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<img src="../logo.png" class="logo" alt=""><h1>Dummify discrete features to binary columns</h1>
<small class="dont-index">Source: <a href="https://github.com/boxuancui/DataExplorer/blob/HEAD/R/dummify.r" class="external-link"><code>R/dummify.r</code></a></small>
<div class="d-none name"><code>dummify.Rd</code></div>
</div>
<div class="ref-description section level2">
<p>Data dummification is also known as one hot encoding or feature binarization. It turns each category to a distinct column with binary (numeric) values.</p>
</div>
<div class="section level2">
<h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
<div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">dummify</span><span class="op">(</span><span class="va">data</span>, maxcat <span class="op">=</span> <span class="fl">50L</span>, select <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></span></code></pre></div>
</div>
<div class="section level2">
<h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
<dl><dt>data</dt>
<dd><p>input data</p></dd>
<dt>maxcat</dt>
<dd><p>maximum categories allowed for each discrete feature. Default is 50.</p></dd>
<dt>select</dt>
<dd><p>names of selected features to be dummified. Default is <code>NULL</code>.</p></dd>
</dl></div>
<div class="section level2">
<h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
<p>dummified dataset (discrete features only) preserving original features. However, column order might be different.</p>
</div>
<div class="section level2">
<h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
<p>Continuous features will be ignored if added in <code>select</code>.</p>
<p><code>select</code> features will be ignored if categories exceed <code>maxcat</code>.</p>
</div>
<div class="section level2">
<h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
<p>This is different from <a href="https://rdrr.io/r/stats/model.matrix.html" class="external-link">model.matrix</a>, where the latter aims to create a full rank matrix for regression-like use cases. If your intention is to create a design matrix, use <a href="https://rdrr.io/r/stats/model.matrix.html" class="external-link">model.matrix</a> instead.</p>
</div>
<div class="section level2">
<h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
<div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co">## Dummify iris dataset</span></span></span>
<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/str.html" class="external-link">str</a></span><span class="op">(</span><span class="fu">dummify</span><span class="op">(</span><span class="va">iris</span><span class="op">)</span><span class="op">)</span></span></span>
<span class="r-out co"><span class="r-pr">#></span> 'data.frame': 150 obs. of 7 variables:</span>
<span class="r-out co"><span class="r-pr">#></span> $ Sepal.Length : num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...</span>
<span class="r-out co"><span class="r-pr">#></span> $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...</span>
<span class="r-out co"><span class="r-pr">#></span> $ Petal.Length : num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...</span>
<span class="r-out co"><span class="r-pr">#></span> $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...</span>
<span class="r-out co"><span class="r-pr">#></span> $ Species_setosa : int 1 1 1 1 1 1 1 1 1 1 ...</span>
<span class="r-out co"><span class="r-pr">#></span> $ Species_versicolor: int 0 0 0 0 0 0 0 0 0 0 ...</span>
<span class="r-out co"><span class="r-pr">#></span> $ Species_virginica : int 0 0 0 0 0 0 0 0 0 0 ...</span>
<span class="r-out co"><span class="r-pr">#></span> - attr(*, ".internal.selfref")=<externalptr> </span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co">## Dummify diamonds dataset ignoring features with more than 5 categories</span></span></span>
<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/data.html" class="external-link">data</a></span><span class="op">(</span><span class="st">"diamonds"</span>, package <span class="op">=</span> <span class="st">"ggplot2"</span><span class="op">)</span></span></span>
<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/str.html" class="external-link">str</a></span><span class="op">(</span><span class="fu">dummify</span><span class="op">(</span><span class="va">diamonds</span>, maxcat <span class="op">=</span> <span class="fl">5</span><span class="op">)</span><span class="op">)</span></span></span>
<span class="r-msg co"><span class="r-pr">#></span> 2 features with more than 5 categories ignored!</span>
<span class="r-msg co"><span class="r-pr">#></span> color: 7 categories</span>
<span class="r-msg co"><span class="r-pr">#></span> clarity: 8 categories</span>
<span class="r-out co"><span class="r-pr">#></span> tibble [53,940 × 14] (S3: tbl_df/tbl/data.frame)</span>
<span class="r-out co"><span class="r-pr">#></span> $ carat : num [1:53940] 0.23 0.21 0.23 0.29 0.31 0.24 0.24 0.26 0.22 0.23 ...</span>
<span class="r-out co"><span class="r-pr">#></span> $ depth : num [1:53940] 61.5 59.8 56.9 62.4 63.3 62.8 62.3 61.9 65.1 59.4 ...</span>
<span class="r-out co"><span class="r-pr">#></span> $ table : num [1:53940] 55 61 65 58 58 57 57 55 61 61 ...</span>
<span class="r-out co"><span class="r-pr">#></span> $ price : int [1:53940] 326 326 327 334 335 336 336 337 337 338 ...</span>
<span class="r-out co"><span class="r-pr">#></span> $ x : num [1:53940] 3.95 3.89 4.05 4.2 4.34 3.94 3.95 4.07 3.87 4 ...</span>
<span class="r-out co"><span class="r-pr">#></span> $ y : num [1:53940] 3.98 3.84 4.07 4.23 4.35 3.96 3.98 4.11 3.78 4.05 ...</span>
<span class="r-out co"><span class="r-pr">#></span> $ z : num [1:53940] 2.43 2.31 2.31 2.63 2.75 2.48 2.47 2.53 2.49 2.39 ...</span>
<span class="r-out co"><span class="r-pr">#></span> $ color : Ord.factor w/ 7 levels "D"<"E"<"F"<"G"<..: 2 2 2 6 7 7 6 5 2 5 ...</span>
<span class="r-out co"><span class="r-pr">#></span> $ clarity : Ord.factor w/ 8 levels "I1"<"SI2"<"SI1"<..: 2 3 5 4 2 6 7 3 4 5 ...</span>
<span class="r-out co"><span class="r-pr">#></span> $ cut_Fair : int [1:53940] 0 0 0 0 0 0 0 0 1 0 ...</span>
<span class="r-out co"><span class="r-pr">#></span> $ cut_Good : int [1:53940] 0 0 1 0 1 0 0 0 0 0 ...</span>
<span class="r-out co"><span class="r-pr">#></span> $ cut_Ideal : int [1:53940] 1 0 0 0 0 0 0 0 0 0 ...</span>
<span class="r-out co"><span class="r-pr">#></span> $ cut_Premium : int [1:53940] 0 1 0 1 0 0 0 0 0 0 ...</span>
<span class="r-out co"><span class="r-pr">#></span> $ cut_Very.Good: int [1:53940] 0 0 0 0 0 1 1 1 0 1 ...</span>
<span class="r-out co"><span class="r-pr">#></span> - attr(*, ".internal.selfref")=<externalptr> </span>
<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/str.html" class="external-link">str</a></span><span class="op">(</span><span class="fu">dummify</span><span class="op">(</span><span class="va">diamonds</span>, select <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"cut"</span>, <span class="st">"color"</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></span>
<span class="r-out co"><span class="r-pr">#></span> tibble [53,940 × 20] (S3: tbl_df/tbl/data.frame)</span>
<span class="r-out co"><span class="r-pr">#></span> $ carat : num [1:53940] 0.23 0.21 0.23 0.29 0.31 0.24 0.24 0.26 0.22 0.23 ...</span>
<span class="r-out co"><span class="r-pr">#></span> $ depth : num [1:53940] 61.5 59.8 56.9 62.4 63.3 62.8 62.3 61.9 65.1 59.4 ...</span>
<span class="r-out co"><span class="r-pr">#></span> $ table : num [1:53940] 55 61 65 58 58 57 57 55 61 61 ...</span>
<span class="r-out co"><span class="r-pr">#></span> $ price : int [1:53940] 326 326 327 334 335 336 336 337 337 338 ...</span>
<span class="r-out co"><span class="r-pr">#></span> $ x : num [1:53940] 3.95 3.89 4.05 4.2 4.34 3.94 3.95 4.07 3.87 4 ...</span>
<span class="r-out co"><span class="r-pr">#></span> $ y : num [1:53940] 3.98 3.84 4.07 4.23 4.35 3.96 3.98 4.11 3.78 4.05 ...</span>
<span class="r-out co"><span class="r-pr">#></span> $ z : num [1:53940] 2.43 2.31 2.31 2.63 2.75 2.48 2.47 2.53 2.49 2.39 ...</span>
<span class="r-out co"><span class="r-pr">#></span> $ clarity : Ord.factor w/ 8 levels "I1"<"SI2"<"SI1"<..: 2 3 5 4 2 6 7 3 4 5 ...</span>
<span class="r-out co"><span class="r-pr">#></span> $ cut_Fair : int [1:53940] 0 0 0 0 0 0 0 0 1 0 ...</span>
<span class="r-out co"><span class="r-pr">#></span> $ cut_Good : int [1:53940] 0 0 1 0 1 0 0 0 0 0 ...</span>
<span class="r-out co"><span class="r-pr">#></span> $ cut_Ideal : int [1:53940] 1 0 0 0 0 0 0 0 0 0 ...</span>
<span class="r-out co"><span class="r-pr">#></span> $ cut_Premium : int [1:53940] 0 1 0 1 0 0 0 0 0 0 ...</span>
<span class="r-out co"><span class="r-pr">#></span> $ cut_Very.Good: int [1:53940] 0 0 0 0 0 1 1 1 0 1 ...</span>
<span class="r-out co"><span class="r-pr">#></span> $ color_D : int [1:53940] 0 0 0 0 0 0 0 0 0 0 ...</span>
<span class="r-out co"><span class="r-pr">#></span> $ color_E : int [1:53940] 1 1 1 0 0 0 0 0 1 0 ...</span>
<span class="r-out co"><span class="r-pr">#></span> $ color_F : int [1:53940] 0 0 0 0 0 0 0 0 0 0 ...</span>
<span class="r-out co"><span class="r-pr">#></span> $ color_G : int [1:53940] 0 0 0 0 0 0 0 0 0 0 ...</span>
<span class="r-out co"><span class="r-pr">#></span> $ color_H : int [1:53940] 0 0 0 0 0 0 0 1 0 1 ...</span>
<span class="r-out co"><span class="r-pr">#></span> $ color_I : int [1:53940] 0 0 0 1 0 0 1 0 0 0 ...</span>
<span class="r-out co"><span class="r-pr">#></span> $ color_J : int [1:53940] 0 0 0 0 1 1 0 0 0 0 ...</span>
<span class="r-out co"><span class="r-pr">#></span> - attr(*, ".internal.selfref")=<externalptr> </span>
</code></pre></div>
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