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Merge pull request #63 from UBC-DSCI/patch-my-section
removed my-section tags
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‎03-viz.Rmd

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- Use `ggsave` to save visualizations in `.png` and `.svg` format
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## Choosing the visualization
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#### *Ask a question, and answer it* {-#my-section}
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#### *Ask a question, and answer it*
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The purpose of a visualization is to answer a question about a data set of interest. So naturally, the first thing to do **before** creating
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a visualization is to formulate the question about the data that you are trying to answer.
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## Refining the visualization
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#### *Convey the message, minimize noise* {-#my-section}
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#### *Convey the message, minimize noise*
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Just being able to make a visualization in R with `ggplot2` (or any other tool for that matter) doesn't mean that it is effective at communicating
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your message to others. Once you have selected a broad type of visualization to use, you will have to refine it to suit your particular need.
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## Creating visualizations with `ggplot2`
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#### *Build the visualization iteratively* {-#my-section}
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#### *Build the visualization iteratively*
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This section will cover examples of how to choose and refine a visualization given a data set and a question that you want to answer,
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and then how to create the visualization in R using `ggplot2`. To use the `ggplot2` library, we need to load the `tidyverse` metapackage.
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## Explaining the visualization
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#### *Tell a story* {-#my-section}
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#### *Tell a story*
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Typically, your visualization will not be shown completely on its own, but rather it will be part of a larger presentation.
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Further, visualizations can provide supporting information for any part of a presentation, from opening to conclusion.
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## Saving the visualization
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#### *Choose the right output format for your needs* {-#my-section}
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#### *Choose the right output format for your needs*
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Just as there are many ways to store data sets, there are many ways to store visualizations and images.
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Which one you choose can depend on a number of factors, such as file size/type limitations

‎docs/GitHub.html

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<meta name="author" content="Melissa Lee" />
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<meta name="date" content="2020-11-22" />
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<meta name="date" content="2020-11-24" />
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‎docs/classification-continued.html

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<meta name="author" content="Melissa Lee" />
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<meta name="date" content="2020-11-22" />
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<div class="sourceCode" id="cb213"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb213-1"><a href="classification-continued.html#cb213-1"></a><span class="kw">glimpse</span>(cancer_train)</span></code></pre></div>
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<pre><code>## Rows: 427
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## Columns: 12
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## $ ID &lt;dbl&gt; 842302, 842517, 84300903, 84348301, 84358402, 84378…
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## $ Class &lt;fct&gt; M, M, M, M, M, M, M, M, M, M, M, M, M, M, M, B, B, …
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## $ Radius &lt;dbl&gt; 17.990, 20.570, 19.690, 11.420, 20.290, 12.450, 18.…
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## $ Texture &lt;dbl&gt; 10.38, 17.77, 21.25, 20.38, 14.34, 15.70, 19.98, 20…
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## $ Perimeter &lt;dbl&gt; 122.80, 132.90, 130.00, 77.58, 135.10, 82.57, 119.6…
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## $ Area &lt;dbl&gt; 1001.0, 1326.0, 1203.0, 386.1, 1297.0, 477.1, 1040.…
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## $ Smoothness &lt;dbl&gt; 0.11840, 0.08474, 0.10960, 0.14250, 0.10030, 0.1278…
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## $ Compactness &lt;dbl&gt; 0.27760, 0.07864, 0.15990, 0.28390, 0.13280, 0.1700…
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## $ Concavity &lt;dbl&gt; 0.30010, 0.08690, 0.19740, 0.24140, 0.19800, 0.1578…
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## $ Concave_Points &lt;dbl&gt; 0.14710, 0.07017, 0.12790, 0.10520, 0.10430, 0.0808…
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## $ Symmetry &lt;dbl&gt; 0.2419, 0.1812, 0.2069, 0.2597, 0.1809, 0.2087, 0.1…
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## $ Fractal_Dimension &lt;dbl&gt; 0.07871, 0.05667, 0.05999, 0.09744, 0.05883, 0.0761…</code></pre>
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## $ ID &lt;dbl&gt; 842302, 842517, 84300903, 84348301, 84358402, 843786, 844359, 84458202, 84501001, 845…
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## $ Class &lt;fct&gt; M, M, M, M, M, M, M, M, M, M, M, M, M, M, M, B, B, M, M, M, M, M, M, M, M, M, M, M, M…
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## $ Radius &lt;dbl&gt; 17.990, 20.570, 19.690, 11.420, 20.290, 12.450, 18.250, 13.710, 12.460, 16.020, 15.78…
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## $ Texture &lt;dbl&gt; 10.38, 17.77, 21.25, 20.38, 14.34, 15.70, 19.98, 20.83, 24.04, 23.24, 17.89, 24.80, 2…
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## $ Perimeter &lt;dbl&gt; 122.80, 132.90, 130.00, 77.58, 135.10, 82.57, 119.60, 90.20, 83.97, 102.70, 103.60, 1…
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## $ Area &lt;dbl&gt; 1001.0, 1326.0, 1203.0, 386.1, 1297.0, 477.1, 1040.0, 577.9, 475.9, 797.8, 781.0, 112…
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## $ Smoothness &lt;dbl&gt; 0.11840, 0.08474, 0.10960, 0.14250, 0.10030, 0.12780, 0.09463, 0.11890, 0.11860, 0.08…
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## $ Compactness &lt;dbl&gt; 0.27760, 0.07864, 0.15990, 0.28390, 0.13280, 0.17000, 0.10900, 0.16450, 0.23960, 0.06…
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## $ Concavity &lt;dbl&gt; 0.30010, 0.08690, 0.19740, 0.24140, 0.19800, 0.15780, 0.11270, 0.09366, 0.22730, 0.03…
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## $ Concave_Points &lt;dbl&gt; 0.147100, 0.070170, 0.127900, 0.105200, 0.104300, 0.080890, 0.074000, 0.059850, 0.085…
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## $ Symmetry &lt;dbl&gt; 0.2419, 0.1812, 0.2069, 0.2597, 0.1809, 0.2087, 0.1794, 0.2196, 0.2030, 0.1528, 0.184…
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## $ Fractal_Dimension &lt;dbl&gt; 0.07871, 0.05667, 0.05999, 0.09744, 0.05883, 0.07613, 0.05742, 0.07451, 0.08243, 0.05…</code></pre>
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<div class="sourceCode" id="cb215"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb215-1"><a href="classification-continued.html#cb215-1"></a><span class="kw">glimpse</span>(cancer_test)</span></code></pre></div>
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<pre><code>## Rows: 142
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## Columns: 12
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## $ ID &lt;dbl&gt; 844981, 84799002, 848406, 849014, 8510426, 8511133,…
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## $ Class &lt;fct&gt; M, M, M, M, B, M, M, M, M, M, M, B, B, M, M, M, B, …
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## $ Radius &lt;dbl&gt; 13.000, 14.540, 14.680, 19.810, 13.540, 15.340, 18.…
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## $ Texture &lt;dbl&gt; 21.82, 27.54, 20.13, 22.15, 14.36, 14.26, 25.11, 26…
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## $ Perimeter &lt;dbl&gt; 87.50, 96.73, 94.74, 130.00, 87.46, 102.50, 124.80,…
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## $ Area &lt;dbl&gt; 519.8, 658.8, 684.5, 1260.0, 566.3, 704.4, 1088.0, …
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## $ Smoothness &lt;dbl&gt; 0.12730, 0.11390, 0.09867, 0.09831, 0.09779, 0.1073…
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## $ Compactness &lt;dbl&gt; 0.19320, 0.15950, 0.07200, 0.10270, 0.08129, 0.2135…
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## $ Concavity &lt;dbl&gt; 0.18590, 0.16390, 0.07395, 0.14790, 0.06664, 0.2077…
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## $ Concave_Points &lt;dbl&gt; 0.093530, 0.073640, 0.052590, 0.094980, 0.047810, 0…
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## $ Symmetry &lt;dbl&gt; 0.2350, 0.2303, 0.1586, 0.1582, 0.1885, 0.2521, 0.2…
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## $ Fractal_Dimension &lt;dbl&gt; 0.07389, 0.07077, 0.05922, 0.05395, 0.05766, 0.0703…</code></pre>
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## $ ID &lt;dbl&gt; 844981, 84799002, 848406, 849014, 8510426, 8511133, 853401, 854002, 855167, 856106, 8…
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## $ Class &lt;fct&gt; M, M, M, M, B, M, M, M, M, M, M, B, B, M, M, M, B, M, M, B, B, B, M, M, B, B, M, B, B…
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## $ Radius &lt;dbl&gt; 13.000, 14.540, 14.680, 19.810, 13.540, 15.340, 18.630, 19.270, 13.440, 13.280, 18.22…
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## $ Texture &lt;dbl&gt; 21.82, 27.54, 20.13, 22.15, 14.36, 14.26, 25.11, 26.47, 21.58, 20.28, 18.70, 11.79, 1…
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## $ Perimeter &lt;dbl&gt; 87.50, 96.73, 94.74, 130.00, 87.46, 102.50, 124.80, 127.90, 86.18, 87.32, 120.30, 54.…
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## $ Area &lt;dbl&gt; 519.8, 658.8, 684.5, 1260.0, 566.3, 704.4, 1088.0, 1162.0, 563.0, 545.2, 1033.0, 224.…
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## $ Smoothness &lt;dbl&gt; 0.12730, 0.11390, 0.09867, 0.09831, 0.09779, 0.10730, 0.10640, 0.09401, 0.08162, 0.10…
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## $ Compactness &lt;dbl&gt; 0.19320, 0.15950, 0.07200, 0.10270, 0.08129, 0.21350, 0.18870, 0.17190, 0.06031, 0.14…
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## $ Concavity &lt;dbl&gt; 0.18590, 0.16390, 0.07395, 0.14790, 0.06664, 0.20770, 0.23190, 0.16570, 0.03110, 0.09…
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## $ Concave_Points &lt;dbl&gt; 0.093530, 0.073640, 0.052590, 0.094980, 0.047810, 0.097560, 0.124400, 0.075930, 0.020…
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## $ Symmetry &lt;dbl&gt; 0.2350, 0.2303, 0.1586, 0.1582, 0.1885, 0.2521, 0.2183, 0.1853, 0.1784, 0.1974, 0.209…
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## $ Fractal_Dimension &lt;dbl&gt; 0.07389, 0.07077, 0.05922, 0.05395, 0.05766, 0.07032, 0.06197, 0.06261, 0.05587, 0.06…</code></pre>
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<p>We can see from <code>glimpse</code> in the code above that the training set contains 427
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observations, while the test set contains 142 observations. This corresponds to
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a train / test split of 75% / 25%, as desired.</p>
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<span id="cb218-9"><a href="classification-continued.html#cb218-9"></a><span class="st"> </span><span class="kw">fit</span>(<span class="dt">data =</span> cancer_train)</span>
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<span id="cb218-10"><a href="classification-continued.html#cb218-10"></a></span>
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<span id="cb218-11"><a href="classification-continued.html#cb218-11"></a>knn_fit</span></code></pre></div>
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<pre><code>## ══ Workflow [trained] ══════════════════════════════════════════════════════════
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<pre><code>## ══ Workflow [trained] ════════════════════════════════════════════════════════════════════════════════════════════
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## Preprocessor: Recipe
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## Model: nearest_neighbor()
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##
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## ── Preprocessor ────────────────────────────────────────────────────────────────
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## ── Preprocessor ──────────────────────────────────────────────────────────────────────────────────────────────────
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## 2 Recipe Steps
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## ── Model ─────────────────────────────────────────────────────────────────────────────────────────────────────────
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##
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## kknn::train.kknn(formula = formula, data = data, ks = ~3, kernel = ~&quot;rectangular&quot;)
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<span id="cb220-2"><a href="classification-continued.html#cb220-2"></a><span class="st"> </span><span class="kw">bind_cols</span>(cancer_test)</span>
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<span id="cb220-3"><a href="classification-continued.html#cb220-3"></a><span class="kw">head</span>(cancer_test_predictions)</span></code></pre></div>
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<pre><code>## # A tibble: 6 x 13
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## .pred_class ID Class Radius Texture Perimeter Area Smoothness Compactness
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## &lt;fct&gt; &lt;dbl&gt; &lt;fct&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;
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## 1 M 8.45e5 M 13 21.8 87.5 520. 0.127 0.193
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## 2 M 8.48e7 M 14.5 27.5 96.7 659. 0.114 0.160
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## 3 B 8.48e5 M 14.7 20.1 94.7 684. 0.0987 0.072
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## 4 M 8.49e5 M 19.8 22.2 130 1260 0.0983 0.103
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## 5 B 8.51e6 B 13.5 14.4 87.5 566. 0.0978 0.0813
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## 6 M 8.51e6 M 15.3 14.3 102. 704. 0.107 0.214
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## # … with 4 more variables: Concavity &lt;dbl&gt;, Concave_Points &lt;dbl&gt;,
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## # Symmetry &lt;dbl&gt;, Fractal_Dimension &lt;dbl&gt;</code></pre>
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## .pred_class ID Class Radius Texture Perimeter Area Smoothness Compactness Concavity Concave_Points Symmetry
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## &lt;fct&gt; &lt;dbl&gt; &lt;fct&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;
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## 1 M 8.45e5 M 13 21.8 87.5 520. 0.127 0.193 0.186 0.0935 0.235
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## 2 M 8.48e7 M 14.5 27.5 96.7 659. 0.114 0.160 0.164 0.0736 0.230
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## 3 B 8.48e5 M 14.7 20.1 94.7 684. 0.0987 0.072 0.0740 0.0526 0.159
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## 4 M 8.49e5 M 19.8 22.2 130 1260 0.0983 0.103 0.148 0.0950 0.158
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## 5 B 8.51e6 B 13.5 14.4 87.5 566. 0.0978 0.0813 0.0666 0.0478 0.188
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## 6 M 8.51e6 M 15.3 14.3 102. 704. 0.107 0.214 0.208 0.0976 0.252
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## # … with 1 more variable: Fractal_Dimension &lt;dbl&gt;</code></pre>
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<p><strong>5. Compute the accuracy</strong></p>
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<p>Finally we can assess our classifier’s accuracy. To do this we use the <code>metrics</code> function
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from <code>tidymodels</code> to get the statistics about the quality of our model, specifying

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