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added vignette of freq
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<h1 class="title toc-ignore">Creating Frequency Tables</h1>
<h4 class="author"><em>Matthijs S. Berends</em></h4>
<div id="TOC">
<ul>
<li><a href="#introduction">Introduction</a></li>
<li><a href="#frequencies-of-one-variable">Frequencies of one variable</a></li>
<li><a href="#frequencies-of-more-than-one-variable">Frequencies of more than one variable</a></li>
<li><a href="#frequencies-of-numeric-values">Frequencies of numeric values</a></li>
<li><a href="#frequencies-of-factors">Frequencies of factors</a></li>
<li><a href="#frequencies-of-dates">Frequencies of dates</a></li>
<li><a href="#additional-parameters">Additional parameters</a><ul>
<li><a href="#parameter-na.rm">Parameter <code>na.rm</code></a></li>
<li><a href="#parameter-markdown">Parameter <code>markdown</code></a></li>
<li><a href="#parameter-as.data.frame">Parameter <code>as.data.frame</code></a></li>
</ul></li>
</ul>
</div>
<div id="introduction" class="section level2">
<h2>Introduction</h2>
<p>Frequency tables (or frequency distributions) are summaries of the distribution of values in a sample. With the <code>freq</code> function, you can create univariate frequency tables. Multiple variables will be pasted into one variable, so it forces a univariate distribution. We take the <code>septic_patients</code> dataset (included in this AMR package) as example.</p>
</div>
<div id="frequencies-of-one-variable" class="section level2">
<h2>Frequencies of one variable</h2>
<p>To only show and quickly review the content of one variable, you can just select this variable in various ways. Let’s say we want to get the frequencies of the <code>sex</code> variable of the <code>septic_patients</code> dataset:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># # just using base R</span>
<span class="kw">freq</span>(septic_patients$sex)
<span class="co"># # using base R to select the variable and pass it on with a pipe</span>
septic_patients$sex %&gt;%<span class="st"> </span><span class="kw">freq</span>()
<span class="co"># # do it all with pipes, using the `select` function of the dplyr package</span>
septic_patients %&gt;%
<span class="st"> </span><span class="kw">select</span>(sex) %&gt;%
<span class="st"> </span><span class="kw">freq</span>()</code></pre></div>
<p>This will all lead to the following table:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">freq</span>(septic_patients$sex)
<span class="co"># Class: character</span>
<span class="co"># Length: 2000 (of which NA: 0 = 0.0%)</span>
<span class="co"># Unique: 2</span>
<span class="co"># </span>
<span class="co"># Item Count Percent Cum. Count Cum. Percent</span>
<span class="co"># ----- ------ -------- ----------- -------------</span>
<span class="co"># M 1112 55.6% 1112 55.6%</span>
<span class="co"># F 888 44.4% 2000 100.0%</span></code></pre></div>
<p>This immediately shows the class of the variable, its length and availability (i.e. the amount of <code>NA</code>), the amount of unique values and (most importantly) that among septic patients men are more prevalent than women.</p>
</div>
<div id="frequencies-of-more-than-one-variable" class="section level2">
<h2>Frequencies of more than one variable</h2>
<p>Multiple variables will be pasted into one variable to review individual cases, keeping a univariate frequency table.</p>
<p>For illustration, we could add some more variables to the <code>septic_patients</code> dataset to learn about bacterial properties:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">my_patients &lt;-<span class="st"> </span>septic_patients %&gt;%<span class="st"> </span>
<span class="st"> </span><span class="kw">left_join_microorganisms</span>()</code></pre></div>
<p>Now all variables of the <code>microorganisms</code> dataset have been joined to the <code>septic_patients</code> dataset. The <code>microorganisms</code> dataset consists of the following variables:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">colnames</span>(microorganisms)
<span class="co"># [1] &quot;bactid&quot; &quot;bactsys&quot; &quot;family&quot; &quot;genus&quot; </span>
<span class="co"># [5] &quot;species&quot; &quot;subspecies&quot; &quot;fullname&quot; &quot;type&quot; </span>
<span class="co"># [9] &quot;gramstain&quot; &quot;aerobic&quot; &quot;type_nl&quot; &quot;gramstain_nl&quot;</span></code></pre></div>
<p>If we compare the dimensions between the old and new dataset, we can see that these 11 variables were added:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">dim</span>(septic_patients)
<span class="co"># [1] 2000 47</span>
<span class="kw">dim</span>(my_patients)
<span class="co"># [1] 2000 58</span></code></pre></div>
<p>So now the <code>genus</code> and <code>species</code> variables are available. A frequency table of these combined variables can be created like this:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">my_patients %&gt;%
<span class="st"> </span><span class="kw">select</span>(genus, species) %&gt;%
<span class="st"> </span><span class="kw">freq</span>()
<span class="co"># Columns: 2</span>
<span class="co"># Length: 2000 (of which NA: 0 = 0.0%)</span>
<span class="co"># Unique: 137</span>
<span class="co"># </span>
<span class="co"># Item Count Percent Cum. Count Cum. Percent</span>
<span class="co"># ---------------------------------- ------ -------- ----------- -------------</span>
<span class="co"># Escherichia coli 485 24.2% 485 24.2%</span>
<span class="co"># Staphylococcus coagulase negatief 297 14.8% 782 39.1%</span>
<span class="co"># Staphylococcus aureus 200 10.0% 982 49.1%</span>
<span class="co"># Staphylococcus epidermidis 150 7.5% 1132 56.6%</span>
<span class="co"># Streptococcus pneumoniae 97 4.9% 1229 61.5%</span>
<span class="co"># Staphylococcus hominis 67 3.4% 1296 64.8%</span>
<span class="co"># Klebsiella pneumoniae 65 3.2% 1361 68.0%</span>
<span class="co"># Enterococcus faecalis 44 2.2% 1405 70.2%</span>
<span class="co"># Proteus mirabilis 33 1.7% 1438 71.9%</span>
<span class="co"># Pseudomonas aeruginosa 31 1.6% 1469 73.5%</span>
<span class="co"># Streptococcus pyogenes 30 1.5% 1499 75.0%</span>
<span class="co"># Enterococcus faecium 27 1.4% 1526 76.3%</span>
<span class="co"># Bacteroides fragilis 26 1.3% 1552 77.6%</span>
<span class="co"># Enterobacter cloacae 25 1.2% 1577 78.8%</span>
<span class="co"># Klebsiella oxytoca 23 1.1% 1600 80.0%</span>
<span class="co"># ... and 122 more (n = 400; 20.0%). Use `nmax` to show more or less rows.</span></code></pre></div>
</div>
<div id="frequencies-of-numeric-values" class="section level2">
<h2>Frequencies of numeric values</h2>
<p>Frequency tables can be created of any input.</p>
<p>In case of numeric values (like integers, doubles, etc.) additional descriptive statistics will be calculated and shown into the header:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># # get age distribution of unique patients</span>
septic_patients %&gt;%<span class="st"> </span>
<span class="st"> </span><span class="kw">distinct</span>(patient_id, <span class="dt">.keep_all =</span> <span class="ot">TRUE</span>) %&gt;%<span class="st"> </span>
<span class="st"> </span><span class="kw">select</span>(age) %&gt;%<span class="st"> </span>
<span class="st"> </span><span class="kw">freq</span>(<span class="dt">nmax =</span> <span class="dv">5</span>)
<span class="co"># Class: integer</span>
<span class="co"># Length: 1920 (of which NA: 0 = 0.0%)</span>
<span class="co"># Unique: 94</span>
<span class="co"># </span>
<span class="co"># Mean: 68</span>
<span class="co"># Std. dev.: 18 (CV: 0.27)</span>
<span class="co"># Five-Num: 0 | 61 | 72 | 80 | 101 (CQV: 0.13)</span>
<span class="co"># Outliers: 94 (unique: 26)</span>
<span class="co"># </span>
<span class="co"># Item Count Percent Cum. Count Cum. Percent</span>
<span class="co"># ----- ------ -------- ----------- -------------</span>
<span class="co"># 0 34 1.8% 34 1.8%</span>
<span class="co"># 1 5 0.3% 39 2.0%</span>
<span class="co"># 2 5 0.3% 44 2.3%</span>
<span class="co"># 3 2 0.1% 46 2.4%</span>
<span class="co"># 4 1 0.1% 47 2.4%</span>
<span class="co"># ... and 89 more (n = 1873; 97.6%).</span></code></pre></div>
<p>So the following properties are determined, where <code>NA</code> values are always ignored:</p>
<ul>
<li><p><strong>Mean</strong></p></li>
<li><p><strong>Standard deviation</strong></p></li>
<li><p><strong>Coefficient of variation</strong> (CV), the standard deviation divided by the mean</p></li>
<li><p><strong>Five numbers of Tukey</strong> (min, Q1, median, Q3, max)</p></li>
<li><p><strong>Coefficient of quartile variation</strong> (CQV, sometimes called coefficient of dispersion), calculated as (Q3 - Q1) / (Q3 + Q1) using quantile with <code>type = 6</code> as quantile algorithm to comply with SPSS standards</p></li>
<li><p><strong>Outliers</strong> (total count and unique count)</p></li>
</ul>
<p>So for example, the above frequency table quickly shows the median age of patients being 72.</p>
</div>
<div id="frequencies-of-factors" class="section level2">
<h2>Frequencies of factors</h2>
<p>Frequencies of factors will be sorted on factor level instead of item count by default. This can be changed with the <code>sort.count</code> parameter. Frequency tables of factors always show the factor level as an additional last column.</p>
<p><code>sort.count</code> is <code>TRUE</code> by default, except for factors. Compare this default behaviour:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">septic_patients %&gt;%
<span class="st"> </span><span class="kw">select</span>(hospital_id) %&gt;%<span class="st"> </span>
<span class="st"> </span><span class="kw">freq</span>()
<span class="co"># Class: factor</span>
<span class="co"># Length: 2000 (of which NA: 0 = 0.0%)</span>
<span class="co"># Unique: 5</span>
<span class="co"># </span>
<span class="co"># Item Count Percent Cum. Count Cum. Percent (Factor Level)</span>
<span class="co"># ----- ------ -------- ----------- ------------- ---------------</span>
<span class="co"># A 233 11.7% 233 11.7% 1</span>
<span class="co"># B 583 29.1% 816 40.8% 2</span>
<span class="co"># C 221 11.1% 1037 51.8% 3</span>
<span class="co"># D 650 32.5% 1687 84.4% 4</span>
<span class="co"># E 313 15.7% 2000 100.0% 5</span></code></pre></div>
<p>To this, where items are now sorted on item count:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">septic_patients %&gt;%
<span class="st"> </span><span class="kw">select</span>(hospital_id) %&gt;%<span class="st"> </span>
<span class="st"> </span><span class="kw">freq</span>(<span class="dt">sort.count =</span> <span class="ot">TRUE</span>)
<span class="co"># Class: factor</span>
<span class="co"># Length: 2000 (of which NA: 0 = 0.0%)</span>
<span class="co"># Unique: 5</span>
<span class="co"># </span>
<span class="co"># Item Count Percent Cum. Count Cum. Percent (Factor Level)</span>
<span class="co"># ----- ------ -------- ----------- ------------- ---------------</span>
<span class="co"># D 650 32.5% 650 32.5% 4</span>
<span class="co"># B 583 29.1% 1233 61.7% 2</span>
<span class="co"># E 313 15.7% 1546 77.3% 5</span>
<span class="co"># A 233 11.7% 1779 88.9% 1</span>
<span class="co"># C 221 11.1% 2000 100.0% 3</span></code></pre></div>
<p>All classes will be printed into the header. Variables with the new <code>rsi</code> class of this AMR package are actually ordered factors and have three classes (look at <code>Class</code> in the header):</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">septic_patients %&gt;%
<span class="st"> </span><span class="kw">select</span>(amox) %&gt;%<span class="st"> </span>
<span class="st"> </span><span class="kw">freq</span>()
<span class="co"># Class: factor &gt; ordered &gt; rsi</span>
<span class="co"># Length: 2000 (of which NA: 678 = 33.9%)</span>
<span class="co"># Unique: 3</span>
<span class="co"># </span>
<span class="co"># Item Count Percent Cum. Count Cum. Percent (Factor Level)</span>
<span class="co"># ----- ------ -------- ----------- ------------- ---------------</span>
<span class="co"># S 561 42.4% 561 42.4% 1</span>
<span class="co"># I 49 3.7% 610 46.1% 2</span>
<span class="co"># R 712 53.9% 1322 100.0% 3</span></code></pre></div>
</div>
<div id="frequencies-of-dates" class="section level2">
<h2>Frequencies of dates</h2>
<p>Frequencies of dates will show the oldest and newest date in the data, and the amount of days between them:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">septic_patients %&gt;%
<span class="st"> </span><span class="kw">select</span>(date) %&gt;%<span class="st"> </span>
<span class="st"> </span><span class="kw">freq</span>(<span class="dt">nmax =</span> <span class="dv">5</span>)
<span class="co"># Class: Date</span>
<span class="co"># Length: 2000 (of which NA: 0 = 0.0%)</span>
<span class="co"># Unique: 1662</span>
<span class="co"># </span>
<span class="co"># Oldest: 2 januari 2001</span>
<span class="co"># Newest: 18 oktober 2017 (+6133)</span>
<span class="co"># </span>
<span class="co"># Item Count Percent Cum. Count Cum. Percent</span>
<span class="co"># ----------- ------ -------- ----------- -------------</span>
<span class="co"># 2008-12-24 5 0.2% 5 0.2%</span>
<span class="co"># 2010-12-10 4 0.2% 9 0.4%</span>
<span class="co"># 2011-03-03 4 0.2% 13 0.6%</span>
<span class="co"># 2013-06-24 4 0.2% 17 0.8%</span>
<span class="co"># 2017-09-01 4 0.2% 21 1.1%</span>
<span class="co"># ... and 1657 more (n = 1979; 99.0%).</span></code></pre></div>
</div>
<div id="additional-parameters" class="section level2">
<h2>Additional parameters</h2>
<div id="parameter-na.rm" class="section level3">
<h3>Parameter <code>na.rm</code></h3>
<p>With the <code>na.rm</code> parameter (defaults to <code>TRUE</code>, but they will always be shown into the header), you can include <code>NA</code> values in the frequency table:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">septic_patients %&gt;%
<span class="st"> </span><span class="kw">select</span>(amox) %&gt;%<span class="st"> </span>
<span class="st"> </span><span class="kw">freq</span>(<span class="dt">na.rm =</span> <span class="ot">FALSE</span>)
<span class="co"># Class: factor &gt; ordered &gt; rsi</span>
<span class="co"># Length: 2678 (of which NA: 678 = 25.3%)</span>
<span class="co"># Unique: 4</span>
<span class="co"># </span>
<span class="co"># Item Count Percent Cum. Count Cum. Percent (Factor Level)</span>
<span class="co"># ----- ------ -------- ----------- ------------- ---------------</span>
<span class="co"># S 561 28.1% 561 28.1% 1</span>
<span class="co"># I 49 2.5% 610 30.5% 2</span>
<span class="co"># R 712 35.6% 1322 66.1% 3</span>
<span class="co"># &lt;NA&gt; 678 33.9% 2000 100.0% &lt;NA&gt;</span></code></pre></div>
</div>
<div id="parameter-markdown" class="section level3">
<h3>Parameter <code>markdown</code></h3>
<p>The <code>markdown</code> parameter can be used in reports created with R Markdown. This will always print all rows:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">septic_patients %&gt;%
<span class="st"> </span><span class="kw">select</span>(hospital_id) %&gt;%<span class="st"> </span>
<span class="st"> </span><span class="kw">freq</span>(<span class="dt">markdown =</span> <span class="ot">TRUE</span>)
<span class="co"># </span>
<span class="co"># Class: factor</span>
<span class="co"># </span>
<span class="co"># Length: 2000 (of which NA: 0 = 0.0%)</span>
<span class="co"># </span>
<span class="co"># Unique: 5</span>
<span class="co"># </span>
<span class="co"># |Item | Count| Percent| Cum. Count| Cum. Percent| (Factor Level)|</span>
<span class="co"># |:----|-----:|-------:|----------:|------------:|--------------:|</span>
<span class="co"># |A | 233| 11.7%| 233| 11.7%| 1|</span>
<span class="co"># |B | 583| 29.1%| 816| 40.8%| 2|</span>
<span class="co"># |C | 221| 11.1%| 1037| 51.8%| 3|</span>
<span class="co"># |D | 650| 32.5%| 1687| 84.4%| 4|</span>
<span class="co"># |E | 313| 15.7%| 2000| 100.0%| 5|</span></code></pre></div>
</div>
<div id="parameter-as.data.frame" class="section level3">
<h3>Parameter <code>as.data.frame</code></h3>
<p>With the <code>as.data.frame</code> parameter you can assign the frequency table to an object, or just print it as a <code>data.frame</code> to the console:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">my_df &lt;-<span class="st"> </span>septic_patients %&gt;%
<span class="st"> </span><span class="kw">select</span>(hospital_id) %&gt;%<span class="st"> </span>
<span class="st"> </span><span class="kw">freq</span>(<span class="dt">as.data.frame =</span> <span class="ot">TRUE</span>)
my_df
<span class="co"># item count percent cum_count cum_percent factor_level</span>
<span class="co"># 1 A 233 0.1165 233 0.1165 1</span>
<span class="co"># 2 B 583 0.2915 816 0.4080 2</span>
<span class="co"># 3 C 221 0.1105 1037 0.5185 3</span>
<span class="co"># 4 D 650 0.3250 1687 0.8435 4</span>
<span class="co"># 5 E 313 0.1565 2000 1.0000 5</span>
<span class="kw">class</span>(my_df)
<span class="co"># [1] &quot;data.frame&quot;</span></code></pre></div>
<hr />
<p>AMR, (c) 2018, <a href="https://github.com/msberends/AMR" class="uri">https://github.com/msberends/AMR</a></p>
<p>Licensed under the <a href="https://github.com/msberends/AMR/blob/master/LICENSE">GNU General Public License v2.0</a>.</p>
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