSCkDH9mNQ7fMqAB33ikHl2dtqQDJaA+h4JYCOVwLouC+gbRK6PAa9BkDZlQDKdgW06Sx0aSlevpOaB1T5bB5Q5XO3UvzHA6jpSCzi6godlPM7LJOXZSR81uignN1hmZLtSCx8VuignNhhudli0sIc9HA4XEU1k/LZfA6qfO7akvQxAXo4iBRtPgbVPlsHVPvcCVD1eHxUgE6vhNvbKwBU+mwfUOlzH0DV49Ekn0sBFUG1AtSE1w0CqnwOyGeDgCqfF+RzU0Cnx6NJralmkoDaAmB7gDIAqPXZHqAMAGp9bgbodMKPEtDTlQB6uhJATzsBKp+OW7wkss9mWgjoLU/PKwBU+WweUOVzT0DdBZGdttIyQO/uTlzNx6DaZ+sxqPa5Twyqsm9nQWSnrbQIUF4lwdOz9VK88dl4Kd743K+pU5wezF8/oHe3XqTSJKCEzyYBJXxuBuitzL+t2uFzBaD+c9YmoL7PNgH1fW4F6K0HaENaGoPeES+C5gCd8dkcoDM+twGUZ963zfIZSVc+luXsALYNAcrHBp0dwLYhQL30hOzsA6gML1qtBY2l65v7R38AW6N2YtBXwzN/ANtZn/sB6qUnYmeXGFQBSpy+DQXT9f3f/MOjN4AtlETpcDiAObxuZrYsoO/+6n8/8wawhRqQz90A9dOTREn73A5QPtFS0R0olK4//elf/+gPYOtJNOXu2R/0x3/8f3/wB7D1JHzu2R/UTU8G+4NaCZ/b9QcFw9pdG6BvvuKvI3cAWyiR14kENXN43dxs0Rz01ef89e4OYAs1IJ975aBEelJ5nfG5ZX/QawR0etR/cp94iMsUWTcB6JR1/ohyUIYva/LZBKBUekJ2Jp97Anp9MSj/1ZT7+6/mYtDbW1322zkGfSXesJ/PxaC+z50ApdITsOP73BjQNhWvZqIHsIUJqrVvNRM9gC3lc99qJnoAW8pnB5StqQe9vdVPulab9aC+zzbrQX2fewLqetlNy1qSRNHPxEpa7bUk0T7ba0mife4IqOdlNy1u6jwej+0DSvtsD1DaZweULQf0yMU/FQArWwSU8tkioJTPDihbDyismWgZ0NFfSc7tDejoryTnygOKi2it8Lk4BsUJKv+2Byjtsz1AaZ+bAupXIrShZYBOCajSc2Sn00lMhMv0+wCa4rMFQFN8dkBTBIdfFH8Ooq+rStBAkWk3QKM+GwE06rMDmiIEqEjFKwA07LMdQMM+68egzcSdUAtjUJWe4ImP1IruFIMm+GwB0BSfFQGVHZraKblDLQFUXsetipRkevrlvv0BTfO5P6BpPusBqrqEfhyAmv6ftzpBa3YGTesPSl2W6f8JfdbrDJrUH5SQ7f8JfVbrDEr1B9XNBB8JoEw/aTBB1crGqplmfTZWzTTrc6tqJlULK8doSvO9nVYAKsecmqZl4c/tTtgMoBGfzQAa8VkzBpWAjqN7zga0IgblaTma2gnv6nYHNNHn7oAm+qwHqKiE9QBtBNaFpXj+nIsBbI/6i+oGAbU+h4DP/QG1Pi8Bn9UAHWUzgXNKNbN7WLoMUJ6eR56WRzkmxeFwaBLQFJ8tAJrisyqgpoVV8MgtyKX7F5wWAyrS8+bmhieouIzmYtBEn40AGvW5AaAKR9NWcPWA3tzIFFWX0VgpftZnY6X4WZ9bleLNo+AC2kLdKEzXD08/j22uceFBtUnQcfp3S0ATfOrLIn1uBWiCT80O6XPj/qAjeMNrQBuLQfn3kc+CmytcRKnvKBN0HMUftmEOGvc5hHxuloPGfV5CPrcFdDSv+oOKQZN2qy03XV8Owydfz2+OAD3eTQl5d3e3OaBxnwhQ1+eWr/iITwSo63MLQG0OOSo18FqHItL15c++md0cATqK3zjfB9CITwSo63PjGDTkEwHq+twAUAvjrQbU76ayq4gcNJCeMAa9E1ejU5QvfI1qd6vnoCGfIAb1fQ7IZ/UcNOQTxKC+zwvyWRdQ2ZDVOqCR9xEDuEzJaBNULXsNq3rDtU7rAI37NJdF+RxCPksCGvdp2KF8XkI+ywOqCbWtrS0Il+JxRP/9/f2vv5sbuEEl6M2dSFXRPvca1/mmj26bXYrHPt/KXIoeuIHwOYR8Fi3FR9PTskP4vIR8Fo5Bb2V/ZX6StvrWB6rv+ABCb347O4CtTE/1Q+d3suFzHM/ns10Lj1avHpQPyPTq09kBbH2fQ8hnvXpQKj0BO77PS8hnhU8+iMn9C/MmXaf8RwsETVOizgwedlJlPgQoO3OpR7EOoLTPCdKZwcMIn0PIZylA09LTskP4vIR8FgTUCTnhePXeaTcXrgeVb0rwYpoedXoAW9UWpiQSlC/WCXp4PS2t1mHZ9zllnfQAtoRPvtj4HKal1TosR9KTMTuALeGTLzY+L9PSSh2WdXc/mW0e0S9+tAWoSsq35ol//+UXL2YGsD3ZFOUlUP4zPzAH9dqVKsSg1ue7z6ayCD2ALeVzQD5rtiRF09PwQvm8IJ/VKuoFoLeiKevEjgZQcdLGAWU8JekBbHWC6ipmEeMzkaJky3xVQBknkx7ANu5zQ0D99PQAnfdZF1DJp+6tcjLl+735JF/xsAX520c6BtXjDOgE1Z96ccmYpmZTp+/z5TM6Bo37rNnUGU1Pw07cZ72mTn78o+5NJQhlLXQTkcLp+nYArcfqXUQOYHuECXpS2q4/KPKp3u3kALYJPmtW1EfTE3YWifis2hZ/BIAeRTDaJqBYb+7vp5iJrAfVTXMwPU/6nSQ32a673StZH07Vgyb43K67HZGeENCIz5qAqs4qElCJpZnYWcv6g+reDUJTEsoE5U0gIqoXmyDMTDIrbfXJR4JPlAKuz436g6b4RJi5PlcBqs7Lc0zR0Y/HFro/wO7C6TrlRc9emWyIEIxBbXrKFBWNdCSgo12qtBLQqE8Yg0Z8wjp9z+dKQKM+YQwa8Qnr9D2fawDVJ2aiwfVwEAW0NgF9+fP/ePrsxz98OrcxxkW+lmQSHsfj3SygY2lA4z7hZUV82k0Jnyvb4qM+ITsRn3ZTwmcBQO9kPwCpNgH98PQZrxl5G+9uJzVd1XQVKkF1NwciBi0NaIJPhEvYZz1AE3widsI+6wIq67UsoKwRPtcAOgUr4jnj6anfSGbVAQE6nkvGoJmARnxCQD2fWwIa8QkB9XyujUFRZypwYnDKyEFqCdeD8ldS8EsaCtCTKnja6xJVFDAGnS6vaD1o1CcF6IxP3K+kbD1o1CcF6IxP3K+kcDWTBdT+eCeQ26C0Ia9EPWjoSy8M6EF/5RUBlKt8PWjIJwY06LN6PWjIJwY06LPuN0m6tx/9XncA3bIBdGE1k5BM0ZNNULCiLqBRocsK+9yuHpQQYifssy6gTNQuYT5tt7srBZRHL6OK2MUDb0zjGFRoR0DDPtsBNOxzC0Bt7+g70aEKEgq23Q3Qd5+5/Rddgf6gouuLTs/z+e7Jkyd30HVFQOM+QX/QmM+KgMZ9gv6gMZ+VAQWfHeviEupZj4plO8WgwRo7KdAfVHZnUBXL5/MTLlEcfP1aTB2LdAel+oMm+AT9QWd9DoP0WaY7KAFogk/QH3TW5+UifRbpDjoDq+5MJWYoQFXF1vbt80R3u5Bgd7uTbDqGCSrrKFTaHtGeFb9JIgS72+X5LN/dLiTY3S7PZ9Ec9HQy3VEZAtRsIQHdoQcJzkEzARUVyzJFedIuBNTdMgpogk8MaI7PeSQ9nzFAE3xiQHN8ziPp+UwD1BKq+kyjLZoANFilLIVi0PEou2eZFD0tAVS3A89sSsWgcZ8oBs3yOQuo7zMag8Z9ohg0y+csoL7PREBvTBaq25bgJvoNvyugOYWk6TKeuAkqrupuHKf0NDG1ni4JaE4hKddnSUBzCkm5PosCyiyfusv0UTUo4fLQvjEo7/IbkdNZxElQXvi05VCRouLp59MFAU3w6byaM3wWBDTBp/NqzvBZFFBxaswnBxQVnXbS0kKSSgiToja8N+mZCGh+DJpTSMr2WTAGzSkkZfssGYPKUwsWbxWevKHARKax/WtqaSEJJegUz4sehGcsnlIJgGaX4nMKSdk+C5bicwpJ2T5LluJVpsv5lIDeml79ANCz00FlG+EY9FfphSSmnnGVoDe6Iw5KT5mi52AM6s0lxKBRnw6gGT6LVtRHfTqAZvgsD6hE81b8gwCVG5lMfFvhV/yQVUhS6SlS1CSoTECToEcdEr1G3WRW1oNGfeJXc47PAflcWQ8a9YlfzTk+L8jn+m+STMdl3gZ/Fn36edHJluajgNbhd1VbPEjQo0pQ/QqyCaoewdf6JyGZnIOq3Raf43NAPrdti8/xeUE+CzR1WkAliye5zJwlBmilHLYIoLzwdzIPvE7RW52eIkVbADTFZwuApvgsDygz9FvW4HiQYf78Xv5ltK43k0xR2ZPwJBw+MSk68l+fbAPQHJ97AprjswKgppuSzLDlArKDPbVnk4DqJxqUOe0jPyXotIUFtFgMmiCvu12yz4IxaIK87nbJPovGoFKqS+jN4SACCrFsTOOT+gyljELp+v7L+/vHuQFsuew7B5Q5ZcOcCprk2Oe8z0HF/qC8webZ3AC2uT4r9gcl0hOzk+OzeHc7+fN24sQHaYSXk4gMlGxNqlahH0hXPsLV+9+9mBvAls0k6Fk2HKsZM17va7MLY2UB5SOGvfvl13MD2Gb6HJDPkoBS6ZkA6IzPC/JZCFD9VdRB8Sk/3bvzNqMJTTtNpgLp+j1PxbnBw4ToBJXFTT0jEnTk1E0JbdrhSgL6llM5N3hYts8B+SwJKJWeKYDSPi/IZylAzVdRk/IAraVIuqLhAuEAtlJH2S15iuthrTJKUP4D0yMfzFb3yTkes/oyp8WgaPhFvzPxUXZLTvApejEbn+s6LMfSk4EOy1JH2S05wafoxWx8ru6wLCTAu5HD22lA/W88mwKUj8RGD2ArJfM6WXcnomSZkiA9ZaLyz45hgh4LF5J4twx6AFs4m+QTAXosXEjy0pPM65J8IkCPhUrxXJJLNUVtsG2PpmC6/vD7rxijB7CVUoAedVeDcbR9b3CKKkBZFUDFp+f0ALZwNsnngHyWBdRPTxrQFJ8X5LMYoBN9J0hgWhm+osKl+KnMyQIxqELJhk4mRVXVnU7Q6Ul8LZbprQuX4nmnjEAMqmaTfA7IZ+FSvJeeJEpJPi/IZyFApyMdVIXRWVQaJdaCVlQgXWV6MnIAWyUAKEjR8Qga6CygZ3nNouA5D+i0USagkk9GDmCLZpN8DsjnPKDTRpmAUuk5D2jM5wX5nAd02iitw7I80i3MsydAn/BvS/fsbxdI1zf3XI+BetAZQE/yI4WzCZyOHFDUVKYZHHUlvpZJdq04oK9EGeVZoB50BlDS54B86uOMuhJ/1mccUCo9kwAlfV6QT32cUVfiz/okpeJO8TCAeFd1P0U1SDyu2FLrWpJs5SZP0CeiqpeX/mwDnUzQo/saVx8MivRfCygl+mWc4hPviX2uA5QS/TJO8Yn3xD4XAqr4BICeFKBH06nJRL6LLneBigCqDIsKiNMN/80InKJH8d4GKcXr8vjsxoCm+BwCPrcCNMXnJeBzOaBHXaV1EHwqQGWXJkGorTtYdL35KgOolKzpFf86jzx4aQjJ9BT1JQ6gC2JQSqHizHKf62JQSsHizGKfC2PQUX33LKIL+dM0k56czKCM4/UCesOHnbIJ+iQxQWXMNDpReEVAi/qsCGhRn8nd7dR3z6dR18EKHZgBVBJKfJeXpaydywAqrou95k8dfz3MPvDgnWRn1WPvH5U6Y6JmAJU+h0I+6wEqfV4K+UzusKy+e36iCvIuoJb+tXxm7F4UUKYC7MNMcqJSvJndHtB0n0PIZ3VA031eQj7TAX2iOvQjQGURSVYo8LPwleAEolh/Tgh07Xl2B5RfauBxlzuO/BtCPr0foHGfQ8jnZoDGfV5CPtM/+ZCEHs1pFZ+SyBFWkZoz2Gcn9Zr3AFTGTABQ+cDpyzQXNdodxQYmRemjEmdMVDAGHdJ9DiGftWPQS7rPS8hnGqCyRHRjCdV1sfZ04OFYAegeMagQ9yiv7KzKk+Yx1OlpLgsBWr9HPZrN8YkArd+jHs3m+ESALmvqlNml+WhP6Jb6PF8DKjlbAGiWygEqTZoHfYSRtkpNGlD3ga8MaJbPIeSzLqBZPi8hn2sARR1TpZjhUxMqnphF1x9XLqDzHTeF+ddzCaqb7uz2Ij3lX1HrhruYZvYH9S5rXsLVEPNptxc+5V/hE3cxzewP6irQcVO4usR82u2FT/lX+MRdTBP6gx7Rh/nidU8AKspJtwyEknVrRUvnoEwnpOodYmOmo3ngj6g3k07Qg16Hr7ZSDmp9DiGfYMeIz0o5qPV5CfkEO0Z8BiRPpAkVBaaTAvQJHEFCEqqOrPJae5TC3Z8Kx6BcKsM/uxr1VvaCRlU4hAlq3hZyok4MWt5nnRi0vM+QLKCitUh0rlcZqKyINcWkWznKrf4OFfNZlNCSTZ2w64hNT1PqFBdkp5iOolR6qgRVGYfJQSo0dcKuI8V8VmjqhF1HivkMyQIqX/Li6w/0fb7ORcVnJ7LKHobC7CoAZTblzs4HNTqd1Vhq+tpeg88ItgM03+cQ8FkP0Hyfl4DPoMShRB2sAO9GdKXCgApC1YdR1wqoEJmgZ734tQOosx+YqAhogs/BAXTeZ0VAE3xeHEDnfSZKlcFu9Hi6t7cEoHZDsGe7MagzRyaolgXU+wj5XDsGdeaCPgfkkwxlq8Wg/sBN8z4vyCcZyiY1Rx5VXqwOh4rwT0DHfvgDIKNbrVVU9QB9bV4/fnqKpBpl90Nvx9oV9d5css9tK+q9uWSfa75Jgl35nTom0OLKM1RVNIM/SFdBRQC9kb/xSAPKQHoeUYrKB3ZKiK0AVT5pQBN8bgWo8kkDmuCzEqC8Qt48HqZT6BUAKrs2zLzibZykW+5gipKD+tQCVPskX/ELfNYCVPskX/ELfBYDVBXu1cOhKrBmAC1VeV8N0DNIOpCizQG6wuemgK7wmfFd/BHWB4AyEq8AffLkBkbApgIL8/lEflRXrHmpFqAwKb0FDQG6xueWgK7xmTeyiDqMrgvVDa28belMAspgOUmUo1hjgBIx6HkmQW2Kmk13jUHzfO4Xg+b5XDX0jbEgCB3l1x+jJtMW0VTWqk6pCUWAlmA0nK5iBIzod/HEnEk0k3S6aV6ueK1WydWrARUjikS/iyfmfJ8D8jkgn6sB9dIztRTv+7wgnxfksyygfOAI5tduiX6iehASD9BihAbT9fv7X3/HAuODxgCFT7wHqJ0R6+aOmgLoW/5DGoHxQWOAQp8eoMDngH1mA+qnZx6g0KcHKPB5wT4XA6rDUQ3oeIa/KzateAJm1I6iJkpOVgf02y/+ZXri42MzUXMo+eRKBChabbfyj5oA6MtP/u+Ug8bHZqLmPJ8D8jmEfOYCSqRncj2o5/OCfF5CPpcA6haXNKpH26eJ/4aoOakZBw+cu34OKhIyND5oSCDJvCUoO9VdSVeMD8rBDI0PGhJw4i1B2anuSrpifFCcnizYH5TsHyrlLUHZqe5Kmj0+KJSEa3R1HG1rEup/Z0ZqhA/HNjFofHxQek46PWvDr/GSs5lhq3NQCWh8fFB6zvE5pPtcFoPGxwel5xyfl3SfS3LQo1MlKmV+HPws2pXkSfXPflmfC843q5wclOU1deLEMskHktPZlDzOghyU5TV1pvtcG4MSOSheH2zqTPdZIAY9miwUUCpGaLJji9hOVfzHZ5MO61WBGc1xHQd0WQxq5pwHXy14jRK1SA66LAY1c7TPIeRzGaDLYlAzR/u8hHwujUH1Fx18DDPxE1/ipxF1ryZYqk8NNolKWgYvi1Ac0Pj4oOE5m55nO2OLTI678/m8END4+KDhOdLnEPK5DND4+KDhOdLnJeRzEaA2gBS9n/mMGGJM/eCy/lQuAVDQ0lQF0EX1oIws08PXkU1Q9SjaDTesB3Xm8nxuWA/KyDJ9os9lgBqZ7vl8mKgTf++r326yp1fve3VGvPutHnqPVQDU0ypAGZmgKpoBG27d3W6pz6272y31WQjQG52VKkDl4Y+WT6KGi5mhIQ2hM2eZ4XNLQBlMUPTA65/YbQPQZJ87A5rscyWgDGSgAlD1Ad0IAlVz9gig+doGUFT2NNG1XAXLgzYGNas3BTTPp24Hpc6YqIWA5vnU7aC5Ul0CzKwE1IwlIr6QR3ySgDIC0PmM1NNGgALjR/BCECtAcdDsaee2BTTL5wA33xbQLJ8XkpuodKcqfRZBKLOD3eiBJBjIyeeeBI/PZEIbABTI7NkioJ7PRgH1fBYA1PRlYhZQN99MP8cVAGqr5v30bAnQIeSzIUAvIZ9lABWH0IRiLEHdV+B4trqpRUBBJn/Ev5PkP3s7xqCuT7/jk1NRD7PTBVoMqOPT7/jkVNTn84liUASoHmzZOVUI0JEPHIUITTWxGaD+Sn053oXtV4onV8763K8UT66c9bm2FM+FAZVZoOrNfNaVoPK0FHxi+2UfB5QQ1wAABcxJREFU1+0HqE1H98FrC9B5n20BOu9zHaD6aPJ3FcQcBlQHwQwNHoF0RYCqy0WAru8s4mk1oMoRAnR9ZxFPqwFVjnCYWaCziJE5rIBMzUkK1QveAjrGAF1w/o0B1ZeLASVi0LkzJmotoNrREPLZAKDa0SXkszygDPBpAbX987yjoGUZEWg+oOkdlud7MdtpYumiDsv+Za2UcGSniaWLOix7yuiwPN+L2U4TSxd2WDaysI96uHq4xsagZ9uDNHxEtYnzs/Nz3U32y0GBms5BgZrOQYFK5qAoooVjfZ8JTYDGDygBtSNEqiPPELpfDArX4ZipAUBBDArXnRuOQeG6c7kYFAlgpJB0OAWMqZe+f5C2Ac3edPdSfOKmu5fiUzddJZdPMUIpf8WrvnfOBg516u2v3/AYUPxQmbkOaAd0gfB7/QjHelZIuoB6BXwnBsVhiZ3rgHZAF0ihiaA0xSXYwQmMnj9TwIdHJOY6oB3QBRIAwR/GQR3/YAcntcNmgG6v3LSTl7W9Fvm8Wlk+QYd6CygYpFzKfBgaOiI1ty4HLbRyGYQhBS9r8cr/ZhBGZAbBsT2mDahnF1A9/nP2SA4d0IyVHVAoAlAmI1E4bCnMFhfw2QHNWdkBRTIEHt03Oh4Hb9VJepp3kXI+SKIE6csgNKspvgPaRQp9kETobLvUmwUIxllAc7rTsw5oF60IoJo7ikk00wHtqqIlgCJCQ2/4DmjXaoX4tOC5AAIkZwtI9WJQOKiQK2/UIbDqy/v7x5mV39/zUbGDR84XHKTJlTeKE1j12TA8m1n5duCjjAePnK9rSU9XTkZJrKLYXagMQNHg6o780deN+FiY73/3glzJb8O0MHTkfKHB6h35o9kb8bFF3/3ya3Ilx3paGDpyvq4lPT05pXd/HR2fUpvHlQEoGtgSixh93eh7nlLfPs7uPS0MHHmB0EChWMRo9kZvOXkvn83uPS0MHHmBriU9PUnybDneW3f21vmLUlHNABQNDeytdMcORpqWzq6cHvXgkbOFhlr2VrpjMSNNS2dXTlln8MjZupb09HXWHZWJ93gioMkBQAagaHB1V97o61B8zNaZle+//OJF+MjZQoPVu/JGs4fiY+DOrHz32Sdfh4+crWtJT1pzgILSkVugrw3o4if+h99/Fdg7lBss0uIc9MPTzwN7h3LXRbqW9KRlkSMLSnqKobDUbloD0GBk442+DlZ9+RjcOxBPLVIwUvRGswerPnsW3DsQny7StaTnjOZZc3JJmsUKMSgaXN2VN/q6XSPSk16p3kXBI2cLDVbvyhvN3q4RfNIr1bs9eORsXUt6xjSbGQYBTVX9etA391yPsyunmKmJetBXotvxs9mVUwzaRD3o9ukZ0WytkxuLunslHb23JHWtlVdYJ1d4+xSPQbu6QoKFIadiHq2yG6PZmaN2QLvK6AwpdMLPMyYUDQkNNqfUAe0qIzcDnQcUjNcI96UP2wHtKiP0EncL8D6g/r70YTugXYXkvrTtH6cwj0d4Qn99dUC7iilcH2rn9FA5KWX5eoC++6VtE/zPf2fkdAvqPkvJww3+DJ27kQlLI41MmwAK0xZON6Hus5Rc3MCgTTmA4oUd0O6zlOYARVkiApQMCrYElH9FMTzj/34upz+X0y2p+ywm/w1PDCWiuZyNPjcEVDzd7z57xv9+ePqMsVc/+6ahJ16q+6ymuaFuImWj7WJQ3TWNJ+J//ZnZRG5J3efmSii8G9V9xb8chk+ZjpPeDrxLUHMJ2n1uIioOTVHtQtKHp4N8DX14+snXTT7x3ecW8upCU3esX4qfgiU+yfv8srcNPvHd5xbKyTSRqgL6lg92MP3lAf1b/sB/9omYbkrdZxU5PHqApvJaNweVYRKbYqdPefz0yT9NqflyKPbRRBF1nzUUAzI5R+1t8V01FAOwA9q1qzqgXW0rxt/+MWhXVwF1QLuaVge0q2l1QLuaVge0q2l1QLuaVge0q2n9f2AgN2cgN97wAAAAAElFTkSuQmCC" /><!-- --></p>
|
||
<p>除了ggplot2自带的主题外，还有许多拓展主题包，比如：ggthemes、ggthemr ggthemes在cran上发布，因此推荐使用这个 ggthemr 色彩很好看，因此推荐这个</p>
|
||
<p>ggthemes相关链接：<a href="https://github.com/jrnold/ggthemes" class="uri">https://github.com/jrnold/ggthemes</a></p>
|
||
<p>ggthemr相关链接：<a href="https://github.com/Mikata-Project/ggthemr" class="uri">https://github.com/Mikata-Project/ggthemr</a></p>
|
||
<p>因为ggthemr没有上cran，因此需要通过github安装</p>
|
||
<div class="sourceCode" id="cb31"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb31-1"><a href="#cb31-1"></a><span class="co"># devtools::install_github('Mikata-Project/ggthemr')</span></span></code></pre></div>
|
||
<p>使用方法也是非常简单，这里用我比较喜欢的greyscale主题方案</p>
|
||
<div class="sourceCode" id="cb32"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb32-1"><a href="#cb32-1"></a><span class="kw">library</span>(ggthemr)</span>
|
||
<span id="cb32-2"><a href="#cb32-2"></a><span class="kw">ggthemr</span>(<span class="st">"greyscale"</span>)</span>
|
||
<span id="cb32-3"><a href="#cb32-3"></a>p3 <-<span class="st"> </span><span class="kw">ggplot</span>(<span class="dt">data =</span> boston_data, <span class="kw">aes</span>(<span class="dt">x =</span> lstat, <span class="dt">y =</span> medv, <span class="dt">colour =</span> <span class="kw">factor</span>(rad))) <span class="op">+</span></span>
|
||
<span id="cb32-4"><a href="#cb32-4"></a><span class="st"> </span><span class="kw">geom_point</span>()</span>
|
||
<span id="cb32-5"><a href="#cb32-5"></a>p4 <-<span class="st"> </span><span class="kw">ggplot</span>(<span class="dt">data =</span> boston_data, <span class="kw">aes</span>(<span class="dt">x =</span> lstat, <span class="dt">y =</span> medv, <span class="dt">colour =</span> rad)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_point</span>()</span>
|
||
<span id="cb32-6"><a href="#cb32-6"></a><span class="kw">ggarrange</span>(p3, p4, <span class="dt">nrow =</span> <span class="dv">1</span>)</span></code></pre></div>
|
||
<p><img 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" /><!-- --></p>
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<p>试一试light这个主题，配色非常的温柔</p>
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<div class="sourceCode" id="cb33"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb33-1"><a href="#cb33-1"></a><span class="kw">library</span>(ggthemr)</span>
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<span id="cb33-2"><a href="#cb33-2"></a><span class="kw">ggthemr</span>(<span class="st">"light"</span>)</span>
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<span id="cb33-3"><a href="#cb33-3"></a>p3 <-<span class="st"> </span><span class="kw">ggplot</span>(<span class="dt">data =</span> boston_data, <span class="kw">aes</span>(<span class="dt">x =</span> lstat, <span class="dt">y =</span> medv, <span class="dt">colour =</span> <span class="kw">factor</span>(rad))) <span class="op">+</span></span>
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||
<span id="cb33-4"><a href="#cb33-4"></a><span class="st"> </span><span class="kw">geom_point</span>()</span>
|
||
<span id="cb33-5"><a href="#cb33-5"></a>p4 <-<span class="st"> </span><span class="kw">ggplot</span>(<span class="dt">data =</span> boston_data, <span class="kw">aes</span>(<span class="dt">x =</span> lstat, <span class="dt">y =</span> medv, <span class="dt">colour =</span> rad)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_point</span>()</span>
|
||
<span id="cb33-6"><a href="#cb33-6"></a><span class="kw">ggarrange</span>(p3, p4, <span class="dt">nrow =</span> <span class="dv">1</span>)</span></code></pre></div>
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<p><img 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" /><!-- --></p>
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<p>实战部分： 对提供的数据集我们可以试一试ggthemr中的不同主题，同时对波士顿房价进行其它的数据可视化的探索。</p>
|
||
<p>ggplot2是一个非常经典的数据可视化R包，内容非常丰富，由于篇幅的原因没办法将ggplot2中的各种方法全部讲述，因此选择了几个常见的图形进行相关的讲解，以期达到抛砖引玉的效果。如果对ggplot2感兴趣的同学，可以去官网进行更加详细的学习，也非常期待大家的数据可视化作品~</p>
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<p><strong>Task4 END.</strong></p>
|
||
<p>— By: 牧小熊</p>
|
||
<blockquote>
|
||
<p>华中农业大学研究生，Datawhale成员, Datawhale优秀原创作者</p>
|
||
<p>知乎：<a href="https://www.zhihu.com/people/muxiaoxiong" class="uri">https://www.zhihu.com/people/muxiaoxiong</a></p>
|
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</blockquote>
|
||
<p>关于Datawhale： Datawhale是一个专注于数据科学与AI领域的开源组织，汇集了众多领域院校和知名企业的优秀学习者，聚合了一群有开源精神和探索精神的团队成员。Datawhale 以“for the learner，和学习者一起成长”为愿景，鼓励真实地展现自我、开放包容、互信互助、敢于试错和勇于担当。同时 Datawhale 用开源的理念去探索开源内容、开源学习和开源方案，赋能人才培养，助力人才成长，建立起人与人，人与知识，人与企业和人与未来的联结。 本次数据挖掘路径学习，专题知识将在天池分享，详情可关注Datawhale：</p>
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