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introduce.html
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<meta name="author" content="Choonghyun Ryu" />
<meta name="date" content="2021-10-13" />
<title>Introduce dlookr</title>
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<body>
<h1 class="title toc-ignore">Introduce dlookr</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>Explore data to understand the data and find scenarios for performing the analysis.</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 a data diagnosis or automatically generates a data diagnosis report.</li>
<li>Discover data in a variety of ways, and automatically generate EDA(exploratory data analysis) report.</li>
<li>Impute missing values and outliers, resolve skewed data, and binaries continuous variables into categorical variables. And generates an automated report to support it.</li>
</ul>
<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>table of DBMS : table of the DBMS through tbl_dbi.
<ul>
<li>Use dplyr as the back-end interface for any DBI-compatible database.</li>
</ul></li>
</ul>
</div>
<div id="list-of-supported-tasks-of-data-analytics" class="section level2">
<h2>List of supported tasks of data analytics</h2>
<div id="diagnose-data" class="section level3">
<h3>Diagnose Data</h3>
<div id="overall-diagnose-data" class="section level4">
<h4>Overall Diagnose Data</h4>
<table>
<colgroup>
<col width="25%" />
<col width="37%" />
<col width="16%" />
<col width="20%" />
</colgroup>
<thead>
<tr class="header">
<th align="left">Tasks</th>
<th align="left">Descriptions</th>
<th align="left">Functions</th>
<th align="center">Support DBI</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">describe overview of data</td>
<td align="left">Inquire basic information to understand the data in general</td>
<td align="left"><code>overview()</code></td>
<td align="center"></td>
</tr>
<tr class="even">
<td align="left">summary overview object</td>
<td align="left">summary described overview of data</td>
<td align="left"><code>summary.overview()</code></td>
<td align="center"></td>
</tr>
<tr class="odd">
<td align="left">plot overview object</td>
<td align="left">plot described overview of data</td>
<td align="left"><code>plot.overview()</code></td>
<td align="center"></td>
</tr>
<tr class="even">
<td align="left">diagnose data quality of variables</td>
<td align="left">The scope of data quality diagnosis is information on missing values and unique value information</td>
<td align="left"><code>diagnose()</code></td>
<td align="center">x</td>
</tr>
<tr class="odd">
<td align="left">diagnose data quality of categorical variables</td>
<td align="left">frequency, ratio, rank by levels of each variables</td>
<td align="left"><code>diagnose_category()</code></td>
<td align="center">x</td>
</tr>
<tr class="even">
<td align="left">diagnose data quality of numerical variables</td>
<td align="left">descriptive statistics, number of zero, minus, outliers</td>
<td align="left"><code>diagnose_numeric()</code></td>
<td align="center">x</td>
</tr>
<tr class="odd">
<td align="left">diagnose data quality for outlier</td>
<td align="left">number of outliers, ratio, mean of outliers, mean with outliers, mean without outliers</td>
<td align="left"><code>diagnose_outlier()</code></td>
<td align="center">x</td>
</tr>
<tr class="even">
<td align="left">plot outliers information of numerical data</td>
<td align="left">box plot and histogram whith outliers, without outliers</td>
<td align="left"><code>plot_outlier.data.frame()</code></td>
<td align="center">x</td>
</tr>
<tr class="odd">
<td align="left">plot outliers information of numerical data by target variable</td>
<td align="left">box plot and density plot whith outliers, without outliers</td>
<td align="left"><code>plot_outlier.target_df()</code></td>
<td align="center">x</td>
</tr>
<tr class="even">
<td align="left">diagnose combination of categorical variables</td>
<td align="left">Check for sparse cases of level combinations of categorical variables</td>
<td align="left"><code>diagnose_sparese()</code></td>
<td align="center"></td>
</tr>
</tbody>
</table>
</div>
<div id="visualize-missing-values" class="section level4">
<h4>Visualize Missing Values</h4>
<table>
<colgroup>
<col width="25%" />
<col width="37%" />
<col width="16%" />
<col width="20%" />
</colgroup>
<thead>
<tr class="header">
<th align="left">Tasks</th>
<th align="left">Descriptions</th>
<th align="left">Functions</th>
<th align="center">Support DBI</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">pareto chart for missing value</td>
<td align="left">visualize pareto chart for variables with missing value.</td>
<td align="left"><code>plot_na_pareto()</code></td>
<td align="center"></td>
</tr>
<tr class="even">
<td align="left">combination chart for missing value</td>
<td align="left">visualize distribution of missing value by combination of variables.</td>
<td align="left"><code>plot_na_hclust()</code></td>
<td align="center"></td>
</tr>
<tr class="odd">
<td align="left">plot the combination variables that is include missing value</td>
<td align="left">visualize the combinations of missing value across cases..</td>
<td align="left"><code>plot_na_intersect()</code></td>
<td align="center"></td>
</tr>
</tbody>
</table>
</div>
<div id="reporting" class="section level4">
<h4>Reporting</h4>
<table>
<colgroup>
<col width="26%" />
<col width="34%" />
<col width="17%" />
<col width="21%" />
</colgroup>
<thead>
<tr class="header">
<th align="left">Types</th>
<th align="left">Descriptions</th>
<th align="left">Functions</th>
<th align="center">Support DBI</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">reporting the information of data diagnosis into pdf file</td>
<td align="left">report the information for diagnosing the quality of the data.</td>
<td align="left"><code>diagnose_report()</code></td>
<td align="center">x</td>
</tr>
<tr class="even">
<td align="left">reporting the information of data diagnosis into html file</td>
<td align="left">report the information for diagnosing the quality of the data.</td>
<td align="left"><code>diagnose_report()</code></td>
<td align="center">x</td>
</tr>
<tr class="odd">
<td align="left">reporting the information of data diagnosis into html file</td>
<td align="left">dynamic report the information for diagnosing the quality of the data.</td>
<td align="left"><code>diagnose_web_report()</code></td>
<td align="center">x</td>
</tr>
<tr class="even">
<td align="left">reporting the information of data diagnosis into pdf and html file</td>
<td align="left">paged report the information for diagnosing the quality of the data.</td>
<td align="left"><code>diagnose_paged_report()</code></td>
<td align="center">x</td>
</tr>
</tbody>
</table>
</div>
</div>
<div id="eda" class="section level3">
<h3>EDA</h3>
<div id="univariate-eda" class="section level4">
<h4>Univariate EDA</h4>
<table>
<colgroup>
<col width="16%" />
<col width="16%" />
<col width="32%" />
<col width="16%" />
<col width="20%" />
</colgroup>
<thead>
<tr class="header">
<th align="left">Types</th>
<th align="left">Tasks</th>
<th align="left">Descriptions</th>
<th align="left">Functions</th>
<th align="center">Support DBI</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">categorical</td>
<td align="left">summaries</td>
<td align="left">frequency tables</td>
<td align="left"><code>univar_category()</code></td>
<td align="center"></td>
</tr>
<tr class="even">
<td align="left">categorical</td>
<td align="left">summaries</td>
<td align="left">chi-squared test</td>
<td align="left"><code>summary.univar_category()</code></td>
<td align="center"></td>
</tr>
<tr class="odd">
<td align="left">categorical</td>
<td align="left">visualize</td>
<td align="left">bar charts</td>
<td align="left"><code>plot.univar_category()</code></td>
<td align="center"></td>
</tr>
<tr class="even">
<td align="left">categorical</td>
<td align="left">visualize</td>
<td align="left">bar charts</td>
<td align="left"><code>plot_bar_category()</code></td>
<td align="center"></td>
</tr>
<tr class="odd">
<td align="left">numerical</td>
<td align="left">summaries</td>
<td align="left">descriptive statistics</td>
<td align="left"><code>describe()</code></td>
<td align="center">x</td>
</tr>
<tr class="even">
<td align="left">numerical</td>
<td align="left">summaries</td>
<td align="left">descriptive statistics</td>
<td align="left"><code>univar_numeric()</code></td>
<td align="center"></td>
</tr>
<tr class="odd">
<td align="left">numerical</td>
<td align="left">summaries</td>
<td align="left">descriptive statistics of standardized variable</td>
<td align="left"><code>summary.univar_numeric()</code></td>
<td align="center"></td>
</tr>
<tr class="even">
<td align="left">numerical</td>
<td align="left">visualize</td>
<td align="left">histogram, box plot</td>
<td align="left"><code>plot.univar_numeric()</code></td>
<td align="center"></td>
</tr>
<tr class="odd">
<td align="left">numerical</td>
<td align="left">visualize</td>
<td align="left">Q-Q plots</td>
<td align="left"><code>plot_qq_numeric()</code></td>
<td align="center"></td>
</tr>
<tr class="even">
<td align="left">numerical</td>
<td align="left">visualize</td>
<td align="left">box plot</td>
<td align="left"><code>plot_box_numeric()</code></td>
<td align="center"></td>
</tr>
<tr class="odd">
<td align="left">numerical</td>
<td align="left">visualize</td>
<td align="left">histogram</td>
<td align="left"><code>plot_hist_numeric()</code></td>
<td align="center"></td>
</tr>
</tbody>
</table>
</div>
<div id="bivariate-eda" class="section level4">
<h4>Bivariate EDA</h4>
<table>
<colgroup>
<col width="16%" />
<col width="16%" />
<col width="32%" />
<col width="16%" />
<col width="20%" />
</colgroup>
<thead>
<tr class="header">
<th align="left">Types</th>
<th align="left">Tasks</th>
<th align="left">Descriptions</th>
<th align="left">Functions</th>
<th align="center">Support DBI</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">categorical</td>
<td align="left">summaries</td>
<td align="left">frequency tables cross cases</td>
<td align="left"><code>compare_category()</code></td>
<td align="center"></td>
</tr>
<tr class="even">
<td align="left">categorical</td>
<td align="left">summaries</td>
<td align="left">contingency tables, chi-squared test</td>
<td align="left"><code>summary.compare_category()</code></td>
<td align="center"></td>
</tr>
<tr class="odd">
<td align="left">categorical</td>
<td align="left">visualize</td>
<td align="left">mosaics plot</td>
<td align="left"><code>plot.compare_category()</code></td>
<td align="center"></td>
</tr>
<tr class="even">
<td align="left">numerical</td>
<td align="left">summaries</td>
<td align="left">correlation coefficient, linear model summaries</td>
<td align="left"><code>compare_numeric()</code></td>
<td align="center"></td>
</tr>
<tr class="odd">
<td align="left">numerical</td>
<td align="left">summaries</td>
<td align="left">correlation coefficient, linear model summaries with threshold</td>
<td align="left"><code>summary.compare_numeric()</code></td>
<td align="center"></td>
</tr>
<tr class="even">
<td align="left">numerical</td>
<td align="left">visualize</td>
<td align="left">scatter plot with marginal box plot</td>
<td align="left"><code>plot.compare_numeric()</code></td>
<td align="center"></td>
</tr>
<tr class="odd">
<td align="left">numerical</td>
<td align="left">Correlate</td>
<td align="left">correlation coefficient</td>
<td align="left"><code>correlate()</code></td>
<td align="center">x</td>
</tr>
<tr class="even">
<td align="left">numerical</td>
<td align="left">Correlate</td>
<td align="left">visualization of a correlation matrix</td>
<td align="left"><code>plot_correlate()</code></td>
<td align="center">x</td>
</tr>
</tbody>
</table>
</div>
<div id="normality-test" class="section level4">
<h4>Normality Test</h4>
<table>
<colgroup>
<col width="16%" />
<col width="16%" />
<col width="32%" />
<col width="16%" />
<col width="20%" />
</colgroup>
<thead>
<tr class="header">
<th align="left">Types</th>
<th align="left">Tasks</th>
<th align="left">Descriptions</th>
<th align="left">Functions</th>
<th align="center">Support DBI</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">numerical</td>
<td align="left">summaries</td>
<td align="left">Shapiro-Wilk normality test</td>
<td align="left"><code>normality()</code></td>
<td align="center">x</td>
</tr>
<tr class="even">
<td align="left">numerical</td>
<td align="left">summaries</td>
<td align="left">normality diagnosis plot (histogram, Q-Q plots)</td>
<td align="left"><code>plot_normality()</code></td>
<td align="center">x</td>
</tr>
</tbody>
</table>
</div>
<div id="relationship-between-target-variable-and-predictors" class="section level4">
<h4>Relationship between target variable and predictors</h4>
<table>
<colgroup>
<col width="16%" />
<col width="16%" />
<col width="32%" />
<col width="16%" />
<col width="20%" />
</colgroup>
<thead>
<tr class="header">
<th align="left">Target Variable</th>
<th align="left">Predictor</th>
<th align="left">Descriptions</th>
<th align="left">Functions</th>
<th align="center">Support DBI</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">categorical</td>
<td align="left">categorical</td>
<td align="left">contingency tables</td>
<td align="left"><code>relate()</code></td>
<td align="center">x</td>
</tr>
<tr class="even">
<td align="left">categorical</td>
<td align="left">categorical</td>
<td align="left">mosaics plot</td>
<td align="left"><code>plot.relate()</code></td>
<td align="center">x</td>
</tr>
<tr class="odd">
<td align="left">categorical</td>
<td align="left">numerical</td>
<td align="left">descriptive statistic for each levels and total observation</td>
<td align="left"><code>relate()</code></td>
<td align="center">x</td>
</tr>
<tr class="even">
<td align="left">categorical</td>
<td align="left">numerical</td>
<td align="left">density plot</td>
<td align="left"><code>plot.relate()</code></td>
<td align="center">x</td>
</tr>
<tr class="odd">
<td align="left">categorical</td>
<td align="left">categorical</td>
<td align="left">bar charts</td>
<td align="left"><code>plot_bar_category()</code></td>
<td align="center"></td>
</tr>
<tr class="even">
<td align="left">numerical</td>
<td align="left">categorical</td>
<td align="left">ANOVA test</td>
<td align="left"><code>relate()</code></td>
<td align="center">x</td>
</tr>
<tr class="odd">
<td align="left">numerical</td>
<td align="left">categorical</td>
<td align="left">scatter plot</td>
<td align="left"><code>plot.relate()</code></td>
<td align="center">x</td>
</tr>
<tr class="even">
<td align="left">numerical</td>
<td align="left">numerical</td>
<td align="left">simple linear model</td>
<td align="left"><code>relate()</code></td>
<td align="center">x</td>
</tr>
<tr class="odd">
<td align="left">numerical</td>
<td align="left">numerical</td>
<td align="left">box plot</td>
<td align="left"><code>plot.relate()</code></td>
<td align="center">x</td>
</tr>
<tr class="even">
<td align="left">categorical</td>
<td align="left">numerical</td>
<td align="left">Q-Q plots</td>
<td align="left"><code>plot_qq_numeric()</code></td>
<td align="center"></td>
</tr>
<tr class="odd">
<td align="left">categorical</td>
<td align="left">numerical</td>
<td align="left">box plot</td>
<td align="left"><code>plot_box_numeric()</code></td>
<td align="center"></td>
</tr>
<tr class="even">
<td align="left">categorical</td>
<td align="left">numerical</td>
<td align="left">histogram</td>
<td align="left"><code>plot_hist_numeric()</code></td>
<td align="center"></td>
</tr>
</tbody>
</table>
</div>
<div id="reporting-1" class="section level4">
<h4>Reporting</h4>
<table>
<colgroup>
<col width="25%" />
<col width="37%" />
<col width="16%" />
<col width="20%" />
</colgroup>
<thead>
<tr class="header">
<th align="left">Types</th>
<th align="left">Descriptions</th>
<th align="left">Functions</th>
<th align="center">Support DBI</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">reporting the information of EDA into pdf file</td>
<td align="left">reporting the information of EDA.</td>
<td align="left"><code>eda_report()</code></td>
<td align="center">x</td>
</tr>
<tr class="even">
<td align="left">reporting the information of EDA into html file</td>
<td align="left">reporting the information of EDA.</td>
<td align="left"><code>eda_report()</code></td>
<td align="center">x</td>
</tr>
<tr class="odd">
<td align="left">reporting the information of EDA into pdf file</td>
<td align="left">dynamic reporting the information of EDA.</td>
<td align="left"><code>eda_web_report()</code></td>
<td align="center">x</td>
</tr>
<tr class="even">
<td align="left">reporting the information of EDA into html file</td>
<td align="left">paged reporting the information of EDA.</td>
<td align="left"><code>eda_paged_report()</code></td>
<td align="center">x</td>
</tr>
</tbody>
</table>
</div>
</div>
<div id="transform-data" class="section level3">
<h3>Transform Data</h3>
<div id="find-variables" class="section level4">
<h4>Find Variables</h4>
<table>
<colgroup>
<col width="19%" />
<col width="38%" />
<col width="19%" />
<col width="23%" />
</colgroup>
<thead>
<tr class="header">
<th align="left">Types</th>
<th align="left">Descriptions</th>
<th align="left">Functions</th>
<th align="center">Support DBI</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">missing values</td>
<td align="left">find the variable that contains the missing value in the object that inherits the data.frame</td>
<td align="left"><code>find_na()</code></td>
<td align="center"></td>
</tr>
<tr class="even">
<td align="left">outliers</td>
<td align="left">find the numerical variable that contains outliers in the object that inherits the data.frame</td>
<td align="left"><code>find_outliers()</code></td>
<td align="center"></td>
</tr>
<tr class="odd">
<td align="left">skewed variable</td>
<td align="left">find the numerical variable that skewed variable that inherits the data.frame</td>
<td align="left"><code>find_skewness()</code></td>
<td align="center"></td>
</tr>
</tbody>
</table>
</div>
<div id="imputation" class="section level4">
<h4>Imputation</h4>
<table>
<colgroup>
<col width="19%" />
<col width="38%" />
<col width="19%" />
<col width="23%" />
</colgroup>
<thead>
<tr class="header">
<th align="left">Types</th>
<th align="left">Descriptions</th>
<th align="left">Functions</th>
<th align="center">Support DBI</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">missing values</td>
<td align="left">missing values are imputed with some representative values and statistical methods.</td>
<td align="left"><code>imputate_na()</code></td>
<td align="center"></td>
</tr>
<tr class="even">
<td align="left">outliers</td>
<td align="left">outliers are imputed with some representative values and statistical methods.</td>
<td align="left"><code>imputate_outlier()</code></td>
<td align="center"></td>
</tr>
<tr class="odd">
<td align="left">summaries</td>
<td align="left">calculate descriptive statistics of the original and imputed values.</td>
<td align="left"><code>summary.imputation()</code></td>
<td align="center"></td>
</tr>
<tr class="even">
<td align="left">visualize</td>
<td align="left">the imputation of a numerical variable is a density plot, and the imputation of a categorical variable is a bar plot.</td>
<td align="left"><code>plot.imputation()</code></td>
<td align="center"></td>
</tr>
</tbody>
</table>
</div>
<div id="binning" class="section level4">
<h4>Binning</h4>
<table>
<colgroup>
<col width="19%" />
<col width="38%" />
<col width="19%" />
<col width="23%" />
</colgroup>
<thead>
<tr class="header">
<th align="left">Types</th>
<th align="left">Descriptions</th>
<th align="left">Functions</th>
<th align="center">Support DBI</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">binning</td>
<td align="left">converts a numeric variable to a categorization variable</td>
<td align="left"><code>binning()</code></td>
<td align="center"></td>
</tr>
<tr class="even">
<td align="left">summaries</td>
<td align="left">calculate frequency and relative frequency for each levels(bins)</td>
<td align="left"><code>summary.bins()</code></td>
<td align="center"></td>
</tr>
<tr class="odd">
<td align="left">visualize</td>
<td align="left">visualize two plots on a single screen. The plot at the top is a histogram representing the frequency of the level. The plot at the bottom is a bar chart representing the frequency of the level.</td>
<td align="left"><code>plot.bins()</code></td>
<td align="center"></td>
</tr>
<tr class="even">
<td align="left">optimal binning</td>
<td align="left">categorizes a numeric characteristic into bins for ulterior usage in scoring modeling</td>
<td align="left"><code>binning_by()</code></td>
<td align="center"></td>
</tr>
<tr class="odd">
<td align="left">summaries</td>
<td align="left">summary metrics to evaluate the performance of binomial classification model</td>
<td align="left"><code>summary.optimal_bins()</code></td>
<td align="center"></td>
</tr>
<tr class="even">
<td align="left">visualize</td>
<td align="left">generates plots for understand distribution, bad rate, and weight of evidence after running binning_by()</td>
<td align="left"><code>plot.optimal_bins()</code></td>
<td align="center"></td>
</tr>
<tr class="odd">
<td align="left">evaluate</td>
<td align="left">calculates metrics to evaluate the performance of binned variable for binomial classification model</td>
<td align="left"><code>performance_bin()</code></td>
<td align="center"></td>
</tr>
<tr class="even">
<td align="left">summaries</td>
<td align="left">summary metrics to evaluate the performance of binomial classification model after performance_bin()</td>
<td align="left"><code>summary.performance_bin()</code></td>
<td align="center"></td>
</tr>
<tr class="odd">
<td align="left">visualize</td>
<td align="left">It generates plots for understand frequency, WoE by bins using performance_bin after running binning_by()</td>
<td align="left"><code>plot.performance_bin()</code></td>
<td align="center"></td>
</tr>
<tr class="even">
<td align="left">visualize</td>
<td align="left">extract bins from “bins” and “optimal_bins” objects</td>
<td align="left"><code>extract.bins()</code></td>
<td align="center"></td>
</tr>
</tbody>
</table>
</div>
<div id="diagnose-binned-variable" class="section level4">
<h4>Diagnose Binned Variable</h4>
<table>
<colgroup>
<col width="19%" />
<col width="38%" />
<col width="19%" />
<col width="23%" />
</colgroup>
<thead>
<tr class="header">
<th align="left">Types</th>
<th align="left">Descriptions</th>
<th align="left">Functions</th>
<th align="center">Support DBI</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">diagnosis</td>
<td align="left">performs diagnose performance that calculates metrics to evaluate the performance of binned variable for binomial classification model.</td>
<td align="left"><code>performance_bin()</code></td>
<td align="center"></td>
</tr>
<tr class="even">
<td align="left">summaries</td>
<td align="left">summary method for “performance_bin”. summary metrics to evaluate the performance of binomial classification model.</td>
<td align="left"><code>summary.performance_bin()</code></td>
<td align="center"></td>
</tr>
<tr class="odd">
<td align="left">visualize</td>
<td align="left">visualize for understand frequency, WoE by bins using performance_bin and something else.</td>
<td align="left"><code>plot.performance_bin()</code></td>
<td align="center"></td>
</tr>
</tbody>
</table>
</div>
<div id="transformation" class="section level4">
<h4>Transformation</h4>
<table>
<colgroup>
<col width="19%" />
<col width="38%" />
<col width="19%" />
<col width="23%" />
</colgroup>
<thead>
<tr class="header">
<th align="left">Types</th>
<th align="left">Descriptions</th>
<th align="left">Functions</th>
<th align="center">Support DBI</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">transformation</td>
<td align="left">performs variable transformation for standardization and resolving skewness of numerical variables.</td>
<td align="left"><code>transform()</code></td>
<td align="center"></td>
</tr>
<tr class="even">
<td align="left">summaries</td>
<td align="left">compares the distribution of data before and after data transformation</td>
<td align="left"><code>summary.transform()</code></td>
<td align="center"></td>
</tr>
<tr class="odd">
<td align="left">visualize</td>
<td align="left">visualize two kinds of plot by attribute of ‘transform’ class. The transformation of a numerical variable is a density plot.</td>
<td align="left"><code>plot.transform()</code></td>
<td align="center"></td>
</tr>
</tbody>
</table>
</div>
<div id="reporting-2" class="section level4">
<h4>Reporting</h4>
<table>
<colgroup>
<col width="25%" />
<col width="37%" />
<col width="16%" />
<col width="20%" />
</colgroup>
<thead>
<tr class="header">
<th align="left">Types</th>
<th align="left">Descriptions</th>
<th align="left">Functions</th>
<th align="center">Support DBI</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">reporting the information of transformation into pdf</td>
<td align="left">reporting the information of transformation.</td>
<td align="left"><code>transformation_report()</code></td>
<td align="center"></td>
</tr>
<tr class="even">
<td align="left">reporting the information of transformation into html</td>
<td align="left">reporting the information of transformation.</td>
<td align="left"><code>transformation_report()</code></td>
<td align="center"></td>
</tr>
<tr class="odd">
<td align="left">reporting the information of transformation into pdf</td>
<td align="left">dynamic reporting the information of transformation.</td>
<td align="left"><code>transformation_web_report()</code></td>
<td align="center"></td>
</tr>
<tr class="even">
<td align="left">reporting the information of transformation into html</td>
<td align="left">paged reporting the information of transformation.</td>
<td align="left"><code>transformation_paged_report()</code></td>
<td align="center"></td>
</tr>
</tbody>
</table>
</div>
</div>
<div id="miscellaneous" class="section level3">
<h3>Miscellaneous</h3>
<div id="statistics" class="section level4">
<h4>Statistics</h4>
<table>
<colgroup>
<col width="19%" />
<col width="38%" />
<col width="19%" />
<col width="23%" />
</colgroup>
<thead>
<tr class="header">
<th align="left">Types</th>
<th align="left">Descriptions</th>
<th align="left">Functions</th>
<th align="center">Support DBI</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">statistics</td>
<td align="left">calculate the entropy.</td>
<td align="left"><code>entropy()</code></td>
<td align="center"></td>
</tr>
<tr class="even">
<td align="left">statistics</td>
<td align="left">calculate the skewness of the data.</td>
<td align="left"><code>skewness()</code></td>
<td align="center"></td>
</tr>
<tr class="odd">
<td align="left">statistics</td>
<td align="left">calculate the kurtosis of the data.</td>
<td align="left"><code>kurtosis()</code></td>
<td align="center"></td>
</tr>
<tr class="even">
<td align="left">statistics</td>
<td align="left">calculate the Jensen-Shannon divergence between two probability distributions.</td>
<td align="left"><code>jsd()</code></td>
<td align="center"></td>
</tr>
<tr class="odd">
<td align="left">statistics</td>
<td align="left">calculate the Kullback-Leibler divergence between two probability distributions.</td>
<td align="left"><code>kld()</code></td>
<td align="center"></td>
</tr>
<tr class="even">
<td align="left">statistics</td>
<td align="left">finding percentile of numerical variable.</td>
<td align="left"><code>get_percentile()</code></td>
<td align="center"></td>
</tr>
<tr class="odd">
<td align="left">statistics</td>
<td align="left">transform a numeric vector using several methods like “log”, “sqrt”, “log+1”, “log+a”, “1/x”, “x^2”, “x^3”, “Box-Cox”, “Yeo-Johnson”</td>
<td align="left"><code>get_transform()</code></td>
<td align="center"></td>
</tr>
</tbody>
</table>
</div>
<div id="programming" class="section level4">
<h4>Programming</h4>
<table>
<colgroup>
<col width="19%" />
<col width="38%" />
<col width="19%" />
<col width="23%" />
</colgroup>
<thead>
<tr class="header">
<th align="left">Types</th>
<th align="left">Descriptions</th>
<th align="left">Functions</th>
<th align="center">Support DBI</th>
</tr>
</thead>
<tbody>
<tr class="odd">