882 lines
557 KiB
HTML
882 lines
557 KiB
HTML
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<meta name="author" content="June Yao" />
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<meta name="date" content="2021-07-15" />
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<title>Task02_Data_Preparation.knit</title>
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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 />
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||
Task02 数据清洗与准备</div></h1>
|
||
<h4 class="author project-author">June Yao</h4>
|
||
<h4 class="date project-date">2021-07-15</h4>
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</section>
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" style="width:100.0%" /></p>
|
||
<p>Task02共计6个知识点，预计需学习5~8小时，请安排好学习任务。</p>
|
||
<div id="环境配置" class="section level2">
|
||
<h2>0 环境配置</h2>
|
||
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1"></a><span class="kw">library</span>(mlbench) <span class="co">#将会使用到包中的BostonHousing数据集</span></span>
|
||
<span id="cb1-2"><a href="#cb1-2"></a><span class="kw">library</span>(funModeling) <span class="co"># 探索性数据分析工具包，本节内容中将会使用到它的status()函数，打印整体数据质量</span></span>
|
||
<span id="cb1-3"><a href="#cb1-3"></a><span class="kw">library</span>(tidyverse) <span class="co"># 数据转化工具包，本节内容中将会使用它包含的dplyr中的管道函数 %>%</span></span>
|
||
<span id="cb1-4"><a href="#cb1-4"></a><span class="kw">library</span>(VIM) <span class="co"># 缺失值可视化工具包，本节内容中将会使用到它的aggr()函数</span></span>
|
||
<span id="cb1-5"><a href="#cb1-5"></a><span class="kw">library</span>(mice) <span class="co">#缺失值处理工具包，本节内容会使用它来进行多重插补</span></span>
|
||
<span id="cb1-6"><a href="#cb1-6"></a><span class="kw">library</span>(Rlof) <span class="co">#用于LOF异常值检测方法，本节内容将会使用到它的lof()函数</span></span>
|
||
<span id="cb1-7"><a href="#cb1-7"></a><span class="kw">library</span>(fastDummies) <span class="co">#用于生成dummy的包，本节内容将会使用到它的dummy_cols()函数</span></span>
|
||
<span id="cb1-8"><a href="#cb1-8"></a><span class="kw">library</span>(sjmisc) <span class="co">#用于生成dummy的包，本节内容将会使用到它的to_dummy()函数</span></span>
|
||
<span id="cb1-9"><a href="#cb1-9"></a><span class="kw">library</span>(MASS) <span class="co">#基于此包进行box-cox转换</span></span>
|
||
<span id="cb1-10"><a href="#cb1-10"></a><span class="kw">library</span>(dlookr) <span class="co">#本节内容将会使用到它的transform()函数</span></span></code></pre></div>
|
||
</div>
|
||
<div id="案例数据" class="section level2">
|
||
<h2>0 案例数据</h2>
|
||
<p>本节内容将会使用到两个数据集。</p>
|
||
<div id="数据集1-h1n1流感问卷数据集" class="section level3">
|
||
<h3>数据集1 h1n1流感问卷数据集</h3>
|
||
<div id="数据说明" class="section level4">
|
||
<h4>数据说明</h4>
|
||
<p>目前提供的数据集来自关于h1n1流感调查问卷的部分内容，可以从这个网站上看到具体字段的详细说明：<a href="https://www.drivendata.org/competitions/66/flu-shot-learning/page/211/" class="uri">https://www.drivendata.org/competitions/66/flu-shot-learning/page/211/</a></p>
|
||
<p>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>
|
||
</div>
|
||
<div id="加载并查看部分数据" class="section level4">
|
||
<h4>加载并查看部分数据</h4>
|
||
<p>首先加载数据，了解数据集大小。</p>
|
||
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1"></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>
|
||
<span id="cb2-2"><a href="#cb2-2"></a><span class="kw">dim</span>(h1n1_data)</span></code></pre></div>
|
||
<pre><code>## [1] 26707 33</code></pre>
|
||
<p>注：为了简化本章的示例，我们在这32个特征中，筛选出了10个特征，作为一个子集，来学习如何使用R做数据清洗与准备。如有兴趣，可以把下面这块筛选去掉，自己用完整数据集做一次探索。</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>h1n1_data[, <span class="kw">c</span>(<span class="dv">1</span>, <span class="dv">3</span>, <span class="dv">11</span>, <span class="dv">12</span>, <span class="dv">15</span>, <span class="dv">16</span>, <span class="dv">19</span>, <span class="dv">20</span>, <span class="dv">22</span>, <span class="dv">23</span>, <span class="dv">33</span>)]</span>
|
||
<span id="cb4-2"><a href="#cb4-2"></a><span class="kw">head</span>(h1n1_data)</span></code></pre></div>
|
||
<pre><code>## respondent_id h1n1_knowledge doctor_recc_h1n1 chronic_med_condition
|
||
## 1 0 0 0 0
|
||
## 2 1 2 0 0
|
||
## 3 2 1 NA 1
|
||
## 4 3 1 0 1
|
||
## 5 4 1 0 0
|
||
## 6 5 1 0 0
|
||
## health_insurance opinion_h1n1_vacc_effective age_group education
|
||
## 1 1 3 55 - 64 Years < 12 Years
|
||
## 2 1 5 35 - 44 Years 12 Years
|
||
## 3 NA 3 18 - 34 Years College Graduate
|
||
## 4 NA 3 65+ Years 12 Years
|
||
## 5 NA 3 45 - 54 Years Some College
|
||
## 6 NA 5 65+ Years 12 Years
|
||
## sex income_poverty h1n1_vaccine
|
||
## 1 Female Below Poverty 0
|
||
## 2 Male Below Poverty 0
|
||
## 3 Male <= $75,000, Above Poverty 0
|
||
## 4 Female Below Poverty 0
|
||
## 5 Female <= $75,000, Above Poverty 0
|
||
## 6 Male <= $75,000, Above Poverty 0</code></pre>
|
||
</div>
|
||
</div>
|
||
<div id="数据集2-波士顿房价数据集" class="section level3">
|
||
<h3>数据集2 波士顿房价数据集</h3>
|
||
<div id="数据说明-1" class="section level4">
|
||
<h4>数据说明</h4>
|
||
<p>数据集来自<code>mlbench</code>包，请提前装好。数据字段说明可从网址查看：<a href="https://blog.csdn.net/weixin_46027193/article/details/112238597" class="uri">https://blog.csdn.net/weixin_46027193/article/details/112238597</a></p>
|
||
<p>数据集包含506条房价信息，共有13个特征+1个预测字段（房屋价格）。</p>
|
||
</div>
|
||
<div id="加载并查看部分数据-1" class="section level4">
|
||
<h4>加载并查看部分数据</h4>
|
||
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1"></a><span class="kw">data</span>(BostonHousing)</span>
|
||
<span id="cb6-2"><a href="#cb6-2"></a><span class="kw">dim</span>(BostonHousing)</span></code></pre></div>
|
||
<pre><code>## [1] 506 14</code></pre>
|
||
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1"></a><span class="kw">head</span>(BostonHousing)</span></code></pre></div>
|
||
<pre><code>## crim zn indus chas nox rm age dis rad tax ptratio b lstat
|
||
## 1 0.00632 18 2.31 0 0.538 6.575 65.2 4.0900 1 296 15.3 396.90 4.98
|
||
## 2 0.02731 0 7.07 0 0.469 6.421 78.9 4.9671 2 242 17.8 396.90 9.14
|
||
## 3 0.02729 0 7.07 0 0.469 7.185 61.1 4.9671 2 242 17.8 392.83 4.03
|
||
## 4 0.03237 0 2.18 0 0.458 6.998 45.8 6.0622 3 222 18.7 394.63 2.94
|
||
## 5 0.06905 0 2.18 0 0.458 7.147 54.2 6.0622 3 222 18.7 396.90 5.33
|
||
## 6 0.02985 0 2.18 0 0.458 6.430 58.7 6.0622 3 222 18.7 394.12 5.21
|
||
## medv
|
||
## 1 24.0
|
||
## 2 21.6
|
||
## 3 34.7
|
||
## 4 33.4
|
||
## 5 36.2
|
||
## 6 28.7</code></pre>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div id="重复值处理" class="section level2">
|
||
<h2>1 重复值处理</h2>
|
||
<p>在某些情况下，我们需要对数据进行去重处理。<code>unique()</code>函数可以对数据进行整体去重，<code>distinct()</code>函数可以针对某些列去重。</p>
|
||
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1"></a><span class="co"># 整体去重</span></span>
|
||
<span id="cb10-2"><a href="#cb10-2"></a>h1n1_data_de_dup1 <-<span class="st"> </span><span class="kw">unique</span>(h1n1_data)</span>
|
||
<span id="cb10-3"><a href="#cb10-3"></a></span>
|
||
<span id="cb10-4"><a href="#cb10-4"></a><span class="co"># 指定根据列respondent_id,h1n1_knowledge去重，并保留所有列</span></span>
|
||
<span id="cb10-5"><a href="#cb10-5"></a>h1n1_data_de_dup2 <-<span class="st"> </span><span class="kw">distinct</span>(h1n1_data, respondent_id, h1n1_knowledge, <span class="dt">.keep_all =</span> T)</span></code></pre></div>
|
||
</div>
|
||
<div id="缺失值识别与处理" class="section level2">
|
||
<h2>2 缺失值识别与处理</h2>
|
||
<p>现实环境中，由于数据来源及搜集过程，可能有各种不规范，导致数据往往会存在缺失。缺失值识别与处理，无论是在统计还是数据管理中，往往是数据清洗的第一步。</p>
|
||
<div id="缺失值识别" class="section level3">
|
||
<h3>2.1 缺失值识别</h3>
|
||
<div id="常用识别方法" class="section level4">
|
||
<h4>2.1.1 常用识别方法</h4>
|
||
<p>在R语言中，惯用会把缺失值表示为NA，一般可使用<code>is.nan(a)</code>，<code>!complete.cases(a)</code>来识别<code>a</code>是否为缺失值。</p>
|
||
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1"></a><span class="co"># 假设定义的一个变量中存在缺失值</span></span>
|
||
<span id="cb11-2"><a href="#cb11-2"></a>y <-<span class="st"> </span><span class="kw">c</span>(<span class="dv">1</span>, <span class="dv">2</span>, <span class="dv">3</span>, <span class="ot">NA</span>)</span>
|
||
<span id="cb11-3"><a href="#cb11-3"></a></span>
|
||
<span id="cb11-4"><a href="#cb11-4"></a><span class="co"># 用is.nan在识别是否为缺失值</span></span>
|
||
<span id="cb11-5"><a href="#cb11-5"></a><span class="kw">is.na</span>(y)</span></code></pre></div>
|
||
<pre><code>## [1] FALSE FALSE FALSE TRUE</code></pre>
|
||
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1"></a><span class="co"># 用!complete.cases()在识别是否为缺失值</span></span>
|
||
<span id="cb13-2"><a href="#cb13-2"></a><span class="op">!</span><span class="kw">complete.cases</span>(y)</span></code></pre></div>
|
||
<pre><code>## [1] FALSE FALSE FALSE TRUE</code></pre>
|
||
</div>
|
||
<div id="缺失值统计" class="section level4">
|
||
<h4>2.1.2 缺失值统计</h4>
|
||
<p>统计缺失值总数。</p>
|
||
<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1"></a><span class="co"># 数据集中总缺失数据量</span></span>
|
||
<span id="cb15-2"><a href="#cb15-2"></a><span class="kw">sum</span>(<span class="kw">is.na</span>(h1n1_data))</span></code></pre></div>
|
||
<pre><code>## [1] 15912</code></pre>
|
||
<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1"></a><span class="co"># 数据集中某一列缺失数据量</span></span>
|
||
<span id="cb17-2"><a href="#cb17-2"></a><span class="kw">sum</span>(<span class="kw">is.na</span>(h1n1_data[<span class="st">"h1n1_knowledge"</span>]))</span></code></pre></div>
|
||
<pre><code>## [1] 116</code></pre>
|
||
<p>如果想按行或按列统计，可以写函数。</p>
|
||
<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1"></a>pMiss <-<span class="st"> </span><span class="cf">function</span>(x) {</span>
|
||
<span id="cb19-2"><a href="#cb19-2"></a> <span class="kw">sum</span>(<span class="kw">is.na</span>(x))<span class="op">/</span><span class="kw">length</span>(x) <span class="op">*</span><span class="st"> </span><span class="dv">100</span></span>
|
||
<span id="cb19-3"><a href="#cb19-3"></a>}</span>
|
||
<span id="cb19-4"><a href="#cb19-4"></a><span class="kw">apply</span>(h1n1_data, <span class="dv">2</span>, pMiss) <span class="co">#按列统计缺失比率%</span></span></code></pre></div>
|
||
<pre><code>## respondent_id h1n1_knowledge
|
||
## 0.0000000 0.4343431
|
||
## doctor_recc_h1n1 chronic_med_condition
|
||
## 8.0877673 3.6357509
|
||
## health_insurance opinion_h1n1_vacc_effective
|
||
## 45.9579885 1.4640356
|
||
## age_group education
|
||
## 0.0000000 0.0000000
|
||
## sex income_poverty
|
||
## 0.0000000 0.0000000
|
||
## h1n1_vaccine
|
||
## 0.0000000</code></pre>
|
||
<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1"></a><span class="co"># apply(h1n1_data,1,pMiss) #按行统计缺失比率%</span></span></code></pre></div>
|
||
<p>或调用一些现成的包。比如，我们可以使用<code>funModeling</code>包中的<code>status()</code>函数，直接观测案例数据中包含的0值，缺失值（NA），在每个特征中的分布情况。以h1n1 flu数据集为例：</p>
|
||
<div class="sourceCode" id="cb22"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1"></a>data_quality <-<span class="st"> </span><span class="kw">status</span>(h1n1_data)</span>
|
||
<span id="cb22-2"><a href="#cb22-2"></a>data_quality <span class="op">%>%</span></span>
|
||
<span id="cb22-3"><a href="#cb22-3"></a><span class="st"> </span><span class="kw">mutate</span>(<span class="kw">across</span>(<span class="kw">where</span>(is.numeric), <span class="op">~</span><span class="kw">round</span>(., <span class="dv">3</span>))) <span class="co">#保留4位小数</span></span></code></pre></div>
|
||
<pre><code>## variable q_zeros p_zeros q_na
|
||
## respondent_id respondent_id 1 0.000 0
|
||
## h1n1_knowledge h1n1_knowledge 2506 0.094 116
|
||
## doctor_recc_h1n1 doctor_recc_h1n1 19139 0.717 2160
|
||
## chronic_med_condition chronic_med_condition 18446 0.691 971
|
||
## health_insurance health_insurance 1736 0.065 12274
|
||
## opinion_h1n1_vacc_effective opinion_h1n1_vacc_effective 0 0.000 391
|
||
## age_group age_group 0 0.000 0
|
||
## education education 0 0.000 0
|
||
## sex sex 0 0.000 0
|
||
## income_poverty income_poverty 0 0.000 0
|
||
## h1n1_vaccine h1n1_vaccine 21033 0.788 0
|
||
## p_na q_inf p_inf type unique
|
||
## respondent_id 0.000 0 0 integer 26707
|
||
## h1n1_knowledge 0.004 0 0 numeric 3
|
||
## doctor_recc_h1n1 0.081 0 0 numeric 2
|
||
## chronic_med_condition 0.036 0 0 numeric 2
|
||
## health_insurance 0.460 0 0 numeric 2
|
||
## opinion_h1n1_vacc_effective 0.015 0 0 numeric 5
|
||
## age_group 0.000 0 0 character 5
|
||
## education 0.000 0 0 character 5
|
||
## sex 0.000 0 0 character 2
|
||
## income_poverty 0.000 0 0 character 4
|
||
## h1n1_vaccine 0.000 0 0 integer 2</code></pre>
|
||
<p>结合案例数据h1n1 flu来看，存在缺失值的有5个特征字段。</p>
|
||
<div class="sourceCode" id="cb24"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1"></a>missing_Value <-<span class="st"> </span>data_quality[<span class="kw">which</span>(data_quality<span class="op">$</span>p_na <span class="op">></span><span class="st"> </span><span class="dv">0</span>), ]</span>
|
||
<span id="cb24-2"><a href="#cb24-2"></a>missing_Value<span class="op">$</span>variable</span></code></pre></div>
|
||
<pre><code>## [1] "h1n1_knowledge" "doctor_recc_h1n1"
|
||
## [3] "chronic_med_condition" "health_insurance"
|
||
## [5] "opinion_h1n1_vacc_effective"</code></pre>
|
||
</div>
|
||
<div id="缺失值机制与分析" class="section level4">
|
||
<h4>2.1.3 缺失值机制与分析</h4>
|
||
<p>统计学家通常将缺失数据分为3类，为了更好的处理缺失值，我们可以基于缺失值机制来识别以下3种缺失模式：</p>
|
||
<ul>
|
||
<li>MCAR（完全随机缺失）：如果数据的缺失与任何值（观察或缺失）之间没有关系，则为MCAR。</li>
|
||
<li>MAR（随机缺失）：考虑MAR与MCAR有何不同，如果缺失和观测值之间存在系统关系，则为MAR。例如-男性比女性更容易告诉自己的体重，因此体重就是MAR。“ Weight”变量的缺失取决于变量“Sex”的观测值。</li>
|
||
<li>MNAR（非随机缺失）：若缺失数据不属于MCAR和MAR，数据的缺失依赖于不完全变量本身，则数据为非随机缺失。例如，抑郁程度高的人更不容易填写抑郁调查问卷。</li>
|
||
</ul>
|
||
<p>MNAR是最复杂的情况，处理 MNAR的策略是找到更多有关缺失原因的数据，或者执行假设分析，查看结果在各种情况下的敏感程度。大部分处理缺失数据的方法都假定数据是MCAR或MAR，此时，可以忽略缺失数据的生成机制，在替换或删除缺失数据后，直接对感兴趣的关系进行建模。</p>
|
||
<p>以下介绍几种可视化分析缺失数据关联的方法：</p>
|
||
<p>我们用<code>VIM</code>包里的<code>aggr()</code>函数，直观看一下具体的缺失情况。</p>
|
||
<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1"></a><span class="kw">aggr</span>(h1n1_data, <span class="dt">cex.axis =</span> <span class="fl">0.6</span>, <span class="dt">oma =</span> <span class="kw">c</span>(<span class="dv">9</span>, <span class="dv">5</span>, <span class="dv">5</span>, <span class="dv">1</span>)) <span class="co">#cex.axis调整轴字体大小，oma调整外边框大小</span></span></code></pre></div>
|
||
<p><img src="data:image/png;base64,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" /><!-- --></p>
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<p>通过用<code>VIM</code>包里的矩阵图<code>matrixplot()</code>函数，可以检查某些变量的缺失值模式是否与其他变量的真实值有关联。矩阵图中，观测数据以黑白色阶显示（颜色越深，数值越高），缺失值会被标记为红色。我们对某一个存在缺失值的变量进行排序，来找寻含缺失值变量与其他变量的关系。</p>
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<p>在此案例中，我们按照<code>health_insurance</code>进行分组排序。可以看到是否有慢性病<code>chronic_med_condition</code>的缺失，与<code>opinion_h1n1_vacc_effective</code>的缺失相对较集中。除此之外，也可以看到有慢性病的人年龄普遍较大。</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="co"># 先简单处理一下一些类别变量的顺序</span></span>
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<span id="cb27-2"><a href="#cb27-2"></a>h1n1_data_matplt <-<span class="st"> </span>h1n1_data</span>
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<span id="cb27-3"><a href="#cb27-3"></a>h1n1_data_matplt<span class="op">$</span>age_group <-<span class="st"> </span><span class="kw">factor</span>(h1n1_data_matplt<span class="op">$</span>age_group)</span>
|
||
<span id="cb27-4"><a href="#cb27-4"></a>h1n1_data_matplt<span class="op">$</span>education <-<span class="st"> </span><span class="kw">factor</span>(h1n1_data_matplt<span class="op">$</span>education, <span class="dt">levels =</span> <span class="kw">c</span>(<span class="st">""</span>, <span class="st">"< 12 Years"</span>,</span>
|
||
<span id="cb27-5"><a href="#cb27-5"></a> <span class="st">"12 Years"</span>, <span class="st">"Some College"</span>, <span class="st">"College Graduate"</span>))</span>
|
||
<span id="cb27-6"><a href="#cb27-6"></a>h1n1_data_matplt<span class="op">$</span>sex <-<span class="st"> </span><span class="kw">factor</span>(h1n1_data_matplt<span class="op">$</span>sex)</span>
|
||
<span id="cb27-7"><a href="#cb27-7"></a>h1n1_data_matplt<span class="op">$</span>income_poverty <-<span class="st"> </span><span class="kw">factor</span>(h1n1_data_matplt<span class="op">$</span>income_poverty, <span class="dt">levels =</span> <span class="kw">c</span>(<span class="st">"18 - 34 Years"</span>,</span>
|
||
<span id="cb27-8"><a href="#cb27-8"></a> <span class="st">"<= $75,000, Above Poverty"</span>, <span class="st">"> $75,000"</span>))</span>
|
||
<span id="cb27-9"><a href="#cb27-9"></a><span class="co"># levels(h1n1_data_matplt$age_group) # 查看顺序</span></span>
|
||
<span id="cb27-10"><a href="#cb27-10"></a></span>
|
||
<span id="cb27-11"><a href="#cb27-11"></a><span class="co"># 矩阵图可视化</span></span>
|
||
<span id="cb27-12"><a href="#cb27-12"></a><span class="kw">par</span>(<span class="dt">mar =</span> <span class="kw">c</span>(<span class="dv">9</span>, <span class="fl">4.1</span>, <span class="fl">2.1</span>, <span class="fl">2.1</span>)) <span class="co"># x轴标签太长，调用par()函数调整外边框的大小</span></span>
|
||
<span id="cb27-13"><a href="#cb27-13"></a><span class="kw">matrixplot</span>(h1n1_data_matplt, <span class="dt">sortby =</span> <span class="st">"chronic_med_condition"</span>, <span class="dt">cex.axis =</span> <span class="fl">0.7</span>) <span class="co">#cex.axis为调整坐标轴字体大小</span></span></code></pre></div>
|
||
<p><img src="data:image/png;base64,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" /><!-- --></p>
|
||
<p>用相关性探索缺失值。首先生成一个影子矩阵，用指示变量替代数据集中的数据（1表示缺失，0表示存在）。</p>
|
||
<div class="sourceCode" id="cb28"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb28-1"><a href="#cb28-1"></a>shadow_mat <-<span class="st"> </span><span class="kw">as.data.frame</span>(<span class="kw">abs</span>(<span class="kw">is.na</span>(h1n1_data[, <span class="dv">-1</span>])))</span>
|
||
<span id="cb28-2"><a href="#cb28-2"></a><span class="kw">head</span>(shadow_mat)</span></code></pre></div>
|
||
<pre><code>## h1n1_knowledge doctor_recc_h1n1 chronic_med_condition health_insurance
|
||
## 1 0 0 0 0
|
||
## 2 0 0 0 0
|
||
## 3 0 1 0 1
|
||
## 4 0 0 0 1
|
||
## 5 0 0 0 1
|
||
## 6 0 0 0 1
|
||
## opinion_h1n1_vacc_effective age_group education sex income_poverty
|
||
## 1 0 0 0 0 0
|
||
## 2 0 0 0 0 0
|
||
## 3 0 0 0 0 0
|
||
## 4 0 0 0 0 0
|
||
## 5 0 0 0 0 0
|
||
## 6 0 0 0 0 0
|
||
## h1n1_vaccine
|
||
## 1 0
|
||
## 2 0
|
||
## 3 0
|
||
## 4 0
|
||
## 5 0
|
||
## 6 0</code></pre>
|
||
<div class="sourceCode" id="cb30"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1"></a><span class="co"># 可提取含缺失值的变量</span></span>
|
||
<span id="cb30-2"><a href="#cb30-2"></a>shadow_mat <-<span class="st"> </span>shadow_mat[<span class="kw">which</span>(<span class="kw">apply</span>(shadow_mat, <span class="dv">2</span>, sum) <span class="op">></span><span class="st"> </span><span class="dv">0</span>)]</span>
|
||
<span id="cb30-3"><a href="#cb30-3"></a></span>
|
||
<span id="cb30-4"><a href="#cb30-4"></a><span class="co"># 计算相关系数</span></span>
|
||
<span id="cb30-5"><a href="#cb30-5"></a><span class="kw">cor</span>(shadow_mat)</span></code></pre></div>
|
||
<pre><code>## h1n1_knowledge doctor_recc_h1n1
|
||
## h1n1_knowledge 1.00000000 0.00546769
|
||
## doctor_recc_h1n1 0.00546769 1.00000000
|
||
## chronic_med_condition 0.02367388 0.09572429
|
||
## health_insurance -0.01292316 0.22136525
|
||
## opinion_h1n1_vacc_effective 0.01565202 0.14793032
|
||
## chronic_med_condition health_insurance
|
||
## h1n1_knowledge 0.02367388 -0.01292316
|
||
## doctor_recc_h1n1 0.09572429 0.22136525
|
||
## chronic_med_condition 1.00000000 0.15724626
|
||
## health_insurance 0.15724626 1.00000000
|
||
## opinion_h1n1_vacc_effective 0.47431031 0.10403005
|
||
## opinion_h1n1_vacc_effective
|
||
## h1n1_knowledge 0.01565202
|
||
## doctor_recc_h1n1 0.14793032
|
||
## chronic_med_condition 0.47431031
|
||
## health_insurance 0.10403005
|
||
## opinion_h1n1_vacc_effective 1.00000000</code></pre>
|
||
<div class="sourceCode" id="cb32"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb32-1"><a href="#cb32-1"></a><span class="co"># 相关系数热力图</span></span>
|
||
<span id="cb32-2"><a href="#cb32-2"></a><span class="kw">heatmap</span>(<span class="kw">cor</span>(shadow_mat))</span></code></pre></div>
|
||
<p><img src="data:image/png;base64,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" /><!-- --></p>
|
||
<p>根据缺失相关性矩阵，<code>opinion_h1n1_vacc_effective</code> 与 <code>chronic_med_condition</code> 缺失相关性较大。</p>
|
||
<p>综上，在案例中，变量之间的存在部分相关性，考虑为MAR。</p>
|
||
<p>其他数据缺失关系分析，可参考附录<code>数据的预处理基础</code>。</p>
|
||
</div>
|
||
</div>
|
||
<div id="缺失值处理" class="section level3">
|
||
<h3>2.2 缺失值处理</h3>
|
||
<p>缺失值一般有三种方式：</p>
|
||
<ul>
|
||
<li>将缺失值作为变量值使用。比如在民意调查中，当选民不投票时，可以将缺失值处理为“无法确定”。</li>
|
||
<li>删除数据。主要有删除样本值和删除特征值。但可能会损失掉一些有用信息。</li>
|
||
<li>插补法。如均值/中位数/同类均值插补（数值变量），众数插补（类别变量），手动插补(根据主观理解)，多重插补等。</li>
|
||
</ul>
|
||
<p>以下我们主要介绍删除法和插补法：</p>
|
||
<div id="删除法" class="section level4">
|
||
<h4>2.2.1 删除法</h4>
|
||
<p>行删除，可以直接用<code>complete.cases()</code>或<code>na.omit()</code>来过滤掉数据集中所有缺失行。</p>
|
||
<div class="sourceCode" id="cb33"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb33-1"><a href="#cb33-1"></a>h1n1_data_row_del1 <-<span class="st"> </span>h1n1_data[<span class="op">!</span><span class="kw">complete.cases</span>(h1n1_data), ]</span>
|
||
<span id="cb33-2"><a href="#cb33-2"></a>h1n1_data_row_del2 <-<span class="st"> </span><span class="kw">na.omit</span>(h1n1_data)</span></code></pre></div>
|
||
<p>列删除，一般对于缺失率极高又没有太大作用的特征值，我们直接删除，如可以用<code>dataset[,-5]</code>去掉第五列，或<code>subset(dataset, select = -c(col1, col2))</code>去掉列col1和列col2。</p>
|
||
<p>比如，我们把<code>health_insurance</code>变量删除。</p>
|
||
<div class="sourceCode" id="cb34"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb34-1"><a href="#cb34-1"></a>h1n1_data_col_del1 <-<span class="st"> </span><span class="kw">subset</span>(h1n1_data, <span class="dt">select =</span> <span class="op">-</span><span class="kw">c</span>(health_insurance))</span></code></pre></div>
|
||
</div>
|
||
<div id="简单插补法" class="section level4">
|
||
<h4>2.2.2 简单插补法</h4>
|
||
<p>注意在空值插补的时候，要区分类别变量与数值变量，均值插补不适用于类别变量。我们这里随机选择了一个变量演示<code>impute()</code>函数用法，在实际插补的时候，请大家根据情况进行选择。</p>
|
||
<div class="sourceCode" id="cb35"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb35-1"><a href="#cb35-1"></a>h1n1_data_sim_imp <-<span class="st"> </span>h1n1_data</span>
|
||
<span id="cb35-2"><a href="#cb35-2"></a>h1n1_data_sim_imp<span class="op">$</span>h1n1_knowledge <-<span class="st"> </span><span class="kw">impute</span>(h1n1_data_sim_imp<span class="op">$</span>h1n1_knowledge, <span class="dv">1</span>) <span class="co">#填充特定值</span></span>
|
||
<span id="cb35-3"><a href="#cb35-3"></a>h1n1_data_sim_imp<span class="op">$</span>h1n1_knowledge <-<span class="st"> </span><span class="kw">impute</span>(h1n1_data_sim_imp<span class="op">$</span>h1n1_knowledge, median) <span class="co">#插补中位数</span></span>
|
||
<span id="cb35-4"><a href="#cb35-4"></a>h1n1_data_sim_imp<span class="op">$</span>h1n1_knowledge <-<span class="st"> </span><span class="kw">impute</span>(h1n1_data_sim_imp<span class="op">$</span>h1n1_knowledge, mean) <span class="co">#插补均值</span></span></code></pre></div>
|
||
</div>
|
||
<div id="拟合插补法" class="section level4">
|
||
<h4>2.2.3 拟合插补法</h4>
|
||
<p>利用有监督的机器学习方法，比如回归、最邻近、随机森林、支持向量机等模型，对缺失值作预测。</p>
|
||
</div>
|
||
<div id="多重插补法" class="section level4">
|
||
<h4>2.2.4 多重插补法</h4>
|
||
<p>多重插补（MI）是一种基于重复模拟的处理缺失值的方法。其思想来源于贝叶斯估计，认为待插补的值是随机的，它的值来自于已观测到的值。具体实践上通常是估计出待插补的值，然后再加上不同的噪声，形成多组可选插补值（通常是3到10个）。根据某种选择依据，选取最合适的插补值。与单个插补（例如均值）相比，创建多个插补可解决缺失值的不确定性。 R中可利用<code>Amelia</code>、<code>mice</code>和<code>mi</code>包来执行这些操作。</p>
|
||
<p>本节中，我们将用案例介绍mice包（通过链式方程进行的多元插补）提供的方法。使用mice生成m个完整的插补数据集。然后利用<code>with-pool</code>的方法来评估选择哪一个数据集。首先使用<code>with()</code>函数依次对每个完整数据集应用统计模型如lm，glm等，用<code>summary()</code>输出数据集检验，看某数据集是否合格。接下来<code>pool()</code>函数把5个回归模型汇总，用<code>summary()</code>输出汇总数据集检验，查看整体插补方法是否合格。检验结果分析可参考附录<code>mice检验结果解释</code></p>
|
||
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style="width:60.0%" /></p>
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<div class="sourceCode" id="cb36"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb36-1"><a href="#cb36-1"></a><span class="co"># 先处理下数据，把数据集中一些类别变量转换回来</span></span>
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<span id="cb36-2"><a href="#cb36-2"></a></span>
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<span id="cb36-3"><a href="#cb36-3"></a><span class="co"># imp是一个包含m个插补数据集的列表对象，同时还含有完成插补过程的信息。</span></span>
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<span id="cb36-4"><a href="#cb36-4"></a><span class="co"># 参数m的默认值为5，这里我们将m设为4，生成4个无缺失数据集 参数method,</span></span>
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<span id="cb36-5"><a href="#cb36-5"></a><span class="co"># 对于每个变量的拟合，可以指定所用的拟合方法,method传入的参数可以是一个具体方法，也可以为不同列指定具体方法，具体方法选择可参考附录mice使用文档。这里我们使用默认值。</span></span>
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<span id="cb36-6"><a href="#cb36-6"></a>imp <-<span class="st"> </span><span class="kw">mice</span>(h1n1_data, <span class="dt">m =</span> <span class="dv">4</span>, <span class="dt">seed =</span> <span class="dv">122</span>, <span class="dt">printFlag =</span> <span class="ot">FALSE</span>)</span>
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<span id="cb36-7"><a href="#cb36-7"></a></span>
|
||
<span id="cb36-8"><a href="#cb36-8"></a><span class="co"># 查看变量h1n1_knowledge在几个插补数据集中的插补结果 imp$imp$h1n1_knowledge</span></span>
|
||
<span id="cb36-9"><a href="#cb36-9"></a></span>
|
||
<span id="cb36-10"><a href="#cb36-10"></a><span class="co"># 查看每个变量所用的插补方法 imp$method</span></span>
|
||
<span id="cb36-11"><a href="#cb36-11"></a></span>
|
||
<span id="cb36-12"><a href="#cb36-12"></a><span class="co"># 设定应用于m个插补数据集的统计分析方法。方法包括做线性回归模型的lm()函数、做广义线性模型的glm()函数、做广义可加模型的gam()，做负二项模型的nbrm()函数</span></span>
|
||
<span id="cb36-13"><a href="#cb36-13"></a>fit <-<span class="st"> </span><span class="kw">with</span>(imp, <span class="kw">lm</span>(h1n1_vaccine <span class="op">~</span><span class="st"> </span>h1n1_knowledge <span class="op">+</span><span class="st"> </span>doctor_recc_h1n1 <span class="op">+</span><span class="st"> </span>chronic_med_condition <span class="op">+</span></span>
|
||
<span id="cb36-14"><a href="#cb36-14"></a><span class="st"> </span>health_insurance <span class="op">+</span><span class="st"> </span>opinion_h1n1_vacc_effective))</span>
|
||
<span id="cb36-15"><a href="#cb36-15"></a></span>
|
||
<span id="cb36-16"><a href="#cb36-16"></a><span class="co"># 输出每个数据集检验</span></span>
|
||
<span id="cb36-17"><a href="#cb36-17"></a><span class="kw">print.data.frame</span>(<span class="kw">summary</span>(fit), <span class="dt">digits =</span> <span class="dv">4</span>)</span></code></pre></div>
|
||
<pre><code>## term estimate std.error statistic p.value nobs
|
||
## 1 (Intercept) -0.30492 0.010809 -28.209 1.557e-172 26707
|
||
## 2 h1n1_knowledge 0.03645 0.003661 9.956 2.596e-23 26707
|
||
## 3 doctor_recc_h1n1 0.34604 0.005568 62.147 0.000e+00 26707
|
||
## 4 chronic_med_condition 0.03033 0.005015 6.048 1.485e-09 26707
|
||
## 5 health_insurance 0.07826 0.006754 11.587 5.706e-31 26707
|
||
## 6 opinion_h1n1_vacc_effective 0.08317 0.002245 37.054 4.116e-293 26707
|
||
## 7 (Intercept) -0.30718 0.010901 -28.179 3.509e-172 26707
|
||
## 8 h1n1_knowledge 0.03689 0.003683 10.016 1.429e-23 26707
|
||
## 9 doctor_recc_h1n1 0.33876 0.005563 60.893 0.000e+00 26707
|
||
## 10 chronic_med_condition 0.02972 0.005031 5.907 3.521e-09 26707
|
||
## 11 health_insurance 0.07776 0.006957 11.178 6.028e-29 26707
|
||
## 12 opinion_h1n1_vacc_effective 0.08385 0.002258 37.128 2.986e-294 26707
|
||
## 13 (Intercept) -0.30981 0.010830 -28.607 2.603e-177 26707
|
||
## 14 h1n1_knowledge 0.03666 0.003679 9.965 2.386e-23 26707
|
||
## 15 doctor_recc_h1n1 0.33489 0.005557 60.262 0.000e+00 26707
|
||
## 16 chronic_med_condition 0.02948 0.005035 5.855 4.814e-09 26707
|
||
## 17 health_insurance 0.08090 0.006742 12.000 4.334e-33 26707
|
||
## 18 opinion_h1n1_vacc_effective 0.08415 0.002258 37.272 1.851e-296 26707
|
||
## 19 (Intercept) -0.30608 0.010910 -28.055 1.047e-170 26707
|
||
## 20 h1n1_knowledge 0.03702 0.003685 10.046 1.056e-23 26707
|
||
## 21 doctor_recc_h1n1 0.33370 0.005564 59.970 0.000e+00 26707
|
||
## 22 chronic_med_condition 0.02969 0.005040 5.891 3.877e-09 26707
|
||
## 23 health_insurance 0.07557 0.006896 10.959 6.877e-28 26707
|
||
## 24 opinion_h1n1_vacc_effective 0.08423 0.002259 37.278 1.490e-296 26707</code></pre>
|
||
<div class="sourceCode" id="cb38"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb38-1"><a href="#cb38-1"></a><span class="co"># 包含m个统计分析平均结果的列表对象</span></span>
|
||
<span id="cb38-2"><a href="#cb38-2"></a>pooled <-<span class="st"> </span><span class="kw">pool</span>(fit)</span>
|
||
<span id="cb38-3"><a href="#cb38-3"></a></span>
|
||
<span id="cb38-4"><a href="#cb38-4"></a><span class="co"># 这是一个总体评估结果</span></span>
|
||
<span id="cb38-5"><a href="#cb38-5"></a>pooled</span></code></pre></div>
|
||
<pre><code>## Class: mipo m = 4
|
||
## term m estimate ubar b
|
||
## 1 (Intercept) 4 -0.30699871 1.179991e-04 4.368721e-06
|
||
## 2 h1n1_knowledge 4 0.03675472 1.352049e-05 6.410610e-08
|
||
## 3 doctor_recc_h1n1 4 0.33834805 3.094965e-05 3.095473e-05
|
||
## 4 chronic_med_condition 4 0.02980518 2.530162e-05 1.342220e-07
|
||
## 5 health_insurance 4 0.07812323 4.675346e-05 4.779575e-06
|
||
## 6 opinion_h1n1_vacc_effective 4 0.08385005 5.085296e-06 2.294296e-07
|
||
## t dfcom df riv lambda fmi
|
||
## 1 1.234600e-04 26701 1446.448456 0.046279160 0.044232134 0.045550936
|
||
## 2 1.360062e-05 26701 20305.470346 0.005926755 0.005891836 0.005989737
|
||
## 3 6.964306e-05 26701 9.710629 1.250205046 0.555596055 0.625522404
|
||
## 4 2.546940e-05 26701 19168.975299 0.006631097 0.006587415 0.006691047
|
||
## 5 5.272792e-05 26701 231.386715 0.127786668 0.113307482 0.120873547
|
||
## 6 5.372083e-06 26701 1010.568516 0.056395341 0.053384693 0.055252579</code></pre>
|
||
<div class="sourceCode" id="cb40"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb40-1"><a href="#cb40-1"></a><span class="co"># 这里修改action的参数（范围1-m），选择一个数据集作为我们已填充完成的数据集</span></span>
|
||
<span id="cb40-2"><a href="#cb40-2"></a>h1n1_data_complete <-<span class="st"> </span><span class="kw">complete</span>(imp, <span class="dt">action =</span> <span class="dv">2</span>)</span></code></pre></div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div id="异常值识别与处理" class="section level2">
|
||
<h2>3 异常值识别与处理</h2>
|
||
<div id="异常值识别" class="section level3">
|
||
<h3>3.1 异常值识别</h3>
|
||
<p>本节的异常值指离群点。为了让数据统计或数据建模更加准确，我们通常会识别并对处理一些离群点。有些模型会对异常值较敏感，参考附录<code>什么样的模型对缺失值更敏感?</code>。 总的来说，有几种常用方法，包括可视化图形分布识别（箱线图）、z-score识别、局部异常因子法（LOF法）、聚类法等。</p>
|
||
<p>我们这里用波士顿房价数据集来演示一下异常值识别的处理过程。</p>
|
||
</div>
|
||
<div id="可视化图形分布" class="section level3">
|
||
<h3>3.1.1 可视化图形分布</h3>
|
||
<p>首先是可视化图形分布识别，将数值型变量筛选出来，用boxlpot看看分布。</p>
|
||
<div class="sourceCode" id="cb41"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb41-1"><a href="#cb41-1"></a><span class="co"># 提取数值字段</span></span>
|
||
<span id="cb41-2"><a href="#cb41-2"></a>nums <-<span class="st"> </span><span class="kw">unlist</span>(<span class="kw">lapply</span>(BostonHousing, is.numeric))</span>
|
||
<span id="cb41-3"><a href="#cb41-3"></a>nums_data <-<span class="st"> </span>BostonHousing[, nums]</span>
|
||
<span id="cb41-4"><a href="#cb41-4"></a></span>
|
||
<span id="cb41-5"><a href="#cb41-5"></a><span class="co"># 数据变形</span></span>
|
||
<span id="cb41-6"><a href="#cb41-6"></a>nums_data.new <-<span class="st"> </span>nums_data <span class="op">%>%</span></span>
|
||
<span id="cb41-7"><a href="#cb41-7"></a><span class="st"> </span>as.data.frame <span class="op">%>%</span></span>
|
||
<span id="cb41-8"><a href="#cb41-8"></a><span class="st"> </span><span class="kw">mutate</span>(<span class="dt">Cell =</span> <span class="kw">rownames</span>(.)) <span class="op">%>%</span></span>
|
||
<span id="cb41-9"><a href="#cb41-9"></a><span class="st"> </span><span class="kw">gather</span>(., <span class="dt">key =</span> colname, <span class="dt">value =</span> <span class="st">"value"</span>, <span class="op">-</span>Cell)</span>
|
||
<span id="cb41-10"><a href="#cb41-10"></a></span>
|
||
<span id="cb41-11"><a href="#cb41-11"></a><span class="co"># 用ggplot画出箱线图</span></span>
|
||
<span id="cb41-12"><a href="#cb41-12"></a><span class="kw">ggplot</span>(<span class="dt">data =</span> nums_data.new, <span class="kw">aes</span>(<span class="dt">x =</span> colname, <span class="dt">y =</span> value)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_boxplot</span>(<span class="kw">aes</span>(<span class="dv">1</span>)) <span class="op">+</span></span>
|
||
<span id="cb41-13"><a href="#cb41-13"></a><span class="st"> </span><span class="kw">facet_wrap</span>(<span class="op">~</span>colname, <span class="dt">scales =</span> <span class="st">"free"</span>) <span class="op">+</span><span class="st"> </span><span class="kw">theme_grey</span>() <span class="op">+</span><span class="st"> </span><span class="kw">labs</span>(<span class="dt">title =</span> <span class="st">"Outlier Detection On Numeric Data By Boxplot"</span>,</span>
|
||
<span id="cb41-14"><a href="#cb41-14"></a> <span class="dt">x =</span> <span class="st">"Numeric Columns"</span>, <span class="dt">y =</span> <span class="st">""</span>) <span class="op">+</span><span class="st"> </span><span class="kw">theme</span>(<span class="dt">legend.position =</span> <span class="st">"top"</span>) <span class="op">+</span><span class="st"> </span><span class="kw">theme_bw</span>()</span></code></pre></div>
|
||
<p><img src="data:image/png;base64,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" /><!-- --></p>
|
||
<p>通过可视化分布，可以选择剔除一些不合理的离群值，比如在数据集中将dis>10.0的数据剔除。</p>
|
||
</div>
|
||
<div id="z-score" class="section level3">
|
||
<h3>3.1.2 z-score</h3>
|
||
<p>z-score是一种一维或低维特征空间中参数异常检测方法。它假定数据是高斯分布，异常值是分布尾部的数据点，因此远离数据的平均值。一般将z-score低于-3或高于3的数据看成是异常值。</p>
|
||
<div class="sourceCode" id="cb42"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb42-1"><a href="#cb42-1"></a><span class="co"># 定义一个识别异常点的函数，x是输入数据（matrix或df）,zs是异常临界值，z-score超过zs的被识别为异常点</span></span>
|
||
<span id="cb42-2"><a href="#cb42-2"></a>outliers =<span class="st"> </span><span class="cf">function</span>(x, zs) {</span>
|
||
<span id="cb42-3"><a href="#cb42-3"></a> temp <-<span class="st"> </span><span class="kw">abs</span>(<span class="kw">apply</span>(x, <span class="dv">1</span>, scale))</span>
|
||
<span id="cb42-4"><a href="#cb42-4"></a> <span class="kw">return</span>(x[temp <span class="op">></span><span class="st"> </span>zs])</span>
|
||
<span id="cb42-5"><a href="#cb42-5"></a>}</span>
|
||
<span id="cb42-6"><a href="#cb42-6"></a><span class="co"># 打印出z-score<3的值</span></span>
|
||
<span id="cb42-7"><a href="#cb42-7"></a><span class="kw">outliers</span>(nums_data, <span class="dv">3</span>)</span></code></pre></div>
|
||
<pre><code>## [1] 7.380 0.700 0.573 5.889 17.400 20.200 392.400 396.900 396.900
|
||
## [10] 393.680 396.900 368.570 396.900 377.730 375.330 396.900 391.980 100.630
|
||
## [19] 388.520 255.230 374.680 392.680 395.770 12.430 11.280 27.710 10.210
|
||
## [28] 6.860 9.880 9.620 4.210 13.000 25.410 16.900 29.550 6.360
|
||
## [37] 4.850 4.700 4.610 13.270 2.960 24.560 19.370 14.100 14.330
|
||
## [46] 22.800 33.400</code></pre>
|
||
</div>
|
||
<div id="局部异常因子法" class="section level3">
|
||
<h3>3.1.3 局部异常因子法</h3>
|
||
<p>局部异常因子法(LOF)，是一种无监督的离群检测方法，是基于密度的离群点检测方法中一个比较有代表性的算法。适用于在中等高维数据集上执行异常值检测。</p>
|
||
<div class="sourceCode" id="cb44"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb44-1"><a href="#cb44-1"></a><span class="co"># k是计算局部异常因子所需要判断异常点周围的点的个数</span></span>
|
||
<span id="cb44-2"><a href="#cb44-2"></a>outlier_score <-<span class="st"> </span><span class="kw">lof</span>(<span class="dt">data =</span> nums_data, <span class="dt">k =</span> <span class="dv">5</span>)</span>
|
||
<span id="cb44-3"><a href="#cb44-3"></a></span>
|
||
<span id="cb44-4"><a href="#cb44-4"></a><span class="co"># 绘制异常值得分的直方分布图</span></span>
|
||
<span id="cb44-5"><a href="#cb44-5"></a><span class="kw">hist</span>(outlier_score, <span class="dt">col =</span> <span class="st">"#8ac6d1"</span>)</span></code></pre></div>
|
||
<p><img src="data:image/png;base64,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" /><!-- --></p>
|
||
<div class="sourceCode" id="cb45"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb45-1"><a href="#cb45-1"></a><span class="co"># 排序，挑出得分排前五的数据（找到索引）作为异常值</span></span>
|
||
<span id="cb45-2"><a href="#cb45-2"></a><span class="kw">names</span>(outlier_score) <-<span class="st"> </span><span class="dv">1</span><span class="op">:</span><span class="kw">nrow</span>(nums_data)</span>
|
||
<span id="cb45-3"><a href="#cb45-3"></a><span class="kw">sort</span>(outlier_score, <span class="dt">decreasing =</span> <span class="ot">TRUE</span>)[<span class="dv">1</span><span class="op">:</span><span class="dv">5</span>]</span></code></pre></div>
|
||
<pre><code>## 489 493 381 492 406
|
||
## 5.133201 4.534088 4.529170 3.732775 3.559666</code></pre>
|
||
</div>
|
||
<div id="异常值处理" class="section level3">
|
||
<h3>3.2 异常值处理</h3>
|
||
<p>首先需要确定是否是真的异常值，有些值虽然离群，但其实并不是异常值，处理掉反而会影响后续任务的准确性。 如果确定需要处理，可以参考缺失值的处理方式进行处理。</p>
|
||
</div>
|
||
</div>
|
||
<div id="特征编码" class="section level2">
|
||
<h2>4 特征编码</h2>
|
||
<p>为什么要进行特征编码？我们拿到的原始数据中，一般会有一些类别变量，但是在统计或机器学习中，我们通常需要把类别变量转化为数值型变量，才能应用于一些方法中。</p>
|
||
<div id="独热编码哑编码" class="section level3">
|
||
<h3>4.1 独热编码/哑编码</h3>
|
||
<p>One-hot encoding 和 dummy,是将类别变量扩充为多个只显示1，0的变量，每个变量代表原类别变量中的一个类。 注意他们之间的区别：<a href="https://www.cnblogs.com/lianyingteng/p/7792693.html" class="uri">https://www.cnblogs.com/lianyingteng/p/7792693.html</a></p>
|
||
<ul>
|
||
<li>优点：解决了分类器不好处理分类数据的问题，在一定程度上也起到了扩充特征的作用。它的值只有0和1，不同的类型存储在垂直的空间。<br />
|
||
</li>
|
||
<li>缺点：当类别的数量很多时，特征空间会变得非常大，容易造成维度灾难。（为避免维度灾难，后续可以考虑降维处理）</li>
|
||
</ul>
|
||
<p>R里面有很多现成的转化编码的包，我们这里使用了<code>dummy_cols()</code>函数做演示，可以看到原来的类别类型字段，已经扩充为多个0，1编码的字段。</p>
|
||
<div class="sourceCode" id="cb47"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb47-1"><a href="#cb47-1"></a>h1n1_data_dummy <-<span class="st"> </span><span class="kw">dummy_cols</span>(<span class="kw">subset</span>(h1n1_data_complete, <span class="dt">select =</span> <span class="kw">c</span>(age_group)),</span>
|
||
<span id="cb47-2"><a href="#cb47-2"></a> <span class="dt">select_columns =</span> <span class="kw">c</span>(<span class="st">"age_group"</span>))</span>
|
||
<span id="cb47-3"><a href="#cb47-3"></a><span class="kw">head</span>(h1n1_data_dummy)</span></code></pre></div>
|
||
<pre><code>## age_group age_group_18 - 34 Years age_group_35 - 44 Years
|
||
## 1 55 - 64 Years 0 0
|
||
## 2 35 - 44 Years 0 1
|
||
## 3 18 - 34 Years 1 0
|
||
## 4 65+ Years 0 0
|
||
## 5 45 - 54 Years 0 0
|
||
## 6 65+ Years 0 0
|
||
## age_group_45 - 54 Years age_group_55 - 64 Years age_group_65+ Years
|
||
## 1 0 1 0
|
||
## 2 0 0 0
|
||
## 3 0 0 0
|
||
## 4 0 0 1
|
||
## 5 1 0 0
|
||
## 6 0 0 1</code></pre>
|
||
</div>
|
||
<div id="标签编码" class="section level3">
|
||
<h3>4.2 标签编码</h3>
|
||
<p>标签编码(Label Encoder)是将类别变量转换成连续的数值型变量，通常对有序的变量进行标签编码，既保留了顺序信息，也节约了空间（不会扩充变量）</p>
|
||
<p>R里有一个特殊的结构factor（factor是有序的分类变量），我们这里可以利用factor来做标签编码。首先根据实际情况设置factor的类别顺序，然后直接用<code>as.numeric()</code>转化为数字。</p>
|
||
<div class="sourceCode" id="cb49"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb49-1"><a href="#cb49-1"></a>h1n1_data_complete_lab_encoder <-<span class="st"> </span>h1n1_data_complete</span>
|
||
<span id="cb49-2"><a href="#cb49-2"></a>h1n1_data_complete_lab_encoder<span class="op">$</span>income_poverty_lab_encoder <-<span class="st"> </span><span class="kw">as.numeric</span>(<span class="kw">factor</span>(h1n1_data_complete_lab_encoder<span class="op">$</span>income_poverty,</span>
|
||
<span id="cb49-3"><a href="#cb49-3"></a> <span class="dt">levels =</span> <span class="kw">c</span>(<span class="st">"Below Poverty"</span>, <span class="st">"<= $75,000, Above Poverty"</span>, <span class="st">"> $75,000"</span>)))</span>
|
||
<span id="cb49-4"><a href="#cb49-4"></a><span class="kw">head</span>(<span class="kw">subset</span>(h1n1_data_complete_lab_encoder, <span class="dt">select =</span> <span class="kw">c</span>(income_poverty, income_poverty_lab_encoder)))</span></code></pre></div>
|
||
<pre><code>## income_poverty income_poverty_lab_encoder
|
||
## 1 Below Poverty 1
|
||
## 2 Below Poverty 1
|
||
## 3 <= $75,000, Above Poverty 2
|
||
## 4 Below Poverty 1
|
||
## 5 <= $75,000, Above Poverty 2
|
||
## 6 <= $75,000, Above Poverty 2</code></pre>
|
||
</div>
|
||
<div id="手动编码" class="section level3">
|
||
<h3>4.3 手动编码</h3>
|
||
<p>比如，当某一个特征中有很多类别，我们认为某些类别可以合为一类，可以用<code>case_when()</code>函数手动处理。</p>
|
||
<div class="sourceCode" id="cb51"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb51-1"><a href="#cb51-1"></a>h1n1_data_manual <-<span class="st"> </span><span class="kw">subset</span>(h1n1_data_complete, <span class="dt">select =</span> <span class="kw">c</span>(age_group))</span>
|
||
<span id="cb51-2"><a href="#cb51-2"></a>h1n1_data_manual<span class="op">$</span>age_group_manual <-<span class="st"> </span><span class="kw">case_when</span>(h1n1_data_manual<span class="op">$</span>age_group <span class="op">%in%</span><span class="st"> </span><span class="kw">c</span>(<span class="st">"18 - 34 Years"</span>) <span class="op">~</span></span>
|
||
<span id="cb51-3"><a href="#cb51-3"></a><span class="st"> </span><span class="dv">1</span>, h1n1_data_manual<span class="op">$</span>age_group <span class="op">%in%</span><span class="st"> </span><span class="kw">c</span>(<span class="st">"35 - 44 Years"</span>, <span class="st">"45 - 54 Years"</span>, <span class="st">"55 - 64 Years"</span>) <span class="op">~</span></span>
|
||
<span id="cb51-4"><a href="#cb51-4"></a><span class="st"> </span><span class="dv">2</span>, h1n1_data_manual<span class="op">$</span>age_group <span class="op">%in%</span><span class="st"> </span><span class="kw">c</span>(<span class="st">"65+ Years"</span>) <span class="op">~</span><span class="st"> </span><span class="dv">3</span>)</span>
|
||
<span id="cb51-5"><a href="#cb51-5"></a><span class="kw">head</span>(h1n1_data_manual)</span></code></pre></div>
|
||
<pre><code>## age_group age_group_manual
|
||
## 1 55 - 64 Years 2
|
||
## 2 35 - 44 Years 2
|
||
## 3 18 - 34 Years 1
|
||
## 4 65+ Years 3
|
||
## 5 45 - 54 Years 2
|
||
## 6 65+ Years 3</code></pre>
|
||
</div>
|
||
<div id="日期特征转换" class="section level3">
|
||
<h3>4.4 日期特征转换</h3>
|
||
<p>参考附录<code>R语言日期时间处理</code></p>
|
||
</div>
|
||
</div>
|
||
<div id="规范化与偏态数据" class="section level2">
|
||
<h2>5 规范化与偏态数据</h2>
|
||
<p>为什么要数据规范化？简单来说是为了去除数据量纲和数据大小的差异，确保数据是在同一量纲或者同一数量级下进行比较，一般用在机器学习算法之前。数据规范化又可以使用0-1规范化，Z-score等方法。 为什么要处理偏态数据？。很多模型会假设数据或参数服从正态分布。例如线性回归(linear regression)，它假设误差服从正态分布。</p>
|
||
<p>提示：注意在测试数据与训练数据分布差别很大的情况下，对测试数据运用一些规范化方法时，可能因为数据分布不匹配而带来误差。</p>
|
||
<p>这里我们使用波士顿房价数据集来做演示。可以看到图中数据的偏态分布及量纲差别。</p>
|
||
<div class="sourceCode" id="cb53"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb53-1"><a href="#cb53-1"></a>BostonHousing <span class="op">%>%</span></span>
|
||
<span id="cb53-2"><a href="#cb53-2"></a><span class="st"> </span><span class="kw">keep</span>(is.numeric) <span class="op">%>%</span></span>
|
||
<span id="cb53-3"><a href="#cb53-3"></a><span class="st"> </span><span class="kw">gather</span>() <span class="op">%>%</span></span>
|
||
<span id="cb53-4"><a href="#cb53-4"></a><span class="st"> </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(value)) <span class="op">+</span><span class="st"> </span><span class="kw">facet_wrap</span>(<span class="op">~</span>key, <span class="dt">scales =</span> <span class="st">"free"</span>) <span class="op">+</span><span class="st"> </span><span class="kw">geom_density</span>(<span class="dt">color =</span> <span class="st">"#348498"</span>,</span>
|
||
<span id="cb53-5"><a href="#cb53-5"></a> <span class="dt">fill =</span> <span class="st">"#8ac6d1"</span>) <span class="op">+</span><span class="st"> </span><span class="kw">theme_bw</span>()</span></code></pre></div>
|
||
<p><img src="data:image/png;base64,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" /><!-- --></p>
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<div id="规范化" class="section level3">
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<h3>5.1 0-1规范化</h3>
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<p>0-1规范化是将原始数据缩放到[0,1]区间内，一般方法是最小最大规范的方法，公式如下：</p>
|
||
<p><img src="data:image/png;base64,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" style="width:20.0%" /></p>
|
||
<p>这里用循环计算出每一列的最大最小值，再根据公式求出缩放后的数据。</p>
|
||
<div class="sourceCode" id="cb54"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb54-1"><a href="#cb54-1"></a>nums_data_norm1 <-<span class="st"> </span>nums_data</span>
|
||
<span id="cb54-2"><a href="#cb54-2"></a><span class="cf">for</span> (col <span class="cf">in</span> <span class="kw">names</span>(nums_data_norm1)) {</span>
|
||
<span id="cb54-3"><a href="#cb54-3"></a> xmin <-<span class="st"> </span><span class="kw">min</span>(nums_data_norm1[col])</span>
|
||
<span id="cb54-4"><a href="#cb54-4"></a> xmax <-<span class="st"> </span><span class="kw">max</span>(nums_data_norm1[col])</span>
|
||
<span id="cb54-5"><a href="#cb54-5"></a> nums_data_norm1[col] <-<span class="st"> </span>(nums_data_norm1[col] <span class="op">-</span><span class="st"> </span>xmin)<span class="op">/</span>(xmax <span class="op">-</span><span class="st"> </span>xmin)</span>
|
||
<span id="cb54-6"><a href="#cb54-6"></a>}</span>
|
||
<span id="cb54-7"><a href="#cb54-7"></a></span>
|
||
<span id="cb54-8"><a href="#cb54-8"></a><span class="kw">head</span>(nums_data_norm1)</span></code></pre></div>
|
||
<pre><code>## crim zn indus nox rm age dis
|
||
## 1 0.0000000000 0.18 0.06781525 0.3148148 0.5775053 0.6416066 0.2692031
|
||
## 2 0.0002359225 0.00 0.24230205 0.1728395 0.5479977 0.7826982 0.3489620
|
||
## 3 0.0002356977 0.00 0.24230205 0.1728395 0.6943859 0.5993821 0.3489620
|
||
## 4 0.0002927957 0.00 0.06304985 0.1502058 0.6585553 0.4418126 0.4485446
|
||
## 5 0.0007050701 0.00 0.06304985 0.1502058 0.6871048 0.5283213 0.4485446
|
||
## 6 0.0002644715 0.00 0.06304985 0.1502058 0.5497222 0.5746653 0.4485446
|
||
## rad tax ptratio b lstat medv
|
||
## 1 0.00000000 0.20801527 0.2872340 1.0000000 0.08967991 0.4222222
|
||
## 2 0.04347826 0.10496183 0.5531915 1.0000000 0.20447020 0.3688889
|
||
## 3 0.04347826 0.10496183 0.5531915 0.9897373 0.06346578 0.6600000
|
||
## 4 0.08695652 0.06679389 0.6489362 0.9942761 0.03338852 0.6311111
|
||
## 5 0.08695652 0.06679389 0.6489362 1.0000000 0.09933775 0.6933333
|
||
## 6 0.08695652 0.06679389 0.6489362 0.9929901 0.09602649 0.5266667</code></pre>
|
||
<p>转换完再看一下分布，已经缩放到0-1之间了。</p>
|
||
<div class="sourceCode" id="cb56"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb56-1"><a href="#cb56-1"></a>nums_data_norm1 <span class="op">%>%</span></span>
|
||
<span id="cb56-2"><a href="#cb56-2"></a><span class="st"> </span><span class="kw">keep</span>(is.numeric) <span class="op">%>%</span></span>
|
||
<span id="cb56-3"><a href="#cb56-3"></a><span class="st"> </span><span class="kw">gather</span>() <span class="op">%>%</span></span>
|
||
<span id="cb56-4"><a href="#cb56-4"></a><span class="st"> </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(value)) <span class="op">+</span><span class="st"> </span><span class="kw">facet_wrap</span>(<span class="op">~</span>key, <span class="dt">scales =</span> <span class="st">"free"</span>) <span class="op">+</span><span class="st"> </span><span class="kw">geom_density</span>(<span class="dt">color =</span> <span class="st">"#348498"</span>,</span>
|
||
<span id="cb56-5"><a href="#cb56-5"></a> <span class="dt">fill =</span> <span class="st">"#8ac6d1"</span>) <span class="op">+</span><span class="st"> </span><span class="kw">theme_bw</span>()</span></code></pre></div>
|
||
<p><img 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" /><!-- --></p>
|
||
<p>此外可以用dlookr包里的<code>transform()</code>函数。</p>
|
||
<div class="sourceCode" id="cb57"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb57-1"><a href="#cb57-1"></a>nums_data_norm2 <-<span class="st"> </span>nums_data</span>
|
||
<span id="cb57-2"><a href="#cb57-2"></a>nums_data_norm2<span class="op">$</span>crim <-<span class="st"> </span>dlookr<span class="op">::</span><span class="kw">transform</span>(nums_data<span class="op">$</span>crim, <span class="dt">method =</span> <span class="st">"minmax"</span>)</span></code></pre></div>
|
||
</div>
|
||
<div id="z-score标准化" class="section level3">
|
||
<h3>5.2 Z-score标准化</h3>
|
||
<p>Z-score标准化是原数据减去期望再除以标准差，将数据按比例缩放，使其落入到一个小的区间内，标准化后的数据可正可负，但是一般绝对值不会太大。</p>
|
||
<p><img src="data:image/png;base64,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" style="width:15.0%" /></p>
|
||
<p>R里面可以用<code>scale()</code>函数来计算z-score。也可以dlookr包里的中<code>transform()</code>函数。</p>
|
||
<div class="sourceCode" id="cb58"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb58-1"><a href="#cb58-1"></a>nums_data_zscore <-<span class="st"> </span>nums_data</span>
|
||
<span id="cb58-2"><a href="#cb58-2"></a>nums_data_zscore <-<span class="st"> </span><span class="kw">scale</span>(nums_data_zscore)</span>
|
||
<span id="cb58-3"><a href="#cb58-3"></a><span class="kw">head</span>(nums_data_zscore)</span></code></pre></div>
|
||
<pre><code>## crim zn indus nox rm age dis
|
||
## 1 -0.4193669 0.2845483 -1.2866362 -0.1440749 0.4132629 -0.1198948 0.140075
|
||
## 2 -0.4169267 -0.4872402 -0.5927944 -0.7395304 0.1940824 0.3668034 0.556609
|
||
## 3 -0.4169290 -0.4872402 -0.5927944 -0.7395304 1.2814456 -0.2655490 0.556609
|
||
## 4 -0.4163384 -0.4872402 -1.3055857 -0.8344581 1.0152978 -0.8090878 1.076671
|
||
## 5 -0.4120741 -0.4872402 -1.3055857 -0.8344581 1.2273620 -0.5106743 1.076671
|
||
## 6 -0.4166314 -0.4872402 -1.3055857 -0.8344581 0.2068916 -0.3508100 1.076671
|
||
## rad tax ptratio b lstat medv
|
||
## 1 -0.9818712 -0.6659492 -1.4575580 0.4406159 -1.0744990 0.1595278
|
||
## 2 -0.8670245 -0.9863534 -0.3027945 0.4406159 -0.4919525 -0.1014239
|
||
## 3 -0.8670245 -0.9863534 -0.3027945 0.3960351 -1.2075324 1.3229375
|
||
## 4 -0.7521778 -1.1050216 0.1129203 0.4157514 -1.3601708 1.1815886
|
||
## 5 -0.7521778 -1.1050216 0.1129203 0.4406159 -1.0254866 1.4860323
|
||
## 6 -0.7521778 -1.1050216 0.1129203 0.4101651 -1.0422909 0.6705582</code></pre>
|
||
<p>转换完再看一下分布，数据缩放后在0周围的一个小区间了。</p>
|
||
<div class="sourceCode" id="cb60"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb60-1"><a href="#cb60-1"></a><span class="kw">data.frame</span>(nums_data_zscore) <span class="op">%>%</span></span>
|
||
<span id="cb60-2"><a href="#cb60-2"></a><span class="st"> </span><span class="kw">keep</span>(is.numeric) <span class="op">%>%</span></span>
|
||
<span id="cb60-3"><a href="#cb60-3"></a><span class="st"> </span><span class="kw">gather</span>() <span class="op">%>%</span></span>
|
||
<span id="cb60-4"><a href="#cb60-4"></a><span class="st"> </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(value)) <span class="op">+</span><span class="st"> </span><span class="kw">facet_wrap</span>(<span class="op">~</span>key, <span class="dt">scales =</span> <span class="st">"free"</span>) <span class="op">+</span><span class="st"> </span><span class="kw">geom_density</span>(<span class="dt">color =</span> <span class="st">"#348498"</span>,</span>
|
||
<span id="cb60-5"><a href="#cb60-5"></a> <span class="dt">fill =</span> <span class="st">"#8ac6d1"</span>) <span class="op">+</span><span class="st"> </span><span class="kw">theme_bw</span>()</span></code></pre></div>
|
||
<p><img src="data:image/png;base64,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" /><!-- --></p>
|
||
</div>
|
||
<div id="对数转换log-transform" class="section level3">
|
||
<h3>5.3 对数转换(log transform)</h3>
|
||
<p>使用对数转换也是一种常见的处理偏斜特征的方法，但要注意原数据中不能含有负值。此外为了避免0值，我们通常使用log1p，公式为<code>lg(x+1)</code>。可以直接用dlookr包里的<code>transform()</code>函数，一般结合mutate函数一起使用。</p>
|
||
<div class="sourceCode" id="cb61"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb61-1"><a href="#cb61-1"></a><span class="co"># 直接公式转换</span></span>
|
||
<span id="cb61-2"><a href="#cb61-2"></a>nums_data_log1p1 <-<span class="st"> </span><span class="kw">log</span>(nums_data <span class="op">+</span><span class="st"> </span><span class="dv">1</span>)</span>
|
||
<span id="cb61-3"><a href="#cb61-3"></a></span>
|
||
<span id="cb61-4"><a href="#cb61-4"></a><span class="co"># 用transform()函数</span></span>
|
||
<span id="cb61-5"><a href="#cb61-5"></a>nums_data_log1p2 <-<span class="st"> </span>nums_data</span>
|
||
<span id="cb61-6"><a href="#cb61-6"></a>nums_data_log1p2<span class="op">$</span>b <-<span class="st"> </span>dlookr<span class="op">::</span><span class="kw">transform</span>(nums_data_log1p2<span class="op">$</span>b, <span class="dt">method =</span> <span class="st">"log+1"</span>)</span></code></pre></div>
|
||
<p>转换完再看一下分布，大多变量转换后接近正态分布了。但是这里要特别注意离散数据。</p>
|
||
<div class="sourceCode" id="cb62"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb62-1"><a href="#cb62-1"></a>nums_data_log1p1 <span class="op">%>%</span></span>
|
||
<span id="cb62-2"><a href="#cb62-2"></a><span class="st"> </span><span class="kw">keep</span>(is.numeric) <span class="op">%>%</span></span>
|
||
<span id="cb62-3"><a href="#cb62-3"></a><span class="st"> </span><span class="kw">gather</span>() <span class="op">%>%</span></span>
|
||
<span id="cb62-4"><a href="#cb62-4"></a><span class="st"> </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(value)) <span class="op">+</span><span class="st"> </span><span class="kw">facet_wrap</span>(<span class="op">~</span>key, <span class="dt">scales =</span> <span class="st">"free"</span>) <span class="op">+</span><span class="st"> </span><span class="kw">geom_density</span>(<span class="dt">color =</span> <span class="st">"#348498"</span>,</span>
|
||
<span id="cb62-5"><a href="#cb62-5"></a> <span class="dt">fill =</span> <span class="st">"#8ac6d1"</span>) <span class="op">+</span><span class="st"> </span><span class="kw">theme_bw</span>()</span></code></pre></div>
|
||
<p><img 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" /><!-- --></p>
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</div>
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<div id="box-cox" class="section level3">
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<h3>5.4 Box-Cox</h3>
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<p>Box-Cox变换是Box和Cox在1964年提出的一种广义幂变换方法，在变换后可以一定程度上减小不可观测的误差和预测变量的相关性，在机器学习中经常用来处理偏态分布。其一个显著优点是通过求变换参数来确定变换形式，而这个过程完全基于数据本身而无须任何先验信息，这无疑比凭经验或通过尝试而选用对数、平方根等变换方式要客观和精确。计算公式如下：</p>
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<p><img 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" style="width:40.0%" /></p>
|
||
<p>示例参考附录<code>基于R语言进行Box-Cox变换</code></p>
|
||
</div>
|
||
</div>
|
||
<div id="小拓展" class="section level2">
|
||
<h2>6 小拓展</h2>
|
||
<p>R语言中，mutate 类似于SQL中，根据表的现有变量，生成新变量。使用mutate集中处理变量转换，代码显示较整洁。</p>
|
||
<div class="sourceCode" id="cb63"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb63-1"><a href="#cb63-1"></a> h1n1_data_de <-<span class="st"> </span>h1n1_data_complete <span class="op">%>%</span><span class="st"> </span><span class="kw">to_dummy</span>(education, <span class="dt">suffix =</span> <span class="st">"label"</span>) <span class="op">%>%</span><span class="st"> </span></span>
|
||
<span id="cb63-2"><a href="#cb63-2"></a><span class="st"> </span><span class="kw">bind_cols</span>(h1n1_data_complete) <span class="op">%>%</span><span class="st"> </span></span>
|
||
<span id="cb63-3"><a href="#cb63-3"></a><span class="st"> </span><span class="kw">mutate</span>(</span>
|
||
<span id="cb63-4"><a href="#cb63-4"></a> <span class="co">#标签编码(label encoder)</span></span>
|
||
<span id="cb63-5"><a href="#cb63-5"></a> <span class="dt">sex =</span> <span class="kw">as.factor</span>(<span class="kw">as.numeric</span>(<span class="kw">factor</span>(sex))),</span>
|
||
<span id="cb63-6"><a href="#cb63-6"></a> <span class="dt">income_poverty =</span> (<span class="kw">as.numeric</span>(<span class="kw">factor</span>(</span>
|
||
<span id="cb63-7"><a href="#cb63-7"></a> income_poverty,</span>
|
||
<span id="cb63-8"><a href="#cb63-8"></a> <span class="dt">levels =</span> <span class="kw">c</span>(</span>
|
||
<span id="cb63-9"><a href="#cb63-9"></a> <span class="st">"Below Poverty"</span>,</span>
|
||
<span id="cb63-10"><a href="#cb63-10"></a> <span class="st">"<= $75,000, Above Poverty"</span>,</span>
|
||
<span id="cb63-11"><a href="#cb63-11"></a> <span class="st">"> $75,000"</span></span>
|
||
<span id="cb63-12"><a href="#cb63-12"></a> )</span>
|
||
<span id="cb63-13"><a href="#cb63-13"></a> ))),</span>
|
||
<span id="cb63-14"><a href="#cb63-14"></a> <span class="co">#手动编码</span></span>
|
||
<span id="cb63-15"><a href="#cb63-15"></a> <span class="dt">age_group =</span> <span class="kw">as.factor</span>(</span>
|
||
<span id="cb63-16"><a href="#cb63-16"></a> <span class="kw">case_when</span>(</span>
|
||
<span id="cb63-17"><a href="#cb63-17"></a> age_group <span class="op">%in%</span><span class="st"> </span><span class="kw">c</span>(<span class="st">"18 - 34 Years"</span>) <span class="op">~</span><span class="st"> </span><span class="dv">1</span>, </span>
|
||
<span id="cb63-18"><a href="#cb63-18"></a> age_group <span class="op">%in%</span><span class="st"> </span><span class="kw">c</span>(<span class="st">"35 - 44 Years"</span>,<span class="st">"45 - 54 Years"</span>,<span class="st">"55 - 64 Years"</span>) <span class="op">~</span><span class="st"> </span><span class="dv">2</span>, </span>
|
||
<span id="cb63-19"><a href="#cb63-19"></a> age_group <span class="op">%in%</span><span class="st"> </span><span class="kw">c</span>(<span class="st">"65+ Years"</span>) <span class="op">~</span><span class="st"> </span><span class="dv">3</span></span>
|
||
<span id="cb63-20"><a href="#cb63-20"></a> )),</span>
|
||
<span id="cb63-21"><a href="#cb63-21"></a> <span class="co">#标准化</span></span>
|
||
<span id="cb63-22"><a href="#cb63-22"></a> <span class="kw">across</span>( </span>
|
||
<span id="cb63-23"><a href="#cb63-23"></a> <span class="kw">c</span>(<span class="st">"h1n1_knowledge"</span>, </span>
|
||
<span id="cb63-24"><a href="#cb63-24"></a> <span class="st">"doctor_recc_h1n1"</span>,</span>
|
||
<span id="cb63-25"><a href="#cb63-25"></a> <span class="st">"chronic_med_condition"</span>,</span>
|
||
<span id="cb63-26"><a href="#cb63-26"></a> <span class="st">"opinion_h1n1_vacc_effective"</span>,</span>
|
||
<span id="cb63-27"><a href="#cb63-27"></a> <span class="st">"age_group"</span>,</span>
|
||
<span id="cb63-28"><a href="#cb63-28"></a> <span class="st">"income_poverty"</span>),</span>
|
||
<span id="cb63-29"><a href="#cb63-29"></a> <span class="op">~</span><span class="st"> </span><span class="kw">scale</span>(<span class="kw">as.numeric</span>(.x))</span>
|
||
<span id="cb63-30"><a href="#cb63-30"></a> )</span>
|
||
<span id="cb63-31"><a href="#cb63-31"></a> ) <span class="op">%>%</span><span class="st"> </span>dplyr<span class="op">::</span><span class="kw">select</span>(<span class="op">-</span><span class="kw">one_of</span>(<span class="st">"education"</span>,<span class="st">"education_"</span>))</span>
|
||
<span id="cb63-32"><a href="#cb63-32"></a></span>
|
||
<span id="cb63-33"><a href="#cb63-33"></a><span class="kw">head</span>( h1n1_data_de)</span></code></pre></div>
|
||
<pre><code>## education_< 12 Years education_12 Years education_College Graduate
|
||
## 1 1 0 0
|
||
## 2 0 1 0
|
||
## 3 0 0 1
|
||
## 4 0 1 0
|
||
## 5 0 0 0
|
||
## 6 0 1 0
|
||
## education_Some College respondent_id h1n1_knowledge doctor_recc_h1n1
|
||
## 1 0 0 -2.0416901 -0.5258839
|
||
## 2 0 1 1.1935904 -0.5258839
|
||
## 3 0 2 -0.4240499 -0.5258839
|
||
## 4 0 3 -0.4240499 -0.5258839
|
||
## 5 1 4 -0.4240499 -0.5258839
|
||
## 6 0 5 -0.4240499 -0.5258839
|
||
## chronic_med_condition health_insurance opinion_h1n1_vacc_effective
|
||
## 1 -0.6284091 1 -0.8439071
|
||
## 2 -0.6284091 1 1.1407906
|
||
## 3 1.5912605 1 -0.8439071
|
||
## 4 1.5912605 1 -0.8439071
|
||
## 5 -0.6284091 0 -0.8439071
|
||
## 6 -0.6284091 1 1.1407906
|
||
## age_group sex income_poverty h1n1_vaccine
|
||
## 1 -0.09109418 1 -1.8905904 0
|
||
## 2 -0.09109418 2 -1.8905904 0
|
||
## 3 -1.58547517 2 -0.2945789 0
|
||
## 4 1.40328681 1 -1.8905904 0
|
||
## 5 -0.09109418 1 -0.2945789 0
|
||
## 6 1.40328681 2 -0.2945789 0</code></pre>
|
||
<p>注意在机器学习中，尽量在数据集划分后，分别在训练集与验证集、测试集上进行数据清洗，避免数据泄露。R中的数据集划分方法参考附录<code>R中数据集分割</code>。</p>
|
||
</div>
|
||
<div id="思考与练习" class="section level2">
|
||
<h2>思考与练习</h2>
|
||
<p>看完了本节数据清洗与准备，尝试着选取一个完整的数据集（从本节中选取或使用自己的数据集），来做一次清洗吧！</p>
|
||
</div>
|
||
<div id="附录参考资料" class="section level2">
|
||
<h2>附录：参考资料</h2>
|
||
<div id="理论资料" class="section level3">
|
||
<h3>理论资料</h3>
|
||
<p><strong>数据的预处理基础：</strong> 如何处理缺失值 <a href="https://cloud.tencent.com/developer/article/1626004" class="uri">https://cloud.tencent.com/developer/article/1626004</a></p>
|
||
<p><strong>多重插补法：</strong> 处理缺失值之多重插补（Multiple Imputation）<a href="https://zhuanlan.zhihu.com/p/36436260" class="uri">https://zhuanlan.zhihu.com/p/36436260</a></p>
|
||
<p><strong>异常值检测：</strong> R语言–异常值检测 <a href="https://blog.csdn.net/kicilove/article/details/76260350" class="uri">https://blog.csdn.net/kicilove/article/details/76260350</a></p>
|
||
<p><strong>异常值检测之LOF：</strong> 异常检测算法之局部异常因子算法-Local Outlier Factor(LOF) <a href="https://blog.csdn.net/BigData_Mining/article/details/102914342" class="uri">https://blog.csdn.net/BigData_Mining/article/details/102914342</a></p>
|
||
<p><strong>规范化：</strong> 规范化、标准化、归一化、正则化 <a href="https://blog.csdn.net/u014381464/article/details/81101551" class="uri">https://blog.csdn.net/u014381464/article/details/81101551</a></p>
|
||
<p><strong>什么样的模型对缺失值更敏感？：</strong> <a href="https://blog.csdn.net/zhang15953709913/article/details/88717220" class="uri">https://blog.csdn.net/zhang15953709913/article/details/88717220</a></p>
|
||
</div>
|
||
<div id="r语言函数用法示例" class="section level3">
|
||
<h3>R语言函数用法示例</h3>
|
||
<p><code>funModeling</code>用法示例：<a href="https://cran.r-project.org/web/packages/funModeling/vignettes/funModeling_quickstart.html" class="uri">https://cran.r-project.org/web/packages/funModeling/vignettes/funModeling_quickstart.html</a></p>
|
||
<p><code>tidyverse</code>官方文档：<a href="https://www.tidyverse.org/" class="uri">https://www.tidyverse.org/</a></p>
|
||
<p><code>VIM</code>教学网页：<a href="https://www.datacamp.com/community/tutorials/visualize-data-vim-package" class="uri">https://www.datacamp.com/community/tutorials/visualize-data-vim-package</a></p>
|
||
<p><code>mice</code>使用文档(Multivariate Imputation by Chained Equations)：<a href="https://cran.r-project.org/web/packages/mice/mice.pdf" class="uri">https://cran.r-project.org/web/packages/mice/mice.pdf</a></p>
|
||
<p><code>mice</code>使用中文解释：<a href="https://blog.csdn.net/sinat_26917383/article/details/51265213" class="uri">https://blog.csdn.net/sinat_26917383/article/details/51265213</a></p>
|
||
<p><code>mice</code>检验结果解释：<a href="http://blog.fens.me/r-na-mice/" class="uri">http://blog.fens.me/r-na-mice/</a></p>
|
||
<p><code>caret</code>包数据预处理：<a href="https://www.cnblogs.com/Hyacinth-Yuan/p/8284612.html" class="uri">https://www.cnblogs.com/Hyacinth-Yuan/p/8284612.html</a></p>
|
||
<p>R语言日期时间处理：<a href="https://zhuanlan.zhihu.com/p/83984803" class="uri">https://zhuanlan.zhihu.com/p/83984803</a></p>
|
||
<p>基于R语言进行Box-Cox变换：<a href="https://ask.hellobi.com/blog/R_shequ/18371" class="uri">https://ask.hellobi.com/blog/R_shequ/18371</a></p>
|
||
<p>R中数据集分割：<a href="https://zhuanlan.zhihu.com/p/45163182" class="uri">https://zhuanlan.zhihu.com/p/45163182</a></p>
|
||
<p><strong>Task2 END.</strong></p>
|
||
<p>— By: June</p>
|
||
<blockquote>
|
||
<p>悉尼大学研究生，Datawhale成员</p>
|
||
</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>
|
||
<p><a href="https://camo.githubusercontent.com/8578ee173c78b587d5058439bbd0b98fa39c173def229a8c3d957e62aac0b649/68747470733a2f2f696d672d626c6f672e6373646e696d672e636e2f323032303039313330313032323639382e706e67237069635f63656e746572"><img src="data:image/png;base64,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