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EDA.html
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<meta name="author" content="Choonghyun Ryu" />
<meta name="date" content="2021-10-13" />
<title>Exploratory Data Analysis</title>
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<h1 class="title toc-ignore">Exploratory Data Analysis</h1>
<h4 class="author">Choonghyun Ryu</h4>
<h4 class="date">2021-10-13</h4>
<div id="preface" class="section level2">
<h2>Preface</h2>
<p>After you have acquired the data, you should do the following:</p>
<ul>
<li>Diagnose data quality.
<ul>
<li>If there is a problem with data quality,</li>
<li>The data must be corrected or re-acquired.</li>
</ul></li>
<li><strong>Explore data to understand the data and find scenarios for performing the analysis.</strong></li>
<li>Derive new variables or perform variable transformations.</li>
</ul>
<p>The dlookr package makes these steps fast and easy:</p>
<ul>
<li>Performs an data diagnosis or automatically generates a data diagnosis report.</li>
<li><strong>Discover data in a variety of ways, and automatically generate EDA(exploratory data analysis) report.</strong></li>
<li>Impute missing values and outliers, resolve skewed data, and categorize continuous variables into categorical variables. And generates an automated report to support it.</li>
</ul>
<p>This document introduces <strong>EDA(Exploratory Data Analysis)</strong> methods provided by the dlookr package. You will learn how to EDA of <code>tbl_df</code> data that inherits from data.frame and <code>data.frame</code> with functions provided by dlookr.</p>
<p>dlookr increases synergy with <code>dplyr</code>. Particularly in data exploration and data wrangle, it increases the efficiency of the <code>tidyverse</code> package group.</p>
</div>
<div id="supported-data-structures" class="section level2">
<h2>Supported data structures</h2>
<p>Data diagnosis supports the following data structures.</p>
<ul>
<li>data frame : data.frame class.</li>
<li>data table : tbl_df class.</li>
<li><strong>table of DBMS</strong> : table of the DBMS through tbl_dbi.
<ul>
<li><strong>Use dplyr as the back-end interface for any DBI-compatible database.</strong></li>
</ul></li>
</ul>
</div>
<div id="datasets" class="section level2">
<h2>datasets</h2>
<p>To illustrate the basic use of EDA in the dlookr package, I use a <code>Carseats</code> dataset. <code>Carseats</code> in the <code>ISLR</code> package is a simulated data set containing sales of child car seats at 400 different stores. This data is a data.frame created for the purpose of predicting sales volume.</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ISLR)</span>
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="fu">str</span>(Carseats)</span>
<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="st">'data.frame'</span><span class="sc">:</span> <span class="dv">400</span> obs. of <span class="dv">11</span> variables<span class="sc">:</span></span>
<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a> <span class="er">$</span> Sales <span class="sc">:</span> num <span class="fl">9.5</span> <span class="fl">11.22</span> <span class="fl">10.06</span> <span class="fl">7.4</span> <span class="fl">4.15</span> ...</span>
<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a> <span class="sc">$</span> CompPrice <span class="sc">:</span> num <span class="dv">138</span> <span class="dv">111</span> <span class="dv">113</span> <span class="dv">117</span> <span class="dv">141</span> <span class="dv">124</span> <span class="dv">115</span> <span class="dv">136</span> <span class="dv">132</span> <span class="dv">132</span> ...</span>
<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a> <span class="sc">$</span> Income <span class="sc">:</span> num <span class="dv">73</span> <span class="dv">48</span> <span class="dv">35</span> <span class="dv">100</span> <span class="dv">64</span> <span class="dv">113</span> <span class="dv">105</span> <span class="dv">81</span> <span class="dv">110</span> <span class="dv">113</span> ...</span>
<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a> <span class="sc">$</span> Advertising<span class="sc">:</span> num <span class="dv">11</span> <span class="dv">16</span> <span class="dv">10</span> <span class="dv">4</span> <span class="dv">3</span> <span class="dv">13</span> <span class="dv">0</span> <span class="dv">15</span> <span class="dv">0</span> <span class="dv">0</span> ...</span>
<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a> <span class="sc">$</span> Population <span class="sc">:</span> num <span class="dv">276</span> <span class="dv">260</span> <span class="dv">269</span> <span class="dv">466</span> <span class="dv">340</span> <span class="dv">501</span> <span class="dv">45</span> <span class="dv">425</span> <span class="dv">108</span> <span class="dv">131</span> ...</span>
<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a> <span class="sc">$</span> Price <span class="sc">:</span> num <span class="dv">120</span> <span class="dv">83</span> <span class="dv">80</span> <span class="dv">97</span> <span class="dv">128</span> <span class="dv">72</span> <span class="dv">108</span> <span class="dv">120</span> <span class="dv">124</span> <span class="dv">124</span> ...</span>
<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a> <span class="sc">$</span> ShelveLoc <span class="sc">:</span> Factor w<span class="sc">/</span> <span class="dv">3</span> levels <span class="st">"Bad"</span>,<span class="st">"Good"</span>,<span class="st">"Medium"</span><span class="sc">:</span> <span class="dv">1</span> <span class="dv">2</span> <span class="dv">3</span> <span class="dv">3</span> <span class="dv">1</span> <span class="dv">1</span> <span class="dv">3</span> <span class="dv">2</span> <span class="dv">3</span> <span class="dv">3</span> ...</span>
<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a> <span class="sc">$</span> Age <span class="sc">:</span> num <span class="dv">42</span> <span class="dv">65</span> <span class="dv">59</span> <span class="dv">55</span> <span class="dv">38</span> <span class="dv">78</span> <span class="dv">71</span> <span class="dv">67</span> <span class="dv">76</span> <span class="dv">76</span> ...</span>
<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a> <span class="sc">$</span> Education <span class="sc">:</span> num <span class="dv">17</span> <span class="dv">10</span> <span class="dv">12</span> <span class="dv">14</span> <span class="dv">13</span> <span class="dv">16</span> <span class="dv">15</span> <span class="dv">10</span> <span class="dv">10</span> <span class="dv">17</span> ...</span>
<span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a> <span class="sc">$</span> Urban <span class="sc">:</span> Factor w<span class="sc">/</span> <span class="dv">2</span> levels <span class="st">"No"</span>,<span class="st">"Yes"</span><span class="sc">:</span> <span class="dv">2</span> <span class="dv">2</span> <span class="dv">2</span> <span class="dv">2</span> <span class="dv">2</span> <span class="dv">1</span> <span class="dv">2</span> <span class="dv">2</span> <span class="dv">1</span> <span class="dv">1</span> ...</span>
<span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a> <span class="sc">$</span> US <span class="sc">:</span> Factor w<span class="sc">/</span> <span class="dv">2</span> levels <span class="st">"No"</span>,<span class="st">"Yes"</span><span class="sc">:</span> <span class="dv">2</span> <span class="dv">2</span> <span class="dv">2</span> <span class="dv">2</span> <span class="dv">1</span> <span class="dv">2</span> <span class="dv">1</span> <span class="dv">2</span> <span class="dv">1</span> <span class="dv">2</span> ...</span></code></pre></div>
<p>The contents of individual variables are as follows. (Refer to ISLR::Carseats Man page)</p>
<ul>
<li>Sales
<ul>
<li>Unit sales (in thousands) at each location</li>
</ul></li>
<li>CompPrice
<ul>
<li>Price charged by competitor at each location</li>
</ul></li>
<li>Income
<ul>
<li>Community income level (in thousands of dollars)</li>
</ul></li>
<li>Advertising
<ul>
<li>Local advertising budget for company at each location (in thousands of dollars)</li>
</ul></li>
<li>Population
<ul>
<li>Population size in region (in thousands)</li>
</ul></li>
<li>Price
<ul>
<li>Price company charges for car seats at each site</li>
</ul></li>
<li>ShelveLoc
<ul>
<li>A factor with levels Bad, Good and Medium indicating the quality of the shelving location for the car seats at each site</li>
</ul></li>
<li>Age
<ul>
<li>Average age of the local population</li>
</ul></li>
<li>Education
<ul>
<li>Education level at each location</li>
</ul></li>
<li>Urban
<ul>
<li>A factor with levels No and Yes to indicate whether the store is in an urban or rural location</li>
</ul></li>
<li>US
<ul>
<li>A factor with levels No and Yes to indicate whether the store is in the US or not</li>
</ul></li>
</ul>
<p>When data analysis is performed, data containing missing values is frequently encountered. However, ‘Carseats’ is complete data without missing values. So the following script created the missing values and saved them as <code>carseats</code>.</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>carseats <span class="ot"><-</span> ISLR<span class="sc">::</span>Carseats</span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a><span class="fu">suppressWarnings</span>(<span class="fu">RNGversion</span>(<span class="st">"3.5.0"</span>))</span>
<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a><span class="fu">set.seed</span>(<span class="dv">123</span>)</span>
<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a>carseats[<span class="fu">sample</span>(<span class="fu">seq</span>(<span class="fu">NROW</span>(carseats)), <span class="dv">20</span>), <span class="st">"Income"</span>] <span class="ot"><-</span> <span class="cn">NA</span></span>
<span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-7"><a href="#cb2-7" aria-hidden="true" tabindex="-1"></a><span class="fu">suppressWarnings</span>(<span class="fu">RNGversion</span>(<span class="st">"3.5.0"</span>))</span>
<span id="cb2-8"><a href="#cb2-8" aria-hidden="true" tabindex="-1"></a><span class="fu">set.seed</span>(<span class="dv">456</span>)</span>
<span id="cb2-9"><a href="#cb2-9" aria-hidden="true" tabindex="-1"></a>carseats[<span class="fu">sample</span>(<span class="fu">seq</span>(<span class="fu">NROW</span>(carseats)), <span class="dv">10</span>), <span class="st">"Urban"</span>] <span class="ot"><-</span> <span class="cn">NA</span></span></code></pre></div>
</div>
<div id="exploratory-data-analysis" class="section level2">
<h2>Exploratory Data Analysis</h2>
<p>dlookr can help to understand the distribution of data by calculating descriptive statistics of numerical data. In addition, correlation between variables is identified and normality test is performed. It also identifies the relationship between target variables and independent variables.:</p>
<p>The following is a list of the EDA functions included in the dlookr package.:</p>
<ul>
<li><code>describe()</code> provides descriptive statistics for numerical data.</li>
<li><code>normality()</code> and <code>plot_normality()</code> perform normalization and visualization of numerical data.</li>
<li><code>correlate()</code> and <code>plot_correlate()</code> calculate the correlation coefficient between two numerical data and provide visualization.</li>
<li><code>target_by()</code> defines the target variable and <code>relate()</code> describes the relationship with the variables of interest corresponding to the target variable.</li>
<li><code>plot.relate()</code> visualizes the relationship to the variable of interest corresponding to the destination variable.</li>
<li><code>eda_report()</code> performs an exploratory data analysis and reports the results.</li>
</ul>
</div>
<div id="univariate-data-eda" class="section level2">
<h2>Univariate data EDA</h2>
<div id="calculating-descriptive-statistics-using-describe" class="section level3">
<h3>Calculating descriptive statistics using <code>describe()</code></h3>
<p><code>describe()</code> computes descriptive statistics for numerical data. The descriptive statistics help determine the distribution of numerical variables. Like function of dplyr, the first argument is the tibble (or data frame). The second and subsequent arguments refer to variables within that data frame.</p>
<p>The variables of the <code>tbl_df</code> object returned by <code>describe()</code> are as follows.</p>
<ul>
<li><code>n</code> : number of observations excluding missing values</li>
<li><code>na</code> : number of missing values</li>
<li><code>mean</code> : arithmetic average</li>
<li><code>sd</code> : standard deviation</li>
<li><code>se_mean</code> : standard error mean. sd/sqrt(n)</li>
<li><code>IQR</code> : interquartile range (Q3-Q1)</li>
<li><code>skewness</code> : skewness</li>
<li><code>kurtosis</code> : kurtosis</li>
<li><code>p25</code> : Q1. 25% percentile</li>
<li><code>p50</code> : median. 50% percentile</li>
<li><code>p75</code> : Q3. 75% percentile</li>
<li><code>p01</code>, <code>p05</code>, <code>p10</code>, <code>p20</code>, <code>p30</code> : 1%, 5%, 20%, 30% percentiles</li>
<li><code>p40</code>, <code>p60</code>, <code>p70</code>, <code>p80</code> : 40%, 60%, 70%, 80% percentiles</li>
<li><code>p90</code>, <code>p95</code>, <code>p99</code>, <code>p100</code> : 90%, 95%, 99%, 100% percentiles</li>
</ul>
<p>For example, <code>describe()</code> can computes the statistics of all numerical variables in <code>carseats</code>:</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="fu">describe</span>(carseats)</span>
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a><span class="co"># A tibble: 8 x 26</span></span>
<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a> variable n na mean sd se_mean IQR skewness kurtosis p00 p01</span>
<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a> <span class="sc"><</span>chr<span class="sc">></span> <span class="er"><</span>int<span class="sc">></span> <span class="er"><</span>int<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span></span>
<span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a><span class="dv">1</span> Sales <span class="dv">400</span> <span class="dv">0</span> <span class="fl">7.50</span> <span class="fl">2.82</span> <span class="fl">0.141</span> <span class="fl">3.93</span> <span class="fl">0.186</span> <span class="sc">-</span><span class="fl">0.0809</span> <span class="dv">0</span> <span class="fl">0.906</span></span>
<span id="cb3-6"><a href="#cb3-6" aria-hidden="true" tabindex="-1"></a><span class="dv">2</span> CompPri… <span class="dv">400</span> <span class="dv">0</span> <span class="fl">125.</span> <span class="fl">15.3</span> <span class="fl">0.767</span> <span class="dv">20</span> <span class="sc">-</span><span class="fl">0.0428</span> <span class="fl">0.0417</span> <span class="dv">77</span> <span class="fl">89.0</span> </span>
<span id="cb3-7"><a href="#cb3-7" aria-hidden="true" tabindex="-1"></a><span class="dv">3</span> Income <span class="dv">380</span> <span class="dv">20</span> <span class="fl">68.9</span> <span class="fl">28.1</span> <span class="fl">1.44</span> <span class="fl">48.2</span> <span class="fl">0.0449</span> <span class="sc">-</span><span class="fl">1.09</span> <span class="dv">21</span> <span class="fl">21.8</span> </span>
<span id="cb3-8"><a href="#cb3-8" aria-hidden="true" tabindex="-1"></a><span class="dv">4</span> Adverti… <span class="dv">400</span> <span class="dv">0</span> <span class="fl">6.64</span> <span class="fl">6.65</span> <span class="fl">0.333</span> <span class="dv">12</span> <span class="fl">0.640</span> <span class="sc">-</span><span class="fl">0.545</span> <span class="dv">0</span> <span class="dv">0</span> </span>
<span id="cb3-9"><a href="#cb3-9" aria-hidden="true" tabindex="-1"></a><span class="co"># … with 4 more rows, and 15 more variables: p05 <dbl>, p10 <dbl>, p20 <dbl>,</span></span>
<span id="cb3-10"><a href="#cb3-10" aria-hidden="true" tabindex="-1"></a><span class="co"># p25 <dbl>, p30 <dbl>, p40 <dbl>, p50 <dbl>, p60 <dbl>, p70 <dbl>,</span></span>
<span id="cb3-11"><a href="#cb3-11" aria-hidden="true" tabindex="-1"></a><span class="co"># p75 <dbl>, p80 <dbl>, p90 <dbl>, p95 <dbl>, p99 <dbl>, p100 <dbl></span></span></code></pre></div>
<ul>
<li><code>skewness</code> : The left-skewed distribution data that is the variables with large positive skewness should consider the log or sqrt transformations to follow the normal distribution. The variables <code>Advertising</code> seem to need to consider variable transformation.</li>
<li><code>mean</code> and <code>sd</code>, <code>se_mean</code> : The<code>Population</code> with a large <code>standard error of the mean</code>(se_mean) has low representativeness of the <code>arithmetic mean</code>(mean). The <code>standard deviation</code>(sd) is much larger than the arithmetic average.</li>
</ul>
<p>The following explains the descriptive statistics only for a few selected variables.:</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Select columns by name</span></span>
<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a><span class="fu">describe</span>(carseats, Sales, CompPrice, Income)</span>
<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a><span class="co"># A tibble: 3 x 26</span></span>
<span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a> variable n na mean sd se_mean IQR skewness kurtosis p00 p01</span>
<span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a> <span class="sc"><</span>chr<span class="sc">></span> <span class="er"><</span>int<span class="sc">></span> <span class="er"><</span>int<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span></span>
<span id="cb4-6"><a href="#cb4-6" aria-hidden="true" tabindex="-1"></a><span class="dv">1</span> Sales <span class="dv">400</span> <span class="dv">0</span> <span class="fl">7.50</span> <span class="fl">2.82</span> <span class="fl">0.141</span> <span class="fl">3.93</span> <span class="fl">0.186</span> <span class="sc">-</span><span class="fl">0.0809</span> <span class="dv">0</span> <span class="fl">0.906</span></span>
<span id="cb4-7"><a href="#cb4-7" aria-hidden="true" tabindex="-1"></a><span class="dv">2</span> CompPri… <span class="dv">400</span> <span class="dv">0</span> <span class="fl">125.</span> <span class="fl">15.3</span> <span class="fl">0.767</span> <span class="dv">20</span> <span class="sc">-</span><span class="fl">0.0428</span> <span class="fl">0.0417</span> <span class="dv">77</span> <span class="fl">89.0</span> </span>
<span id="cb4-8"><a href="#cb4-8" aria-hidden="true" tabindex="-1"></a><span class="dv">3</span> Income <span class="dv">380</span> <span class="dv">20</span> <span class="fl">68.9</span> <span class="fl">28.1</span> <span class="fl">1.44</span> <span class="fl">48.2</span> <span class="fl">0.0449</span> <span class="sc">-</span><span class="fl">1.09</span> <span class="dv">21</span> <span class="fl">21.8</span> </span>
<span id="cb4-9"><a href="#cb4-9" aria-hidden="true" tabindex="-1"></a><span class="co"># … with 15 more variables: p05 <dbl>, p10 <dbl>, p20 <dbl>, p25 <dbl>,</span></span>
<span id="cb4-10"><a href="#cb4-10" aria-hidden="true" tabindex="-1"></a><span class="co"># p30 <dbl>, p40 <dbl>, p50 <dbl>, p60 <dbl>, p70 <dbl>, p75 <dbl>,</span></span>
<span id="cb4-11"><a href="#cb4-11" aria-hidden="true" tabindex="-1"></a><span class="co"># p80 <dbl>, p90 <dbl>, p95 <dbl>, p99 <dbl>, p100 <dbl></span></span>
<span id="cb4-12"><a href="#cb4-12" aria-hidden="true" tabindex="-1"></a><span class="co"># Select all columns between year and day (include)</span></span>
<span id="cb4-13"><a href="#cb4-13" aria-hidden="true" tabindex="-1"></a><span class="fu">describe</span>(carseats, Sales<span class="sc">:</span>Income)</span>
<span id="cb4-14"><a href="#cb4-14" aria-hidden="true" tabindex="-1"></a><span class="co"># A tibble: 3 x 26</span></span>
<span id="cb4-15"><a href="#cb4-15" aria-hidden="true" tabindex="-1"></a> variable n na mean sd se_mean IQR skewness kurtosis p00 p01</span>
<span id="cb4-16"><a href="#cb4-16" aria-hidden="true" tabindex="-1"></a> <span class="sc"><</span>chr<span class="sc">></span> <span class="er"><</span>int<span class="sc">></span> <span class="er"><</span>int<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span></span>
<span id="cb4-17"><a href="#cb4-17" aria-hidden="true" tabindex="-1"></a><span class="dv">1</span> Sales <span class="dv">400</span> <span class="dv">0</span> <span class="fl">7.50</span> <span class="fl">2.82</span> <span class="fl">0.141</span> <span class="fl">3.93</span> <span class="fl">0.186</span> <span class="sc">-</span><span class="fl">0.0809</span> <span class="dv">0</span> <span class="fl">0.906</span></span>
<span id="cb4-18"><a href="#cb4-18" aria-hidden="true" tabindex="-1"></a><span class="dv">2</span> CompPri… <span class="dv">400</span> <span class="dv">0</span> <span class="fl">125.</span> <span class="fl">15.3</span> <span class="fl">0.767</span> <span class="dv">20</span> <span class="sc">-</span><span class="fl">0.0428</span> <span class="fl">0.0417</span> <span class="dv">77</span> <span class="fl">89.0</span> </span>
<span id="cb4-19"><a href="#cb4-19" aria-hidden="true" tabindex="-1"></a><span class="dv">3</span> Income <span class="dv">380</span> <span class="dv">20</span> <span class="fl">68.9</span> <span class="fl">28.1</span> <span class="fl">1.44</span> <span class="fl">48.2</span> <span class="fl">0.0449</span> <span class="sc">-</span><span class="fl">1.09</span> <span class="dv">21</span> <span class="fl">21.8</span> </span>
<span id="cb4-20"><a href="#cb4-20" aria-hidden="true" tabindex="-1"></a><span class="co"># … with 15 more variables: p05 <dbl>, p10 <dbl>, p20 <dbl>, p25 <dbl>,</span></span>
<span id="cb4-21"><a href="#cb4-21" aria-hidden="true" tabindex="-1"></a><span class="co"># p30 <dbl>, p40 <dbl>, p50 <dbl>, p60 <dbl>, p70 <dbl>, p75 <dbl>,</span></span>
<span id="cb4-22"><a href="#cb4-22" aria-hidden="true" tabindex="-1"></a><span class="co"># p80 <dbl>, p90 <dbl>, p95 <dbl>, p99 <dbl>, p100 <dbl></span></span>
<span id="cb4-23"><a href="#cb4-23" aria-hidden="true" tabindex="-1"></a><span class="co"># Select all columns except those from year to day (exclude)</span></span>
<span id="cb4-24"><a href="#cb4-24" aria-hidden="true" tabindex="-1"></a><span class="fu">describe</span>(carseats, <span class="sc">-</span>(Sales<span class="sc">:</span>Income))</span>
<span id="cb4-25"><a href="#cb4-25" aria-hidden="true" tabindex="-1"></a><span class="co"># A tibble: 5 x 26</span></span>
<span id="cb4-26"><a href="#cb4-26" aria-hidden="true" tabindex="-1"></a> variable n na mean sd se_mean IQR skewness kurtosis p00 p01</span>
<span id="cb4-27"><a href="#cb4-27" aria-hidden="true" tabindex="-1"></a> <span class="sc"><</span>chr<span class="sc">></span> <span class="er"><</span>int<span class="sc">></span> <span class="er"><</span>int<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span></span>
<span id="cb4-28"><a href="#cb4-28" aria-hidden="true" tabindex="-1"></a><span class="dv">1</span> Adverti… <span class="dv">400</span> <span class="dv">0</span> <span class="fl">6.64</span> <span class="fl">6.65</span> <span class="fl">0.333</span> <span class="dv">12</span> <span class="fl">0.640</span> <span class="sc">-</span><span class="fl">0.545</span> <span class="dv">0</span> <span class="dv">0</span> </span>
<span id="cb4-29"><a href="#cb4-29" aria-hidden="true" tabindex="-1"></a><span class="dv">2</span> Populat… <span class="dv">400</span> <span class="dv">0</span> <span class="fl">265.</span> <span class="fl">147.</span> <span class="fl">7.37</span> <span class="fl">260.</span> <span class="sc">-</span><span class="fl">0.0512</span> <span class="sc">-</span><span class="fl">1.20</span> <span class="dv">10</span> <span class="fl">16.0</span></span>
<span id="cb4-30"><a href="#cb4-30" aria-hidden="true" tabindex="-1"></a><span class="dv">3</span> Price <span class="dv">400</span> <span class="dv">0</span> <span class="fl">116.</span> <span class="fl">23.7</span> <span class="fl">1.18</span> <span class="dv">31</span> <span class="sc">-</span><span class="fl">0.125</span> <span class="fl">0.452</span> <span class="dv">24</span> <span class="fl">55.0</span></span>
<span id="cb4-31"><a href="#cb4-31" aria-hidden="true" tabindex="-1"></a><span class="dv">4</span> Age <span class="dv">400</span> <span class="dv">0</span> <span class="fl">53.3</span> <span class="fl">16.2</span> <span class="fl">0.810</span> <span class="fl">26.2</span> <span class="sc">-</span><span class="fl">0.0772</span> <span class="sc">-</span><span class="fl">1.13</span> <span class="dv">25</span> <span class="dv">25</span> </span>
<span id="cb4-32"><a href="#cb4-32" aria-hidden="true" tabindex="-1"></a><span class="co"># … with 1 more row, and 15 more variables: p05 <dbl>, p10 <dbl>, p20 <dbl>,</span></span>
<span id="cb4-33"><a href="#cb4-33" aria-hidden="true" tabindex="-1"></a><span class="co"># p25 <dbl>, p30 <dbl>, p40 <dbl>, p50 <dbl>, p60 <dbl>, p70 <dbl>,</span></span>
<span id="cb4-34"><a href="#cb4-34" aria-hidden="true" tabindex="-1"></a><span class="co"># p75 <dbl>, p80 <dbl>, p90 <dbl>, p95 <dbl>, p99 <dbl>, p100 <dbl></span></span></code></pre></div>
<p>The <code>describe()</code> function can be sorted by <code>left or right skewed size</code>(skewness) using <code>dplyr</code>.:</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>carseats <span class="sc">%>%</span></span>
<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">describe</span>() <span class="sc">%>%</span></span>
<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(variable, skewness, mean, p25, p50, p75) <span class="sc">%>%</span> </span>
<span id="cb5-4"><a href="#cb5-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(<span class="sc">!</span><span class="fu">is.na</span>(skewness)) <span class="sc">%>%</span> </span>
<span id="cb5-5"><a href="#cb5-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">arrange</span>(<span class="fu">desc</span>(<span class="fu">abs</span>(skewness)))</span>
<span id="cb5-6"><a href="#cb5-6" aria-hidden="true" tabindex="-1"></a><span class="co"># A tibble: 8 x 6</span></span>
<span id="cb5-7"><a href="#cb5-7" aria-hidden="true" tabindex="-1"></a> variable skewness mean p25 p50 p75</span>
<span id="cb5-8"><a href="#cb5-8" aria-hidden="true" tabindex="-1"></a> <span class="sc"><</span>chr<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span></span>
<span id="cb5-9"><a href="#cb5-9" aria-hidden="true" tabindex="-1"></a><span class="dv">1</span> Advertising <span class="fl">0.640</span> <span class="fl">6.64</span> <span class="dv">0</span> <span class="dv">5</span> <span class="dv">12</span> </span>
<span id="cb5-10"><a href="#cb5-10" aria-hidden="true" tabindex="-1"></a><span class="dv">2</span> Sales <span class="fl">0.186</span> <span class="fl">7.50</span> <span class="fl">5.39</span> <span class="fl">7.49</span> <span class="fl">9.32</span></span>
<span id="cb5-11"><a href="#cb5-11" aria-hidden="true" tabindex="-1"></a><span class="dv">3</span> Price <span class="sc">-</span><span class="fl">0.125</span> <span class="fl">116.</span> <span class="dv">100</span> <span class="dv">117</span> <span class="dv">131</span> </span>
<span id="cb5-12"><a href="#cb5-12" aria-hidden="true" tabindex="-1"></a><span class="dv">4</span> Age <span class="sc">-</span><span class="fl">0.0772</span> <span class="fl">53.3</span> <span class="fl">39.8</span> <span class="fl">54.5</span> <span class="dv">66</span> </span>
<span id="cb5-13"><a href="#cb5-13" aria-hidden="true" tabindex="-1"></a><span class="co"># … with 4 more rows</span></span></code></pre></div>
<p>The <code>describe()</code> function supports the <code>group_by()</code> function syntax of the <code>dplyr</code> package.</p>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>carseats <span class="sc">%>%</span></span>
<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">group_by</span>(US) <span class="sc">%>%</span> </span>
<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">describe</span>(Sales, Income) </span>
<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a><span class="co"># A tibble: 4 x 27</span></span>
<span id="cb6-5"><a href="#cb6-5" aria-hidden="true" tabindex="-1"></a> variable US n na mean sd se_mean IQR skewness kurtosis p00</span>
<span id="cb6-6"><a href="#cb6-6" aria-hidden="true" tabindex="-1"></a> <span class="sc"><</span>chr<span class="sc">></span> <span class="er"><</span>fct<span class="sc">></span> <span class="er"><</span>int<span class="sc">></span> <span class="er"><</span>int<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span></span>
<span id="cb6-7"><a href="#cb6-7" aria-hidden="true" tabindex="-1"></a><span class="dv">1</span> Income No <span class="dv">130</span> <span class="dv">12</span> <span class="fl">65.8</span> <span class="fl">28.2</span> <span class="fl">2.48</span> <span class="dv">50</span> <span class="fl">0.100</span> <span class="sc">-</span><span class="fl">1.14</span> <span class="dv">22</span> </span>
<span id="cb6-8"><a href="#cb6-8" aria-hidden="true" tabindex="-1"></a><span class="dv">2</span> Income Yes <span class="dv">250</span> <span class="dv">8</span> <span class="fl">70.4</span> <span class="fl">27.9</span> <span class="fl">1.77</span> <span class="dv">48</span> <span class="fl">0.0199</span> <span class="sc">-</span><span class="fl">1.06</span> <span class="dv">21</span> </span>
<span id="cb6-9"><a href="#cb6-9" aria-hidden="true" tabindex="-1"></a><span class="dv">3</span> Sales No <span class="dv">142</span> <span class="dv">0</span> <span class="fl">6.82</span> <span class="fl">2.60</span> <span class="fl">0.218</span> <span class="fl">3.44</span> <span class="fl">0.323</span> <span class="fl">0.808</span> <span class="dv">0</span> </span>
<span id="cb6-10"><a href="#cb6-10" aria-hidden="true" tabindex="-1"></a><span class="dv">4</span> Sales Yes <span class="dv">258</span> <span class="dv">0</span> <span class="fl">7.87</span> <span class="fl">2.88</span> <span class="fl">0.179</span> <span class="fl">4.23</span> <span class="fl">0.0760</span> <span class="sc">-</span><span class="fl">0.326</span> <span class="fl">0.37</span></span>
<span id="cb6-11"><a href="#cb6-11" aria-hidden="true" tabindex="-1"></a><span class="co"># … with 16 more variables: p01 <dbl>, p05 <dbl>, p10 <dbl>, p20 <dbl>,</span></span>
<span id="cb6-12"><a href="#cb6-12" aria-hidden="true" tabindex="-1"></a><span class="co"># p25 <dbl>, p30 <dbl>, p40 <dbl>, p50 <dbl>, p60 <dbl>, p70 <dbl>,</span></span>
<span id="cb6-13"><a href="#cb6-13" aria-hidden="true" tabindex="-1"></a><span class="co"># p75 <dbl>, p80 <dbl>, p90 <dbl>, p95 <dbl>, p99 <dbl>, p100 <dbl></span></span></code></pre></div>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>carseats <span class="sc">%>%</span></span>
<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">group_by</span>(US, Urban) <span class="sc">%>%</span> </span>
<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">describe</span>(Sales, Income) </span>
<span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a><span class="co"># A tibble: 12 x 28</span></span>
<span id="cb7-5"><a href="#cb7-5" aria-hidden="true" tabindex="-1"></a> variable US Urban n na mean sd se_mean IQR skewness kurtosis</span>
<span id="cb7-6"><a href="#cb7-6" aria-hidden="true" tabindex="-1"></a> <span class="sc"><</span>chr<span class="sc">></span> <span class="er"><</span>fct<span class="sc">></span> <span class="er"><</span>fct<span class="sc">></span> <span class="er"><</span>int<span class="sc">></span> <span class="er"><</span>int<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span></span>
<span id="cb7-7"><a href="#cb7-7" aria-hidden="true" tabindex="-1"></a><span class="dv">1</span> Income No No <span class="dv">42</span> <span class="dv">4</span> <span class="fl">60.2</span> <span class="fl">29.1</span> <span class="fl">4.49</span> <span class="fl">45.2</span> <span class="fl">0.408</span> <span class="sc">-</span><span class="fl">1.00</span></span>
<span id="cb7-8"><a href="#cb7-8" aria-hidden="true" tabindex="-1"></a><span class="dv">2</span> Income No Yes <span class="dv">84</span> <span class="dv">8</span> <span class="fl">69.5</span> <span class="fl">27.4</span> <span class="fl">2.99</span> <span class="dv">47</span> <span class="sc">-</span><span class="fl">0.0497</span> <span class="sc">-</span><span class="fl">1.11</span></span>
<span id="cb7-9"><a href="#cb7-9" aria-hidden="true" tabindex="-1"></a><span class="dv">3</span> Income No <span class="sc"><</span><span class="cn">NA</span><span class="sc">></span> <span class="dv">4</span> <span class="dv">0</span> <span class="fl">48.2</span> <span class="fl">24.7</span> <span class="fl">12.3</span> <span class="fl">40.8</span> <span class="sc">-</span><span class="fl">0.0496</span> <span class="sc">-</span><span class="fl">5.70</span></span>
<span id="cb7-10"><a href="#cb7-10" aria-hidden="true" tabindex="-1"></a><span class="dv">4</span> Income Yes No <span class="dv">65</span> <span class="dv">4</span> <span class="fl">70.5</span> <span class="fl">29.9</span> <span class="fl">3.70</span> <span class="dv">48</span> <span class="fl">0.0736</span> <span class="sc">-</span><span class="fl">1.08</span></span>
<span id="cb7-11"><a href="#cb7-11" aria-hidden="true" tabindex="-1"></a><span class="co"># … with 8 more rows, and 17 more variables: p00 <dbl>, p01 <dbl>, p05 <dbl>,</span></span>
<span id="cb7-12"><a href="#cb7-12" aria-hidden="true" tabindex="-1"></a><span class="co"># p10 <dbl>, p20 <dbl>, p25 <dbl>, p30 <dbl>, p40 <dbl>, p50 <dbl>,</span></span>
<span id="cb7-13"><a href="#cb7-13" aria-hidden="true" tabindex="-1"></a><span class="co"># p60 <dbl>, p70 <dbl>, p75 <dbl>, p80 <dbl>, p90 <dbl>, p95 <dbl>,</span></span>
<span id="cb7-14"><a href="#cb7-14" aria-hidden="true" tabindex="-1"></a><span class="co"># p99 <dbl>, p100 <dbl></span></span></code></pre></div>
</div>
<div id="test-of-normality-on-numeric-variables-using-normality" class="section level3">
<h3>Test of normality on numeric variables using <code>normality()</code></h3>
<p><code>normality()</code> performs a normality test on numerical data. <code>Shapiro-Wilk normality test</code> is performed. When the number of observations is greater than 5000, it is tested after extracting 5000 samples by random simple sampling.</p>
<p>The variables of <code>tbl_df</code> object returned by <code>normality()</code> are as follows.</p>
<ul>
<li><code>statistic</code> : Statistics of the Shapiro-Wilk test</li>
<li><code>p_value</code> : p-value of the Shapiro-Wilk test</li>
<li><code>sample</code> : Number of sample observations performed Shapiro-Wilk test</li>
</ul>
<p><code>normality()</code> performs the normality test for all numerical variables of <code>carseats</code> as follows.:</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="fu">normality</span>(carseats)</span>
<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a><span class="co"># A tibble: 8 x 4</span></span>
<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a> vars statistic p_value sample</span>
<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a> <span class="sc"><</span>chr<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span></span>
<span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a><span class="dv">1</span> Sales <span class="fl">0.995</span> <span class="fl">2.54</span>e<span class="sc">-</span> <span class="dv">1</span> <span class="dv">400</span></span>
<span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a><span class="dv">2</span> CompPrice <span class="fl">0.998</span> <span class="fl">9.77</span>e<span class="sc">-</span> <span class="dv">1</span> <span class="dv">400</span></span>
<span id="cb8-7"><a href="#cb8-7" aria-hidden="true" tabindex="-1"></a><span class="dv">3</span> Income <span class="fl">0.961</span> <span class="fl">1.52</span>e<span class="sc">-</span> <span class="dv">8</span> <span class="dv">400</span></span>
<span id="cb8-8"><a href="#cb8-8" aria-hidden="true" tabindex="-1"></a><span class="dv">4</span> Advertising <span class="fl">0.874</span> <span class="fl">1.49e-17</span> <span class="dv">400</span></span>
<span id="cb8-9"><a href="#cb8-9" aria-hidden="true" tabindex="-1"></a><span class="co"># … with 4 more rows</span></span></code></pre></div>
<p>The following example performs a normality test on only a few selected variables.</p>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Select columns by name</span></span>
<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a><span class="fu">normality</span>(carseats, Sales, CompPrice, Income)</span>
<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a><span class="co"># A tibble: 3 x 4</span></span>
<span id="cb9-4"><a href="#cb9-4" aria-hidden="true" tabindex="-1"></a> vars statistic p_value sample</span>
<span id="cb9-5"><a href="#cb9-5" aria-hidden="true" tabindex="-1"></a> <span class="sc"><</span>chr<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span></span>
<span id="cb9-6"><a href="#cb9-6" aria-hidden="true" tabindex="-1"></a><span class="dv">1</span> Sales <span class="fl">0.995</span> <span class="fl">0.254</span> <span class="dv">400</span></span>
<span id="cb9-7"><a href="#cb9-7" aria-hidden="true" tabindex="-1"></a><span class="dv">2</span> CompPrice <span class="fl">0.998</span> <span class="fl">0.977</span> <span class="dv">400</span></span>
<span id="cb9-8"><a href="#cb9-8" aria-hidden="true" tabindex="-1"></a><span class="dv">3</span> Income <span class="fl">0.961</span> <span class="fl">0.0000000152</span> <span class="dv">400</span></span>
<span id="cb9-9"><a href="#cb9-9" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-10"><a href="#cb9-10" aria-hidden="true" tabindex="-1"></a><span class="co"># Select all columns between year and day (inclusive)</span></span>
<span id="cb9-11"><a href="#cb9-11" aria-hidden="true" tabindex="-1"></a><span class="fu">normality</span>(carseats, Sales<span class="sc">:</span>Income)</span>
<span id="cb9-12"><a href="#cb9-12" aria-hidden="true" tabindex="-1"></a><span class="co"># A tibble: 3 x 4</span></span>
<span id="cb9-13"><a href="#cb9-13" aria-hidden="true" tabindex="-1"></a> vars statistic p_value sample</span>
<span id="cb9-14"><a href="#cb9-14" aria-hidden="true" tabindex="-1"></a> <span class="sc"><</span>chr<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span></span>
<span id="cb9-15"><a href="#cb9-15" aria-hidden="true" tabindex="-1"></a><span class="dv">1</span> Sales <span class="fl">0.995</span> <span class="fl">0.254</span> <span class="dv">400</span></span>
<span id="cb9-16"><a href="#cb9-16" aria-hidden="true" tabindex="-1"></a><span class="dv">2</span> CompPrice <span class="fl">0.998</span> <span class="fl">0.977</span> <span class="dv">400</span></span>
<span id="cb9-17"><a href="#cb9-17" aria-hidden="true" tabindex="-1"></a><span class="dv">3</span> Income <span class="fl">0.961</span> <span class="fl">0.0000000152</span> <span class="dv">400</span></span>
<span id="cb9-18"><a href="#cb9-18" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-19"><a href="#cb9-19" aria-hidden="true" tabindex="-1"></a><span class="co"># Select all columns except those from year to day (inclusive)</span></span>
<span id="cb9-20"><a href="#cb9-20" aria-hidden="true" tabindex="-1"></a><span class="fu">normality</span>(carseats, <span class="sc">-</span>(Sales<span class="sc">:</span>Income))</span>
<span id="cb9-21"><a href="#cb9-21" aria-hidden="true" tabindex="-1"></a><span class="co"># A tibble: 5 x 4</span></span>
<span id="cb9-22"><a href="#cb9-22" aria-hidden="true" tabindex="-1"></a> vars statistic p_value sample</span>
<span id="cb9-23"><a href="#cb9-23" aria-hidden="true" tabindex="-1"></a> <span class="sc"><</span>chr<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span></span>
<span id="cb9-24"><a href="#cb9-24" aria-hidden="true" tabindex="-1"></a><span class="dv">1</span> Advertising <span class="fl">0.874</span> <span class="fl">1.49e-17</span> <span class="dv">400</span></span>
<span id="cb9-25"><a href="#cb9-25" aria-hidden="true" tabindex="-1"></a><span class="dv">2</span> Population <span class="fl">0.952</span> <span class="fl">4.08e-10</span> <span class="dv">400</span></span>
<span id="cb9-26"><a href="#cb9-26" aria-hidden="true" tabindex="-1"></a><span class="dv">3</span> Price <span class="fl">0.996</span> <span class="fl">3.90</span>e<span class="sc">-</span> <span class="dv">1</span> <span class="dv">400</span></span>
<span id="cb9-27"><a href="#cb9-27" aria-hidden="true" tabindex="-1"></a><span class="dv">4</span> Age <span class="fl">0.957</span> <span class="fl">1.86</span>e<span class="sc">-</span> <span class="dv">9</span> <span class="dv">400</span></span>
<span id="cb9-28"><a href="#cb9-28" aria-hidden="true" tabindex="-1"></a><span class="co"># … with 1 more row</span></span></code></pre></div>
<p>You can use <code>dplyr</code> to sort variables that do not follow a normal distribution in order of <code>p_value</code>:</p>
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(dplyr)</span>
<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a>carseats <span class="sc">%>%</span></span>
<span id="cb10-4"><a href="#cb10-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">normality</span>() <span class="sc">%>%</span></span>
<span id="cb10-5"><a href="#cb10-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(p_value <span class="sc"><=</span> <span class="fl">0.01</span>) <span class="sc">%>%</span> </span>
<span id="cb10-6"><a href="#cb10-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">arrange</span>(<span class="fu">abs</span>(p_value))</span>
<span id="cb10-7"><a href="#cb10-7" aria-hidden="true" tabindex="-1"></a><span class="co"># A tibble: 5 x 4</span></span>
<span id="cb10-8"><a href="#cb10-8" aria-hidden="true" tabindex="-1"></a> vars statistic p_value sample</span>
<span id="cb10-9"><a href="#cb10-9" aria-hidden="true" tabindex="-1"></a> <span class="sc"><</span>chr<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span></span>
<span id="cb10-10"><a href="#cb10-10" aria-hidden="true" tabindex="-1"></a><span class="dv">1</span> Advertising <span class="fl">0.874</span> <span class="fl">1.49e-17</span> <span class="dv">400</span></span>
<span id="cb10-11"><a href="#cb10-11" aria-hidden="true" tabindex="-1"></a><span class="dv">2</span> Education <span class="fl">0.924</span> <span class="fl">2.43e-13</span> <span class="dv">400</span></span>
<span id="cb10-12"><a href="#cb10-12" aria-hidden="true" tabindex="-1"></a><span class="dv">3</span> Population <span class="fl">0.952</span> <span class="fl">4.08e-10</span> <span class="dv">400</span></span>
<span id="cb10-13"><a href="#cb10-13" aria-hidden="true" tabindex="-1"></a><span class="dv">4</span> Age <span class="fl">0.957</span> <span class="fl">1.86</span>e<span class="sc">-</span> <span class="dv">9</span> <span class="dv">400</span></span>
<span id="cb10-14"><a href="#cb10-14" aria-hidden="true" tabindex="-1"></a><span class="co"># … with 1 more row</span></span></code></pre></div>
<p>In particular, the <code>Advertising</code> variable is considered to be the most out of the normal distribution.</p>
<p>The <code>normality()</code> function supports the <code>group_by()</code> function syntax in the <code>dplyr</code> package.</p>
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a>carseats <span class="sc">%>%</span></span>
<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">group_by</span>(ShelveLoc, US) <span class="sc">%>%</span></span>
<span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">normality</span>(Income) <span class="sc">%>%</span> </span>
<span id="cb11-4"><a href="#cb11-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">arrange</span>(<span class="fu">desc</span>(p_value))</span>
<span id="cb11-5"><a href="#cb11-5" aria-hidden="true" tabindex="-1"></a><span class="co"># A tibble: 6 x 6</span></span>
<span id="cb11-6"><a href="#cb11-6" aria-hidden="true" tabindex="-1"></a> variable ShelveLoc US statistic p_value sample</span>
<span id="cb11-7"><a href="#cb11-7" aria-hidden="true" tabindex="-1"></a> <span class="sc"><</span>chr<span class="sc">></span> <span class="er"><</span>fct<span class="sc">></span> <span class="er"><</span>fct<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span></span>
<span id="cb11-8"><a href="#cb11-8" aria-hidden="true" tabindex="-1"></a><span class="dv">1</span> Income Bad No <span class="fl">0.969</span> <span class="fl">0.470</span> <span class="dv">34</span></span>
<span id="cb11-9"><a href="#cb11-9" aria-hidden="true" tabindex="-1"></a><span class="dv">2</span> Income Bad Yes <span class="fl">0.958</span> <span class="fl">0.0343</span> <span class="dv">62</span></span>
<span id="cb11-10"><a href="#cb11-10" aria-hidden="true" tabindex="-1"></a><span class="dv">3</span> Income Good No <span class="fl">0.902</span> <span class="fl">0.0328</span> <span class="dv">24</span></span>
<span id="cb11-11"><a href="#cb11-11" aria-hidden="true" tabindex="-1"></a><span class="dv">4</span> Income Good Yes <span class="fl">0.955</span> <span class="fl">0.0296</span> <span class="dv">61</span></span>
<span id="cb11-12"><a href="#cb11-12" aria-hidden="true" tabindex="-1"></a><span class="co"># … with 2 more rows</span></span></code></pre></div>
<p>The <code>Income</code> variable does not follow the normal distribution. However, the case where <code>US</code> is <code>No</code> and <code>ShelveLoc</code> is <code>Good</code> and <code>Bad</code> at the significance level of 0.01, it follows the normal distribution.</p>
<p>The following example performs <code>normality test of log(Income)</code> for each combination of <code>ShelveLoc</code> and <code>US</code> categorical variables to search for variables that follow the normal distribution.</p>
<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a>carseats <span class="sc">%>%</span></span>
<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">log_income =</span> <span class="fu">log</span>(Income)) <span class="sc">%>%</span></span>
<span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">group_by</span>(ShelveLoc, US) <span class="sc">%>%</span></span>
<span id="cb12-4"><a href="#cb12-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">normality</span>(log_income) <span class="sc">%>%</span></span>
<span id="cb12-5"><a href="#cb12-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(p_value <span class="sc">></span> <span class="fl">0.01</span>)</span>
<span id="cb12-6"><a href="#cb12-6" aria-hidden="true" tabindex="-1"></a><span class="co"># A tibble: 1 x 6</span></span>
<span id="cb12-7"><a href="#cb12-7" aria-hidden="true" tabindex="-1"></a> variable ShelveLoc US statistic p_value sample</span>
<span id="cb12-8"><a href="#cb12-8" aria-hidden="true" tabindex="-1"></a> <span class="sc"><</span>chr<span class="sc">></span> <span class="er"><</span>fct<span class="sc">></span> <span class="er"><</span>fct<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span></span>
<span id="cb12-9"><a href="#cb12-9" aria-hidden="true" tabindex="-1"></a><span class="dv">1</span> log_income Bad No <span class="fl">0.940</span> <span class="fl">0.0737</span> <span class="dv">34</span></span></code></pre></div>
</div>
<div id="visualization-of-normality-of-numerical-variables-using-plot_normality" class="section level3">
<h3>Visualization of normality of numerical variables using <code>plot_normality()</code></h3>
<p><code>plot_normality()</code> visualizes the normality of numeric data.</p>
<p>The information visualized by <code>plot_normality()</code> is as follows.:</p>
<ul>
<li><code>Histogram of original data</code></li>
<li><code>Q-Q plot of original data</code></li>
<li><code>histogram of log transformed data</code></li>
<li><code>Histogram of square root transformed data</code></li>
</ul>
<p>In the data analysis process, it often encounters numerical data that follows the <code>power-law distribution</code>. Since the numerical data that follows the <code>power-law distribution</code> is converted into a normal distribution by performing the <code>log</code> or <code>sqrt</code> transformation, so draw a histogram of the <code>log</code> and <code>sqrt</code> transformed data.</p>
<p><code>plot_normality()</code> can also specify several variables like <code>normality()</code> function.</p>
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Select columns by name</span></span>
<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a><span class="fu">plot_normality</span>(carseats, Sales, CompPrice)</span></code></pre></div>
<p><img src="data:image/png;base64,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" width="600px" style="display: block; margin: auto;" /></p>
<p>The <code>plot_normality()</code> function also supports the <code>group_by()</code> function syntax in the <code>dplyr</code> package.</p>
<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a>carseats <span class="sc">%>%</span></span>
<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(ShelveLoc <span class="sc">==</span> <span class="st">"Good"</span>) <span class="sc">%>%</span></span>
<span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">group_by</span>(US) <span class="sc">%>%</span></span>
<span id="cb14-4"><a href="#cb14-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">plot_normality</span>(Income)</span></code></pre></div>
</div>
</div>
<div id="eda-of-bivariate-data" class="section level2">
<h2>EDA of bivariate data</h2>
<div id="calculation-of-correlation-coefficient-using-correlate" class="section level3">
<h3>Calculation of <code>correlation coefficient</code> using <code>correlate()</code></h3>
<p><code>correlate()</code> calculates the correlation coefficient of all combinations of <code>carseats</code> numerical variables as follows:</p>
<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a><span class="fu">correlate</span>(carseats)</span>
<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a><span class="co"># A tibble: 56 x 3</span></span>
<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a> var1 var2 coef_corr</span>
<span id="cb15-4"><a href="#cb15-4" aria-hidden="true" tabindex="-1"></a> <span class="sc"><</span>fct<span class="sc">></span> <span class="er"><</span>fct<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span></span>
<span id="cb15-5"><a href="#cb15-5" aria-hidden="true" tabindex="-1"></a><span class="dv">1</span> CompPrice Sales <span class="fl">0.0641</span></span>
<span id="cb15-6"><a href="#cb15-6" aria-hidden="true" tabindex="-1"></a><span class="dv">2</span> Income Sales <span class="fl">0.151</span> </span>
<span id="cb15-7"><a href="#cb15-7" aria-hidden="true" tabindex="-1"></a><span class="dv">3</span> Advertising Sales <span class="fl">0.270</span> </span>
<span id="cb15-8"><a href="#cb15-8" aria-hidden="true" tabindex="-1"></a><span class="dv">4</span> Population Sales <span class="fl">0.0505</span></span>
<span id="cb15-9"><a href="#cb15-9" aria-hidden="true" tabindex="-1"></a><span class="co"># … with 52 more rows</span></span></code></pre></div>
<p>The following example performs a normality test only on combinations that include several selected variables.</p>
<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Select columns by name</span></span>
<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a><span class="fu">correlate</span>(carseats, Sales, CompPrice, Income)</span>
<span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a><span class="co"># A tibble: 21 x 3</span></span>
<span id="cb16-4"><a href="#cb16-4" aria-hidden="true" tabindex="-1"></a> var1 var2 coef_corr</span>
<span id="cb16-5"><a href="#cb16-5" aria-hidden="true" tabindex="-1"></a> <span class="sc"><</span>fct<span class="sc">></span> <span class="er"><</span>fct<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span></span>
<span id="cb16-6"><a href="#cb16-6" aria-hidden="true" tabindex="-1"></a><span class="dv">1</span> CompPrice Sales <span class="fl">0.0641</span></span>
<span id="cb16-7"><a href="#cb16-7" aria-hidden="true" tabindex="-1"></a><span class="dv">2</span> Income Sales <span class="fl">0.151</span> </span>
<span id="cb16-8"><a href="#cb16-8" aria-hidden="true" tabindex="-1"></a><span class="dv">3</span> Sales CompPrice <span class="fl">0.0641</span></span>
<span id="cb16-9"><a href="#cb16-9" aria-hidden="true" tabindex="-1"></a><span class="dv">4</span> Income CompPrice <span class="sc">-</span><span class="fl">0.0761</span></span>
<span id="cb16-10"><a href="#cb16-10" aria-hidden="true" tabindex="-1"></a><span class="co"># … with 17 more rows</span></span>
<span id="cb16-11"><a href="#cb16-11" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb16-12"><a href="#cb16-12" aria-hidden="true" tabindex="-1"></a><span class="co"># Select all columns between year and day (include)</span></span>
<span id="cb16-13"><a href="#cb16-13" aria-hidden="true" tabindex="-1"></a><span class="fu">correlate</span>(carseats, Sales<span class="sc">:</span>Income)</span>
<span id="cb16-14"><a href="#cb16-14" aria-hidden="true" tabindex="-1"></a><span class="co"># A tibble: 21 x 3</span></span>
<span id="cb16-15"><a href="#cb16-15" aria-hidden="true" tabindex="-1"></a> var1 var2 coef_corr</span>
<span id="cb16-16"><a href="#cb16-16" aria-hidden="true" tabindex="-1"></a> <span class="sc"><</span>fct<span class="sc">></span> <span class="er"><</span>fct<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span></span>
<span id="cb16-17"><a href="#cb16-17" aria-hidden="true" tabindex="-1"></a><span class="dv">1</span> CompPrice Sales <span class="fl">0.0641</span></span>
<span id="cb16-18"><a href="#cb16-18" aria-hidden="true" tabindex="-1"></a><span class="dv">2</span> Income Sales <span class="fl">0.151</span> </span>
<span id="cb16-19"><a href="#cb16-19" aria-hidden="true" tabindex="-1"></a><span class="dv">3</span> Sales CompPrice <span class="fl">0.0641</span></span>
<span id="cb16-20"><a href="#cb16-20" aria-hidden="true" tabindex="-1"></a><span class="dv">4</span> Income CompPrice <span class="sc">-</span><span class="fl">0.0761</span></span>
<span id="cb16-21"><a href="#cb16-21" aria-hidden="true" tabindex="-1"></a><span class="co"># … with 17 more rows</span></span>
<span id="cb16-22"><a href="#cb16-22" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb16-23"><a href="#cb16-23" aria-hidden="true" tabindex="-1"></a><span class="co"># Select all columns except those from year to day (exclude)</span></span>
<span id="cb16-24"><a href="#cb16-24" aria-hidden="true" tabindex="-1"></a><span class="fu">correlate</span>(carseats, <span class="sc">-</span>(Sales<span class="sc">:</span>Income))</span>
<span id="cb16-25"><a href="#cb16-25" aria-hidden="true" tabindex="-1"></a><span class="co"># A tibble: 35 x 3</span></span>
<span id="cb16-26"><a href="#cb16-26" aria-hidden="true" tabindex="-1"></a> var1 var2 coef_corr</span>
<span id="cb16-27"><a href="#cb16-27" aria-hidden="true" tabindex="-1"></a> <span class="sc"><</span>fct<span class="sc">></span> <span class="er"><</span>fct<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span></span>
<span id="cb16-28"><a href="#cb16-28" aria-hidden="true" tabindex="-1"></a><span class="dv">1</span> Advertising Sales <span class="fl">0.270</span> </span>
<span id="cb16-29"><a href="#cb16-29" aria-hidden="true" tabindex="-1"></a><span class="dv">2</span> Population Sales <span class="fl">0.0505</span></span>
<span id="cb16-30"><a href="#cb16-30" aria-hidden="true" tabindex="-1"></a><span class="dv">3</span> Price Sales <span class="sc">-</span><span class="fl">0.445</span> </span>
<span id="cb16-31"><a href="#cb16-31" aria-hidden="true" tabindex="-1"></a><span class="dv">4</span> Age Sales <span class="sc">-</span><span class="fl">0.232</span> </span>
<span id="cb16-32"><a href="#cb16-32" aria-hidden="true" tabindex="-1"></a><span class="co"># … with 31 more rows</span></span></code></pre></div>
<p><code>correlate()</code> produces <code>two pairs of variables</code>. So the following example uses <code>filter()</code> to get the correlation coefficient for <code>a pair of variable</code> combinations:</p>
<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a>carseats <span class="sc">%>%</span></span>
<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">correlate</span>(Sales<span class="sc">:</span>Income) <span class="sc">%>%</span></span>
<span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(<span class="fu">as.integer</span>(var1) <span class="sc">></span> <span class="fu">as.integer</span>(var2))</span>
<span id="cb17-4"><a href="#cb17-4" aria-hidden="true" tabindex="-1"></a><span class="co"># A tibble: 3 x 3</span></span>
<span id="cb17-5"><a href="#cb17-5" aria-hidden="true" tabindex="-1"></a> var1 var2 coef_corr</span>
<span id="cb17-6"><a href="#cb17-6" aria-hidden="true" tabindex="-1"></a> <span class="sc"><</span>fct<span class="sc">></span> <span class="er"><</span>fct<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span></span>
<span id="cb17-7"><a href="#cb17-7" aria-hidden="true" tabindex="-1"></a><span class="dv">1</span> CompPrice Sales <span class="fl">0.0641</span></span>
<span id="cb17-8"><a href="#cb17-8" aria-hidden="true" tabindex="-1"></a><span class="dv">2</span> Income Sales <span class="fl">0.151</span> </span>
<span id="cb17-9"><a href="#cb17-9" aria-hidden="true" tabindex="-1"></a><span class="dv">3</span> Income CompPrice <span class="sc">-</span><span class="fl">0.0761</span></span></code></pre></div>
<p>The <code>correlate()</code> also supports the <code>group_by()</code> function syntax in the <code>dplyr</code> package.</p>
<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a>carseats <span class="sc">%>%</span></span>
<span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(ShelveLoc <span class="sc">==</span> <span class="st">"Good"</span>) <span class="sc">%>%</span></span>
<span id="cb18-3"><a href="#cb18-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">group_by</span>(Urban, US) <span class="sc">%>%</span></span>
<span id="cb18-4"><a href="#cb18-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">correlate</span>(Sales) <span class="sc">%>%</span></span>
<span id="cb18-5"><a href="#cb18-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(<span class="fu">abs</span>(coef_corr) <span class="sc">></span> <span class="fl">0.5</span>)</span>
<span id="cb18-6"><a href="#cb18-6" aria-hidden="true" tabindex="-1"></a><span class="co"># A tibble: 10 x 5</span></span>
<span id="cb18-7"><a href="#cb18-7" aria-hidden="true" tabindex="-1"></a> Urban US var1 var2 coef_corr</span>
<span id="cb18-8"><a href="#cb18-8" aria-hidden="true" tabindex="-1"></a> <span class="sc"><</span>fct<span class="sc">></span> <span class="er"><</span>fct<span class="sc">></span> <span class="er"><</span>fct<span class="sc">></span> <span class="er"><</span>fct<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span></span>
<span id="cb18-9"><a href="#cb18-9" aria-hidden="true" tabindex="-1"></a><span class="dv">1</span> No No Sales Population <span class="sc">-</span><span class="fl">0.530</span></span>
<span id="cb18-10"><a href="#cb18-10" aria-hidden="true" tabindex="-1"></a><span class="dv">2</span> No No Sales Price <span class="sc">-</span><span class="fl">0.838</span></span>
<span id="cb18-11"><a href="#cb18-11" aria-hidden="true" tabindex="-1"></a><span class="dv">3</span> No Yes Sales Price <span class="sc">-</span><span class="fl">0.630</span></span>
<span id="cb18-12"><a href="#cb18-12" aria-hidden="true" tabindex="-1"></a><span class="dv">4</span> Yes No Sales Price <span class="sc">-</span><span class="fl">0.833</span></span>
<span id="cb18-13"><a href="#cb18-13" aria-hidden="true" tabindex="-1"></a><span class="co"># … with 6 more rows</span></span></code></pre></div>
</div>
<div id="visualization-of-the-correlation-matrix-using-plot_correlate" class="section level3">
<h3>Visualization of the correlation matrix using <code>plot_correlate()</code></h3>
<p><code>plot_correlate()</code> visualizes the correlation matrix.</p>
<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plot_correlate</span>(carseats)</span></code></pre></div>
<p><img src="data:image/png;base64,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" width="600px" style="display: block; margin: auto;" /></p>
<p><code>plot_correlate()</code> can also specify multiple variables, like the <code>correlate()</code> function. The following is a visualization of the correlation matrix including several selected variables.</p>
<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Select columns by name</span></span>
<span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a><span class="fu">plot_correlate</span>(carseats, Sales, Price)</span></code></pre></div>
<p>The <code>plot_correlate()</code> function also supports the <code>group_by()</code> function syntax in the <code>dplyr</code> package.</p>
<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a>carseats <span class="sc">%>%</span></span>
<span id="cb21-2"><a href="#cb21-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(ShelveLoc <span class="sc">==</span> <span class="st">"Good"</span>) <span class="sc">%>%</span></span>
<span id="cb21-3"><a href="#cb21-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">group_by</span>(Urban) <span class="sc">%>%</span></span>
<span id="cb21-4"><a href="#cb21-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">plot_correlate</span>(Sales)</span></code></pre></div>
</div>
</div>
<div id="eda-based-on-target-variable" class="section level2">
<h2>EDA based on target variable</h2>
<div id="definition-of-target-variable" class="section level3">
<h3>Definition of target variable</h3>
<p>To perform EDA based on <code>target variable</code>, you need to create a <code>target_by</code> class object. <code>target_by()</code> creates a <code>target_by</code> class with an object inheriting data.frame or data.frame. <code>target_by()</code> is similar to <code>group_by()</code> in <code>dplyr</code> which creates <code>grouped_df</code>. The difference is that you specify only one variable.</p>
<p>The following is an example of specifying <code>US</code> as target variable in <code>carseats</code> data.frame.:</p>
<div class="sourceCode" id="cb22"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a>categ <span class="ot"><-</span> <span class="fu">target_by</span>(carseats, US)</span></code></pre></div>
</div>
<div id="eda-when-target-variable-is-categorical-variable" class="section level3">
<h3>EDA when target variable is categorical variable</h3>
<p>Let’s perform EDA when the target variable is a categorical variable. When the categorical variable <code>US</code> is the target variable, we examine the relationship between the target variable and the predictor.</p>
<div id="cases-where-predictors-are-numeric-variable" class="section level4">
<h4>Cases where predictors are numeric variable</h4>
<p><code>relate()</code> shows the relationship between the target variable and the predictor. The following example shows the relationship between <code>Sales</code> and the target variable <code>US</code>. The predictor <code>Sales</code> is a numeric variable. In this case, the descriptive statistics are shown for each level of the target variable.</p>
<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a><span class="co"># If the variable of interest is a numerical variable</span></span>
<span id="cb23-2"><a href="#cb23-2" aria-hidden="true" tabindex="-1"></a>cat_num <span class="ot"><-</span> <span class="fu">relate</span>(categ, Sales)</span>
<span id="cb23-3"><a href="#cb23-3" aria-hidden="true" tabindex="-1"></a>cat_num</span>
<span id="cb23-4"><a href="#cb23-4" aria-hidden="true" tabindex="-1"></a><span class="co"># A tibble: 3 x 27</span></span>
<span id="cb23-5"><a href="#cb23-5" aria-hidden="true" tabindex="-1"></a> variable US n na mean sd se_mean IQR skewness kurtosis p00</span>
<span id="cb23-6"><a href="#cb23-6" aria-hidden="true" tabindex="-1"></a> <span class="sc"><</span>chr<span class="sc">></span> <span class="er"><</span>fct<span class="sc">></span> <span class="er"><</span>int<span class="sc">></span> <span class="er"><</span>int<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span> <span class="er"><</span>dbl<span class="sc">></span></span>
<span id="cb23-7"><a href="#cb23-7" aria-hidden="true" tabindex="-1"></a><span class="dv">1</span> Sales No <span class="dv">142</span> <span class="dv">0</span> <span class="fl">6.82</span> <span class="fl">2.60</span> <span class="fl">0.218</span> <span class="fl">3.44</span> <span class="fl">0.323</span> <span class="fl">0.808</span> <span class="dv">0</span> </span>
<span id="cb23-8"><a href="#cb23-8" aria-hidden="true" tabindex="-1"></a><span class="dv">2</span> Sales Yes <span class="dv">258</span> <span class="dv">0</span> <span class="fl">7.87</span> <span class="fl">2.88</span> <span class="fl">0.179</span> <span class="fl">4.23</span> <span class="fl">0.0760</span> <span class="sc">-</span><span class="fl">0.326</span> <span class="fl">0.37</span></span>
<span id="cb23-9"><a href="#cb23-9" aria-hidden="true" tabindex="-1"></a><span class="dv">3</span> Sales total <span class="dv">400</span> <span class="dv">0</span> <span class="fl">7.50</span> <span class="fl">2.82</span> <span class="fl">0.141</span> <span class="fl">3.93</span> <span class="fl">0.186</span> <span class="sc">-</span><span class="fl">0.0809</span> <span class="dv">0</span> </span>
<span id="cb23-10"><a href="#cb23-10" aria-hidden="true" tabindex="-1"></a><span class="co"># … with 16 more variables: p01 <dbl>, p05 <dbl>, p10 <dbl>, p20 <dbl>,</span></span>
<span id="cb23-11"><a href="#cb23-11" aria-hidden="true" tabindex="-1"></a><span class="co"># p25 <dbl>, p30 <dbl>, p40 <dbl>, p50 <dbl>, p60 <dbl>, p70 <dbl>,</span></span>
<span id="cb23-12"><a href="#cb23-12" aria-hidden="true" tabindex="-1"></a><span class="co"># p75 <dbl>, p80 <dbl>, p90 <dbl>, p95 <dbl>, p99 <dbl>, p100 <dbl></span></span>
<span id="cb23-13"><a href="#cb23-13" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(cat_num)</span>
<span id="cb23-14"><a href="#cb23-14" aria-hidden="true" tabindex="-1"></a> variable US n na mean </span>
<span id="cb23-15"><a href="#cb23-15" aria-hidden="true" tabindex="-1"></a> Length<span class="sc">:</span><span class="dv">3</span> No <span class="sc">:</span><span class="dv">1</span> Min. <span class="sc">:</span><span class="fl">142.0</span> Min. <span class="sc">:</span><span class="dv">0</span> Min. <span class="sc">:</span><span class="fl">6.823</span> </span>
<span id="cb23-16"><a href="#cb23-16" aria-hidden="true" tabindex="-1"></a> Class <span class="sc">:</span>character Yes <span class="sc">:</span><span class="dv">1</span> 1st Qu.<span class="sc">:</span><span class="fl">200.0</span> 1st Qu.<span class="sc">:</span><span class="dv">0</span> 1st Qu.<span class="sc">:</span><span class="fl">7.160</span> </span>
<span id="cb23-17"><a href="#cb23-17" aria-hidden="true" tabindex="-1"></a> Mode <span class="sc">:</span>character total<span class="sc">:</span><span class="dv">1</span> Median <span class="sc">:</span><span class="fl">258.0</span> Median <span class="sc">:</span><span class="dv">0</span> Median <span class="sc">:</span><span class="fl">7.496</span> </span>
<span id="cb23-18"><a href="#cb23-18" aria-hidden="true" tabindex="-1"></a> Mean <span class="sc">:</span><span class="fl">266.7</span> Mean <span class="sc">:</span><span class="dv">0</span> Mean <span class="sc">:</span><span class="fl">7.395</span> </span>
<span id="cb23-19"><a href="#cb23-19" aria-hidden="true" tabindex="-1"></a> 3rd Qu.<span class="sc">:</span><span class="fl">329.0</span> 3rd Qu.<span class="sc">:</span><span class="dv">0</span> 3rd Qu.<span class="sc">:</span><span class="fl">7.682</span> </span>
<span id="cb23-20"><a href="#cb23-20" aria-hidden="true" tabindex="-1"></a> Max. <span class="sc">:</span><span class="fl">400.0</span> Max. <span class="sc">:</span><span class="dv">0</span> Max. <span class="sc">:</span><span class="fl">7.867</span> </span>
<span id="cb23-21"><a href="#cb23-21" aria-hidden="true" tabindex="-1"></a> sd se_mean IQR skewness </span>
<span id="cb23-22"><a href="#cb23-22" aria-hidden="true" tabindex="-1"></a> Min. <span class="sc">:</span><span class="fl">2.603</span> Min. <span class="sc">:</span><span class="fl">0.1412</span> Min. <span class="sc">:</span><span class="fl">3.442</span> Min. <span class="sc">:</span><span class="fl">0.07603</span> </span>
<span id="cb23-23"><a href="#cb23-23" aria-hidden="true" tabindex="-1"></a> 1st Qu.<span class="sc">:</span><span class="fl">2.713</span> 1st Qu.<span class="sc">:</span><span class="fl">0.1602</span> 1st Qu.<span class="sc">:</span><span class="fl">3.686</span> 1st Qu.<span class="sc">:</span><span class="fl">0.13080</span> </span>
<span id="cb23-24"><a href="#cb23-24" aria-hidden="true" tabindex="-1"></a> Median <span class="sc">:</span><span class="fl">2.824</span> Median <span class="sc">:</span><span class="fl">0.1791</span> Median <span class="sc">:</span><span class="fl">3.930</span> Median <span class="sc">:</span><span class="fl">0.18556</span> </span>
<span id="cb23-25"><a href="#cb23-25" aria-hidden="true" tabindex="-1"></a> Mean <span class="sc">:</span><span class="fl">2.768</span> Mean <span class="sc">:</span><span class="fl">0.1796</span> Mean <span class="sc">:</span><span class="fl">3.866</span> Mean <span class="sc">:</span><span class="fl">0.19489</span> </span>
<span id="cb23-26"><a href="#cb23-26" aria-hidden="true" tabindex="-1"></a> 3rd Qu.<span class="sc">:</span><span class="fl">2.851</span> 3rd Qu.<span class="sc">:</span><span class="fl">0.1988</span> 3rd Qu.<span class="sc">:</span><span class="fl">4.077</span> 3rd Qu.<span class="sc">:</span><span class="fl">0.25432</span> </span>
<span id="cb23-27"><a href="#cb23-27" aria-hidden="true" tabindex="-1"></a> Max. <span class="sc">:</span><span class="fl">2.877</span> Max. <span class="sc">:</span><span class="fl">0.2184</span> Max. <span class="sc">:</span><span class="fl">4.225</span> Max. <span class="sc">:</span><span class="fl">0.32308</span> </span>
<span id="cb23-28"><a href="#cb23-28" aria-hidden="true" tabindex="-1"></a> kurtosis p00 p01 p05 </span>
<span id="cb23-29"><a href="#cb23-29" aria-hidden="true" tabindex="-1"></a> Min. <span class="sc">:-</span><span class="fl">0.32638</span> Min. <span class="sc">:</span><span class="fl">0.0000</span> Min. <span class="sc">:</span><span class="fl">0.4675</span> Min. <span class="sc">:</span><span class="fl">3.147</span> </span>
<span id="cb23-30"><a href="#cb23-30" aria-hidden="true" tabindex="-1"></a> 1st Qu.<span class="sc">:-</span><span class="fl">0.20363</span> 1st Qu.<span class="sc">:</span><span class="fl">0.0000</span> 1st Qu.<span class="sc">:</span><span class="fl">0.6868</span> 1st Qu.<span class="sc">:</span><span class="fl">3.148</span> </span>
<span id="cb23-31"><a href="#cb23-31" aria-hidden="true" tabindex="-1"></a> Median <span class="sc">:-</span><span class="fl">0.08088</span> Median <span class="sc">:</span><span class="fl">0.0000</span> Median <span class="sc">:</span><span class="fl">0.9062</span> Median <span class="sc">:</span><span class="fl">3.149</span> </span>
<span id="cb23-32"><a href="#cb23-32" aria-hidden="true" tabindex="-1"></a> Mean <span class="sc">:</span> <span class="fl">0.13350</span> Mean <span class="sc">:</span><span class="fl">0.1233</span> Mean <span class="sc">:</span><span class="fl">1.0072</span> Mean <span class="sc">:</span><span class="fl">3.183</span> </span>
<span id="cb23-33"><a href="#cb23-33" aria-hidden="true" tabindex="-1"></a> 3rd Qu.<span class="sc">:</span> <span class="fl">0.36344</span> 3rd Qu.<span class="sc">:</span><span class="fl">0.1850</span> 3rd Qu.<span class="sc">:</span><span class="fl">1.2771</span> 3rd Qu.<span class="sc">:</span><span class="fl">3.200</span> </span>
<span id="cb23-34"><a href="#cb23-34" aria-hidden="true" tabindex="-1"></a> Max. <span class="sc">:</span> <span class="fl">0.80776</span> Max. <span class="sc">:</span><span class="fl">0.3700</span> Max. <span class="sc">:</span><span class="fl">1.6480</span> Max. <span class="sc">:</span><span class="fl">3.252</span> </span>
<span id="cb23-35"><a href="#cb23-35" aria-hidden="true" tabindex="-1"></a> p10 p20 p25 p30 </span>
<span id="cb23-36"><a href="#cb23-36" aria-hidden="true" tabindex="-1"></a> Min. <span class="sc">:</span><span class="fl">3.917</span> Min. <span class="sc">:</span><span class="fl">4.754</span> Min. <span class="sc">:</span><span class="fl">5.080</span> Min. <span class="sc">:</span><span class="fl">5.306</span> </span>
<span id="cb23-37"><a href="#cb23-37" aria-hidden="true" tabindex="-1"></a> 1st Qu.<span class="sc">:</span><span class="fl">4.018</span> 1st Qu.<span class="sc">:</span><span class="fl">4.910</span> 1st Qu.<span class="sc">:</span><span class="fl">5.235</span> 1st Qu.<span class="sc">:</span><span class="fl">5.587</span> </span>
<span id="cb23-38"><a href="#cb23-38" aria-hidden="true" tabindex="-1"></a> Median <span class="sc">:</span><span class="fl">4.119</span> Median <span class="sc">:</span><span class="fl">5.066</span> Median <span class="sc">:</span><span class="fl">5.390</span> Median <span class="sc">:</span><span class="fl">5.867</span> </span>
<span id="cb23-39"><a href="#cb23-39" aria-hidden="true" tabindex="-1"></a> Mean <span class="sc">:</span><span class="fl">4.073</span> Mean <span class="sc">:</span><span class="fl">5.051</span> Mean <span class="sc">:</span><span class="fl">5.411</span> Mean <span class="sc">:</span><span class="fl">5.775</span> </span>
<span id="cb23-40"><a href="#cb23-40" aria-hidden="true" tabindex="-1"></a> 3rd Qu.<span class="sc">:</span><span class="fl">4.152</span> 3rd Qu.<span class="sc">:</span><span class="fl">5.199</span> 3rd Qu.<span class="sc">:</span><span class="fl">5.576</span> 3rd Qu.<span class="sc">:</span><span class="fl">6.010</span> </span>
<span id="cb23-41"><a href="#cb23-41" aria-hidden="true" tabindex="-1"></a> Max. <span class="sc">:</span><span class="fl">4.184</span> Max. <span class="sc">:</span><span class="fl">5.332</span> Max. <span class="sc">:</span><span class="fl">5.763</span> Max. <span class="sc">:</span><span class="fl">6.153</span> </span>
<span id="cb23-42"><a href="#cb23-42" aria-hidden="true" tabindex="-1"></a> p40 p50 p60 p70 </span>
<span id="cb23-43"><a href="#cb23-43" aria-hidden="true" tabindex="-1"></a> Min. <span class="sc">:</span><span class="fl">5.994</span> Min. <span class="sc">:</span><span class="fl">6.660</span> Min. <span class="sc">:</span><span class="fl">7.496</span> Min. <span class="sc">:</span><span class="fl">7.957</span> </span>
<span id="cb23-44"><a href="#cb23-44" aria-hidden="true" tabindex="-1"></a> 1st Qu.<span class="sc">:</span><span class="fl">6.301</span> 1st Qu.<span class="sc">:</span><span class="fl">7.075</span> 1st Qu.<span class="sc">:</span><span class="fl">7.787</span> 1st Qu.<span class="sc">:</span><span class="fl">8.386</span> </span>
<span id="cb23-45"><a href="#cb23-45" aria-hidden="true" tabindex="-1"></a> Median <span class="sc">:</span><span class="fl">6.608</span> Median <span class="sc">:</span><span class="fl">7.490</span> Median <span class="sc">:</span><span class="fl">8.078</span> Median <span class="sc">:</span><span class="fl">8.815</span> </span>
<span id="cb23-46"><a href="#cb23-46" aria-hidden="true" tabindex="-1"></a> Mean <span class="sc">:</span><span class="fl">6.506</span> Mean <span class="sc">:</span><span class="fl">7.313</span> Mean <span class="sc">:</span><span class="fl">8.076</span> Mean <span class="sc">:</span><span class="fl">8.740</span> </span>
<span id="cb23-47"><a href="#cb23-47" aria-hidden="true" tabindex="-1"></a> 3rd Qu.<span class="sc">:</span><span class="fl">6.762</span> 3rd Qu.<span class="sc">:</span><span class="fl">7.640</span> 3rd Qu.<span class="sc">:</span><span class="fl">8.366</span> 3rd Qu.<span class="sc">:</span><span class="fl">9.132</span> </span>
<span id="cb23-48"><a href="#cb23-48" aria-hidden="true" tabindex="-1"></a> Max. <span class="sc">:</span><span class="fl">6.916</span> Max. <span class="sc">:</span><span class="fl">7.790</span> Max. <span class="sc">:</span><span class="fl">8.654</span> Max. <span class="sc">:</span><span class="fl">9.449</span> </span>
<span id="cb23-49"><a href="#cb23-49" aria-hidden="true" tabindex="-1"></a> p75 p80 p90 p95 </span>
<span id="cb23-50"><a href="#cb23-50" aria-hidden="true" tabindex="-1"></a> Min. <span class="sc">:</span><span class="fl">8.523</span> Min. <span class="sc">:</span> <span class="fl">8.772</span> Min. <span class="sc">:</span> <span class="fl">9.349</span> Min. <span class="sc">:</span><span class="fl">11.28</span> </span>
<span id="cb23-51"><a href="#cb23-51" aria-hidden="true" tabindex="-1"></a> 1st Qu.<span class="sc">:</span><span class="fl">8.921</span> 1st Qu.<span class="sc">:</span> <span class="fl">9.265</span> 1st Qu.<span class="sc">:</span><span class="fl">10.325</span> 1st Qu.<span class="sc">:</span><span class="fl">11.86</span> </span>
<span id="cb23-52"><a href="#cb23-52" aria-hidden="true" tabindex="-1"></a> Median <span class="sc">:</span><span class="fl">9.320</span> Median <span class="sc">:</span> <span class="fl">9.758</span> Median <span class="sc">:</span><span class="fl">11.300</span> Median <span class="sc">:</span><span class="fl">12.44</span> </span>
<span id="cb23-53"><a href="#cb23-53" aria-hidden="true" tabindex="-1"></a> Mean <span class="sc">:</span><span class="fl">9.277</span> Mean <span class="sc">:</span> <span class="fl">9.665</span> Mean <span class="sc">:</span><span class="fl">10.795</span> Mean <span class="sc">:</span><span class="fl">12.08</span> </span>
<span id="cb23-54"><a href="#cb23-54" aria-hidden="true" tabindex="-1"></a> 3rd Qu.<span class="sc">:</span><span class="fl">9.654</span> 3rd Qu.<span class="sc">:</span><span class="fl">10.111</span> 3rd Qu.<span class="sc">:</span><span class="fl">11.518</span> 3rd Qu.<span class="sc">:</span><span class="fl">12.49</span> </span>
<span id="cb23-55"><a href="#cb23-55" aria-hidden="true" tabindex="-1"></a> Max. <span class="sc">:</span><span class="fl">9.988</span> Max. <span class="sc">:</span><span class="fl">10.464</span> Max. <span class="sc">:</span><span class="fl">11.736</span> Max. <span class="sc">:</span><span class="fl">12.54</span> </span>
<span id="cb23-56"><a href="#cb23-56" aria-hidden="true" tabindex="-1"></a> p99 p100 </span>
<span id="cb23-57"><a href="#cb23-57" aria-hidden="true" tabindex="-1"></a> Min. <span class="sc">:</span><span class="fl">13.64</span> Min. <span class="sc">:</span><span class="fl">14.90</span> </span>
<span id="cb23-58"><a href="#cb23-58" aria-hidden="true" tabindex="-1"></a> 1st Qu.<span class="sc">:</span><span class="fl">13.78</span> 1st Qu.<span class="sc">:</span><span class="fl">15.59</span> </span>
<span id="cb23-59"><a href="#cb23-59" aria-hidden="true" tabindex="-1"></a> Median <span class="sc">:</span><span class="fl">13.91</span> Median <span class="sc">:</span><span class="fl">16.27</span> </span>
<span id="cb23-60"><a href="#cb23-60" aria-hidden="true" tabindex="-1"></a> Mean <span class="sc">:</span><span class="fl">13.86</span> Mean <span class="sc">:</span><span class="fl">15.81</span> </span>
<span id="cb23-61"><a href="#cb23-61" aria-hidden="true" tabindex="-1"></a> 3rd Qu.<span class="sc">:</span><span class="fl">13.97</span> 3rd Qu.<span class="sc">:</span><span class="fl">16.27</span> </span>
<span id="cb23-62"><a href="#cb23-62" aria-hidden="true" tabindex="-1"></a> Max. <span class="sc">:</span><span class="fl">14.03</span> Max. <span class="sc">:</span><span class="fl">16.27</span> </span></code></pre></div>
<p><code>plot()</code> visualizes the <code>relate</code> class object created by <code>relate()</code> as the relationship between the target variable and the predictor variable. The relationship between <code>US</code> and <code>Sales</code> is visualized by density plot.</p>
<div class="sourceCode" id="cb24"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(cat_num)</span></code></pre></div>
<p><img 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Ggzj1/+8pfSupqsjl9//XUlSqdvpadNVt9KIAtpJSj3afMT6kEbfmipkhLI6tuYlbdy/9e//jXIApp6e0pPk+ql1FmJ1/VbPPryklAy0tpdKB38/e9/l1bhtKTLVKAerBLIIp2CUifluvKtbCJDm70oQTkWc/PKJfmtyNXposEJ9WbJupuCGO6HULbymMq2tC7mPAfr16+HMDqDWIuPH3/8UT5bymY61DZdg8Kgp3a1ts26lsXnTIAJaI/Ahb8a2qtDryRWlkAJa2y55IkUKe1iRbta0fKjxMREmb+wFJbfomcDMecshzDffPNNiN6xvC56aZ3koHXIYh4YdJ2W5tBOZ4qC7jpMrSSk4XEKtLsZhQMHDnR6gaAhUAqk/GkpmDBog7DCxiOPPNJp3bOM1M0/H374oVxWJiywcfvtt4OGr4UFtXw56W4YWhn+p+FiJShDxmJ+W14yNexNkahe9EJDw7/C4lumU+osTwz+GT16tDwTIwkgRSuMzuRQP10UhlfynjG55A0j/4gRB3n1s88+k3UVIymgDwVL6mLOc0DLySjQNMeYMWPkEDvtZEfBcH26vCD+MdWu1rSZkjd/MwEm4AIEXGuAwPLaiB6PfhhSNKe0yqZv+rzwwgv6DMnal6yWlXs03Ksck4W2sUCW3hSHLMSFUtQviXrqqaeMRdfRULqSp3iBkBbKNFRO1xSrb2VZElltk5U13Zs3b54+Pxr6JstyJXQd+hZz1XK4nqyfr7jiCp1QUjIPsuQWvU6ZzHBYli4IZaOPowxfk8GUMsxOFuZkINZdUIa+ybCOjLhEb1nmR8PBlA+FrkPfZA2uDM+TIZZyLLYY1Yl13TKNMbnkjf/9owx909A2GdyNGDFCGvcRM/Fyo49qSV3MeQ7EC4++fnfddZdO2eWOyqUlexS6Mu6pXc1pM31l+IAJMAGXI+D2Vt/Uou+9956Oljsp645pnTKtZ1bmeZVWX7NmjVzWRD+49CHFQ8qOfkiNBbJwFgZF+mVGpEDFhinS2thYfLr24osv6pcQCeMp3cMPPyzLUhQ1rdum+WYqnxQlvSQoP/6U3pSipjiih6YTRlX6lxJSJLSmWAldlQhZOStK2XAdOClNkuPKK69Ukhr9VhT1b3/7Wx29gFAamjcmnkroqqjpOllo09w3xacXCzHsLK2/lTTdyaXcVxQ1vdDQOmTlRYuWTAnjPyWa/Da3LhTZnOfgV7/6lZzHJ9lpmdzSpUtlPRTr866MTbWrqTbrVBk+YQJMwKUIsKI2aM6amhqdsjbY4PIFh9Tbo56qGAq+4J6xC7Rel4yilB6rsTiG1yiestzK8LrhMW1yQsu3ehPoBUMMRV/wQmIsT1J6hhurUBxl7a+YAjCWRH9NUdSkLCnQi0VPPXB9wv8dkIzUNsaCMbmMxaNr9OLU3dI4c+timLep54Bkps1wLAmm2tWSNrOkXI7LBJiAegl03utRvP67c6A5afqYCklJSaCPuYEMiJRlUuakIW9P9Okp0BKj3gZanmVuIKM7xfBOWL9DrLuWW6eKIWmYY0hmWA5tH2pJULyXGUtjKJex+4bXyKBLMepSrvemLqaeA3OfJ0UW+jbVrpa0mWG+fMwEmIB2CbCi1m7bOU1yMe8KsYRMlk/uPGmNs1aDK9VFq23AcjMBJtAzAVbUPfPhu0YIkLU4OYcg62tz/G6L/c6l1zGyglZbsLQuapOf5WECTMD1CXjQqLzrV5NryASYABNgAkxAmwTcfh21NpuNpWYCTIAJMAF3IcCK2l1amuvJBJgAE2ACmiTAilqTzcZCMwEmwASYgLsQYEXtLi3N9WQCTIAJMAFNEmBFrclmY6GZABNgAkzAXQiwonaXluZ6MgEmwASYgCYJsKLWZLOx0EyACTABJuAuBFhRu0tLcz2ZABNgAkxAkwScqqj/9a9/gXztCicUmoMnnCdoTmZDgYVDD8NTTR0LxxRoa2vTlMyGwmqZfWVlpfQnblgfLR1rmX19fb30za4l3iyrbQg4VVHTlpJ79uzBrFmzsGDBArz88svQugK0TbNwLkyACTABJsAEzhFwqqIeMmQIHn30UQi/w3jmmWekR6bU1FRMmDAB1NsWbiS5nZgAE2ACTIAJuDUBpypq4WcYL7zwAubNm4err74aaWlp2LRpE9auXQvhNxi33XabWzcOV54JMAEmwASYgFO9Z9FQN/kTfvbZZ9G/f/9OrXHnnXdi165dna7xCRNgAkyACTABdyPgVEVNQ9+XXXYZAgMD9dw///xzxMbGYuzYsaBh8K6BnH15eHh0vaw/p/sdHR3w8vLSX+MDJsAEmAATYAJaJeAURf36669j3bp1+Pbbb7F69Wr4+flJfqRgN2zYgLfeessoT7r30ksvwdfXF08++STi4+MviLd8+XJERERg4cKF8h712tesWSOPlTnxCxLxBSbABJgAE2ACKiXgFEV9+eWXY9iwYbJnTPPQISEhEo+npyeeeuopJCYmXoCLlDgpcFLU+/fvx4oVK/DLX/6yU7wPPvhAXr/33nv1148cOYJly5YhICAAlD8HJsAEmAATYAJaIuBwRU0K97XXXsMtt9yCRYsWYceOHRfwuvTSS9G3b99O1/Pz85GRkYHIyEhMmTIF1HM2DLSuNi8vT+ZreL2xsRGtra1yODw6OtrwFh8zASbABJgAE1A9AYcrappfpqFpmkOmnjQddw00tN010EYLZHhGgfLouuGFt7c3HnjgAXz88cf6pLS8Kzc3Fy+++CKOHTuGG2+8UVqYKxFqa2tBmwhYE2pqajTdQ6e6d2VoDQdnpCH27e3tmuWvZfb09+Lj4yOnn5zR9r0tU8vsm5ubZYeDOh9qDKGhoZ3sjdQoo1Zlcoqi/slPfiJ5zZ49G6WlpYiJicGZM2fw/fffg64ZU95djcjo3FSgl4HnnnsOgwcPljv6/Pa3v+2kqIODgxEUFGQqG6P3SVGQ0ZtWA00DaHmEgUZW6OVMraGpvAS1J4+jsfgMmqsr0dHSDE9voeDCI+EZEoHIUePhHWjds+fMOtMLs7+/v5xKcqYc1pat5eeeXpLo5VrpsFjLwF7pejLytVeZ7pKvU3/pSImuWrVKbnQyefJkzJ8/X26AQkZmXZUgPZz0NqwEc626lfluSk89bBp6V+aq6cGy9uGidEo+ikxa+ibZtSq/wl5t8teezEXZnu2oOLAb7c2NCEnLRmBCMsIy+8FLGEx2iCmY5soyVB/Iwc61qxHRfwiSL74UQQkpmnl0lOdGbezNBajIb258NcVTZNcqezWx1JosTlXU7777Lr744gt8+eWXsndHm5+QNTddozlsw0CbodAcNClbGsbOysqSt+ktk364DZd4KekoLg2Hv/rqq6A57pSUFM0qJ6VO/K0uAs1VFSjZ8YP8QNeB6GFj0ff6OxGcnA6PbowXA8U+62GB/ije+h0OvPwkokeMQ9qCK+Hlc+GUj7pqy9IwASbgDAJOVdTjx4/Hf/7zH3z00UegDU4OHjwIstwmK+2ugeataT/wpUuXyl4xbTlKgXrkpKSvvPLKrknkEPqkSZNk3jTE/uc///mCOHyBCVhKoF0MY1cc2CWU82bUnspD1OBRyL7qFoRm9DV7hMYnMBjJMxYgbuxFyPvwTez75+Pov/Q++EeywaOl7cHxmYCrE/AQc72mJ3vtRIGGsmmZFQ1j33rrrdKTFs1Pz5w5s9sSqZdM667NHfqmjMj4ggxgbDmnSc5DlGH1boVV8Q3yIqTVOWrynhUVFWXT9jTVVG2NDag6sl8q6IrDexGclIqYkRNFD3qMGNb2N5W8031j7AvWfY6iLd9i0O2/QmDchcsTO2XgxBOtz1EbY+9EnBYVrfY5aosqw5EtIuDUHjVZ79KQNBmSrV+/Xi84GXmNGzdOf254YGyI2/C+sWNaQ82BCZgioBMGgu3NTWitrwMZg9Xk56K9sV72mhuKC+Wcc+TA4XKY2k8YhdkyJM+8RBqXHXjtaQy55wHRs46xZfacFxNgAhom4FRFfccdd2DUqFG455579LuTEUtaL82BCdiCAFlcVx3eJ5VtY+lZtNRUCeXbiI62VjGl3CGKEANK9L+YXxb/wMPLG95BwfAJChFzzF6IGjISafOvFEo6C16+53bQs4VcxvKInzBdvigcXPYchv70QXgHnN9a11h8vsYEmIB7EHCqoqaNSK644goMHTrUPWhzLR1GoDrvCM58+wVqTuYhPHuAnD+OGjoavmER8PYPhKeYCvHwEDvVCUNECmT45SGWe3kKRe3MkDRtHuiF4th7yzBAzFlzYAJMgAk49VeJthIlRT1nzhykp6frW8PYzmT6m3zABHog0NpQh+MfvY3aE8eQJIy1+t10r917wj2IY9WtzEU3YO8/H0Ph998gcfLFVuXBiZgAE3AdAk5V1DTETcunugZjO5N1jcPnTKArgfqi0zj0+vOI6DcEI37zuMVGXl3zc9Y59fZpide+F/4q6xIQE+csUbhcJsAEVEDAqV4qaJMT2hbvs88+w6lTp6R1Nhl+GfauVcCIRdAAgdpTx3HglaeQMusyZF1+k2aVtIKaLL9pGJyWbnFgAkzAvQk4VVGT96sTJ07g2muvRUVFBZYsWQLa9GTPnj3u3Spce4sINJQUiZ70P5C55AbEjZlsUVo1R06cOgetdTUo3blFzWKybEyACdiZgFMV9c6dO6XFt7K3d0JCAqZPn46tW7faudqcvasQoPXNh8VwN/Wko4eOcZVqyXqQYVvGoutx8stVoE1WODABJuCeBJyqqG+44QbcfPPNWLt2LQoKCvD444/j7bffxty5c92zNbjWFhPIW/WGsOjug4SJMyxOq4UEZLEelJSGok1fa0FclpEJMAE7EHCqov7Zz36GV155Re7BHRcXh8zMTOmfmvb15sAETBEoydmM+qIC2es0FVfL91PnLUHhxrWg0QMOTIAJuB8Bp1p9E+7+/fvLj/uh5xr3hgAtw8r//D30v/mnmlt+ZWm9g+KTEd5vkFDWa5A6Z7GlyTk+E2ACGifgcEVNbiZvu+22HrHRTmWjR4/uMQ7fdG8CBWs+RuSgEQhN7+MWIJJnXop9L/4fEi+awzuWuUWLcyWZwHkCDh/6JpeUt99+u/yQUwja53v27Nm4++675f7eOTk5CA0NPS8hHzGBLgSaxc5dFftzkDb38i53XPeUlmuFZfXH2a0bXLeSXDMmwASMEnB4j5oU9YQJE6Qwv/rVr7B8+XJkZ2fL8zFjxsj11Bs2bEDfvn2NCswXmUDF92uRMGU2fIJD3ApG0tS5OPzWi6JXPdvpW526FXiuLBNwMgGHK2rD+pJ/6fvuuw833XQTkpOT8eOPP2LlypX46quvDKPxMRPQE6g7fQLNYgeyuJvu0V9zlwNyDOIXHoXyPduFi81zL7vuUneuJxNwZwIOH/o2hP3ggw/KIe/t27fjzTffBFl+b9myBX36uMe8oyELPjaPwGnhtzlszBSXNyDrjgbt/V30w7rubvN1JsAEXJCAU3vUxHPhwoXy44JsuUo2JkA+ocnZRsrMy2ycs3ayixoyCic+W4m6gnwEJ6drR3CWlAkwAasJOLVHbbXUnNAtCdDypLjxU+FpZ7/Qaobr4eWFuLFThFHZd2oWk2VjAkzAhgRYUdsQJmdlPwKt9XUo27sD8S66A5kl5GKFoi7f8yPam5ssScZxmQAT0CgBVtQabTh3E7tk+ybh8nEw/MIi3K3qF9TXPyIawamZKBNGZRyYABNwfQKsqF2/jTVfQ51OJ9cPx0+Yofm62KoCscJLWMmO722VHefDBJiAigmwolZx47Bo5whUHzso1w2HZfVjJP8jEDVoJBrOnkFTeSkzYQJMwMUJsKJ28QZ2heqVbP8eNC/L4TwBTx8fkAV46S52CXueCh8xAdckwIraNdvVZWpFHqMqDu3hDT6MtGjMiPEoY0VthAxfYgKuRYAVtWu1p8vVpnzvdoRm9oVvSJjL1a23FQrN7Ie2pkbUF57qbVacngkwARUTYEWt4sZh0YDSnVu5N93Ng+Dh6YnoYWNQtvvHbmLwZSbABFyBACtqV2hFF61Dc1WF7C1GDhzuojXsfbWihgpFLUYdODABJuC6BFhRu27bar5mtE44YsAwt93X25wGJEcduvZ2saXoSXOicxwmwAQ0SIAVtQYbzV1EpvlpGtrl0D0BchsbKZZqlQv/3ByYABNwTQKsqF2zXTVfq+bKcjSWFCFc7EbGoWcCUUNGomL/zp4j8V0mwAQ0S4AVtWabzrUFL9+XI4e9Pb19XLuiNqhdaEZftNbVoLG02Aa5cRZMgAmojYDT3Vw6E0hDQwPoY02oqxNOIsrKrEmqijQ1NTWqkKM7Ic7u2oaI0ZONMq6trZXJvIQnKS0Ge7APzOiP0z9+j8hxU+2KhJ57+pvx8/Ozazn2ytwe7O0la9d8m5qa0NHRgdbW1q63VHEeFBSEgIAAVcjiakK4taIODAwEfawJLS0tiI6OtiapatKoVf4W0TtsKSlE6piJRg3J2oXxVFRUFLy9tfv42pq9x8hxKNy0FtELLrfr80UvR/7+/pr+QbY1e7sCN8i8vr4ebW1tCAvjPQUMsLjFIQ99u0Uza6uSlQf3ICx7gFElra2aWCdtc3sT6ltr0dBWh/aONrMyCe87CPVnToHcgXJgAkzAtQhot0viWu3AtTEgUHFgl7BkHmFwxTUPy5uKUVB/HEX1+ShpPIOK5hLUtlahQ9cBb08f6MR3m64NAV5BiPSPRXxAClJD+iIrdBCCfUI7QfHy80doRh9UHdnHG8R0IsMnTED7BFhRa78NXaoG7a0tqM49hKwrbnapelFlqJecW7MfB0tzUHjmBNqFEk4OykJiUDrGxM6QyjjUJxIB3uenY6hHXdtajfLmYqHQT2Jf+VZ8fnI5koIyMSJ6MgZFjBFK/dyfccSAoag8tJcVtcs9OVwhdyfAitrdnwCV1Z+UdGB8ksvs7V3SWIgjVbvlp7jxNFKD+yDBNwMz0hch1j8JtA66p+AllHC4X5T8ZIUOxOSEeWhpb8ahqp3IKd2Arws+wKS4uULRT0dE/6E4vfZT6ITBEW0vyoEJMAHXIMCK2jXa0WVqQT1CUjhaDTRsfarumF45N7c3om/YMEyOn4dMoWh9vfykJXt0gPWGiJTHsKgJ8nO6Lg/fFn6MbSXfYE7KNfAWlre1p44jND1bqwhZbibABLoQYEXdBQifOpdA1eF96HfTPc4VwsLSG9saxJD2Phyt2ovc6n0I8glBv/DhWJRxqxjazoSnh/16tynBWbip769E2Xvw31MrEDLBA0FHd7CitrANOToTUDMBVtRqbh03k62huBAdba0ISkpTfc1pSJuU47HqvThTfwIpwdmi5zwU0xIXIso/zuHy9w0fhvSQ/vhi/yv4yGs9rqoZJnrwAxwuBxfIBJiA7QmworY9U87RSgKVojdNW4aamre1MvteJdPpdDhdn4dDlTk4XLVLzhP3CRuCcXEzpRW2n5fzN3qgIfGFA+7EF6/8DB8EvCTms+djUvzcXtWbEzMBJuB8Aqyond8GLMH/CNDSotixU1TFo6zpLHaXfY+9wtray8MLAyNHY0nGbdLq2p5D2tZC8PL1Q5ZfHwzCMHxV9h3KGotwSfpNUnZr8+R0TIAJOJcAK2rn8ufS/0egvaUZtfm56Hv9XapgQop5U9EXcl3z0KjxuDr7XqGcM1QhmykhaPOTxtwC3Lr4D1iZ9wJW5v4LV2XdDR9PX1NJ+T4TYAIqJMCKWoWN4o4i1Zw4Kpdl+QQFO7X6x4Qx2LqC1aDdwfoLg7AZyYs1p+DC+gxE0eb1yBbrsW/o83OsOv4K3jn2D1yX/TNpde5UwFw4E2ACFhNgRW0xMk5gDwJVRw8gTPQEnRUqmkrw5al3QEPdM5IWYXDkOLtaa9uznkGJqegQe9GTN62AmDhcKXrTq4+/ihW5z+P6Pvdr7sXDnqw4byagBQL2WzeihdqzjKohUH30IML7OEdRbyteh1cPPYrk4EzcO/gxDBVrlNU4/2xuY5ExXlh2f7HD20GZhObWl2TeIbYiDcR7uS+YvX+4ueVxPCbABOxLgBW1ffly7mYQaKmpQlNlGULSs8yIbbsoTWIzkndz/4kcYXR1S/8HxNKqy0Rv0zX8X4dlD5RbsSq0SFlfnnmn2Ee8HZ/kvy72Edcpt/ibCTABlRNgRa3yBnIH8aqOHURoZl94ejluJqayuRT/PvSYdHhx+4A/IS4g2aVQh/cZgOq8w50UMu0JTkZxtAZ8/ZmPXKq+XBkm4MoEWFG7cutqpG60v3e4cGvpqHC24TSWHforhkdNFruH/cRletGG/PyjYuEplmo1FJ02vAxa703z1HvKN4tlZ5s73eMTJsAE1EmAFbU628WtpCJFTZbKjgiFwgPV8qNP4eLkK6WDC0eU6awywrJonvrwBcWH+Ibj2j73Yc3plSioO37Bfb7ABJiAugiwolZXe7idNGSZ3NHaKpZm2X/omXw+v33sGcxNuRbDoye6PGupqMXwt7GQEJiGBWk34j2xzrpOuNHkwASYgHoJsKJWb9u4hWRkmUwWyvbeNrSmpRJvH30WM5OWCKvu8W7BlhQ1rU8nt5fGwuDIMWIZ2li5zpq8fnFgAkxAnQRYUauzXdxGKjJ4IoViz0D+m1eIDT9GRE/GqJip9ixKVXn7RUTBOyAI9YWd56kNhbw4+QppCU6uMjkwASagTgKsqNXZLm4hFS0Rqsk7YndF/Un+f6RHq+liIxN3C6GZ/VBz3PjwN7GgZVtXZN6FnNLvcLzm3Lprd2PE9WUCaifAilrtLeTC8jUUnxGawgsBsQl2q+WW4q9RKhxTXJb+E7uVoeaMw7L6ofr40R5FDPWNEHxuEUPgr4KWrXFgAkxAXQRYUaurPdxKGtmbFj0+ewXyE/1d4ae4Kvtut93jWvaoe5inVtj3E/ua01ryT/PfVC7xNxNgAiohwIpaJQ3hjmLYc36a5qVXH38Nc1KuRrS//Xrsam83/8hokOvLhrNi9MJEuK7Pz4S3sErsLNtkIibfZgJMwJEEWFE7kjaXpScg56fFkCz1+OwRvi74ALEBSdKAzB75aynPc73qIyZFJjeYi4Wv7a9Pf4Cq5jKT8TkCE2ACjiHAitoxnLmULgRoftrD21t6d+pyq9en+bVHcLByBy5Ju6nXeblCBqEZfcUyrWNmVYV8bo+JnS72A3+j0/ajZiXmSEyACdiFACtqu2DlTE0RqBG96TCxv7etQ2tHq5hnfQNzU69FkE+IrbPXZH60jzrxNjdMTbgUDW210hLc3DQcjwkwAfsRYEVtP7accw8Eao4fscuw96aizxHpF4shwp80h3MEAoVVvU5saEK7wJkTvITzDrKSX3dmNapbKsxJwnGYABOwIwFW1HaEy1l3T4B6eLaeny5vKsaPJevE1pg3dF+wm94JTe8jdykzt/qJQWkYGXMRvjj5lrlJOB4TYAJ2IsCK2k5gOdvuCTSWFEF08UA9PVuGr06/i/FxsxDhF2PLbF0ir9CMPqjNN2+eWqkw+ecuazqL/RXblUv8zQSYgBMIaE5Rm3J4T/fb29s7oTSVplNkPrE7Adp/muZNbRmOVe+TfpYnxc+3ZbYuk1eIBQZlSqV9PH2kQd5Xp1agsa1BuczfTIAJOJiAphT1hg0bcM011+DGG2/E2bNnjaJavnw5vvjiC/09c9LoI/OBQwjQTlm2VNTkUGLt6fcwS+xbTcqFw4UEghNT0VJThZZayzxlZYYOQFbYIDFf/eGFmfIVJsAEHEJAM4q6Q3gAeuutt/DSSy/h9ttvx4oVKy4A9MEHH3S6bk6aCzLhC3YnIHvUGbZbP72zdCP8vAKkJyi7C6/RAjzEVq0hqZkWD39TdWcnXy2Xu52uy9No7VlsJqBtAppR1Pn5+cjIyEBkZCSmTJmCgwc7OxBoa2tDXl4ebrnlFn2LmEqjj8gHDiPQVFmG9qZG4X86ySZl0g5kG4o+Fb3pq2ySnytnEiLmqWvycy2uIi1zm5l0uTQsY3eYFuPjBEyg1wS8e52DgzKorKxEWFiYLI18F5NiNgzeYvOMBx54AB9/fN5dn6k0NTU1qKurM8zG7OPqasuGEM3O2EERqd4tLS0OKu18MXWHdsM3PgVF3UxdnI/Z/VFtbS1aW1vh6emJXbXfIdIzHj61QSisLew+kYruOIt9a0gkKvd9A79CyznF67LQ0bYem4vWYkjQBPj6+qqIqPmiOIu9+RJ2H7O5uRk0SlhfX999JCfeod/noKAgJ0rgukVrRlGTQRgpaCWYYyBmKk1oaCjoY21ITEy0NqnT05WVlSE6OtrhcuRt+RoxA4agN+yKi4sRFRWFVrRgf/EW3NzvN4gP1E5bOIt9W2QESj59B3HRUXL/b0sbf0nErXj98BMYnzIDMaFxliZXRXxnsbdF5UlBUwdF6bDYIk/OQxsENDP0TQ8n9aSU4CXm3EwFa9KYypPv944AbWVJW1raImwpXoOs0EFCSafYIjuXz8PbP0C6FK07fcKquhLn/sEj8W3xR1al50RMgAlYR0AzijotLU3OQTc0NGDPnj3IysqSNaa3TLpmLHSXxlhcvmZ/Aq31tWiuKENwcnqvC2tsq5ebm0xNXNjrvNwpg9D0bGFQZvk8tcJoQuRc5Ncdxsla87ckVdLyNxNgAtYR0MzQN82JLViwAEuXLpXzNM8884ys8apVqxAYGIgrr7zyAgLdpbkgIl9wCAHqTQenZsBT2BP0Nmwr/QZ9woYiJsC2m6b0Vi61pw8Rirp05xarxSTr+mlxl+HLU+/gzoEPw9NDM+/6VteZEzIBZxPo/S+mA2uwePFizJkzB35+flCGvm+++eZOEixatKjTubE0nSLwicMI0M5YIWIry96G5o5G7CjbgFv7/6G3WbldelLUxz96W3rGMrT5sATE4PBx2Fu9BdtL1mNc3MWWJOW4TIAJWEFAc6/D1HtWlLS59bUmjbl5czzzCdhqfnpf7RZkhAzg3rT56PUx/SOipSFZY7Hllt9KJqTg56dejw2Fn6K+9bzdiHKfv5kAE7AtAc0pattWn3NzFIH2lmbUF55GSNo52wJry6V10/vqNmNy3Dxrs3D7dNSrtmY9tSE4ctoxMHI071hmCIWPmYCdCLCithNYzrYzgdpTxxEYlwiyPO5NyCnbiFjfZMQFsKW3tRxD0oRB2UnrDcqUcmcmLcHhql04U2+dFbmSD38zASbQMwFW1D3z4bs2IlArDMl6Oz/d3tGGLWfXYGToNBtJ5Z7ZUI/aFoo60DsY5GHrv8Jphzn7Grgnba41E+g9AVbUvWfIOZhBoEYYkpGrxd6EfRU/Isw3Egl+6b3Jxu3TBiWmoKW6Cq11vZ9fHh0zDa0dLdhd/oPbc2UATMBeBFhR24ss56snoBPbHtaezAOt4e1N2Hz2K0yK57np3jCktJ5e3nItuy161bQ8iwzL1hV8iKb2xt6KxumZABMwQoAVtREofMm2BMiIzCc4BL5hEVZnnFu9H+26NvQNH2Z1HpzwPAEy6qOXJ1uEtJC+SA/tL6zAP7FFdpwHE2ACXQiwou4ChE9tT0C6tezl+unNYrvQ8XGzeIMNGzWPtPy2gUGZIs5s4b1sd9n3KGm0ftmXkhd/M4GeCJBDJHJO4k6BFbU7tbaT6kobnfRmfrq4oQBnG05hePQkJ9XA9YqlHnX96Xx0tHf2QmdtTUN9IzA5fr40LLM2D07HBIhAZmZmJxC062ROTg7Ky8sxd+5cjBw5ErGxsZ08JXZK4IInrKhdsFHVViVas9sbi+8txWtBRks+nr5qq5pm5fEJElMR4ZGoP3PKZnUYHzcbNS0VOFCx3WZ5ckZMQCGwfPly0M6TeXl5UnHfd999brPagBW18hTwt10INJWXQCd6bQGx1u3JXddag4OVORgTM90u8rlzpufWU9tmnpo4ent6Y17qdVhz+j3QxjQcmIAtCeTn54N8clMgh0vvvvuudPtpyzLUmhcrarW2jIvIRduGUm/a2n2ld5RuQP/w4QjxDXcRIuqpRmg6GZT1fuMTwxplhw1GYlA6NhZ9ZniZj5lArwn8/Oc/xz//+U8MGTIETzzxBAYPHgwfH59e56uFDFhRa6GVNCyjXD9t5bIs2uCEFDU7frDPAyB71L1wedmdVHNTrpXtVtpY1F0Uvs4EzCZAhmOenp7IyMjA0aNHpZLetWsXJk+ejJaWFrPz0XJEVtRabj0NyE47kllrSHagcjsifKORFJShgZpqT8QAsaUr7cHeXFluU+HD/aLkendyhcmBCVhKgEbf2tvb9ckqKioQHx+Pv/zlL3JOev78+Vi5ciWGDx+OHTt26OO58gEralduXSfXrbW+ViqBoKR0qyTZVrKOe9NWkTMvEf0ghqRmocbGw99U+oS4OahtrcS+8m3mCcOxmMD/CNDQ9scffyzPqOd88uRJqaip97xs2TJ5vba2Fnv27EFqaqpbcGNF7RbN7JxK1oph1eCUDHh6W+72nBw91LRUYkD4SOcI7yalyn2/821nUKZgI8OyBak3YE3Be2hqa1Au8zcTMEng//7v//D2229Lg7GFCxfixRdflDYud911F9atWyeXb5GCvummm5CcnGwyP1eIYPkvqCvUmuvgEALSkMzK/b23Fa/DqJip8BI/+BzsR4DWU5/88gO7FJAROgCZwm/4ujOrsSDtBruUwZm6HoEBAwbgo48+AvWaQ0JC9BVMSkrCe++9J68HBATA24oOgD4zjR1wj1pjDaYlca11xFHfWivdJ5Ki5mBfAiGpmWg4Wyjnqu1R0pyUa0C2BgV1x+2RPefpwgQMlbRhNem6OylpqjsrasMngI9tRqC9tQX1hafkHKilme4UPqf7hg9FiE+YpUk5voUEvPz8pZ/wOuEv3B4hyCcEFyddgU9PvgGy4ufABJiA5QRYUVvOjFOYQYB++ANi4uEdEGhG7PNROnQdcmnP2JgZ5y/ykV0J2NJBhzFBR0RPBvmu/qH4K2O3+RoTYAImCLCiNgGIb1tHgOanQzP6Wpz4aNUe+HsFIjWkd76rLS7YjRPQhjQ0TWGvQNbll6bdDHJTWtbEa6vtxZnzdV0CrKhdt22dWjOpqK3wmLW99FuMjeXetCMbL0TuUJZn132To/zjpNOOT/PfsGs5juTGZTEBRxFgRe0o0m5Ujk7sJFR7Ks/ijU7Km4pxpv44hkSOdyNazq+qf0Q0vHz90FhsXxeVE+PnorWjFbQ+ngMTMEaANuBpravt1cdYvlq/xmtftN6CKpSfjMh8goLhGxZhkXS0XeiwqInw9fKzKB1H7j0B6Z9arHsPjE/qfWbd5ODp4YlF6T/B60eeQN+woYj0j+0mJl92VwJFP6zD6TUfw8OapVc6HTqEEevEv5/bFMWVGLKidqXWVEldrJmfbu1owe6yH/CT/r9XSS3cSwyapya/4fHj7bskLi4wGRPFrmUf5y/D0n6/AylvDkzAkEDc+GnIXHSd4SWzjqk3vu2P95gVV2uR+K9Eay2mAXlrThy12JBsf8WPiAtMQUyAde4wNYBF1SKGCscp9jQoM6z85IT5aNe1g/yMc2ACTMA0AVbUphlxDAsJWOOIY3vJt8Ln9DQLS+LotiIQlJgq5wVbaqttlWW3+VAvenHGrdhU9AWKGwq6jcc3mAATOEeAFTU/CTYl0Fgilt+I5Ti0htrcUFifjxrhwKF/+Ahzk3A8GxPwEG4EvQODUJqzxcY5G88u2j8BM5IWY/WJ19AmDMw4MAEm0D0BVtTds+E7VhA4N+xt2RpoWpI1MnoK7+ttBW9bJokdNQktNZW2zLLHvGgZXohPOL4pWNVjPL7JBGxBYNWq888ZeeUi95laCayotdJSGpGz+vhRhFiw0Ql5VjpQsUM64NBIFV1WzNDMviBDQEeGRWIInOwTjlXvc2SxXJYbErjllluwYcMGWfMvv/wSBQXamXZxa6tvck7eIdb8WhMobWurdofs7CV/zfEjiJs402w2OaWbkB7cD4EeIWanIdnb2to0u3GGvdhb8xwbpvFPSBEOOs6gSaxjpT3AjQXiTs+9rZwi+CEAl6TcjI9PLMNtff+IYDvv765W9sZYd71G3NUsv5eXFzzFFIpaw/jx40EuNKdOPb+ygTx0Pffcc5LrL37xC4SFqdO/gFsranrwm5ubrXquyIl5Q4N2/exSvW0tf0t1BdqaGqET66fNzTtHrJ2eGrPI7PjUWAp7+mHQYrAHe1tx8IuNR/mxQwjO7Gc0S5JdJ9ar0sdWIcE7A/2DR8v56suT7hImDvb7sVcze1M8m5qapEKx1UuSqfIsve/v7w8/P/XugRAeHo4JEybg3Xff1Vdt2bJlIAXu4+ODl19+Gb/73e/099R04NaKmh4s+lgT6uvrVfv2ZU596CXF1m+PJbkHECaGT8PDzdvo5ETNYeg8OjA0Yax0DG+O3BSHfrBCQ0Nt1qszt1xbxbMHe1vJFpE9AO0lZxA2YqzRLGkEiv5myB+wLcP8kGvERih/x676jZiedJkts+6Ul5rZdxLUyAkpaBrRsPXfrZGiXPbSvffei3nz5mHy5Mmyjjt27MD9998vf39effVV1dbbfq+uqq0yC2YvApaun94hjMhGiSVZ5LSBgzoIkCMVR89TU829PL1xRead2F66Hnk1B9UBg6VwOQLU47/tttvwn//8R9YtKSkJJSUl0rAsIUG9eziwona5R9F5FaL56dBuhky7SlXXWi0MiPaDXCByUA+BULFDWd3pE+gQPTdHh3C/KLG++jasPv4aqlu0Y5HraE5cXu8IXHvttYiPP7d89IYbbsB9992Hu+++G9dff33vMrZjarce+rYjV7fLuqW6UiztqUZwUppZdd8pjMj6hw9HkE+IWfE5kmMI+ASHwC8iCnUF+aDdyhwd+oQNEaMsF+GDvJfkFqPeoqfNgQnYgsAHH3wgs6ERPBryVsLKlSulzYWabV64R620Fn/3igAty6Ifdg8zDLw6dB3IKfsOo2On96pMTmwfAueGv4/aJ3Mzcp2WeBn8vAKw5vRKM2JzFCbQOwJkqa5mJU21Y0Xduzbm1P8jIIe9s/qbxeNY9V74ewUiNdjxPTazBHTzSHI9tZjGcFagLUYvz7xDTI3slY5anCUHl8sE1EKAx5XU0hIal6Na/LD3GT3JrFqc29ebe9NmwXJCJLIzyFv9FsivOG0t6owQ6B2Mq7PvxZtHnkJsQBISg9KdIQaX6QQCVUf24cjbL1lcMj2vrhpYUbtqyzqwXi01VaA56uDkdJOlVjSVoKA+D1dm3W0yLkdwDgE/sQ7eJygEdWdOIiQlwzlCiFITAtMwL/VavJf3Au4Y8BDbMzitJRxXsEefZLSHDEONlUX6DpxtZUp1J2NFre720YR01XnC2tvM+ekdYoOToVETxBykdevXNQHEBYQMy+qHmrzDTlXUhHFY1ESQ05YPjr+MG/v+El4e2tzkxgUeCYdU4YRXAbb5bEekX6zF5enQgVN1uRiNqy1Oq/YErKjV3kIakK9a/KCHmjE/3Sq8JO0q+x639H9AA7VybxHDRHuW7t6GpGnznA5idsrVWC6GwL8+/T7mih42B9cmMCBiFOanXmdxJVvam/HXXfdYnE4LCZwzAaUFMiyj2QSo50U/7KbC/optiAtMFnOOiaai8n0nE6B5avIrroZ5P+pF01TJwcoc7C13jBtOJ+Pn4plAJwKsqDvh4BNLCTRXVaC1rsas+elzRmQzLC2C4zuBgF94JHxCQuV6aicUf0GRwT6h0rjsy1MrUNRw6oL7fIEJuDIBVtSu3LoOqJsc9hb7e5uyDi6oO47a1ir0jxjhAKm4CFsQoFESal+1hKSgDMwRw+Dv5b6AxrZ6tYjFcjABuxNgRW13xK5dQPWxg2LYe4DJSv5Ysh6jxb7ebAxkEpVqIlC7VuceUo08JAhtOZsVNggfHn8VtHEOByZgDoFjx45h165d+qg//PCD3ONbf0HlB6yoVd5AahePelxhfXpW1PWttThctUtuDan2+rB85wmECU9atfm5Yt9vdfldn5dyHRrb67Gx6LPzwvIRE+iBQGxsrPSSRf686+rq8OijjyI6OrqHFOq6xYpaXe2hKWkaS8/KH/HA+OQe5abtQvuFD0OwjzqdsvcovBvfpH2//aNipbJWEwba//vKzLtBozS5wrELByZgigC5Br388suxfPlyPPPMM3jggQdAW4du3boVd955JzZu3Ciz+Oc//wly2pGTk2MqS4feZ0XtUNyuVVgVDXuLXldPbirbde3YUbIBY2Nnulbl3aQ2YX0Gokplw9+EXvG09dGJf6OmpdJNWoOr2RsC5CHrnXfewfHjxzF9+nTpiOP555/H008/jddff10OhR86dAh//OMfOznt6E2ZtkrLitpWJN0wH5qfDs8e2GPND1fulD3plOCsHuPxTXUSCBeKmtpZjYE8bY2Mvgirjr/C89VqbCCVyeTr64sZM2bgxhtvlJKRUj548KDsUZ84cUIq6pqaGvzsZz9Dnz59VCU9K2pVNYd2hKH1tefmp3tW1NtKvsG4OO5Na6dlO0tKDjoaigrQ1qBOK+vpSYuEwDpsKPyks+B8xgSMECBl7efnJ++kpqZi0qRJspf9pz/9CUFBQbjnnnvw1ltv4cknnzSS2nmXeGcy57HXdMnkr5j2g/aP7N4go6jhJMrF3t6DIsZquq7uLLyXrx+C07Lk8Hf00NGqQ3HO09adeOXgI8gMHYj0kH6qk5EFUieB4OBgaVC2dOlS6ebytddew69//WtERUVh9mx17RnOilqdz5Dqpao6ekBYe/fcm95a/I1YkjUVZPzDQbsEwvsOQtXR/VCjoiaqYb6RuCTtRtB89V0DH0GAd6B2YbPkdiVAitgwPPLII2hpaQH1tCl8+OGHaGpqgr+/unwR8NC3YavxsdkE6IebfsC7C3WtNTgk5qdHx7I7y+4YaeV6RL/BqDqibuvqgRGjkRkyEF+ceksrWFlOlRBQlLQijtqUNMnFilppHf42m0BbUyPqC05Ki+/uEu0o/Rb9w0cghJdkdYdIM9cDE1Lknt/1ZwtULfPc1GtAO+Dtr9iuajlZOCZgKQEek7SUGMeXu1UFJafB2z/AKI024SVru1iSdUPfnxu9zxe1RYCW38le9eF9CBw2XrXC+3kFYHHGrdJ/dXpIX163r9qW6lmwPeWbcax6b8+RjNzV6XRGrrrGJVbUrtGODq0FDYNG9BvSbZn7hJesaP94JASmdRuHb2iLQHj/oTi7eb2qFTURTRMKemjkeHx+8i1ck/1TbUFmacUWsVPQJ3wok+hCgBV1FyB8apoAKep+N93TbcQtxWsxI3Fxt/f5hvYIkD1C7sp/o11MewhLG1VXYEbSErx08GExBP4jBkfyigNVN1YX4YJ8QkAfDp0J8Bx1Zx58ZoJAQ3EhOlpbEJRkvLdMWzrS0HdfsWUoB9chQNMctEyr/vgR1VfK18sPC9OW4r+n3kVDW53q5WUBmYApAqyoTRHi+50IVIp5ynBhBdzdtqHUmx4fNwu0vpWDaxGIHDAMtbkHNFGpjND+cn/5taff14S8LCQT6IkA/5r2RIfvXUCg6vBeRIgfbGPhbMNp0CYnw6MmGbvN1zROIHLQcNQJb2k64YFIC2FW8pXCKGkf8mvVPwqgBZ4so/MIsKJ2HnvNlUzLsmpP5nW7fnrz2TUYEzMdNPTIwfUIkCctn+Aw1J3M1UTlAryDMCflKmlY1t7RpgmZWUgmYIwAK2pjVPiaUQK0G1lwaga8Ay7c+am6pQJHhM9p9pJlFJ3LXAyhzU8O7dFMfYZGTZDLtLYUf60ZmVlQJtCVgOYUtam1cqbudwXA5+YTqDy4u9th763ih3BI1Hi22DQfpyZjhohledVCUWvp72x+6vX4/uwX7A5Tk08cC00ENKWoN2zYgGuuuUa6KTt79mynFqQfjieeeAJLlizBY489pv8hefnll7F48WL5eeihhzql4RPzCZC3LDIkI4OirqGxrR47yzZhYtycrrf43MUI+MclwcPbG3Wn8jRTs9iARLk+d20BG5ZpptFY0E4ENKOoO4SiIPdjL730Em6//XasWLGiU0V2796N5uZmvP/++1JJ0zmFI0eOYNmyZTL+gw8+2CkNn5hPgOamvQODEBCbcEGiH0vWIzt0MCL9Yy+4xxdcj0D4wBEo27tDUxWbmrgQJ2oOo6gpX1Nys7BMgAhoRlHn5+cjIyMDkZGRmDJlinT4bdiEO3fuxMyZM+Hj4yO/c3Jy5O3Gxka0traivr5e74fUMB0fm0egfH8OIgeNuCByS3szyOf05IR5F9zjC65JIGLwSJQLRa2l4W9/sb3ojKTF2FTxqabkds0niGtlKQHN7ExWWVmJsLAwWT9aw9vW1tmKk+6Hh4fL+xSPzhsaGpCbm4sXX3wRx44dk0Pm8+adVyg1NTWoq7NuQ4Tq6mokzbTzAAAj4klEQVRLWasqPtWb3LuZG85u/Q7Rc69AYWFhpyT767Yg0iseuiofFFZ1vtcpog1Pamtr5cuXp6dm3jM71d5S9p0SO/mE/qa8vf3QIdbJn9i+Bf7J6U6WyPzi43WZaGlrwXd5X6Jv4IUvnebn5JyYNGJII4vU6VBjoN/doKAgNYqmeZk0o6jp7d1wk42ub/OG5/QD3i7Wenp5eeG5557D4MGDQYr1t7/9LQwVdWhoKOhjbUhMTLQ2qdPTlZWVSafp5ghSX1QAbz9/ZE+e0akNaMnLyn1bcHnmHUgMcRyL4uJi6dzdW8yVajFYwl5t9aMXYHID6Dl6IlpP5yJx7ES1idijPNM6FmF9xSpMypwFH0/fHuOq7SYpaOqgKB0WtcnH8tiPgGa6JPRwUk9KCaSEDYPhfeopK71rRZnSfeoN0BspB8sIVNCwtxjuNHxRohz2lG9BuF+UdIRgWY4cW+sEYkaMl8PfHV1GttRer+SAbMQFpoB20OPABLRCQDOKOi0tDXl5eVLZ7tmzB1lZWZIxvWWSAqZe8+bNm+U1+h46dKi8/sADD8hrNMedkpICrQ6XOvOBKt+Xg6ghozqJ0KHrEEtevsRFCZd2us4n7kEgICYe/tGxqNTQmmqlZWYlXwHanKe+9fyLv3KPv5mAGgloZuzQ19cXCxYswNKlS2Wv+JlnnpE8V61ahcDAQLn8avXq1bjuuutAveixY8eKuTRvTJo0CXfeeSdKS0vx5z//WY1toGqZGkvPoqWmCmGZ/TrJSa4s/b0CkR02uNN1PnEfArGjJ6Fkx/cXvMSpnUBsQBIGRIzExqLPMC/1OrWLy/IxAXiIuV1Nedum3rOfn5+cfzbWfjQ8HhLS2U0aWX6TNbgt5zTJqEoZVjcmh9qvmTtPevqbz9BcVY7sK5bqq0S96RcP/Am0l3K/8OH664464DlqR5G+sBxljjogIABtjQ3Y8fivMfK3f4Vv6DlDzgtTqOuK8tzXtFTiBfEM3zXwYUT4xahLyG6k4TnqbsC4wWXNDH0rbUG9567z08o9+u6qpOka/ajYUklTnu4SyvdsR/TQMZ2qe6ByO7w9fJ2ipDsJwidOJUBbyUYJ24WS7d87VQ5rCg/1jcCo6Cn4LP9Na5JzGibgUAKaU9QOpePmhZHv6Za6aoRlD9CToN70xsLPMDWR56b1UNz4IG78VBRv2wjauU5rYVzsbJyuz0NxY4HWRGd53YwAK2o3a3BLqlu2e5vsTXsYrFem3rSXhzf6h2tvHaoldee45hEITe8DL7Fcq1K4P9VaCPOLwJT4Bfj2zMdaE53ldTMCrKjdrMEtqW7prq2IHjFOn4R6098VfoppiZddsFRLH4kP3I5A/MSZKPphnSbrPS7uYpyuy8WZ+hOalJ+Fdg8CrKjdo50trmVNvvA5LMwMQ9Ky9WnJ0tvH0w/9I7g3rYfCB4gZOR71Z06BNsbRWvDz8sfk+PlYf+YjrYnO8roRAVbUbtTYllS1NGez/AFWNjlp17XL3vR00ZvmwAQMCXj5+iF+wjQUfveV4WXNHI+OnY6SxjM4WXtMMzKzoO5FgBW1e7W3WbXtEE5MyvZuR8yo89tD7i77HoHewegbfqGbS7My5UguTSBh0sWoOLALTZVlmqunj6cPpiSIuepC7lVrrvHcRGBW1G7S0JZUs/zATgTGJSIgOk4ma+toxVenV2J87CxLsuG4bkTAJzgEtAHKmW//q8laj4y+CJXNpcIV5iFNys9CuzYBVtSu3b5W1Y7WxcaOnqxPu6N0A5KDsjA4aqz+Gh8wga4EkqbNA60UaK4s73pL9efent6iV32J6FWzBbjqG8sNBWRF7YaN3lOVmyrKUHf6BKKHn1PKze1N2FT0BWYmLekpGd9jAvANi0DsmCk4tfYTTdIYETUJtGNZXs1BTcrPQrsuAVbUrtu2VtWs+MeNiB42FmQgRIG8DKUEZyM5ONOq/DiRexFInrEAlQd3a9IC3Ot/veoNvK7avR5aDdSWFbUGGslRIpLLwpIfN0kLXiqTvAttLf6ae9OOagAXKMcnKBhJ0+ch/7OVmqzN8OhJqG2tRl71AU3Kz0K7JgFW1K7ZrlbVitxZ+kXGICgxVaYn70LkZSgmINGq/DiRexJImDxLOnKhlQNaC14eXrgo8RJsKNTm8L3WeLO85hFgRW0eJ7eIdXbzOiRMmiHrWiEsYHeXb8b0xEVuUXeupO0IeAr3spmLb8SJT96VHrZsl7NjchoWNVH2qnOr9zumQC6FCZggwIraBCB3uV0rDMiaykuFb+HRssrrCj7EmJjpIC9DHJiApQTC+wxEeN9BmhwCP9+r/tTSanN8JmAXAqyo7YJVe5kWbVwretMzQb2hgrrjyK89jMkJ87VXEZZYNQQyFl6LqmMHUb5/p2pkMlcQ6lXXi7lq7lWbS4zj2ZMAK2p70tVI3rQki7wfxYltICmsLXhPuLFcCH+vAHnO/zABawiQv+o+19yGvFVvam5tNfWqp8i5au5VW9P2nMa2BFhR25anJnMr3LhG7irlExiMg5U7RE+iDqNipmqyLiy0ugiEZfWXIzVH3noRtDWtlgL3qrXUWq4tKytq125fk7Vrqa0GOeBIvGgOaKvQr09/gNkpVwmf014m03IEJmAOgeSLL4VPaDhyP/gPdDrhkk0jgXvVGmkoNxCTFbUbNHJPVSSPR1FDR8MvIkqsmf4GEX4x6MeON3pCxvcsJEAe2PpeezsaS87i1H8/tDC1c6Nzr9q5/Ln0cwRYUbvxk0C96WKxwUnyzEtQJwxnvj/7JeakXuPGRLjq9iLg5eePAbf+XHhl24GC9V/Yqxib53vOAvxS3gPc5mQ5Q0sIsKK2hJaLxS1Y94XcLtRfbHLyjViONSRyHOICkl2sllwdtRDwDQnDoDt/jeJt36Fg3edqEcukHEOjJqCxrR5Hq/aajMsRmIA9CLCitgdVDeTZWlWB0p2bQfOHtBzraPUeTE/izU000HSaFtE/IhqD7/odSnJ+wAmxzagW5qypVz018VLerUzTT562hWdFre32s1r60o3/Rfz4afAVRj5fnnpH7kAW6B1sdX6ckAmYS4DsIYbc8wfU5ufiyFsvob2l2dykTos3JHI8yJPc4cpdTpOBC3ZfAqyo3bDta/KPofHUcSQJT0c7yzZChw5ejuWGz4Ezq+wTHCKGwX8DYWeGfS/8H5oqSp0pjsmyPT08MS1poZyr1sIogMkKcQRNEWBFranm6r2wuo4OsQfzCkSL5Vgt3u1Yd2Y15qfeAPoh4sAEHEmAXKn2veFuYScxBnuffwwVB9TdWx0cMVa81OpwQOw1wIEJOJIA/zo7krYKyjq7dQM8PL0QOmQMvi74AP3DRwh/01kqkIxFcEcCtHSLfFj3u+Eu5H30No5/vEK1G6OQrDMSF+Nb4a+6Q9fhjs3FdXYSAVbUTgLvjGKbqytxes3HyFxyI842nxRWrHtwcfIVzhCFy2QCnQiEZQ/A8J//Gc1iCHzv839BfeHpTvfVctI/YgT8vQOwR3iW48AEHEWAFbWjSKugnOOrlyN27BQEJCRhffmHmJV8FdiATAUNwyJIAjRvPeAn9yN+4nTsf+XvcgkXTdWoLcxMWiItwGknPw5MwBEEWFE7grIKyijZ8T0ay0qQOnuR2NjkvwjyCsHw6IkqkIxFYAKdCcRPmI6h9/1ROIrZJwzN/oqGkqLOEZx8lhk6EFF+cdhe8q2TJeHi3YWAh7Bg1M7muzZulaamJjQ3W7c0pKysDNHR0TaWyD7ZtVSW4/h/nkXatXegIdoP753+By4Nvx0pURn2KdDOuVZWViI0NBReXtrcj7y6uhphYWF2pmSf7Gtra+Hr6ws/Pz/7FGCQq07MA5eLzVFKv/8aMZNnIWrcVGEl3ru+ha3YFzedxuozr+In6Q/Cz8vfQGr7HTY2NqK9vR3BwepcRunv7++Q58J+hNWbs7d6RbO/ZD4+Plb/2NOPVWBgoP2F7GUJHeIPO//TdxA/ZRYis/ris9wnMTluAWK8EzQhv7Hq19fXS9m1qqjpBVELz44x9i0tLfLHOCDAMS5Qg4ShWcyQUTix6g3UHzuAjMtvhn90nDHRzLpmK/YZgf2QUdsfu+s2YnqCYzYKoj4VKWq1Pjta/Xs068FxciS3VtT0YFn7cFE6UvRqDye+/ADkFzhV7Oe9uWQtINatTkqci4ryCk3Ib4wvsff29pYfY/fVfk0rz44xjsSdnntHPvs+iSkY+tMHcUY4kDn48hNi+uYyMY89U/SuxcNsYbAl+1kpV+KVg49gXPxMhPlGWiiJ5dGJOdXZkewtl5JT2INA78aR7CER52kzAiXCfWW5WJva97o7UNZ8FpuKvsCi9Ft5zbTNCHNGjiLg4emJ5OnzMfhusf3o9h9w8NWn0CymdJwZIoWnuRHRk7Fe7EXAgQnYkwAranvSdWLetSdzceLTd9H/pp/CUwxTrj7xb0xNuBQxAQlOlIqLZgK9IxAUn4wh9z2IkPRs7HnuEZTs+KF3GfYyNf1NHaveh8L6/F7mxMmZQPcEWFF3z0azdxpLi3H4zReQfcXNCE5OE0tJPoW/VwDGx83SbJ1YcCagEPD08kbqnMXSbSa5zDz85r/QWl+r3Hbod4B3EC4Syvqr0+86tFwuzL0IsKJ2sfZuEZuaHPz3M0gSw4RRQ0Yjv/YIcsq+w6KMW62a03MxPFwdFyIQkpqJYT9/WDqW2f30Q6g4tMcptRsTO126wdxXvs0p5XOhrk+AFbULtXFLbTUOvPY0YkaOR6Kw8q5vrcWHx1/FpWk3O8TYxYVQclU0QoD2C89cfAOyr74VeR8uF583He6Ni9xgzk29DmsL3pcetjSCjsXUEAFW1BpqrJ5EbampwoFXnkTEgGFyWJD2Il594jUMiBgpPz2l5XtMQOsEIvoNxvBfPoK2xgbsfuZh1AgXmo4MWWITFNoz/7vCzxxZLJflJgRYUbtAQzeVl2Dfi39D5OCRSF9wpazRxqLP0NTegNnJV7tADbkKTMA0AZ/AYOHc427xoroIh994Hie/XIWONsdt8zkn5RrpNra4scC0sByDCVhAgBW1BbDUGLVW+JUmf74JYn/ktLlLpIiHq3aJ7Q034Kqse+Dt6dZL5dXYZCyTnQnEjBiPYb94RDr22POPR1F3+oSdSzyXPa2lJivwz/OXw403fHQIa3crhBW1hlu8dOcWHFz2LDIuuw6Jwr80BXqb/+TE67gy6y6el9Zw27LovSPgFxaBgbf9Aoli69GD/372XO+61f6963FxF6NN14btpbwPeO9akFMbEmBFbUhDI8c0nHdc+O49+dVqDLr9V4geNkZKXttajRXHnheuKy9Hekg/jdSGxWQC9iMQN+4i0bv+MxrOnsHuZx9Cg9hfwJ7BU+xFvjB9qdgE5SNUNZfZsyjO240IsKLWWGPXFxUIf72PyV2Zht3/sFgnnS5r0NzeiHeOPofBkWMxKmaqxmrF4jIB+xHwC4+U7jNTZy9G0ecrcXTFqyDjS3uFhMBUjIudiU/yX+chcHtBdrN8WVFrpMGpF31q7SfYL/Y6jhs/Vf7w+ASd86LTKvzivpv7L8QFJuHipMs1UiMWkwk4lkD08LFIv+3XIL/Xu57+E2izlA47DYdflHCJNObcWvKNYyvJpbkkAVbUGmjWCrFfN23oUHcqD8Puf0gYjs3QS03O698TSpp2HluYfgtvaqInwwdM4EICXn7+yFh4LYbc83tU5x7Cj4/cj+IfN0EnvFLZMngJI84lGbeL5Vqf4mzDaVtmzXm5IQE2CVZxo1cdO4jTaz+W2yOmiWVXUWL5lWFo7WjBSqGkvTy8cUXmXeJbm/6ZDevEx0zAEQQC4xIx6I5fo+LIPhR8/ZnoXX8unX7EjJoIT2/beMWLCUiUI1yrjr+MOwY8BF8v+/vwdgQ7LsPxBFhRO555jyXSUFzZ3u0o+v4bsXlDvfjxWIDY0ZPgIVw7GoaGtjox3P1PhPiE4fKMO0Bv8ByYABOwjEBkvyGgT9XR/XIo/NSajxE/YRrixk2VW5NaltuFsUfHTsOJ2sP47OSbuDzzjgsj8BUmYAYB/nU3A5K9o+g6OlBz4hjKdm+TSpo8BCVNnyd60KNA7v26hoqmErxz7B/ICO2P+anXs9vKroD4nAlYSCC872DQp64gH0U/rMPOJx9EWGY/xIyaIHf78/LxtTDH89HJCvzfhx7DlrNrMSF+9vkbfMQEzCTAitpMULaO1lxVgeq8w+JN/gCqjuwHGYZFiWVWQ3/6RwTExHVbHLnU+0i4rJwcPx8T48+tne42Mt9gAkzAIgK0iqKP2Dec5rHpxblo0zfI/eANhPcZhMiBQ+W3r1ijbUnw8/LHNdn3YdnhvyJauJntEzbEkuQclwmAFbUDHoKOtjY0FJ1GrdghqfZkHmrFPsStDXUIzegj//BTLr5UKOf4HiVp72jD+sKPsbvseznUnRU2qMf4fJMJMAHrCXgHBIoh8Ony0yw80lUe3I3yfTk4/vEK0GYqIRl9EZKWheCUdATGJhod+TIsPco/TtiR3IkP8l7GTf1+hYTANMPbfMwEeiTAirpHPNbdpD/sWjGUXSustGmLz/rCU6C1nMEpmSDXfIlTZiMoMcXkH7dSemH9SXwq1mQGegfjzoEPI9TXsjd6JR/+ZgJMwHICpJgVpd3R3oZ6MTxOU1WkvMnYs62+DgHxSQiME5/4RATEJgjlnQC/iOhOf+OZwnHH3NRr5X4Ht/R/AKS8OTABcwiwojaHkok45LGn6pgYwhbD2LTko1X84YakZiE0PQspsy6Tb93kMMDSQAZjG8Tyjn3lWzEjaTFGx0zj5VeWQuT4TMCGBDy9vEVPOlt+lGxb62tBGxHR7meNxYVCge9BY+lZaQzqHxUrFbeivLOFQm+Mm4c3jzyJpf1+i0j/WCUb/mYC3RJgRd0tmp5vtAuL7KLN61EhhsNoODtYDIOF9x2EeGEtGpSU1ulNuuecLrzb3N6EH0vWYfPZNegbPgz3DP6LtO6+MCZfYQJMwNkEfIJCEJ49QH4MZaEXeFLYjSVF8lO2dwcaxKZFOmGfkjo8CK81PoRLOi5GasooBCemXrCywzAvPnZvAqyorWz/qq3fwqe9FbFjL0K/m+4FzWn1NtS0VMrN/HcIz1epIX1wc7/fID4wpbfZcnomwAScQIB+E2iqiz6Gob25SfbAc4rW41OvrzHyqy0Iy2+Qc95hfQZKu5WgJKG4PTwMk/GxGxNgRW1l40dNvwSJiYlWpj6fjIzEcmv2Y5cwEjtecwiDIkbjlv6/Q2xA0vlIfMQEmIDLEKDd0ULTszFdfJLFKo7Vvq9h0ux5iKuMR5WYOju85V/QCQPUiIHDEDVoJEh5e3rzT7XLPABWVIRb3wpovU1Cypk2QThYmYPDVTulO8phUZOk1x0yGOPABJiAexCgpVq3Dvg93s97CaeConDpZUuR7XMz6s8WoPLAbrG//0doevdVuSth8IDh8E/JcA8wXMtOBDyEg3Ndpyt8YhaBwsJCi3rU5IIyV7w90zrovOr9iPCLwYCIURgoetAxYm2lo0NZWRmio6MdXaxNyisuLkZUVBS8NdrL0DL7yspK+Pv7IyAgwCZt6ehM1MqeHOusP7NaLL/8AdOTFkkPeMqWwE3lpXJNd3HOZtCweZzY5pR2KyQDNQ7uQYAVtZXtbEpRk7OMk3XHhFI+gDwxtF3ZXCp9RPcJGyo2PBAbJ/hFWVmybZKp9QfLnNqxojaHkn3isKK2D1cl1zP1J/DVqXdBKz6mCA9cQ4TbWmV74Pr6erHc8wQaDu8WivtHBMQlIE7YyEQNHQ0vX95HXGHoit+sqK1sVWOKurihQChlUswHcEoo6Si/ONDGJFmhg5Aa3AfenrbZ7N9KkTslY0XdCYdDT7TMnhW1Yx6VI1W78X3Rl/IFf1j0ROlnPlQXjTYxdx0WFgZye1u+bydKftyIujMnETNivDBsnYJgseKEg+sRYEVtZZuSog6LCREGYAelMRgpZw/xH21qQIqZvoN9Qq3M3f7JtKwsuEdt/+ejuxJYUXdHxj7XixpOyuFwsmehWcrkgGxkRvRHXECK3DCFfmOayktQvG0jSnb8ANreNE4o7Ojh42yyEsU+teJcLSXAitpSYv+Lv+rQv3GkMQcpwdnIDh0se85xAclW5ub4ZKyoHc9cKVHL7FlRK63o+O8T5UfFdNpRVLSfRXHjaVQ0l0ghgr1DEegTAn9PYTdQ34S28gq0V9cgICwaQWJ708CIWHh5+Uh3uE1t9QgQBqthvpHwEz7s/b0CRdpgBFEe4rqnx4VOgBxfUy6xKwG2+u5KxMzzEcFTcVm/m+Djab1XHTOL4mhMgAkwAcT6JyHSO04OfSs46ltrUScMVRvaatHU3oiWjia0prSiubEWNQViC+OjYgvj1oPwi4uHX2wsav1b4NFcJhR9AZpF/EahuBvFfHhdWw1oNcoE4ehnVvIVSvb8rRICmlPUNPzT00YAxu4bu9Zb/gFeQaykewuR0zMBJtArAkGiJ00foyH13NUGsa0peQIrX5MD8tpHW6DGjR0v1mcPgOHWxqS4PT06+703mi9fdDgBTSnqDRs24KWXXoKvry+efPJJxMef9zhFyvjvf/87tm7dilGjRuHBBx+UCr2nNA6nzQUyASbABBxMIDAuEalzFstPjfDcVyPc6xZv3YDc95ZJy/FQ8gSW3gchYo22T4RzV6M4GI1mitPMhERHRwfeeustqahvv/12rFixohPk3bt3o7m5Ge+//740uqBzU2k6ZcAnTIAJMAEXJ0A7oiXPvASD7vwNxjzyPNLmXwkv/wCpuHc/+zBOfPquixPQZvU006POz89HRkYGIiMjMWXKFCxfvrwT8Z07d2LmzJnw8fGR3zk5OXIup6c0nTLgEybABJiAGxHw8vEV+4rT3uIDZa1pVFL0ctyIgHaqqhlFTdamtH6QAs1R03pCw0D3w8PD5SWKR+em0tTU1KCurs4wG7OPq6urzY6rxohU75aWFjWKZlKm2tpatLa2wtNTMwNCneqkZfYNDQ1yRziaftJi0DJ7GjGkUULa+ESNgX53g4KC1Cia5mXSjKLuahAm3/4M8Bue0w94e3u7HAI3NDwzjENJQ0ND5ccgG4sObeGUw6ICbRhZy0uEeB21DR8EC7Oil1/eQtRCaDaKTgpa2fDERllyNhohoJkuCb2tUU9KCV5ena0TDe9TT5l614bXKF3XNEpe/M0EmAATYAJMQK0ENKOo09LSkJeXBxp627NnD7KysiRTesuka4MHD8bmzZvlNfoeOnQoukuj1sZguZgAE2ACTIAJdCWgmaFvmhNbsGABli5dKudpnnnmGVmXVatWITAwEIsXL8bq1atx3XXXSa9WY8eOlXNpxtJ0hcDnTIAJMAEmwATUSkBzW4hS79nPz6/bYWwaHg8J6bwBgKk01jSOMacc1uTjrDQ8R+0s8oCW2fMctfOeG56jdh57Z5esmR61Aop6zz2Frkqa4ppK01N+fI8JMAEmwASYgDMJaGaO2pmQuGwmwASYABNgAs4iwIraWeS5XCbABJgAE2ACZhBgRW0GJI7CBJgAE2ACTMBZBFhRO4s8l8sEmAATYAJMwAwCmrP6NqNODonSdac0hxRqw0K0LL+WZacm1LL8JDsFwx3/5AWN/MPsNdJQLGYnAqyoO+HgEybABJgAE2AC6iLAQ99WtofSs7AyOSdjApoi0NUJDgnPfwOOaUJj7B1TMpeiFgKsqC1sCfpxeuKJJ7BkyRI89thj/GNlIb/eRr/pppvkLnS0E92aNWt6mx2nN4MAKYq77rpLv9c+bdhyyy234IorrsDXX39tRg4cxVoCXdlTPvw3YC1N7abT3IYnzka9e/dukLu5999/H3/7299A5yNGjHC2WG5RPjlbiY6OxiuvvCLrS77HOdiXAO2G9eijj+LEiRP6gsgXPCnqQYMG4YEHHsCMGTO63SlQn4gPLCZgjD3/DViM0SUScI/awmbcuXMnZs6cCVIS9J2Tk2NhDhzdWgKnTp1CZmYmCgoKJH9vb37PtJaluen27duHiy66SDq9UdIcPHgQkydPRlRUFNLT05Gfn6/c4m8bEjDGnv8GbAhYQ1nxL52FjUV7HZMLTQrkRpPOOTiGwMmTJ7Fx40aUlJQgNzcXTz/9NBISEhxTuJuWMn78eFnztWvX6gnQcCz5fKfAfwN6LDY/MMae/wZsjlkTGbKitrCZDA1o6Meqvb3dwhw4urUERo0aJacZEhMT8emnn2L9+vW4/vrrrc2O09mAAC3T4r8BG4A0Mwv+GzATlItF46FvCxuUehDkoYsCzRcpvWsLs+HoVhDw9/dHRESETJmdnc1DrlYwtEUSLy8vfTb0t8B/A3ocdj/gvwG7I1ZlAayoLWyWwYMHY/PmzTIVfQ8dOtTCHDi6tQQ2bdokjfgo/bZt20A+xzk4ngDZCezatQvkPpamILKyshwvhJuWyH8D7tnwrKgtbHdSDuSL+rrrrsOZM2dYWVjIrzfRL774Yuzduxe33XYbtm7dikmTJvUmO05rJYEbbrgBjz/+uLT8nj9/Ptioz0qQViTjvwEroLlAEt6ZzMpGpCE/Y76vrcyOk1lAgNlbAMtOUWlempYpsq93OwE2kS3/DZgA5GK3WVG7WINydZgAE2ACTMC1CPDQt2u1J9eGCTABJsAEXIwAK2oXa1CuDhNgAkyACbgWAVbUrtWeXBsmwASYABNwMQKsqF2sQbk6TIAJMIGeCFRXV+ONN97AqlWrQPuJdxdaWlrwzTffdHebrzuQACtqB8LmopgAE2ACziRAzlWGDRuG7du34/XXX8fs2bPRnRvNuro6PPLII84Ul8v+HwFW1PwoMAEmwATchAD1pP/yl7/ghRdewBdffCG9nhk6FqI9ImjZnbFA7k0rKio63aJlYnSdg30JsKK2L1/OnQkwASagGgIBAQFyn3xSyBTInzht4tTU1IQpU6bgxhtvRFJSktxH31Dop556Ssaj3vi///1veYvczc6dOxfTpk3Dc889Zxidj21M4P/bu2NX7No4DuBX6YlSRoMyWCQWKTGYmGzKYkIpgwWTwmDkH/AniEkmFoNBysTIapDNZlB6uq5675633uEdvsP91Ocudbrvc77d98fw7TrndH6KOgwqjgABAt0qsL293UbE1mflLy4utsch18Eq9/f3ZW1trdze3rYivry87PyEOojo4uKivLy8tL/z8/P2WS3sm5ub8vDwUL6+vjr728gLmJ6VN5VIgACBrhSoK+qzs7Py/v5eauHWx8HWUl5YWCh3d3dlb2+vlfXExETn+z8/P7dxvltbW+2919fXNhN+fn6+jI+Pl42NjbK5udnZ30ZewIo6byqRAAECXSmws7PTpv7VOe67u7vl8PCwnf6u166Pjo7a6e363p+v+rjYkZGRtuKuq+5a9AMDA+Xk5KRcXV2Vj4+PMjc39+chtsMCijoMKo4AAQLdKjA8PFyOj4/Lz89PqXd1X19fl7GxsfL4+FjqafHl5eXy9PT0rzvBp6amWhlPTk6W6enpsr+/325CqwNCRkdHy+npaenv7y/f39/d+rP/+u+lqP/6f6EfQIAAgf8nsL6+3kq3FvbQ0FAZHBwsS0tL7RT4wcFBOwX++flZ3t7eOoH1GnadWFeva9cbz1ZWVloxr66ulpmZmTI7O9smqf369atzjI2sgKEcWU9pBAgQ6HqBOku8FnC9Zv3Pq66I60NO6ur4v171mJ6entLb29v5uO5fV+d9fX2d92zkBRR13lQiAQIECBCICTj1HaMURIAAAQIE8gKKOm8qkQABAgQIxAQUdYxSEAECBAgQyAso6rypRAIECBAgEBNQ1DFKQQQIECBAIC+gqPOmEgkQIECAQExAUccoBREgQIAAgbyAos6bSiRAgAABAjEBRR2jFESAAAECBPICijpvKpEAAQIECMQEFHWMUhABAgQIEMgLKOq8qUQCBAgQIBATUNQxSkEECBAgQCAvoKjzphIJECBAgEBMQFHHKAURIECAAIG8gKLOm0okQIAAAQIxAUUdoxREgAABAgTyAoo6byqRAAECBAjEBBR1jFIQAQIECBDICyjqvKlEAgQIECAQE1DUMUpBBAgQIEAgL6Co86YSCRAgQIBATEBRxygFESBAgACBvICizptKJECAAAECMQFFHaMURIAAAQIE8gKKOm8qkQABAgQIxAQUdYxSEAECBAgQyAso6rypRAIECBAgEBNQ1DFKQQQIECBAIC/wG+9S//B1Flc+AAAAAElFTkSuQmCC" width="600px" style="display: block; margin: auto;" /></p>
</div>
<div id="cases-where-predictors-are-categorical-variable" class="section level4">
<h4>Cases where predictors are categorical variable</h4>
<p>The following example shows the relationship between <code>ShelveLoc</code> and the target variable <code>US</code>. The predictor variable <code>ShelveLoc</code> is a categorical variable. In this case, it shows the <code>contingency table</code> of two variables. The <code>summary()</code> function performs <code>independence test</code> on the contingency table.</p>
<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1" aria-hidden="true" tabindex="-1"></a><span class="co"># If the variable of interest is a categorical variable</span></span>
<span id="cb25-2"><a href="#cb25-2" aria-hidden="true" tabindex="-1"></a>cat_cat <span class="ot"><-</span> <span class="fu">relate</span>(categ, ShelveLoc)</span>
<span id="cb25-3"><a href="#cb25-3" aria-hidden="true" tabindex="-1"></a>cat_cat</span>
<span id="cb25-4"><a href="#cb25-4" aria-hidden="true" tabindex="-1"></a> ShelveLoc</span>
<span id="cb25-5"><a href="#cb25-5" aria-hidden="true" tabindex="-1"></a>US Bad Good Medium</span>
<span id="cb25-6"><a href="#cb25-6" aria-hidden="true" tabindex="-1"></a> No <span class="dv">34</span> <span class="dv">24</span> <span class="dv">84</span></span>
<span id="cb25-7"><a href="#cb25-7" aria-hidden="true" tabindex="-1"></a> Yes <span class="dv">62</span> <span class="dv">61</span> <span class="dv">135</span></span>
<span id="cb25-8"><a href="#cb25-8" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(cat_cat)</span>
<span id="cb25-9"><a href="#cb25-9" aria-hidden="true" tabindex="-1"></a>Call<span class="sc">:</span> <span class="fu">xtabs</span>(<span class="at">formula =</span> formula_str, <span class="at">data =</span> data, <span class="at">addNA =</span> <span class="cn">TRUE</span>)</span>
<span id="cb25-10"><a href="#cb25-10" aria-hidden="true" tabindex="-1"></a>Number of cases <span class="cf">in</span> table<span class="sc">:</span> <span class="dv">400</span> </span>
<span id="cb25-11"><a href="#cb25-11" aria-hidden="true" tabindex="-1"></a>Number of factors<span class="sc">:</span> <span class="dv">2</span> </span>
<span id="cb25-12"><a href="#cb25-12" aria-hidden="true" tabindex="-1"></a>Test <span class="cf">for</span> independence of all factors<span class="sc">:</span></span>
<span id="cb25-13"><a href="#cb25-13" aria-hidden="true" tabindex="-1"></a> Chisq <span class="ot">=</span> <span class="fl">2.7397</span>, df <span class="ot">=</span> <span class="dv">2</span>, p<span class="sc">-</span>value <span class="ot">=</span> <span class="fl">0.2541</span></span></code></pre></div>
<p><code>plot()</code> visualizes the relationship between the target variable and the predictor. The relationship between <code>US</code> and <code>ShelveLoc</code> is represented by a <code>mosaics plot</code>.</p>
<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(cat_cat)</span></code></pre></div>
<p><img src="data:image/png;base64,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" width="600px" style="display: block; margin: auto;" /></p>
</div>
</div>
<div id="eda-when-target-variable-is-numerical-variable" class="section level3">
<h3>EDA when target variable is numerical variable</h3>
<p>Let’s perform EDA when the target variable is numeric. When the numeric variable <code>Sales</code> is the target variable, we examine the relationship between the target variable and the predictor.</p>
<div class="sourceCode" id="cb27"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1" aria-hidden="true" tabindex="-1"></a><span class="co"># If the variable of interest is a numerical variable</span></span>
<span id="cb27-2"><a href="#cb27-2" aria-hidden="true" tabindex="-1"></a>num <span class="ot"><-</span> <span class="fu">target_by</span>(carseats, Sales)</span></code></pre></div>
<div id="cases-where-predictors-are-numeric-variable-1" class="section level4">
<h4>Cases where predictors are numeric variable</h4>
<p>The following example shows the relationship between <code>Price</code> and the target variable <code>Sales</code>. The predictor variable <code>Price</code> is a numeric variable. In this case, it shows the result of a <code>simple linear model</code> of the <code>target ~ predictor</code> formula. The <code>summary()</code> function expresses the details of the model.</p>
<div class="sourceCode" id="cb28"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb28-1"><a href="#cb28-1" aria-hidden="true" tabindex="-1"></a><span class="co"># If the variable of interest is a numerical variable</span></span>
<span id="cb28-2"><a href="#cb28-2" aria-hidden="true" tabindex="-1"></a>num_num <span class="ot"><-</span> <span class="fu">relate</span>(num, Price)</span>
<span id="cb28-3"><a href="#cb28-3" aria-hidden="true" tabindex="-1"></a>num_num</span>
<span id="cb28-4"><a href="#cb28-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-5"><a href="#cb28-5" aria-hidden="true" tabindex="-1"></a>Call<span class="sc">:</span></span>
<span id="cb28-6"><a href="#cb28-6" aria-hidden="true" tabindex="-1"></a><span class="fu">lm</span>(<span class="at">formula =</span> formula_str, <span class="at">data =</span> data)</span>
<span id="cb28-7"><a href="#cb28-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-8"><a href="#cb28-8" aria-hidden="true" tabindex="-1"></a>Coefficients<span class="sc">:</span></span>
<span id="cb28-9"><a href="#cb28-9" aria-hidden="true" tabindex="-1"></a>(Intercept) Price </span>
<span id="cb28-10"><a href="#cb28-10" aria-hidden="true" tabindex="-1"></a> <span class="fl">13.64192</span> <span class="sc">-</span><span class="fl">0.05307</span> </span>
<span id="cb28-11"><a href="#cb28-11" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(num_num)</span>
<span id="cb28-12"><a href="#cb28-12" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-13"><a href="#cb28-13" aria-hidden="true" tabindex="-1"></a>Call<span class="sc">:</span></span>
<span id="cb28-14"><a href="#cb28-14" aria-hidden="true" tabindex="-1"></a><span class="fu">lm</span>(<span class="at">formula =</span> formula_str, <span class="at">data =</span> data)</span>
<span id="cb28-15"><a href="#cb28-15" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-16"><a href="#cb28-16" aria-hidden="true" tabindex="-1"></a>Residuals<span class="sc">:</span></span>
<span id="cb28-17"><a href="#cb28-17" aria-hidden="true" tabindex="-1"></a> Min 1Q Median 3Q Max </span>
<span id="cb28-18"><a href="#cb28-18" aria-hidden="true" tabindex="-1"></a><span class="sc">-</span><span class="fl">6.5224</span> <span class="sc">-</span><span class="fl">1.8442</span> <span class="sc">-</span><span class="fl">0.1459</span> <span class="fl">1.6503</span> <span class="fl">7.5108</span> </span>
<span id="cb28-19"><a href="#cb28-19" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-20"><a href="#cb28-20" aria-hidden="true" tabindex="-1"></a>Coefficients<span class="sc">:</span></span>
<span id="cb28-21"><a href="#cb28-21" aria-hidden="true" tabindex="-1"></a> Estimate Std. Error t value <span class="fu">Pr</span>(<span class="sc">></span><span class="er">|</span>t<span class="sc">|</span>) </span>
<span id="cb28-22"><a href="#cb28-22" aria-hidden="true" tabindex="-1"></a>(Intercept) <span class="fl">13.641915</span> <span class="fl">0.632812</span> <span class="fl">21.558</span> <span class="sc"><</span><span class="fl">2e-16</span> <span class="sc">**</span><span class="er">*</span></span>
<span id="cb28-23"><a href="#cb28-23" aria-hidden="true" tabindex="-1"></a>Price <span class="sc">-</span><span class="fl">0.053073</span> <span class="fl">0.005354</span> <span class="sc">-</span><span class="fl">9.912</span> <span class="sc"><</span><span class="fl">2e-16</span> <span class="sc">**</span><span class="er">*</span></span>
<span id="cb28-24"><a href="#cb28-24" aria-hidden="true" tabindex="-1"></a><span class="sc">---</span></span>
<span id="cb28-25"><a href="#cb28-25" aria-hidden="true" tabindex="-1"></a>Signif. codes<span class="sc">:</span> <span class="dv">0</span> <span class="st">'***'</span> <span class="fl">0.001</span> <span class="st">'**'</span> <span class="fl">0.01</span> <span class="st">'*'</span> <span class="fl">0.05</span> <span class="st">'.'</span> <span class="fl">0.1</span> <span class="st">' '</span> <span class="dv">1</span></span>
<span id="cb28-26"><a href="#cb28-26" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-27"><a href="#cb28-27" aria-hidden="true" tabindex="-1"></a>Residual standard error<span class="sc">:</span> <span class="fl">2.532</span> on <span class="dv">398</span> degrees of freedom</span>
<span id="cb28-28"><a href="#cb28-28" aria-hidden="true" tabindex="-1"></a>Multiple R<span class="sc">-</span>squared<span class="sc">:</span> <span class="fl">0.198</span>, Adjusted R<span class="sc">-</span>squared<span class="sc">:</span> <span class="fl">0.196</span> </span>
<span id="cb28-29"><a href="#cb28-29" aria-hidden="true" tabindex="-1"></a>F<span class="sc">-</span>statistic<span class="sc">:</span> <span class="fl">98.25</span> on <span class="dv">1</span> and <span class="dv">398</span> DF, p<span class="sc">-</span>value<span class="sc">:</span> <span class="er"><</span> <span class="fl">2.2e-16</span></span></code></pre></div>
<p><code>plot()</code> visualizes the relationship between the target and predictor variables. The relationship between <code>Sales</code> and <code>Price</code> is visualized with a scatter plot. The figure on the left shows the scatter plot of <code>Sales</code> and <code>Price</code> and the confidence interval of the regression line and regression line. The figure on the right shows the relationship between the original data and the predicted values of the linear model as a scatter plot. If there is a linear relationship between the two variables, the scatter plot of the observations converges on the red diagonal line.</p>
<div class="sourceCode" id="cb29"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(num_num)</span></code></pre></div>
<p><img 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" width="600px" style="display: block; margin: auto;" /></p>
<p>The scatter plot of the data with a large number of observations is output as overlapping points. This makes it difficult to judge the relationship between the two variables. It also takes a long time to perform the visualization. In this case, the above problem can be solved by <code>hexabin plot</code>.</p>
<p>In <code>plot()</code>, the <code>hex_thres</code> argument provides a basis for drawing <code>hexabin plot</code>. If the number of observations is greater than <code>hex_thres</code>, draw a <code>hexabin plot</code>.</p>
<p>The following example visualizes the <code>hexabin plot</code> rather than the scatter plot by specifying 350 for the <code>hex_thres</code> argument. This is because the number of observations is 400.</p>
<div class="sourceCode" id="cb30"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(num_num, <span class="at">hex_thres =</span> <span class="dv">350</span>)</span></code></pre></div>
<p><img 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" width="600px" style="display: block; margin: auto;" /></p>
</div>
<div id="cases-where-predictors-are-categorical-variable-1" class="section level4">
<h4>Cases where predictors are categorical variable</h4>
<p>The following example shows the relationship between <code>ShelveLoc</code> and the target variable <code>Sales</code>. The predictor <code>ShelveLoc</code> is a categorical variable and shows the result of <code>one-way ANOVA</code> of <code>target ~ predictor</code> relationship. The results are expressed in terms of ANOVA. The <code>summary()</code> function shows the <code>regression coefficients</code> for each level of the predictor. In other words, it shows detailed information about <code>simple regression analysis</code> of <code>target ~ predictor</code> relationship.</p>
<div class="sourceCode" id="cb31"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb31-1"><a href="#cb31-1" aria-hidden="true" tabindex="-1"></a><span class="co"># If the variable of interest is a categorical variable</span></span>
<span id="cb31-2"><a href="#cb31-2" aria-hidden="true" tabindex="-1"></a>num_cat <span class="ot"><-</span> <span class="fu">relate</span>(num, ShelveLoc)</span>
<span id="cb31-3"><a href="#cb31-3" aria-hidden="true" tabindex="-1"></a>num_cat</span>
<span id="cb31-4"><a href="#cb31-4" aria-hidden="true" tabindex="-1"></a>Analysis of Variance Table</span>
<span id="cb31-5"><a href="#cb31-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb31-6"><a href="#cb31-6" aria-hidden="true" tabindex="-1"></a>Response<span class="sc">:</span> Sales</span>
<span id="cb31-7"><a href="#cb31-7" aria-hidden="true" tabindex="-1"></a> Df Sum Sq Mean Sq F value <span class="fu">Pr</span>(<span class="sc">></span>F) </span>
<span id="cb31-8"><a href="#cb31-8" aria-hidden="true" tabindex="-1"></a>ShelveLoc <span class="dv">2</span> <span class="fl">1009.5</span> <span class="fl">504.77</span> <span class="fl">92.23</span> <span class="sc"><</span> <span class="fl">2.2e-16</span> <span class="sc">**</span><span class="er">*</span></span>
<span id="cb31-9"><a href="#cb31-9" aria-hidden="true" tabindex="-1"></a>Residuals <span class="dv">397</span> <span class="fl">2172.7</span> <span class="fl">5.47</span> </span>
<span id="cb31-10"><a href="#cb31-10" aria-hidden="true" tabindex="-1"></a><span class="sc">---</span></span>
<span id="cb31-11"><a href="#cb31-11" aria-hidden="true" tabindex="-1"></a>Signif. codes<span class="sc">:</span> <span class="dv">0</span> <span class="st">'***'</span> <span class="fl">0.001</span> <span class="st">'**'</span> <span class="fl">0.01</span> <span class="st">'*'</span> <span class="fl">0.05</span> <span class="st">'.'</span> <span class="fl">0.1</span> <span class="st">' '</span> <span class="dv">1</span></span>
<span id="cb31-12"><a href="#cb31-12" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(num_cat)</span>
<span id="cb31-13"><a href="#cb31-13" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb31-14"><a href="#cb31-14" aria-hidden="true" tabindex="-1"></a>Call<span class="sc">:</span></span>
<span id="cb31-15"><a href="#cb31-15" aria-hidden="true" tabindex="-1"></a><span class="fu">lm</span>(<span class="at">formula =</span> <span class="fu">formula</span>(formula_str), <span class="at">data =</span> data)</span>
<span id="cb31-16"><a href="#cb31-16" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb31-17"><a href="#cb31-17" aria-hidden="true" tabindex="-1"></a>Residuals<span class="sc">:</span></span>
<span id="cb31-18"><a href="#cb31-18" aria-hidden="true" tabindex="-1"></a> Min 1Q Median 3Q Max </span>
<span id="cb31-19"><a href="#cb31-19" aria-hidden="true" tabindex="-1"></a><span class="sc">-</span><span class="fl">7.3066</span> <span class="sc">-</span><span class="fl">1.6282</span> <span class="sc">-</span><span class="fl">0.0416</span> <span class="fl">1.5666</span> <span class="fl">6.1471</span> </span>
<span id="cb31-20"><a href="#cb31-20" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb31-21"><a href="#cb31-21" aria-hidden="true" tabindex="-1"></a>Coefficients<span class="sc">:</span></span>
<span id="cb31-22"><a href="#cb31-22" aria-hidden="true" tabindex="-1"></a> Estimate Std. Error t value <span class="fu">Pr</span>(<span class="sc">></span><span class="er">|</span>t<span class="sc">|</span>) </span>
<span id="cb31-23"><a href="#cb31-23" aria-hidden="true" tabindex="-1"></a>(Intercept) <span class="fl">5.5229</span> <span class="fl">0.2388</span> <span class="fl">23.131</span> <span class="sc"><</span> <span class="fl">2e-16</span> <span class="sc">**</span><span class="er">*</span></span>
<span id="cb31-24"><a href="#cb31-24" aria-hidden="true" tabindex="-1"></a>ShelveLocGood <span class="fl">4.6911</span> <span class="fl">0.3484</span> <span class="fl">13.464</span> <span class="sc"><</span> <span class="fl">2e-16</span> <span class="sc">**</span><span class="er">*</span></span>
<span id="cb31-25"><a href="#cb31-25" aria-hidden="true" tabindex="-1"></a>ShelveLocMedium <span class="fl">1.7837</span> <span class="fl">0.2864</span> <span class="fl">6.229</span> <span class="fl">1.2e-09</span> <span class="sc">**</span><span class="er">*</span></span>
<span id="cb31-26"><a href="#cb31-26" aria-hidden="true" tabindex="-1"></a><span class="sc">---</span></span>
<span id="cb31-27"><a href="#cb31-27" aria-hidden="true" tabindex="-1"></a>Signif. codes<span class="sc">:</span> <span class="dv">0</span> <span class="st">'***'</span> <span class="fl">0.001</span> <span class="st">'**'</span> <span class="fl">0.01</span> <span class="st">'*'</span> <span class="fl">0.05</span> <span class="st">'.'</span> <span class="fl">0.1</span> <span class="st">' '</span> <span class="dv">1</span></span>
<span id="cb31-28"><a href="#cb31-28" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb31-29"><a href="#cb31-29" aria-hidden="true" tabindex="-1"></a>Residual standard error<span class="sc">:</span> <span class="fl">2.339</span> on <span class="dv">397</span> degrees of freedom</span>
<span id="cb31-30"><a href="#cb31-30" aria-hidden="true" tabindex="-1"></a>Multiple R<span class="sc">-</span>squared<span class="sc">:</span> <span class="fl">0.3172</span>, Adjusted R<span class="sc">-</span>squared<span class="sc">:</span> <span class="fl">0.3138</span> </span>
<span id="cb31-31"><a href="#cb31-31" aria-hidden="true" tabindex="-1"></a>F<span class="sc">-</span>statistic<span class="sc">:</span> <span class="fl">92.23</span> on <span class="dv">2</span> and <span class="dv">397</span> DF, p<span class="sc">-</span>value<span class="sc">:</span> <span class="er"><</span> <span class="fl">2.2e-16</span></span></code></pre></div>
<p><code>plot()</code> visualizes the relationship between the target variable and the predictor. The relationship between <code>Sales</code> and <code>ShelveLoc</code> is represented by a <code>box plot</code>.</p>
<div class="sourceCode" id="cb32"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb32-1"><a href="#cb32-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(num_cat)</span></code></pre></div>
<p><img 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" width="600px" style="display: block; margin: auto;" /></p>
</div>
</div>
</div>
<div id="automated-report" class="section level2">
<h2>Automated report</h2>
<p>dlookr provides two automated EDA reports:</p>
<ul>
<li>Web page-based dynamic reports can perform in-depth analysis through visualization and statistical tables.</li>
<li>Static reports generated as pdf files or html files can be archived as output of data analysis.</li>
</ul>
<div id="create-a-dynamic-report-using-eda_web_report" class="section level3">
<h3>Create a dynamic report using <code>eda_web_report()</code></h3>
<p><code>eda_web_report()</code> create dynamic report for object inherited from data.frame(<code>tbl_df</code>, <code>tbl</code>, etc) or data.frame.</p>
<div id="contents-of-dynamic-web-report" class="section level4">
<h4>Contents of dynamic web report</h4>
<p>The contents of the report are as follows.:</p>
<ul>
<li>Overview
<ul>
<li>Data Structures</li>
<li>Data Types</li>
<li>Job Informations</li>
</ul></li>
<li>Univariate Analysis
<ul>
<li>Descriptive Statistics</li>
<li>Normality Test</li>
</ul></li>
<li>Bivariate Analysis
<ul>
<li>Compare Numerical Variables</li>
<li>Compare Categorical Variables</li>
</ul></li>
<li>Multivariate Analysis
<ul>
<li>Correlation Analysis
<ul>
<li>Correlation Matrix</li>
<li>Correlation Plot</li>
</ul></li>
</ul></li>
<li>Target based Analysis
<ul>
<li>Grouped Numerical Variables</li>
<li>Grouped Categorical Variables</li>
<li>Grouped Correlation</li>
</ul></li>
</ul>
</div>
<div id="some-arguments-for-dynamic-web-report" class="section level4">
<h4>Some arguments for dynamic web report</h4>
<p>eda_web_report() generates various reports with the following arguments.</p>
<ul>
<li>target
<ul>
<li>target variable</li>
</ul></li>
<li>output_file
<ul>
<li>name of generated file.</li>
</ul></li>
<li>output_dir
<ul>
<li>name of directory to generate report file.</li>
</ul></li>
<li>title
<ul>
<li>title of report.</li>
</ul></li>
<li>subtitle
<ul>
<li>subtitle of report.</li>
</ul></li>
<li>author
<ul>
<li>author of report.</li>
</ul></li>
<li>title_color
<ul>
<li>color of title.</li>
</ul></li>
<li>logo_img
<ul>
<li>name of logo image file on top left.</li>
</ul></li>
<li>create_date
<ul>
<li>The date on which the report is generated.</li>
</ul></li>
<li>theme
<ul>
<li>name of theme for report. support “orange” and “blue”.</li>
</ul></li>
<li>sample_percent
<ul>
<li>Sample percent of data for performing EDA.</li>
</ul></li>
</ul>
<p>The following script creates a EDA report for the <code>data.frame</code> class object, <code>heartfailure</code>.</p>
<div class="sourceCode" id="cb33"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb33-1"><a href="#cb33-1" aria-hidden="true" tabindex="-1"></a>heartfailure <span class="sc">%>%</span></span>
<span id="cb33-2"><a href="#cb33-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">eda_web_report</span>(<span class="at">target =</span> <span class="st">"death_event"</span>, <span class="at">subtitle =</span> <span class="st">"heartfailure"</span>, </span>
<span id="cb33-3"><a href="#cb33-3" aria-hidden="true" tabindex="-1"></a> <span class="at">output_dir =</span> <span class="st">"./"</span>, <span class="at">output_file =</span> <span class="st">"EDA.html"</span>, <span class="at">theme =</span> <span class="st">"blue"</span>)</span></code></pre></div>
</div>
<div id="screenshot-of-dynamic-report" class="section level4">
<h4>Screenshot of dynamic report</h4>
<ul>
<li>The dynamic contents of the report is shown in the following figure.:</li>
</ul>
<div class="figure" style="text-align: center">
<img 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alt="The part of the report" width="80%" />
<p class="caption">
The part of the report
</p>
</div>
</div>
</div>
<div id="create-a-eda-report-using-eda_paged_report" class="section level3">
<h3>Create a EDA report using <code>eda_paged_report()</code></h3>
<p><code>eda_paged_report()</code> create static report for object inherited from data.frame(<code>tbl_df</code>, <code>tbl</code>, etc) or data.frame.</p>
<div id="contents-of-static-paged-report" class="section level4">
<h4>Contents of static paged report</h4>
<p>The contents of the report are as follows.:</p>
<ul>
<li>Overview
<ul>
<li>Data Structures</li>
<li>Job Informations</li>
</ul></li>
<li>Univariate Analysis
<ul>
<li>Descriptive Statistics
<ul>
<li>Numerical Variables</li>
<li>Categorical Variables</li>
</ul></li>
<li>Normality Test<br />
</li>
</ul></li>
<li>Bivariate Analysis
<ul>
<li>Compare Numerical Variables</li>
<li>Compare Categorical Variables</li>
</ul></li>
<li>Multivariate Analysis
<ul>
<li>Correlation Analysis
<ul>
<li>Correlation Coefficient Matrix</li>
<li>Correlation Plot</li>
</ul></li>
</ul></li>
<li>Target based Analysis
<ul>
<li>Grouped Numerical Variables</li>
<li>Grouped Categorical Variables</li>
<li>Grouped Correlation</li>
</ul></li>
</ul>
</div>
<div id="some-arguments-for-static-paged-report" class="section level4">
<h4>Some arguments for static paged report</h4>
<p>eda_paged_report() generates various reports with the following arguments.</p>
<ul>
<li>target
<ul>
<li>target variable</li>
</ul></li>
<li>output_format
<ul>
<li>report output type. Choose either “pdf” and “html”.</li>
</ul></li>
<li>output_file
<ul>
<li>name of generated file.</li>
</ul></li>
<li>output_dir
<ul>
<li>name of directory to generate report file.</li>
</ul></li>
<li>title
<ul>
<li>title of report.</li>
</ul></li>
<li>subtitle
<ul>
<li>subtitle of report.</li>
</ul></li>
<li>abstract_title
<ul>
<li>abstract of report</li>
</ul></li>
<li>author
<ul>
<li>author of report.</li>
</ul></li>
<li>title_color
<ul>
<li>color of title.</li>
</ul></li>
<li>subtitle_color
<ul>
<li>color of subtitle.</li>
</ul></li>
<li>logo_img
<ul>
<li>name of logo image file on top left.</li>
</ul></li>
<li>cover_img
<ul>
<li>name of cover image file on center.</li>
</ul></li>
<li>create_date
<ul>
<li>The date on which the report is generated.</li>
</ul></li>
<li>theme
<ul>
<li>name of theme for report. support “orange” and “blue”.</li>
</ul></li>
<li>sample_percent
<ul>
<li>Sample percent of data for performing EDA.</li>
</ul></li>
</ul>
<p>The following script creates a EDA report for the <code>data.frame</code> class object, <code>heartfailure</code>.</p>
<div class="sourceCode" id="cb34"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb34-1"><a href="#cb34-1" aria-hidden="true" tabindex="-1"></a>heartfailure <span class="sc">%>%</span></span>
<span id="cb34-2"><a href="#cb34-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">eda_paged_report</span>(<span class="at">target =</span> <span class="st">"death_event"</span>, <span class="at">subtitle =</span> <span class="st">"heartfailure"</span>, </span>
<span id="cb34-3"><a href="#cb34-3" aria-hidden="true" tabindex="-1"></a> <span class="at">output_dir =</span> <span class="st">"./"</span>, <span class="at">output_file =</span> <span class="st">"EDA.pdf"</span>, <span class="at">theme =</span> <span class="st">"blue"</span>)</span></code></pre></div>
</div>
<div id="screenshot-of-static-report" class="section level4">
<h4>Screenshot of static report</h4>
<ul>
<li>The cover of the report is shown in the following figure.:</li>
</ul>
<div class="figure" style="text-align: center">