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README.html
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<!DOCTYPE html>
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<meta charset="utf-8">
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
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<h1 id="weighted-data-survey-tables-in-r">Weighted data survey tables in R</h1>
<p>John Johnson 3/9/2020</p>
<p><code>pollster</code> is an R package for making topline and crosstab tables of simple weighted survey data. The package is designed for use with labelled data, like what you might use the <code>haven</code> package to import from Stata or SPSS. It follows tidyverse programming conventions, and output tables are also in the form of a tidy data frame, or tibble.</p>
<p>Only simple weights are currently supported. For complex survey designs, we recommend the excellent <a href="http://r-survey.r-forge.r-project.org/survey/"><code>survey</code> package</a>.</p>
<p>The core functions are:</p>
<ul>
<li><code>topline()</code></li>
<li><code>crosstab()</code></li>
<li><code>crosstab_3way()</code></li>
</ul>
<p>Each of these functions also has a twin version which includes a column for the margin of error calculated to include the design effect of the weights.</p>
<ul>
<li><code>moe_topline()</code></li>
<li><code>moe_crosstab()</code></li>
<li><code>moe_crosstab_3way()</code></li>
</ul>
<p>There are also two special functions which calculate the design effect component of the margin of error for each survey wave independently.</p>
<ul>
<li><code>moe_wave_crosstab()</code></li>
<li><code>moe_wave_crosstab_3way()</code></li>
</ul>
<p>Other functions are included to calculate simple weighted summary statistics.</p>
<ul>
<li><code>wtd_mean()</code> is a tidy-compliant wrapper around <code>stats::weighted.mean()</code></li>
<li><code>summary_table()</code> returns a tible with summary statistics similar to the Stata command <code>sum</code></li>
</ul>
<h2 id="installation">Installation</h2>
<p>Install it this way.</p>
<pre><code>install.packages("pollster")
</code></pre>
<p>Or get the development version.</p>
<pre><code>remotes::install_github("jdjohn215/pollster")
</code></pre>
<h2 id="basic-usage">Basic usage</h2>
<p><code>pollster</code> includes a dataset of Illinois responses to the Current Population Survey’s voter registration supplement.</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb3-1" data-line-number="1"><span class="kw">library</span>(pollster)</a>
<a class="sourceLine" id="cb3-2" data-line-number="2"><span class="kw">head</span>(illinois)</a>
<a class="sourceLine" id="cb3-3" data-line-number="3"><span class="co">#> # A tibble: 6 x 10</span></a>
<a class="sourceLine" id="cb3-4" data-line-number="4"><span class="co">#> year fips sex educ6 raceethnic maritalstatus rv voter age</span></a>
<a class="sourceLine" id="cb3-5" data-line-number="5"><span class="co">#> <dbl> <dbl+l> <dbl+l> <dbl+l> <dbl+lbl> <dbl+lbl> <dbl+l> <dbl+l> <dbl></span></a>
<a class="sourceLine" id="cb3-6" data-line-number="6"><span class="co">#> 1 1996 17 [IL] 1 [Mal… 2 [HS] 1 [White] 1 [Married] 2 [Not… 2 [Not… 29</span></a>
<a class="sourceLine" id="cb3-7" data-line-number="7"><span class="co">#> 2 1996 17 [IL] 2 [Fem… 3 [Som… 1 [White] 1 [Married] 1 [Reg… 1 [Vot… 28</span></a>
<a class="sourceLine" id="cb3-8" data-line-number="8"><span class="co">#> 3 1996 17 [IL] 2 [Fem… 2 [HS] 1 [White] 3 [Never Mar… 1 [Reg… 1 [Vot… 82</span></a>
<a class="sourceLine" id="cb3-9" data-line-number="9"><span class="co">#> 4 1996 17 [IL] 2 [Fem… 2 [HS] 1 [White] 3 [Never Mar… 1 [Reg… 1 [Vot… 72</span></a>
<a class="sourceLine" id="cb3-10" data-line-number="10"><span class="co">#> 5 1996 17 [IL] 1 [Mal… 2 [HS] 2 [Black] 1 [Married] 1 [Reg… 1 [Vot… 75</span></a>
<a class="sourceLine" id="cb3-11" data-line-number="11"><span class="co">#> 6 1996 17 [IL] 2 [Fem… 2 [HS] 2 [Black] 1 [Married] 1 [Reg… 1 [Vot… 60</span></a>
<a class="sourceLine" id="cb3-12" data-line-number="12"><span class="co">#> # … with 1 more variable: weight <dbl></span></a></code></pre></div>
<p>Make a topline table like this. The output is a tibble.</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb4-1" data-line-number="1"><span class="kw">topline</span>(<span class="dt">df =</span> illinois, <span class="dt">variable =</span> maritalstatus, <span class="dt">weight =</span> weight)</a>
<a class="sourceLine" id="cb4-2" data-line-number="2"><span class="co">#> # A tibble: 3 x 5</span></a>
<a class="sourceLine" id="cb4-3" data-line-number="3"><span class="co">#> Response Frequency Percent `Valid Percent` `Cumulative Percent`</span></a>
<a class="sourceLine" id="cb4-4" data-line-number="4"><span class="co">#> <fct> <dbl> <dbl> <dbl> <dbl></span></a>
<a class="sourceLine" id="cb4-5" data-line-number="5"><span class="co">#> 1 Married 55001786. 53.6 53.6 53.6</span></a>
<a class="sourceLine" id="cb4-6" data-line-number="6"><span class="co">#> 2 Widow/divorced/Sep 18635087. 18.1 18.1 71.7</span></a>
<a class="sourceLine" id="cb4-7" data-line-number="7"><span class="co">#> 3 Never Married 29041640. 28.3 28.3 100</span></a></code></pre></div>
<p>Make a crosstab like this.</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" data-line-number="1"><span class="kw">crosstab</span>(<span class="dt">df =</span> illinois, <span class="dt">x =</span> educ6, <span class="dt">y =</span> maritalstatus, <span class="dt">weight =</span> weight)</a>
<a class="sourceLine" id="cb5-2" data-line-number="2"><span class="co">#> # A tibble: 6 x 5</span></a>
<a class="sourceLine" id="cb5-3" data-line-number="3"><span class="co">#> educ6 Married `Widow/divorced/Sep` `Never Married` n</span></a>
<a class="sourceLine" id="cb5-4" data-line-number="4"><span class="co">#> <fct> <dbl> <dbl> <dbl> <dbl></span></a>
<a class="sourceLine" id="cb5-5" data-line-number="5"><span class="co">#> 1 LT HS 40.0 29.1 30.9 10770999.</span></a>
<a class="sourceLine" id="cb5-6" data-line-number="6"><span class="co">#> 2 HS 52.9 21.0 26.1 31409418.</span></a>
<a class="sourceLine" id="cb5-7" data-line-number="7"><span class="co">#> 3 Some Col 44.6 17.4 38.0 21745113.</span></a>
<a class="sourceLine" id="cb5-8" data-line-number="8"><span class="co">#> 4 AA 57.4 18.4 24.2 8249909.</span></a>
<a class="sourceLine" id="cb5-9" data-line-number="9"><span class="co">#> 5 BA 61.1 11.3 27.6 19937965.</span></a>
<a class="sourceLine" id="cb5-10" data-line-number="10"><span class="co">#> 6 Post-BA 70.7 12.9 16.5 10565110.</span></a></code></pre></div>
<p>If you prefer, you can also get the output in long format.</p>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb6-1" data-line-number="1"><span class="kw">crosstab</span>(<span class="dt">df =</span> illinois, <span class="dt">x =</span> educ6, <span class="dt">y =</span> maritalstatus, <span class="dt">weight =</span> weight, <span class="dt">format =</span> <span class="st">"long"</span>)</a>
<a class="sourceLine" id="cb6-2" data-line-number="2"><span class="co">#> # A tibble: 18 x 4</span></a>
<a class="sourceLine" id="cb6-3" data-line-number="3"><span class="co">#> educ6 maritalstatus pct n</span></a>
<a class="sourceLine" id="cb6-4" data-line-number="4"><span class="co">#> <fct> <fct> <dbl> <dbl></span></a>
<a class="sourceLine" id="cb6-5" data-line-number="5"><span class="co">#> 1 LT HS Married 40.0 10770999.</span></a>
<a class="sourceLine" id="cb6-6" data-line-number="6"><span class="co">#> 2 LT HS Widow/divorced/Sep 29.1 10770999.</span></a>
<a class="sourceLine" id="cb6-7" data-line-number="7"><span class="co">#> 3 LT HS Never Married 30.9 10770999.</span></a>
<a class="sourceLine" id="cb6-8" data-line-number="8"><span class="co">#> 4 HS Married 52.9 31409418.</span></a>
<a class="sourceLine" id="cb6-9" data-line-number="9"><span class="co">#> 5 HS Widow/divorced/Sep 21.0 31409418.</span></a>
<a class="sourceLine" id="cb6-10" data-line-number="10"><span class="co">#> 6 HS Never Married 26.1 31409418.</span></a>
<a class="sourceLine" id="cb6-11" data-line-number="11"><span class="co">#> 7 Some Col Married 44.6 21745113.</span></a>
<a class="sourceLine" id="cb6-12" data-line-number="12"><span class="co">#> 8 Some Col Widow/divorced/Sep 17.4 21745113.</span></a>
<a class="sourceLine" id="cb6-13" data-line-number="13"><span class="co">#> 9 Some Col Never Married 38.0 21745113.</span></a>
<a class="sourceLine" id="cb6-14" data-line-number="14"><span class="co">#> 10 AA Married 57.4 8249909.</span></a>
<a class="sourceLine" id="cb6-15" data-line-number="15"><span class="co">#> 11 AA Widow/divorced/Sep 18.4 8249909.</span></a>
<a class="sourceLine" id="cb6-16" data-line-number="16"><span class="co">#> 12 AA Never Married 24.2 8249909.</span></a>
<a class="sourceLine" id="cb6-17" data-line-number="17"><span class="co">#> 13 BA Married 61.1 19937965.</span></a>
<a class="sourceLine" id="cb6-18" data-line-number="18"><span class="co">#> 14 BA Widow/divorced/Sep 11.3 19937965.</span></a>
<a class="sourceLine" id="cb6-19" data-line-number="19"><span class="co">#> 15 BA Never Married 27.6 19937965.</span></a>
<a class="sourceLine" id="cb6-20" data-line-number="20"><span class="co">#> 16 Post-BA Married 70.7 10565110.</span></a>
<a class="sourceLine" id="cb6-21" data-line-number="21"><span class="co">#> 17 Post-BA Widow/divorced/Sep 12.9 10565110.</span></a>
<a class="sourceLine" id="cb6-22" data-line-number="22"><span class="co">#> 18 Post-BA Never Married 16.5 10565110.</span></a></code></pre></div>
<p>A three-way crosstab is just a normal crosstab with a third control variable. Often, this third variable is time.</p>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb7-1" data-line-number="1"><span class="kw">crosstab_3way</span>(<span class="dt">df =</span> illinois, <span class="dt">x =</span> educ6, <span class="dt">y =</span> maritalstatus, <span class="dt">z =</span> year, <span class="dt">weight =</span> weight)</a>
<a class="sourceLine" id="cb7-2" data-line-number="2"><span class="co">#> # A tibble: 72 x 6</span></a>
<a class="sourceLine" id="cb7-3" data-line-number="3"><span class="co">#> educ6 year n Married `Widow/divorced/Sep` `Never Married`</span></a>
<a class="sourceLine" id="cb7-4" data-line-number="4"><span class="co">#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl></span></a>
<a class="sourceLine" id="cb7-5" data-line-number="5"><span class="co">#> 1 LT HS 1996 1182402. 41.0 28.8 30.2</span></a>
<a class="sourceLine" id="cb7-6" data-line-number="6"><span class="co">#> 2 LT HS 1998 1159148. 42.2 33.6 24.2</span></a>
<a class="sourceLine" id="cb7-7" data-line-number="7"><span class="co">#> 3 LT HS 2000 1036154. 44.3 32.6 23.1</span></a>
<a class="sourceLine" id="cb7-8" data-line-number="8"><span class="co">#> 4 LT HS 2002 1074704. 38.0 30.4 31.6</span></a>
<a class="sourceLine" id="cb7-9" data-line-number="9"><span class="co">#> 5 LT HS 2004 936926. 41.0 30.3 28.6</span></a>
<a class="sourceLine" id="cb7-10" data-line-number="10"><span class="co">#> 6 LT HS 2006 918858. 38.6 31.7 29.7</span></a>
<a class="sourceLine" id="cb7-11" data-line-number="11"><span class="co">#> 7 LT HS 2008 909755. 42.1 28.1 29.8</span></a>
<a class="sourceLine" id="cb7-12" data-line-number="12"><span class="co">#> 8 LT HS 2010 806647. 40.6 24.6 34.7</span></a>
<a class="sourceLine" id="cb7-13" data-line-number="13"><span class="co">#> 9 LT HS 2012 705132. 35.7 26.9 37.4</span></a>
<a class="sourceLine" id="cb7-14" data-line-number="14"><span class="co">#> 10 LT HS 2014 782926. 43.7 23.7 32.7</span></a>
<a class="sourceLine" id="cb7-15" data-line-number="15"><span class="co">#> # … with 62 more rows</span></a></code></pre></div>
<h2 id="making-tables-and-graphs">Making tables and graphs</h2>
<p>Wide format is best for displaying table output. Long format is best for making graphs. <code>pollster</code> outputs dovetail seamlessly with <a href="https://www.rdocumentation.org/packages/knitr/versions/1.28/topics/kable"><code>knitr::kable()</code></a> and <a href="https://ggplot2.tidyverse.org/"><code>ggplot2::ggplot()</code></a>. These examples show very basic html table output, but you can customize the appearance of your tables almost endlessly in either html or pdf formats using Hao Zhu’s excellent <a href="https://haozhu233.github.io/kableExtra/"><code>kableExtra</code> package</a>.</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb8-1" data-line-number="1"><span class="kw">library</span>(dplyr)</a>
<a class="sourceLine" id="cb8-2" data-line-number="2"><span class="co">#> </span></a>
<a class="sourceLine" id="cb8-3" data-line-number="3"><span class="co">#> Attaching package: 'dplyr'</span></a>
<a class="sourceLine" id="cb8-4" data-line-number="4"><span class="co">#> The following objects are masked from 'package:stats':</span></a>
<a class="sourceLine" id="cb8-5" data-line-number="5"><span class="co">#> </span></a>
<a class="sourceLine" id="cb8-6" data-line-number="6"><span class="co">#> filter, lag</span></a>
<a class="sourceLine" id="cb8-7" data-line-number="7"><span class="co">#> The following objects are masked from 'package:base':</span></a>
<a class="sourceLine" id="cb8-8" data-line-number="8"><span class="co">#> </span></a>
<a class="sourceLine" id="cb8-9" data-line-number="9"><span class="co">#> intersect, setdiff, setequal, union</span></a>
<a class="sourceLine" id="cb8-10" data-line-number="10"><span class="kw">crosstab</span>(<span class="dt">df =</span> illinois, <span class="dt">x =</span> sex, <span class="dt">y =</span> educ6, <span class="dt">weight =</span> weight) <span class="op">%>%</span></a>
<a class="sourceLine" id="cb8-11" data-line-number="11"><span class="st"> </span>knitr<span class="op">::</span><span class="kw">kable</span>(<span class="dt">digits =</span> <span class="dv">0</span>)</a></code></pre></div>
<table>
<thead>
<tr class="header">
<th align="left">sex</th>
<th align="right">LT HS</th>
<th align="right">HS</th>
<th align="right">Some Col</th>
<th align="right">AA</th>
<th align="right">BA</th>
<th align="right">Post-BA</th>
<th align="right">n</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">Male</td>
<td align="right">11</td>
<td align="right">31</td>
<td align="right">21</td>
<td align="right">7</td>
<td align="right">20</td>
<td align="right">11</td>
<td align="right">49108796</td>
</tr>
<tr class="even">
<td align="left">Female</td>
<td align="right">10</td>
<td align="right">30</td>
<td align="right">22</td>
<td align="right">9</td>
<td align="right">19</td>
<td align="right">10</td>
<td align="right">53569718</td>
</tr>
</tbody>
</table>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb9-1" data-line-number="1"><span class="kw">library</span>(ggplot2)</a>
<a class="sourceLine" id="cb9-2" data-line-number="2"><span class="kw">crosstab</span>(<span class="dt">df =</span> illinois, <span class="dt">x =</span> sex, <span class="dt">y =</span> educ6, <span class="dt">weight =</span> weight, <span class="dt">format =</span> <span class="st">"long"</span>) <span class="op">%>%</span></a>
<a class="sourceLine" id="cb9-3" data-line-number="3"><span class="st"> </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(educ6, pct, <span class="dt">fill =</span> sex)) <span class="op">+</span></a>
<a class="sourceLine" id="cb9-4" data-line-number="4"><span class="st"> </span><span class="kw">geom_bar</span>(<span class="dt">stat =</span> <span class="st">"identity"</span>, <span class="dt">position =</span> <span class="st">"dodge"</span>)</a></code></pre></div>
<p><img 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" /><!-- --></p>
<p>Three-way crosstabs are ideal for plotting time series graphs and/or faceted plots.</p>
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb10-1" data-line-number="1"><span class="kw">crosstab_3way</span>(<span class="dt">df =</span> illinois, <span class="dt">x =</span> sex, <span class="dt">y =</span> educ6, <span class="dt">z =</span> year, <span class="dt">weight =</span> weight, <span class="dt">format =</span> <span class="st">"long"</span>) <span class="op">%>%</span></a>
<a class="sourceLine" id="cb10-2" data-line-number="2"><span class="st"> </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(year, pct, <span class="dt">col =</span> sex)) <span class="op">+</span></a>
<a class="sourceLine" id="cb10-3" data-line-number="3"><span class="st"> </span><span class="kw">geom_line</span>() <span class="op">+</span></a>
<a class="sourceLine" id="cb10-4" data-line-number="4"><span class="st"> </span><span class="kw">facet_wrap</span>(<span class="dt">facets =</span> <span class="kw">vars</span>(educ6))</a></code></pre></div>
<p><img 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" /><!-- --></p>
<h2 id="margin-of-error">Margin of error</h2>
<p>Each <code>pollster</code> function comes with a twin function which includes a margin of error column. For example:</p>
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb11-1" data-line-number="1"><span class="kw">moe_topline</span>(<span class="dt">df =</span> illinois, <span class="dt">variable =</span> voter, <span class="dt">weight =</span> weight)</a>
<a class="sourceLine" id="cb11-2" data-line-number="2"><span class="co">#> # A tibble: 2 x 6</span></a>
<a class="sourceLine" id="cb11-3" data-line-number="3"><span class="co">#> Response Frequency Percent `Valid Percent` MOE `Cumulative Percent`</span></a>
<a class="sourceLine" id="cb11-4" data-line-number="4"><span class="co">#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl></span></a>
<a class="sourceLine" id="cb11-5" data-line-number="5"><span class="co">#> 1 Voted 56230937. 63.7 63.7 0.551 63.7</span></a>
<a class="sourceLine" id="cb11-6" data-line-number="6"><span class="co">#> 2 Not voted 32070164. 36.3 36.3 0.551 100</span></a></code></pre></div>
<p>By default, <code>moe_crosstab</code> output comes in long format, but you can also specify wide format.</p>
<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb12-1" data-line-number="1"><span class="kw">moe_crosstab</span>(<span class="dt">df =</span> illinois, <span class="dt">x =</span> raceethnic, <span class="dt">y =</span> voter, <span class="dt">weight =</span> weight, <span class="dt">format =</span> <span class="st">"wide"</span>)</a>
<a class="sourceLine" id="cb12-2" data-line-number="2"><span class="co">#> # A tibble: 4 x 6</span></a>
<a class="sourceLine" id="cb12-3" data-line-number="3"><span class="co">#> raceethnic n pct_Voted `pct_Not voted` moe_Voted `moe_Not voted`</span></a>
<a class="sourceLine" id="cb12-4" data-line-number="4"><span class="co">#> <fct> <int> <dbl> <dbl> <dbl> <dbl></span></a>
<a class="sourceLine" id="cb12-5" data-line-number="5"><span class="co">#> 1 White 24167 64.4 35.6 0.624 0.624</span></a>
<a class="sourceLine" id="cb12-6" data-line-number="6"><span class="co">#> 2 Black 3980 71.6 28.4 1.45 1.45 </span></a>
<a class="sourceLine" id="cb12-7" data-line-number="7"><span class="co">#> 3 Hispanic 2106 48.3 51.7 2.21 2.21 </span></a>
<a class="sourceLine" id="cb12-8" data-line-number="8"><span class="co">#> 4 Other 1006 48.7 51.3 3.19 3.19</span></a></code></pre></div>
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb13-1" data-line-number="1"><span class="kw">moe_crosstab</span>(<span class="dt">df =</span> illinois, <span class="dt">x =</span> raceethnic, <span class="dt">y =</span> voter, <span class="dt">weight =</span> weight) <span class="op">%>%</span></a>
<a class="sourceLine" id="cb13-2" data-line-number="2"><span class="st"> </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(<span class="dt">x =</span> pct, <span class="dt">y =</span> raceethnic, <span class="dt">xmin =</span> (pct <span class="op">-</span><span class="st"> </span>moe), <span class="dt">xmax =</span> (pct <span class="op">+</span><span class="st"> </span>moe), <span class="dt">color =</span> voter)) <span class="op">+</span></a>
<a class="sourceLine" id="cb13-3" data-line-number="3"><span class="st"> </span><span class="kw">geom_pointrange</span>(<span class="dt">position =</span> <span class="kw">position_dodge</span>(<span class="dt">width =</span> <span class="fl">0.2</span>))</a></code></pre></div>
<p><img 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" /><!-- --></p>
<h2 id="summary-table">Summary table</h2>
<p><code>summary_table()</code> creates a simple summary table of a weighted numeric variable.</p>
<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb14-1" data-line-number="1"><span class="kw">summary_table</span>(<span class="dt">df =</span> illinois, <span class="dt">variable =</span> age, <span class="dt">weight =</span> weight)</a>
<a class="sourceLine" id="cb14-2" data-line-number="2"><span class="co">#> # A tibble: 1 x 8</span></a>
<a class="sourceLine" id="cb14-3" data-line-number="3"><span class="co">#> variable_name unweighted_obse… weighted_observ… weighted_mean min_value</span></a>
<a class="sourceLine" id="cb14-4" data-line-number="4"><span class="co">#> <chr> <int> <dbl> <dbl> <dbl></span></a>
<a class="sourceLine" id="cb14-5" data-line-number="5"><span class="co">#> 1 age 36207 102678514. 46.2 18</span></a>
<a class="sourceLine" id="cb14-6" data-line-number="6"><span class="co">#> # … with 3 more variables: max_value <dbl>, missing_observations <int>,</span></a>
<a class="sourceLine" id="cb14-7" data-line-number="7"><span class="co">#> # missing_weighted_observations <dbl></span></a></code></pre></div>
<p>You can choose <code>name_style = "pretty"</code> if you want column headings appropriate for a formatted table.</p>
<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb15-1" data-line-number="1"><span class="kw">summary_table</span>(<span class="dt">df =</span> illinois, <span class="dt">variable =</span> age, </a>
<a class="sourceLine" id="cb15-2" data-line-number="2"> <span class="dt">weight =</span> weight, <span class="dt">name_style =</span> <span class="st">"pretty"</span>) <span class="op">%>%</span></a>
<a class="sourceLine" id="cb15-3" data-line-number="3"><span class="st"> </span>knitr<span class="op">::</span><span class="kw">kable</span>()</a></code></pre></div>
<table>
<thead>
<tr class="header">
<th align="left">Variable</th>
<th align="right">Unweighted obs</th>
<th align="right">Weighted obs</th>
<th align="right">Weighted mean</th>
<th align="right">Min</th>
<th align="right">Max</th>
<th align="right">Unweighted missing</th>
<th align="right">Weighted missing</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">age</td>
<td align="right">36207</td>
<td align="right">102678514</td>
<td align="right">46.19646</td>
<td align="right">18</td>
<td align="right">90</td>
<td align="right">0</td>
<td align="right">0</td>
</tr>
</tbody>
</table>
</body>
</html>