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<title>Selecting Variables</title>



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<h1 class="title toc-ignore">Selecting Variables</h1>



<p>When recipe steps are used, there are different approaches that can be used to select which variables or features should be used.</p>
<p>The three main characteristics of variables that can be queried:</p>
<ul>
<li>the name of the variable</li>
<li>the data type (e.g. numeric or nominal)</li>
<li>the role that was declared by the recipe</li>
</ul>
<p>The manual pages for <code>?selections</code> and <code>?has_role</code> have details about the available selection methods.</p>
<p>To illustrate this, the credit data will be used:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(recipes)
<span class="kw">data</span>(<span class="st">&quot;credit_data&quot;</span>)
<span class="kw">str</span>(credit_data)
<span class="co">#&gt; 'data.frame':    4454 obs. of  14 variables:</span>
<span class="co">#&gt;  $ Status   : Factor w/ 2 levels &quot;bad&quot;,&quot;good&quot;: 2 2 1 2 2 2 2 2 2 1 ...</span>
<span class="co">#&gt;  $ Seniority: int  9 17 10 0 0 1 29 9 0 0 ...</span>
<span class="co">#&gt;  $ Home     : Factor w/ 6 levels &quot;ignore&quot;,&quot;other&quot;,..: 6 6 3 6 6 3 3 4 3 4 ...</span>
<span class="co">#&gt;  $ Time     : int  60 60 36 60 36 60 60 12 60 48 ...</span>
<span class="co">#&gt;  $ Age      : int  30 58 46 24 26 36 44 27 32 41 ...</span>
<span class="co">#&gt;  $ Marital  : Factor w/ 5 levels &quot;divorced&quot;,&quot;married&quot;,..: 2 5 2 4 4 2 2 4 2 2 ...</span>
<span class="co">#&gt;  $ Records  : Factor w/ 2 levels &quot;no&quot;,&quot;yes&quot;: 1 1 2 1 1 1 1 1 1 1 ...</span>
<span class="co">#&gt;  $ Job      : Factor w/ 4 levels &quot;fixed&quot;,&quot;freelance&quot;,..: 2 1 2 1 1 1 1 1 2 4 ...</span>
<span class="co">#&gt;  $ Expenses : int  73 48 90 63 46 75 75 35 90 90 ...</span>
<span class="co">#&gt;  $ Income   : int  129 131 200 182 107 214 125 80 107 80 ...</span>
<span class="co">#&gt;  $ Assets   : int  0 0 3000 2500 0 3500 10000 0 15000 0 ...</span>
<span class="co">#&gt;  $ Debt     : int  0 0 0 0 0 0 0 0 0 0 ...</span>
<span class="co">#&gt;  $ Amount   : int  800 1000 2000 900 310 650 1600 200 1200 1200 ...</span>
<span class="co">#&gt;  $ Price    : int  846 1658 2985 1325 910 1645 1800 1093 1957 1468 ...</span>

rec &lt;-<span class="st"> </span><span class="kw">recipe</span>(Status <span class="op">~</span><span class="st"> </span>Seniority <span class="op">+</span><span class="st"> </span>Time <span class="op">+</span><span class="st"> </span>Age <span class="op">+</span><span class="st"> </span>Records, <span class="dt">data =</span> credit_data)
rec
<span class="co">#&gt; Data Recipe</span>
<span class="co">#&gt; </span>
<span class="co">#&gt; Inputs:</span>
<span class="co">#&gt; </span>
<span class="co">#&gt;       role #variables</span>
<span class="co">#&gt;    outcome          1</span>
<span class="co">#&gt;  predictor          4</span></code></pre></div>
<p>Before any steps are used the information on the original variables is:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(rec, <span class="dt">original =</span> <span class="ot">TRUE</span>)
<span class="co">#&gt; # A tibble: 5 x 4</span>
<span class="co">#&gt;    variable    type      role   source</span>
<span class="co">#&gt;       &lt;chr&gt;   &lt;chr&gt;     &lt;chr&gt;    &lt;chr&gt;</span>
<span class="co">#&gt; 1 Seniority numeric predictor original</span>
<span class="co">#&gt; 2      Time numeric predictor original</span>
<span class="co">#&gt; 3       Age numeric predictor original</span>
<span class="co">#&gt; 4   Records nominal predictor original</span>
<span class="co">#&gt; 5    Status nominal   outcome original</span></code></pre></div>
<p>We can add a step to compute dummy variables on the non-numeric data after we impute any missing data:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">dummied &lt;-<span class="st"> </span>rec <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">step_dummy</span>(<span class="kw">all_nominal</span>())</code></pre></div>
<p>This will capture <em>any</em> variables that are either character strings or factors: <code>Status</code> and <code>Records</code>. However, since <code>Status</code> is our outcome, we might want to keep it as a factor so we can <em>subtract</em> that variable out either by name or by role:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">dummied &lt;-<span class="st"> </span>rec <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">step_dummy</span>(Records) <span class="co"># or</span>
dummied &lt;-<span class="st"> </span>rec <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">step_dummy</span>(<span class="kw">all_nominal</span>(), <span class="op">-</span><span class="st"> </span>Status) <span class="co"># or</span>
dummied &lt;-<span class="st"> </span>rec <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">step_dummy</span>(<span class="kw">all_nominal</span>(), <span class="op">-</span><span class="st"> </span><span class="kw">all_outcomes</span>()) </code></pre></div>
<p>Using the last definition:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">dummied &lt;-<span class="st"> </span><span class="kw">prep</span>(dummied, <span class="dt">training =</span> credit_data)
<span class="co">#&gt; step 1 dummy training</span>
with_dummy &lt;-<span class="st"> </span><span class="kw">bake</span>(dummied, <span class="dt">newdata =</span> credit_data)
with_dummy
<span class="co">#&gt; # A tibble: 4,454 x 4</span>
<span class="co">#&gt;    Seniority  Time   Age Records_yes</span>
<span class="co">#&gt;        &lt;int&gt; &lt;int&gt; &lt;int&gt;       &lt;dbl&gt;</span>
<span class="co">#&gt;  1         9    60    30           0</span>
<span class="co">#&gt;  2        17    60    58           0</span>
<span class="co">#&gt;  3        10    36    46           1</span>
<span class="co">#&gt;  4         0    60    24           0</span>
<span class="co">#&gt;  5         0    36    26           0</span>
<span class="co">#&gt;  6         1    60    36           0</span>
<span class="co">#&gt;  7        29    60    44           0</span>
<span class="co">#&gt;  8         9    12    27           0</span>
<span class="co">#&gt;  9         0    60    32           0</span>
<span class="co">#&gt; 10         0    48    41           0</span>
<span class="co">#&gt; # ... with 4,444 more rows</span></code></pre></div>
<p><code>Status</code> is unaffected.</p>
<p>One important aspect about selecting variables in steps is that the variable names and types may change as steps are being executed. In the above example, <code>Records</code> is a factor variable before the step is executed. Afterwards, <code>Records</code> is gone and the binary variable <code>Records_yes</code> is in its place. One reason to have general selection routines like <code>all_predictors</code> or <code>contains</code> is to be able to select variables that have not be created yet.</p>

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r-cran-recipes 0.1.0-1 / usr / lib / R / site-library / recipes / doc / Selecting_Variables.html
