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<!DOCTYPE html>
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<title>C Tips and Tricks | Statistical Inference via Data Science</title>
<meta name="description" content="An open-source and fully-reproducible electronic textbook for teaching statistical inference using tidyverse data science tools." />
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<meta name="twitter:description" content="An open-source and fully-reproducible electronic textbook for teaching statistical inference using tidyverse data science tools." />
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<li class="chapter" data-level="1.4.3" data-path="1-getting-started.html"><a href="1-getting-started.html#exploredataframes"><i class="fa fa-check"></i><b>1.4.3</b> Exploring data frames</a></li>
<li class="chapter" data-level="1.4.4" data-path="1-getting-started.html"><a href="1-getting-started.html#identification-vs-measurement-variables"><i class="fa fa-check"></i><b>1.4.4</b> Identification and measurement variables</a></li>
<li class="chapter" data-level="1.4.5" data-path="1-getting-started.html"><a href="1-getting-started.html#help-files"><i class="fa fa-check"></i><b>1.4.5</b> Help files</a></li>
</ul></li>
<li class="chapter" data-level="1.5" data-path="1-getting-started.html"><a href="1-getting-started.html#conclusion"><i class="fa fa-check"></i><b>1.5</b> Conclusion</a>
<ul>
<li class="chapter" data-level="1.5.1" data-path="1-getting-started.html"><a href="1-getting-started.html#additional-resources"><i class="fa fa-check"></i><b>1.5.1</b> Additional resources</a></li>
<li class="chapter" data-level="1.5.2" data-path="1-getting-started.html"><a href="1-getting-started.html#whats-to-come"><i class="fa fa-check"></i><b>1.5.2</b> What’s to come?</a></li>
</ul></li>
</ul></li>
<li class="part"><span><b>I Data Science with tidyverse</b></span></li>
<li class="chapter" data-level="2" data-path="2-viz.html"><a href="2-viz.html"><i class="fa fa-check"></i><b>2</b> Data Visualization</a>
<ul>
<li class="chapter" data-level="" data-path="2-viz.html"><a href="2-viz.html#needed-packages"><i class="fa fa-check"></i>Needed packages</a></li>
<li class="chapter" data-level="2.1" data-path="2-viz.html"><a href="2-viz.html#grammarofgraphics"><i class="fa fa-check"></i><b>2.1</b> The grammar of graphics</a>
<ul>
<li class="chapter" data-level="2.1.1" data-path="2-viz.html"><a href="2-viz.html#components-of-the-grammar"><i class="fa fa-check"></i><b>2.1.1</b> Components of the grammar</a></li>
<li class="chapter" data-level="2.1.2" data-path="2-viz.html"><a href="2-viz.html#gapminder"><i class="fa fa-check"></i><b>2.1.2</b> Gapminder data</a></li>
<li class="chapter" data-level="2.1.3" data-path="2-viz.html"><a href="2-viz.html#other-components"><i class="fa fa-check"></i><b>2.1.3</b> Other components</a></li>
<li class="chapter" data-level="2.1.4" data-path="2-viz.html"><a href="2-viz.html#ggplot2-package"><i class="fa fa-check"></i><b>2.1.4</b> ggplot2 package</a></li>
</ul></li>
<li class="chapter" data-level="2.2" data-path="2-viz.html"><a href="2-viz.html#FiveNG"><i class="fa fa-check"></i><b>2.2</b> Five named graphs - the 5NG</a></li>
<li class="chapter" data-level="2.3" data-path="2-viz.html"><a href="2-viz.html#scatterplots"><i class="fa fa-check"></i><b>2.3</b> 5NG#1: Scatterplots</a>
<ul>
<li class="chapter" data-level="2.3.1" data-path="2-viz.html"><a href="2-viz.html#geompoint"><i class="fa fa-check"></i><b>2.3.1</b> Scatterplots via <code>geom_point</code></a></li>
<li class="chapter" data-level="2.3.2" data-path="2-viz.html"><a href="2-viz.html#overplotting"><i class="fa fa-check"></i><b>2.3.2</b> Overplotting</a></li>
<li class="chapter" data-level="2.3.3" data-path="2-viz.html"><a href="2-viz.html#summary"><i class="fa fa-check"></i><b>2.3.3</b> Summary</a></li>
</ul></li>
<li class="chapter" data-level="2.4" data-path="2-viz.html"><a href="2-viz.html#linegraphs"><i class="fa fa-check"></i><b>2.4</b> 5NG#2: Linegraphs</a>
<ul>
<li class="chapter" data-level="2.4.1" data-path="2-viz.html"><a href="2-viz.html#geomline"><i class="fa fa-check"></i><b>2.4.1</b> Linegraphs via <code>geom_line</code></a></li>
<li class="chapter" data-level="2.4.2" data-path="2-viz.html"><a href="2-viz.html#summary-1"><i class="fa fa-check"></i><b>2.4.2</b> Summary</a></li>
</ul></li>
<li class="chapter" data-level="2.5" data-path="2-viz.html"><a href="2-viz.html#facets"><i class="fa fa-check"></i><b>2.5</b> Facets</a></li>
<li class="chapter" data-level="2.6" data-path="2-viz.html"><a href="2-viz.html#histograms"><i class="fa fa-check"></i><b>2.6</b> 5NG#3: Histograms</a>
<ul>
<li class="chapter" data-level="2.6.1" data-path="2-viz.html"><a href="2-viz.html#geomhistogram"><i class="fa fa-check"></i><b>2.6.1</b> Histograms via <code>geom_histogram</code></a></li>
<li class="chapter" data-level="2.6.2" data-path="2-viz.html"><a href="2-viz.html#adjustbins"><i class="fa fa-check"></i><b>2.6.2</b> Adjusting the bins</a></li>
<li class="chapter" data-level="2.6.3" data-path="2-viz.html"><a href="2-viz.html#summary-2"><i class="fa fa-check"></i><b>2.6.3</b> Summary</a></li>
</ul></li>
<li class="chapter" data-level="2.7" data-path="2-viz.html"><a href="2-viz.html#boxplots"><i class="fa fa-check"></i><b>2.7</b> 5NG#4: Boxplots</a>
<ul>
<li class="chapter" data-level="2.7.1" data-path="2-viz.html"><a href="2-viz.html#geomboxplot"><i class="fa fa-check"></i><b>2.7.1</b> Boxplots via <code>geom_boxplot</code></a></li>
<li class="chapter" data-level="2.7.2" data-path="2-viz.html"><a href="2-viz.html#summary-3"><i class="fa fa-check"></i><b>2.7.2</b> Summary</a></li>
</ul></li>
<li class="chapter" data-level="2.8" data-path="2-viz.html"><a href="2-viz.html#geombar"><i class="fa fa-check"></i><b>2.8</b> 5NG#5: Barplots</a>
<ul>
<li class="chapter" data-level="2.8.1" data-path="2-viz.html"><a href="2-viz.html#barplots-via-geom_bar-or-geom_col"><i class="fa fa-check"></i><b>2.8.1</b> Barplots via <code>geom_bar</code> or <code>geom_col</code></a></li>
<li class="chapter" data-level="2.8.2" data-path="2-viz.html"><a href="2-viz.html#must-avoid-pie-charts"><i class="fa fa-check"></i><b>2.8.2</b> Must avoid pie charts!</a></li>
<li class="chapter" data-level="2.8.3" data-path="2-viz.html"><a href="2-viz.html#two-categ-barplot"><i class="fa fa-check"></i><b>2.8.3</b> Two categorical variables</a></li>
<li class="chapter" data-level="2.8.4" data-path="2-viz.html"><a href="2-viz.html#summary-4"><i class="fa fa-check"></i><b>2.8.4</b> Summary</a></li>
</ul></li>
<li class="chapter" data-level="2.9" data-path="2-viz.html"><a href="2-viz.html#data-vis-conclusion"><i class="fa fa-check"></i><b>2.9</b> Conclusion</a>
<ul>
<li class="chapter" data-level="2.9.1" data-path="2-viz.html"><a href="2-viz.html#summary-table"><i class="fa fa-check"></i><b>2.9.1</b> Summary table</a></li>
<li class="chapter" data-level="2.9.2" data-path="2-viz.html"><a href="2-viz.html#function-argument-specification"><i class="fa fa-check"></i><b>2.9.2</b> Function argument specification</a></li>
<li class="chapter" data-level="2.9.3" data-path="2-viz.html"><a href="2-viz.html#additional-resources-1"><i class="fa fa-check"></i><b>2.9.3</b> Additional resources</a></li>
<li class="chapter" data-level="2.9.4" data-path="2-viz.html"><a href="2-viz.html#whats-to-come-3"><i class="fa fa-check"></i><b>2.9.4</b> What’s to come</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="3" data-path="3-wrangling.html"><a href="3-wrangling.html"><i class="fa fa-check"></i><b>3</b> Data Wrangling</a>
<ul>
<li class="chapter" data-level="" data-path="3-wrangling.html"><a href="3-wrangling.html#wrangling-packages"><i class="fa fa-check"></i>Needed packages</a></li>
<li class="chapter" data-level="3.1" data-path="3-wrangling.html"><a href="3-wrangling.html#piping"><i class="fa fa-check"></i><b>3.1</b> The pipe operator: <code>%>%</code></a></li>
<li class="chapter" data-level="3.2" data-path="3-wrangling.html"><a href="3-wrangling.html#filter"><i class="fa fa-check"></i><b>3.2</b> <code>filter</code> rows</a></li>
<li class="chapter" data-level="3.3" data-path="3-wrangling.html"><a href="3-wrangling.html#slice-rows"><i class="fa fa-check"></i><b>3.3</b> <code>slice</code> rows</a></li>
<li class="chapter" data-level="3.4" data-path="3-wrangling.html"><a href="3-wrangling.html#select"><i class="fa fa-check"></i><b>3.4</b> <code>select</code> variables</a>
<ul>
<li class="chapter" data-level="3.4.1" data-path="3-wrangling.html"><a href="3-wrangling.html#rename"><i class="fa fa-check"></i><b>3.4.1</b> <code>rename</code> variables</a></li>
</ul></li>
<li class="chapter" data-level="3.5" data-path="3-wrangling.html"><a href="3-wrangling.html#summarize"><i class="fa fa-check"></i><b>3.5</b> <code>summarize</code> variables</a></li>
<li class="chapter" data-level="3.6" data-path="3-wrangling.html"><a href="3-wrangling.html#groupby"><i class="fa fa-check"></i><b>3.6</b> <code>group_by</code> rows</a>
<ul>
<li class="chapter" data-level="3.6.1" data-path="3-wrangling.html"><a href="3-wrangling.html#grouping-by-more-than-one-variable"><i class="fa fa-check"></i><b>3.6.1</b> Grouping by more than one variable</a></li>
</ul></li>
<li class="chapter" data-level="3.7" data-path="3-wrangling.html"><a href="3-wrangling.html#mutate"><i class="fa fa-check"></i><b>3.7</b> <code>mutate</code> existing variables</a></li>
<li class="chapter" data-level="3.8" data-path="3-wrangling.html"><a href="3-wrangling.html#arrange"><i class="fa fa-check"></i><b>3.8</b> <code>arrange</code> and sort rows</a></li>
<li class="chapter" data-level="3.9" data-path="3-wrangling.html"><a href="3-wrangling.html#joins"><i class="fa fa-check"></i><b>3.9</b> <code>join</code> data frames</a></li>
<li class="chapter" data-level="3.10" data-path="3-wrangling.html"><a href="3-wrangling.html#wrangling-conclusion"><i class="fa fa-check"></i><b>3.10</b> Conclusion</a>
<ul>
<li class="chapter" data-level="3.10.1" data-path="3-wrangling.html"><a href="3-wrangling.html#summary-table-1"><i class="fa fa-check"></i><b>3.10.1</b> Summary table</a></li>
<li class="chapter" data-level="3.10.2" data-path="3-wrangling.html"><a href="3-wrangling.html#additional-resources-2"><i class="fa fa-check"></i><b>3.10.2</b> Additional resources</a></li>
<li class="chapter" data-level="3.10.3" data-path="3-wrangling.html"><a href="3-wrangling.html#whats-to-come-1"><i class="fa fa-check"></i><b>3.10.3</b> What’s to come?</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="4" data-path="4-tidy.html"><a href="4-tidy.html"><i class="fa fa-check"></i><b>4</b> Data Importing and “Tidy” Data</a>
<ul>
<li class="chapter" data-level="" data-path="4-tidy.html"><a href="4-tidy.html#tidy-packages"><i class="fa fa-check"></i>Needed packages</a></li>
<li class="chapter" data-level="4.1" data-path="4-tidy.html"><a href="4-tidy.html#csv"><i class="fa fa-check"></i><b>4.1</b> Importing data</a>
<ul>
<li class="chapter" data-level="4.1.1" data-path="4-tidy.html"><a href="4-tidy.html#using-the-console"><i class="fa fa-check"></i><b>4.1.1</b> Using the console</a></li>
<li class="chapter" data-level="4.1.2" data-path="4-tidy.html"><a href="4-tidy.html#using-rstudios-interface"><i class="fa fa-check"></i><b>4.1.2</b> Using RStudio’s interface</a></li>
</ul></li>
<li class="chapter" data-level="4.2" data-path="4-tidy.html"><a href="4-tidy.html#tidy-data-ex"><i class="fa fa-check"></i><b>4.2</b> “Tidy” data</a>
<ul>
<li class="chapter" data-level="4.2.1" data-path="4-tidy.html"><a href="4-tidy.html#tidy-definition"><i class="fa fa-check"></i><b>4.2.1</b> Definition of “tidy” data</a></li>
<li class="chapter" data-level="4.2.2" data-path="4-tidy.html"><a href="4-tidy.html#converting-to-tidy-data"><i class="fa fa-check"></i><b>4.2.2</b> Converting to “tidy” data</a></li>
</ul></li>
<li class="chapter" data-level="4.3" data-path="4-tidy.html"><a href="4-tidy.html#case-study-tidy"><i class="fa fa-check"></i><b>4.3</b> Case study: Weight loss data</a></li>
<li class="chapter" data-level="4.4" data-path="4-tidy.html"><a href="4-tidy.html#tidyverse-package"><i class="fa fa-check"></i><b>4.4</b> <code>tidyverse</code> package</a></li>
<li class="chapter" data-level="4.5" data-path="4-tidy.html"><a href="4-tidy.html#tidy-data-conclusion"><i class="fa fa-check"></i><b>4.5</b> Conclusion</a>
<ul>
<li class="chapter" data-level="4.5.1" data-path="4-tidy.html"><a href="4-tidy.html#additional-resources-3"><i class="fa fa-check"></i><b>4.5.1</b> Additional resources</a></li>
<li class="chapter" data-level="4.5.2" data-path="4-tidy.html"><a href="4-tidy.html#whats-to-come-2"><i class="fa fa-check"></i><b>4.5.2</b> What’s to come?</a></li>
</ul></li>
</ul></li>
<li class="part"><span><b>II Data Modeling with moderndive</b></span></li>
<li class="chapter" data-level="5" data-path="5-regression.html"><a href="5-regression.html"><i class="fa fa-check"></i><b>5</b> Basic Regression</a>
<ul>
<li class="chapter" data-level="" data-path="5-regression.html"><a href="5-regression.html#reg-packages"><i class="fa fa-check"></i>Needed packages</a></li>
<li class="chapter" data-level="5.1" data-path="5-regression.html"><a href="5-regression.html#model1"><i class="fa fa-check"></i><b>5.1</b> One numerical explanatory variable</a>
<ul>
<li class="chapter" data-level="5.1.1" data-path="5-regression.html"><a href="5-regression.html#model1EDA"><i class="fa fa-check"></i><b>5.1.1</b> Exploratory data analysis</a></li>
<li class="chapter" data-level="5.1.2" data-path="5-regression.html"><a href="5-regression.html#model1table"><i class="fa fa-check"></i><b>5.1.2</b> Simple linear regression</a></li>
<li class="chapter" data-level="5.1.3" data-path="5-regression.html"><a href="5-regression.html#model1points"><i class="fa fa-check"></i><b>5.1.3</b> Observed/fitted values and residuals</a></li>
</ul></li>
<li class="chapter" data-level="5.2" data-path="5-regression.html"><a href="5-regression.html#model2"><i class="fa fa-check"></i><b>5.2</b> One categorical explanatory variable</a>
<ul>
<li class="chapter" data-level="5.2.1" data-path="5-regression.html"><a href="5-regression.html#model2EDA"><i class="fa fa-check"></i><b>5.2.1</b> Exploratory data analysis</a></li>
<li class="chapter" data-level="5.2.2" data-path="5-regression.html"><a href="5-regression.html#model2table"><i class="fa fa-check"></i><b>5.2.2</b> Linear regression</a></li>
<li class="chapter" data-level="5.2.3" data-path="5-regression.html"><a href="5-regression.html#model2points"><i class="fa fa-check"></i><b>5.2.3</b> Observed/fitted values and residuals</a></li>
</ul></li>
<li class="chapter" data-level="5.3" data-path="5-regression.html"><a href="5-regression.html#reg-related-topics"><i class="fa fa-check"></i><b>5.3</b> Related topics</a>
<ul>
<li class="chapter" data-level="5.3.1" data-path="5-regression.html"><a href="5-regression.html#correlation-is-not-causation"><i class="fa fa-check"></i><b>5.3.1</b> Correlation is not necessarily causation</a></li>
<li class="chapter" data-level="5.3.2" data-path="5-regression.html"><a href="5-regression.html#leastsquares"><i class="fa fa-check"></i><b>5.3.2</b> Best-fitting line</a></li>
<li class="chapter" data-level="5.3.3" data-path="5-regression.html"><a href="5-regression.html#underthehood"><i class="fa fa-check"></i><b>5.3.3</b> <code>get_regression_x()</code> functions</a></li>
</ul></li>
<li class="chapter" data-level="5.4" data-path="5-regression.html"><a href="5-regression.html#reg-conclusion"><i class="fa fa-check"></i><b>5.4</b> Conclusion</a>
<ul>
<li class="chapter" data-level="5.4.1" data-path="5-regression.html"><a href="5-regression.html#additional-resources-basic-regression"><i class="fa fa-check"></i><b>5.4.1</b> Additional resources</a></li>
<li class="chapter" data-level="5.4.2" data-path="5-regression.html"><a href="5-regression.html#whats-to-come-4"><i class="fa fa-check"></i><b>5.4.2</b> What’s to come?</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="6" data-path="6-multiple-regression.html"><a href="6-multiple-regression.html"><i class="fa fa-check"></i><b>6</b> Multiple Regression</a>
<ul>
<li class="chapter" data-level="" data-path="6-multiple-regression.html"><a href="6-multiple-regression.html#mult-reg-packages"><i class="fa fa-check"></i>Needed packages</a></li>
<li class="chapter" data-level="6.1" data-path="6-multiple-regression.html"><a href="6-multiple-regression.html#model4"><i class="fa fa-check"></i><b>6.1</b> One numerical and one categorical explanatory variable</a>
<ul>
<li class="chapter" data-level="6.1.1" data-path="6-multiple-regression.html"><a href="6-multiple-regression.html#model4EDA"><i class="fa fa-check"></i><b>6.1.1</b> Exploratory data analysis</a></li>
<li class="chapter" data-level="6.1.2" data-path="6-multiple-regression.html"><a href="6-multiple-regression.html#model4interactiontable"><i class="fa fa-check"></i><b>6.1.2</b> Interaction model</a></li>
<li class="chapter" data-level="6.1.3" data-path="6-multiple-regression.html"><a href="6-multiple-regression.html#model4table"><i class="fa fa-check"></i><b>6.1.3</b> Parallel slopes model</a></li>
<li class="chapter" data-level="6.1.4" data-path="6-multiple-regression.html"><a href="6-multiple-regression.html#model4points"><i class="fa fa-check"></i><b>6.1.4</b> Observed/fitted values and residuals</a></li>
</ul></li>
<li class="chapter" data-level="6.2" data-path="6-multiple-regression.html"><a href="6-multiple-regression.html#model3"><i class="fa fa-check"></i><b>6.2</b> Two categorical explanatory variables</a>
<ul>
<li class="chapter" data-level="6.2.1" data-path="6-multiple-regression.html"><a href="6-multiple-regression.html#model3EDA"><i class="fa fa-check"></i><b>6.2.1</b> Exploratory data analysis</a></li>
<li class="chapter" data-level="6.2.2" data-path="6-multiple-regression.html"><a href="6-multiple-regression.html#model3table"><i class="fa fa-check"></i><b>6.2.2</b> Regression lines</a></li>
<li class="chapter" data-level="6.2.3" data-path="6-multiple-regression.html"><a href="6-multiple-regression.html#model3points"><i class="fa fa-check"></i><b>6.2.3</b> Observed/fitted values and residuals</a></li>
</ul></li>
<li class="chapter" data-level="6.3" data-path="6-multiple-regression.html"><a href="6-multiple-regression.html#mult-reg-related-topics"><i class="fa fa-check"></i><b>6.3</b> Related topics</a>
<ul>
<li class="chapter" data-level="6.3.1" data-path="6-multiple-regression.html"><a href="6-multiple-regression.html#model-selection"><i class="fa fa-check"></i><b>6.3.1</b> Model selection using visualizations</a></li>
<li class="chapter" data-level="6.3.2" data-path="6-multiple-regression.html"><a href="6-multiple-regression.html#rsquared"><i class="fa fa-check"></i><b>6.3.2</b> Model selection using R-squared</a></li>
</ul></li>
<li class="chapter" data-level="6.4" data-path="6-multiple-regression.html"><a href="6-multiple-regression.html#mult-reg-conclusion"><i class="fa fa-check"></i><b>6.4</b> Conclusion</a>
<ul>
<li class="chapter" data-level="6.4.1" data-path="6-multiple-regression.html"><a href="6-multiple-regression.html#additional-resources-4"><i class="fa fa-check"></i><b>6.4.1</b> Additional resources</a></li>
<li class="chapter" data-level="6.4.2" data-path="6-multiple-regression.html"><a href="6-multiple-regression.html#whats-to-come-5"><i class="fa fa-check"></i><b>6.4.2</b> What’s to come?</a></li>
</ul></li>
</ul></li>
<li class="part"><span><b>III Statistical Inference with infer</b></span></li>
<li class="chapter" data-level="7" data-path="7-sampling.html"><a href="7-sampling.html"><i class="fa fa-check"></i><b>7</b> Sampling</a>
<ul>
<li class="chapter" data-level="" data-path="7-sampling.html"><a href="7-sampling.html#sampling-packages"><i class="fa fa-check"></i>Needed packages</a></li>
<li class="chapter" data-level="7.1" data-path="7-sampling.html"><a href="7-sampling.html#sampling-activity"><i class="fa fa-check"></i><b>7.1</b> Sampling bowl activity</a>
<ul>
<li class="chapter" data-level="7.1.1" data-path="7-sampling.html"><a href="7-sampling.html#what-proportion-of-this-bowls-balls-are-red"><i class="fa fa-check"></i><b>7.1.1</b> What proportion of this bowl’s balls are red?</a></li>
<li class="chapter" data-level="7.1.2" data-path="7-sampling.html"><a href="7-sampling.html#using-the-shovel-once"><i class="fa fa-check"></i><b>7.1.2</b> Using the shovel once</a></li>
<li class="chapter" data-level="7.1.3" data-path="7-sampling.html"><a href="7-sampling.html#student-shovels"><i class="fa fa-check"></i><b>7.1.3</b> Using the shovel 33 times</a></li>
<li class="chapter" data-level="7.1.4" data-path="7-sampling.html"><a href="7-sampling.html#sampling-what-did-we-just-do"><i class="fa fa-check"></i><b>7.1.4</b> What did we just do?</a></li>
</ul></li>
<li class="chapter" data-level="7.2" data-path="7-sampling.html"><a href="7-sampling.html#sampling-simulation"><i class="fa fa-check"></i><b>7.2</b> Virtual sampling</a>
<ul>
<li class="chapter" data-level="7.2.1" data-path="7-sampling.html"><a href="7-sampling.html#using-the-virtual-shovel-once"><i class="fa fa-check"></i><b>7.2.1</b> Using the virtual shovel once</a></li>
</ul></li>
<li class="chapter" data-level="7.3" data-path="7-sampling.html"><a href="7-sampling.html#sampling-framework"><i class="fa fa-check"></i><b>7.3</b> Sampling framework</a>
<ul>
<li class="chapter" data-level="7.3.1" data-path="7-sampling.html"><a href="7-sampling.html#terminology-and-notation"><i class="fa fa-check"></i><b>7.3.1</b> Terminology and notation</a></li>
<li class="chapter" data-level="7.3.2" data-path="7-sampling.html"><a href="7-sampling.html#sampling-definitions"><i class="fa fa-check"></i><b>7.3.2</b> Statistical definitions</a></li>
<li class="chapter" data-level="7.3.3" data-path="7-sampling.html"><a href="7-sampling.html#moral-of-the-story"><i class="fa fa-check"></i><b>7.3.3</b> The moral of the story</a></li>
</ul></li>
<li class="chapter" data-level="7.4" data-path="7-sampling.html"><a href="7-sampling.html#sampling-case-study"><i class="fa fa-check"></i><b>7.4</b> Case study: Polls</a></li>
<li class="chapter" data-level="7.5" data-path="7-sampling.html"><a href="7-sampling.html#sampling-conclusion-central-limit-theorem"><i class="fa fa-check"></i><b>7.5</b> Central Limit Theorem</a></li>
<li class="chapter" data-level="7.6" data-path="7-sampling.html"><a href="7-sampling.html#sampling-conclusion"><i class="fa fa-check"></i><b>7.6</b> Conclusion</a>
<ul>
<li class="chapter" data-level="7.6.1" data-path="7-sampling.html"><a href="7-sampling.html#sampling-conclusion-table"><i class="fa fa-check"></i><b>7.6.1</b> Sampling scenarios</a></li>
<li class="chapter" data-level="7.6.2" data-path="7-sampling.html"><a href="7-sampling.html#additional-resources-5"><i class="fa fa-check"></i><b>7.6.2</b> Additional resources</a></li>
<li class="chapter" data-level="7.6.3" data-path="7-sampling.html"><a href="7-sampling.html#whats-to-come-6"><i class="fa fa-check"></i><b>7.6.3</b> What’s to come?</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="8" data-path="8-confidence-intervals.html"><a href="8-confidence-intervals.html"><i class="fa fa-check"></i><b>8</b> Bootstrapping and Confidence Intervals</a>
<ul>
<li class="chapter" data-level="" data-path="8-confidence-intervals.html"><a href="8-confidence-intervals.html#CI-packages"><i class="fa fa-check"></i>Needed packages</a></li>
<li class="chapter" data-level="8.1" data-path="8-confidence-intervals.html"><a href="8-confidence-intervals.html#resampling-tactile"><i class="fa fa-check"></i><b>8.1</b> Pennies activity</a>
<ul>
<li class="chapter" data-level="8.1.1" data-path="8-confidence-intervals.html"><a href="8-confidence-intervals.html#what-is-the-average-year-on-us-pennies-in-2019"><i class="fa fa-check"></i><b>8.1.1</b> What is the average year on US pennies in 2019?</a></li>
<li class="chapter" data-level="8.1.2" data-path="8-confidence-intervals.html"><a href="8-confidence-intervals.html#resampling-once"><i class="fa fa-check"></i><b>8.1.2</b> Resampling once</a></li>
<li class="chapter" data-level="8.1.3" data-path="8-confidence-intervals.html"><a href="8-confidence-intervals.html#student-resamples"><i class="fa fa-check"></i><b>8.1.3</b> Resampling 35 times</a></li>
<li class="chapter" data-level="8.1.4" data-path="8-confidence-intervals.html"><a href="8-confidence-intervals.html#ci-what-did-we-just-do"><i class="fa fa-check"></i><b>8.1.4</b> What did we just do?</a></li>
</ul></li>
<li class="chapter" data-level="8.2" data-path="8-confidence-intervals.html"><a href="8-confidence-intervals.html#resampling-simulation"><i class="fa fa-check"></i><b>8.2</b> Computer simulation of resampling</a>
<ul>
<li class="chapter" data-level="8.2.1" data-path="8-confidence-intervals.html"><a href="8-confidence-intervals.html#virtually-resampling-once"><i class="fa fa-check"></i><b>8.2.1</b> Virtually resampling once</a></li>
<li class="chapter" data-level="8.2.2" data-path="8-confidence-intervals.html"><a href="8-confidence-intervals.html#bootstrap-35-replicates"><i class="fa fa-check"></i><b>8.2.2</b> Virtually resampling 35 times</a></li>
<li class="chapter" data-level="8.2.3" data-path="8-confidence-intervals.html"><a href="8-confidence-intervals.html#bootstrap-1000-replicates"><i class="fa fa-check"></i><b>8.2.3</b> Virtually resampling 1000 times</a></li>
</ul></li>
<li class="chapter" data-level="8.3" data-path="8-confidence-intervals.html"><a href="8-confidence-intervals.html#ci-build-up"><i class="fa fa-check"></i><b>8.3</b> Understanding confidence intervals</a>
<ul>
<li class="chapter" data-level="8.3.1" data-path="8-confidence-intervals.html"><a href="8-confidence-intervals.html#percentile-method"><i class="fa fa-check"></i><b>8.3.1</b> Percentile method</a></li>
<li class="chapter" data-level="8.3.2" data-path="8-confidence-intervals.html"><a href="8-confidence-intervals.html#se-method"><i class="fa fa-check"></i><b>8.3.2</b> Standard error method</a></li>
</ul></li>
<li class="chapter" data-level="8.4" data-path="8-confidence-intervals.html"><a href="8-confidence-intervals.html#bootstrap-process"><i class="fa fa-check"></i><b>8.4</b> Constructing confidence intervals</a>
<ul>
<li class="chapter" data-level="8.4.1" data-path="8-confidence-intervals.html"><a href="8-confidence-intervals.html#original-workflow"><i class="fa fa-check"></i><b>8.4.1</b> Original workflow</a></li>
<li class="chapter" data-level="8.4.2" data-path="8-confidence-intervals.html"><a href="8-confidence-intervals.html#infer-workflow"><i class="fa fa-check"></i><b>8.4.2</b> <code>infer</code> package workflow</a></li>
<li class="chapter" data-level="8.4.3" data-path="8-confidence-intervals.html"><a href="8-confidence-intervals.html#percentile-method-infer"><i class="fa fa-check"></i><b>8.4.3</b> Percentile method with <code>infer</code></a></li>
<li class="chapter" data-level="8.4.4" data-path="8-confidence-intervals.html"><a href="8-confidence-intervals.html#infer-se"><i class="fa fa-check"></i><b>8.4.4</b> Standard error method with <code>infer</code></a></li>
</ul></li>
<li class="chapter" data-level="8.5" data-path="8-confidence-intervals.html"><a href="8-confidence-intervals.html#one-prop-ci"><i class="fa fa-check"></i><b>8.5</b> Interpreting confidence intervals</a>
<ul>
<li class="chapter" data-level="8.5.1" data-path="8-confidence-intervals.html"><a href="8-confidence-intervals.html#ilyas-yohan"><i class="fa fa-check"></i><b>8.5.1</b> Did the net capture the fish?</a></li>
<li class="chapter" data-level="8.5.2" data-path="8-confidence-intervals.html"><a href="8-confidence-intervals.html#shorthand"><i class="fa fa-check"></i><b>8.5.2</b> Precise and shorthand interpretation</a></li>
<li class="chapter" data-level="8.5.3" data-path="8-confidence-intervals.html"><a href="8-confidence-intervals.html#ci-width"><i class="fa fa-check"></i><b>8.5.3</b> Width of confidence intervals</a></li>
</ul></li>
<li class="chapter" data-level="8.6" data-path="8-confidence-intervals.html"><a href="8-confidence-intervals.html#case-study-two-prop-ci"><i class="fa fa-check"></i><b>8.6</b> Case study: Is yawning contagious?</a>
<ul>
<li class="chapter" data-level="8.6.1" data-path="8-confidence-intervals.html"><a href="8-confidence-intervals.html#mythbusters-study-data"><i class="fa fa-check"></i><b>8.6.1</b> <em>Mythbusters</em> study data</a></li>
<li class="chapter" data-level="8.6.2" data-path="8-confidence-intervals.html"><a href="8-confidence-intervals.html#sampling-scenario"><i class="fa fa-check"></i><b>8.6.2</b> Sampling scenario</a></li>
<li class="chapter" data-level="8.6.3" data-path="8-confidence-intervals.html"><a href="8-confidence-intervals.html#ci-build"><i class="fa fa-check"></i><b>8.6.3</b> Constructing the confidence interval</a></li>
<li class="chapter" data-level="8.6.4" data-path="8-confidence-intervals.html"><a href="8-confidence-intervals.html#interpreting-the-confidence-interval"><i class="fa fa-check"></i><b>8.6.4</b> Interpreting the confidence interval</a></li>
</ul></li>
<li class="chapter" data-level="8.7" data-path="8-confidence-intervals.html"><a href="8-confidence-intervals.html#ci-conclusion"><i class="fa fa-check"></i><b>8.7</b> Conclusion</a>
<ul>
<li class="chapter" data-level="8.7.1" data-path="8-confidence-intervals.html"><a href="8-confidence-intervals.html#bootstrap-vs-sampling"><i class="fa fa-check"></i><b>8.7.1</b> Comparing bootstrap and sampling distributions</a></li>
<li class="chapter" data-level="8.7.2" data-path="8-confidence-intervals.html"><a href="8-confidence-intervals.html#theory-ci"><i class="fa fa-check"></i><b>8.7.2</b> Theory-based confidence intervals</a></li>
<li class="chapter" data-level="8.7.3" data-path="8-confidence-intervals.html"><a href="8-confidence-intervals.html#additional-resources-6"><i class="fa fa-check"></i><b>8.7.3</b> Additional resources</a></li>
<li class="chapter" data-level="8.7.4" data-path="8-confidence-intervals.html"><a href="8-confidence-intervals.html#whats-to-come-7"><i class="fa fa-check"></i><b>8.7.4</b> What’s to come?</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="9" data-path="9-hypothesis-testing.html"><a href="9-hypothesis-testing.html"><i class="fa fa-check"></i><b>9</b> Hypothesis Testing</a>
<ul>
<li class="chapter" data-level="" data-path="9-hypothesis-testing.html"><a href="9-hypothesis-testing.html#nhst-packages"><i class="fa fa-check"></i>Needed packages</a></li>
<li class="chapter" data-level="9.1" data-path="9-hypothesis-testing.html"><a href="9-hypothesis-testing.html#ht-activity"><i class="fa fa-check"></i><b>9.1</b> Promotions activity</a>
<ul>
<li class="chapter" data-level="9.1.1" data-path="9-hypothesis-testing.html"><a href="9-hypothesis-testing.html#does-gender-affect-promotions-at-a-bank"><i class="fa fa-check"></i><b>9.1.1</b> Does gender affect promotions at a bank?</a></li>
<li class="chapter" data-level="9.1.2" data-path="9-hypothesis-testing.html"><a href="9-hypothesis-testing.html#shuffling-once"><i class="fa fa-check"></i><b>9.1.2</b> Shuffling once</a></li>
<li class="chapter" data-level="9.1.3" data-path="9-hypothesis-testing.html"><a href="9-hypothesis-testing.html#shuffling-16-times"><i class="fa fa-check"></i><b>9.1.3</b> Shuffling 16 times</a></li>
<li class="chapter" data-level="9.1.4" data-path="9-hypothesis-testing.html"><a href="9-hypothesis-testing.html#ht-what-did-we-just-do"><i class="fa fa-check"></i><b>9.1.4</b> What did we just do?</a></li>
</ul></li>
<li class="chapter" data-level="9.2" data-path="9-hypothesis-testing.html"><a href="9-hypothesis-testing.html#understanding-ht"><i class="fa fa-check"></i><b>9.2</b> Understanding hypothesis tests</a></li>
<li class="chapter" data-level="9.3" data-path="9-hypothesis-testing.html"><a href="9-hypothesis-testing.html#ht-infer"><i class="fa fa-check"></i><b>9.3</b> Conducting hypothesis tests</a>
<ul>
<li class="chapter" data-level="9.3.1" data-path="9-hypothesis-testing.html"><a href="9-hypothesis-testing.html#infer-workflow-ht"><i class="fa fa-check"></i><b>9.3.1</b> <code>infer</code> package workflow</a></li>
<li class="chapter" data-level="9.3.2" data-path="9-hypothesis-testing.html"><a href="9-hypothesis-testing.html#comparing-infer-workflows"><i class="fa fa-check"></i><b>9.3.2</b> Comparison with confidence intervals</a></li>
<li class="chapter" data-level="9.3.3" data-path="9-hypothesis-testing.html"><a href="9-hypothesis-testing.html#only-one-test"><i class="fa fa-check"></i><b>9.3.3</b> “There is only one test”</a></li>
</ul></li>
<li class="chapter" data-level="9.4" data-path="9-hypothesis-testing.html"><a href="9-hypothesis-testing.html#ht-interpretation"><i class="fa fa-check"></i><b>9.4</b> Interpreting hypothesis tests</a>
<ul>
<li class="chapter" data-level="9.4.1" data-path="9-hypothesis-testing.html"><a href="9-hypothesis-testing.html#trial"><i class="fa fa-check"></i><b>9.4.1</b> Two possible outcomes</a></li>
<li class="chapter" data-level="9.4.2" data-path="9-hypothesis-testing.html"><a href="9-hypothesis-testing.html#types-of-errors"><i class="fa fa-check"></i><b>9.4.2</b> Types of errors</a></li>
<li class="chapter" data-level="9.4.3" data-path="9-hypothesis-testing.html"><a href="9-hypothesis-testing.html#choosing-alpha"><i class="fa fa-check"></i><b>9.4.3</b> How do we choose alpha?</a></li>
</ul></li>
<li class="chapter" data-level="9.5" data-path="9-hypothesis-testing.html"><a href="9-hypothesis-testing.html#ht-case-study"><i class="fa fa-check"></i><b>9.5</b> Case study: Are action or romance movies rated higher?</a>
<ul>
<li class="chapter" data-level="9.5.1" data-path="9-hypothesis-testing.html"><a href="9-hypothesis-testing.html#imdb-data"><i class="fa fa-check"></i><b>9.5.1</b> IMDb ratings data</a></li>
<li class="chapter" data-level="9.5.2" data-path="9-hypothesis-testing.html"><a href="9-hypothesis-testing.html#sampling-scenario-1"><i class="fa fa-check"></i><b>9.5.2</b> Sampling scenario</a></li>
<li class="chapter" data-level="9.5.3" data-path="9-hypothesis-testing.html"><a href="9-hypothesis-testing.html#conducting-the-hypothesis-test"><i class="fa fa-check"></i><b>9.5.3</b> Conducting the hypothesis test</a></li>
</ul></li>
<li class="chapter" data-level="9.6" data-path="9-hypothesis-testing.html"><a href="9-hypothesis-testing.html#nhst-conclusion"><i class="fa fa-check"></i><b>9.6</b> Conclusion</a>
<ul>
<li class="chapter" data-level="9.6.1" data-path="9-hypothesis-testing.html"><a href="9-hypothesis-testing.html#theory-hypo"><i class="fa fa-check"></i><b>9.6.1</b> Theory-based hypothesis tests</a></li>
<li class="chapter" data-level="9.6.2" data-path="9-hypothesis-testing.html"><a href="9-hypothesis-testing.html#when-inference-is-not-needed"><i class="fa fa-check"></i><b>9.6.2</b> When inference is not needed</a></li>
<li class="chapter" data-level="9.6.3" data-path="9-hypothesis-testing.html"><a href="9-hypothesis-testing.html#problems-with-p-values"><i class="fa fa-check"></i><b>9.6.3</b> Problems with p-values</a></li>
<li class="chapter" data-level="9.6.4" data-path="9-hypothesis-testing.html"><a href="9-hypothesis-testing.html#additional-resources-7"><i class="fa fa-check"></i><b>9.6.4</b> Additional resources</a></li>
<li class="chapter" data-level="9.6.5" data-path="9-hypothesis-testing.html"><a href="9-hypothesis-testing.html#whats-to-come-8"><i class="fa fa-check"></i><b>9.6.5</b> What’s to come</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="10" data-path="10-inference-for-regression.html"><a href="10-inference-for-regression.html"><i class="fa fa-check"></i><b>10</b> Inference for Regression</a>
<ul>
<li class="chapter" data-level="" data-path="10-inference-for-regression.html"><a href="10-inference-for-regression.html#inf-packages"><i class="fa fa-check"></i>Needed packages</a></li>
<li class="chapter" data-level="10.1" data-path="10-inference-for-regression.html"><a href="10-inference-for-regression.html#regression-refresher"><i class="fa fa-check"></i><b>10.1</b> Regression refresher</a>
<ul>
<li class="chapter" data-level="10.1.1" data-path="10-inference-for-regression.html"><a href="10-inference-for-regression.html#teaching-evaluations-analysis"><i class="fa fa-check"></i><b>10.1.1</b> Teaching evaluations analysis</a></li>
<li class="chapter" data-level="10.1.2" data-path="10-inference-for-regression.html"><a href="10-inference-for-regression.html#sampling-scenario-2"><i class="fa fa-check"></i><b>10.1.2</b> Sampling scenario</a></li>
</ul></li>
<li class="chapter" data-level="10.2" data-path="10-inference-for-regression.html"><a href="10-inference-for-regression.html#regression-interp"><i class="fa fa-check"></i><b>10.2</b> Interpreting regression tables</a>
<ul>
<li class="chapter" data-level="10.2.1" data-path="10-inference-for-regression.html"><a href="10-inference-for-regression.html#regression-se"><i class="fa fa-check"></i><b>10.2.1</b> Standard error</a></li>
<li class="chapter" data-level="10.2.2" data-path="10-inference-for-regression.html"><a href="10-inference-for-regression.html#regression-test-statistic"><i class="fa fa-check"></i><b>10.2.2</b> Test statistic</a></li>
<li class="chapter" data-level="10.2.3" data-path="10-inference-for-regression.html"><a href="10-inference-for-regression.html#p-value"><i class="fa fa-check"></i><b>10.2.3</b> p-value</a></li>
<li class="chapter" data-level="10.2.4" data-path="10-inference-for-regression.html"><a href="10-inference-for-regression.html#confidence-interval"><i class="fa fa-check"></i><b>10.2.4</b> Confidence interval</a></li>
<li class="chapter" data-level="10.2.5" data-path="10-inference-for-regression.html"><a href="10-inference-for-regression.html#regression-table-computation"><i class="fa fa-check"></i><b>10.2.5</b> How does R compute the table?</a></li>
</ul></li>
<li class="chapter" data-level="10.3" data-path="10-inference-for-regression.html"><a href="10-inference-for-regression.html#regression-conditions"><i class="fa fa-check"></i><b>10.3</b> Conditions for inference for regression</a>
<ul>
<li class="chapter" data-level="10.3.1" data-path="10-inference-for-regression.html"><a href="10-inference-for-regression.html#residuals-refresher"><i class="fa fa-check"></i><b>10.3.1</b> Residuals refresher</a></li>
<li class="chapter" data-level="10.3.2" data-path="10-inference-for-regression.html"><a href="10-inference-for-regression.html#linearity-of-relationship"><i class="fa fa-check"></i><b>10.3.2</b> Linearity of relationship</a></li>
<li class="chapter" data-level="10.3.3" data-path="10-inference-for-regression.html"><a href="10-inference-for-regression.html#independence-of-residuals"><i class="fa fa-check"></i><b>10.3.3</b> Independence of residuals</a></li>
<li class="chapter" data-level="10.3.4" data-path="10-inference-for-regression.html"><a href="10-inference-for-regression.html#normality-of-residuals"><i class="fa fa-check"></i><b>10.3.4</b> Normality of residuals</a></li>
<li class="chapter" data-level="10.3.5" data-path="10-inference-for-regression.html"><a href="10-inference-for-regression.html#equality-of-variance"><i class="fa fa-check"></i><b>10.3.5</b> Equality of variance</a></li>
<li class="chapter" data-level="10.3.6" data-path="10-inference-for-regression.html"><a href="10-inference-for-regression.html#what-is-the-conclusion"><i class="fa fa-check"></i><b>10.3.6</b> What’s the conclusion?</a></li>
</ul></li>
<li class="chapter" data-level="10.4" data-path="10-inference-for-regression.html"><a href="10-inference-for-regression.html#infer-regression"><i class="fa fa-check"></i><b>10.4</b> Simulation-based inference for regression</a>
<ul>
<li class="chapter" data-level="10.4.1" data-path="10-inference-for-regression.html"><a href="10-inference-for-regression.html#confidence-interval-for-slope"><i class="fa fa-check"></i><b>10.4.1</b> Confidence interval for slope</a></li>
<li class="chapter" data-level="10.4.2" data-path="10-inference-for-regression.html"><a href="10-inference-for-regression.html#hypothesis-test-for-slope"><i class="fa fa-check"></i><b>10.4.2</b> Hypothesis test for slope</a></li>
</ul></li>
<li class="chapter" data-level="10.5" data-path="10-inference-for-regression.html"><a href="10-inference-for-regression.html#inference-conclusion"><i class="fa fa-check"></i><b>10.5</b> Conclusion</a>
<ul>
<li class="chapter" data-level="10.5.1" data-path="10-inference-for-regression.html"><a href="10-inference-for-regression.html#theory-regression"><i class="fa fa-check"></i><b>10.5.1</b> Theory-based inference for regression</a></li>
<li class="chapter" data-level="10.5.2" data-path="10-inference-for-regression.html"><a href="10-inference-for-regression.html#summary-of-statistical-inference"><i class="fa fa-check"></i><b>10.5.2</b> Summary of statistical inference</a></li>
<li class="chapter" data-level="10.5.3" data-path="10-inference-for-regression.html"><a href="10-inference-for-regression.html#additional-resources-8"><i class="fa fa-check"></i><b>10.5.3</b> Additional resources</a></li>
<li class="chapter" data-level="10.5.4" data-path="10-inference-for-regression.html"><a href="10-inference-for-regression.html#whats-to-come-9"><i class="fa fa-check"></i><b>10.5.4</b> What’s to come</a></li>
</ul></li>
</ul></li>
<li class="part"><span><b>IV Conclusion</b></span></li>
<li class="chapter" data-level="11" data-path="11-thinking-with-data.html"><a href="11-thinking-with-data.html"><i class="fa fa-check"></i><b>11</b> Tell Your Story with Data</a>
<ul>
<li class="chapter" data-level="11.1" data-path="11-thinking-with-data.html"><a href="11-thinking-with-data.html#review"><i class="fa fa-check"></i><b>11.1</b> Review</a>
<ul>
<li class="chapter" data-level="" data-path="11-thinking-with-data.html"><a href="11-thinking-with-data.html#story-packages"><i class="fa fa-check"></i>Needed packages</a></li>
</ul></li>
<li class="chapter" data-level="11.2" data-path="11-thinking-with-data.html"><a href="11-thinking-with-data.html#seattle-house-prices"><i class="fa fa-check"></i><b>11.2</b> Case study: Seattle house prices</a>
<ul>
<li class="chapter" data-level="11.2.1" data-path="11-thinking-with-data.html"><a href="11-thinking-with-data.html#house-prices-EDA-I"><i class="fa fa-check"></i><b>11.2.1</b> Exploratory data analysis: Part I</a></li>
<li class="chapter" data-level="11.2.2" data-path="11-thinking-with-data.html"><a href="11-thinking-with-data.html#house-prices-EDA-II"><i class="fa fa-check"></i><b>11.2.2</b> Exploratory data analysis: Part II</a></li>
<li class="chapter" data-level="11.2.3" data-path="11-thinking-with-data.html"><a href="11-thinking-with-data.html#house-prices-regression"><i class="fa fa-check"></i><b>11.2.3</b> Regression modeling</a></li>
<li class="chapter" data-level="11.2.4" data-path="11-thinking-with-data.html"><a href="11-thinking-with-data.html#house-prices-making-predictions"><i class="fa fa-check"></i><b>11.2.4</b> Making predictions</a></li>
</ul></li>
<li class="chapter" data-level="11.3" data-path="11-thinking-with-data.html"><a href="11-thinking-with-data.html#data-journalism"><i class="fa fa-check"></i><b>11.3</b> Case study: Effective data storytelling</a>
<ul>
<li class="chapter" data-level="11.3.1" data-path="11-thinking-with-data.html"><a href="11-thinking-with-data.html#bechdel-test-for-hollywood-gender-representation"><i class="fa fa-check"></i><b>11.3.1</b> Bechdel test for Hollywood gender representation</a></li>
<li class="chapter" data-level="11.3.2" data-path="11-thinking-with-data.html"><a href="11-thinking-with-data.html#us-births-in-1999"><i class="fa fa-check"></i><b>11.3.2</b> US Births in 1999</a></li>
<li class="chapter" data-level="11.3.3" data-path="11-thinking-with-data.html"><a href="11-thinking-with-data.html#scripts-of-r-code"><i class="fa fa-check"></i><b>11.3.3</b> Scripts of R code</a></li>
</ul></li>
<li class="chapter" data-level="" data-path="11-thinking-with-data.html"><a href="11-thinking-with-data.html#concluding-remarks"><i class="fa fa-check"></i>Concluding remarks</a></li>
</ul></li>
<li class="appendix"><span><b>Appendix</b></span></li>
<li class="chapter" data-level="A" data-path="A-appendixA.html"><a href="A-appendixA.html"><i class="fa fa-check"></i><b>A</b> Statistical Background</a>
<ul>
<li class="chapter" data-level="A.1" data-path="A-appendixA.html"><a href="A-appendixA.html#appendix-stat-terms"><i class="fa fa-check"></i><b>A.1</b> Basic statistical terms</a>
<ul>
<li class="chapter" data-level="A.1.1" data-path="A-appendixA.html"><a href="A-appendixA.html#mean"><i class="fa fa-check"></i><b>A.1.1</b> Mean</a></li>
<li class="chapter" data-level="A.1.2" data-path="A-appendixA.html"><a href="A-appendixA.html#median"><i class="fa fa-check"></i><b>A.1.2</b> Median</a></li>
<li class="chapter" data-level="A.1.3" data-path="A-appendixA.html"><a href="A-appendixA.html#appendix-sd-variance"><i class="fa fa-check"></i><b>A.1.3</b> Standard deviation and variance</a></li>
<li class="chapter" data-level="A.1.4" data-path="A-appendixA.html"><a href="A-appendixA.html#five-number-summary"><i class="fa fa-check"></i><b>A.1.4</b> Five-number summary</a></li>
<li class="chapter" data-level="A.1.5" data-path="A-appendixA.html"><a href="A-appendixA.html#distribution"><i class="fa fa-check"></i><b>A.1.5</b> Distribution</a></li>
<li class="chapter" data-level="A.1.6" data-path="A-appendixA.html"><a href="A-appendixA.html#outliers"><i class="fa fa-check"></i><b>A.1.6</b> Outliers</a></li>
</ul></li>
<li class="chapter" data-level="A.2" data-path="A-appendixA.html"><a href="A-appendixA.html#appendix-normal-curve"><i class="fa fa-check"></i><b>A.2</b> Normal distribution</a></li>
<li class="chapter" data-level="A.3" data-path="A-appendixA.html"><a href="A-appendixA.html#appendix-log10-transformations"><i class="fa fa-check"></i><b>A.3</b> log10 transformations</a></li>
</ul></li>
<li class="chapter" data-level="B" data-path="B-appendixB.html"><a href="B-appendixB.html"><i class="fa fa-check"></i><b>B</b> Inference Examples</a>
<ul>
<li class="chapter" data-level="" data-path="B-appendixB.html"><a href="B-appendixB.html#needed-packages-1"><i class="fa fa-check"></i>Needed packages</a></li>
<li class="chapter" data-level="B.1" data-path="B-appendixB.html"><a href="B-appendixB.html#inference-mind-map"><i class="fa fa-check"></i><b>B.1</b> Inference mind map</a></li>
<li class="chapter" data-level="B.2" data-path="B-appendixB.html"><a href="B-appendixB.html#one-mean"><i class="fa fa-check"></i><b>B.2</b> One mean</a>
<ul>
<li class="chapter" data-level="B.2.1" data-path="B-appendixB.html"><a href="B-appendixB.html#problem-statement"><i class="fa fa-check"></i><b>B.2.1</b> Problem statement</a></li>
<li class="chapter" data-level="B.2.2" data-path="B-appendixB.html"><a href="B-appendixB.html#competing-hypotheses"><i class="fa fa-check"></i><b>B.2.2</b> Competing hypotheses</a></li>
<li class="chapter" data-level="B.2.3" data-path="B-appendixB.html"><a href="B-appendixB.html#exploring-the-sample-data"><i class="fa fa-check"></i><b>B.2.3</b> Exploring the sample data</a></li>
<li class="chapter" data-level="B.2.4" data-path="B-appendixB.html"><a href="B-appendixB.html#non-traditional-methods"><i class="fa fa-check"></i><b>B.2.4</b> Non-traditional methods</a></li>
<li class="chapter" data-level="B.2.5" data-path="B-appendixB.html"><a href="B-appendixB.html#traditional-methods"><i class="fa fa-check"></i><b>B.2.5</b> Traditional methods</a></li>
<li class="chapter" data-level="B.2.6" data-path="B-appendixB.html"><a href="B-appendixB.html#comparing-results"><i class="fa fa-check"></i><b>B.2.6</b> Comparing results</a></li>
</ul></li>
<li class="chapter" data-level="B.3" data-path="B-appendixB.html"><a href="B-appendixB.html#one-proportion"><i class="fa fa-check"></i><b>B.3</b> One proportion</a>
<ul>
<li class="chapter" data-level="B.3.1" data-path="B-appendixB.html"><a href="B-appendixB.html#problem-statement-1"><i class="fa fa-check"></i><b>B.3.1</b> Problem statement</a></li>
<li class="chapter" data-level="B.3.2" data-path="B-appendixB.html"><a href="B-appendixB.html#competing-hypotheses-1"><i class="fa fa-check"></i><b>B.3.2</b> Competing hypotheses</a></li>
<li class="chapter" data-level="B.3.3" data-path="B-appendixB.html"><a href="B-appendixB.html#exploring-the-sample-data-1"><i class="fa fa-check"></i><b>B.3.3</b> Exploring the sample data</a></li>
<li class="chapter" data-level="B.3.4" data-path="B-appendixB.html"><a href="B-appendixB.html#non-traditional-methods-1"><i class="fa fa-check"></i><b>B.3.4</b> Non-traditional methods</a></li>
<li class="chapter" data-level="B.3.5" data-path="B-appendixB.html"><a href="B-appendixB.html#traditional-methods-1"><i class="fa fa-check"></i><b>B.3.5</b> Traditional methods</a></li>
<li class="chapter" data-level="B.3.6" data-path="B-appendixB.html"><a href="B-appendixB.html#comparing-results-1"><i class="fa fa-check"></i><b>B.3.6</b> Comparing results</a></li>
</ul></li>
<li class="chapter" data-level="B.4" data-path="B-appendixB.html"><a href="B-appendixB.html#two-proportions"><i class="fa fa-check"></i><b>B.4</b> Two proportions</a>
<ul>
<li class="chapter" data-level="B.4.1" data-path="B-appendixB.html"><a href="B-appendixB.html#problem-statement-2"><i class="fa fa-check"></i><b>B.4.1</b> Problem statement</a></li>
<li class="chapter" data-level="B.4.2" data-path="B-appendixB.html"><a href="B-appendixB.html#competing-hypotheses-2"><i class="fa fa-check"></i><b>B.4.2</b> Competing hypotheses</a></li>
<li class="chapter" data-level="B.4.3" data-path="B-appendixB.html"><a href="B-appendixB.html#exploring-the-sample-data-2"><i class="fa fa-check"></i><b>B.4.3</b> Exploring the sample data</a></li>
<li class="chapter" data-level="B.4.4" data-path="B-appendixB.html"><a href="B-appendixB.html#non-traditional-methods-2"><i class="fa fa-check"></i><b>B.4.4</b> Non-traditional methods</a></li>
<li class="chapter" data-level="B.4.5" data-path="B-appendixB.html"><a href="B-appendixB.html#traditional-methods-2"><i class="fa fa-check"></i><b>B.4.5</b> Traditional methods</a></li>
<li class="chapter" data-level="B.4.6" data-path="B-appendixB.html"><a href="B-appendixB.html#test-statistic-2"><i class="fa fa-check"></i><b>B.4.6</b> Test statistic</a></li>
<li class="chapter" data-level="B.4.7" data-path="B-appendixB.html"><a href="B-appendixB.html#state-conclusion-2"><i class="fa fa-check"></i><b>B.4.7</b> State conclusion</a></li>
<li class="chapter" data-level="B.4.8" data-path="B-appendixB.html"><a href="B-appendixB.html#comparing-results-2"><i class="fa fa-check"></i><b>B.4.8</b> Comparing results</a></li>
</ul></li>
<li class="chapter" data-level="B.5" data-path="B-appendixB.html"><a href="B-appendixB.html#two-means-independent-samples"><i class="fa fa-check"></i><b>B.5</b> Two means (independent samples)</a>
<ul>
<li class="chapter" data-level="B.5.1" data-path="B-appendixB.html"><a href="B-appendixB.html#problem-statement-3"><i class="fa fa-check"></i><b>B.5.1</b> Problem statement</a></li>
<li class="chapter" data-level="B.5.2" data-path="B-appendixB.html"><a href="B-appendixB.html#competing-hypotheses-3"><i class="fa fa-check"></i><b>B.5.2</b> Competing hypotheses</a></li>
<li class="chapter" data-level="B.5.3" data-path="B-appendixB.html"><a href="B-appendixB.html#exploring-the-sample-data-3"><i class="fa fa-check"></i><b>B.5.3</b> Exploring the sample data</a></li>
<li class="chapter" data-level="B.5.4" data-path="B-appendixB.html"><a href="B-appendixB.html#non-traditional-methods-3"><i class="fa fa-check"></i><b>B.5.4</b> Non-traditional methods</a></li>
<li class="chapter" data-level="B.5.5" data-path="B-appendixB.html"><a href="B-appendixB.html#traditional-methods-3"><i class="fa fa-check"></i><b>B.5.5</b> Traditional methods</a></li>
<li class="chapter" data-level="B.5.6" data-path="B-appendixB.html"><a href="B-appendixB.html#test-statistic-3"><i class="fa fa-check"></i><b>B.5.6</b> Test statistic</a></li>
<li class="chapter" data-level="B.5.7" data-path="B-appendixB.html"><a href="B-appendixB.html#compute-p-value-1"><i class="fa fa-check"></i><b>B.5.7</b> Compute <span class="math inline">\(p\)</span>-value</a></li>
<li class="chapter" data-level="B.5.8" data-path="B-appendixB.html"><a href="B-appendixB.html#state-conclusion-3"><i class="fa fa-check"></i><b>B.5.8</b> State conclusion</a></li>
<li class="chapter" data-level="B.5.9" data-path="B-appendixB.html"><a href="B-appendixB.html#comparing-results-3"><i class="fa fa-check"></i><b>B.5.9</b> Comparing results</a></li>
</ul></li>
<li class="chapter" data-level="B.6" data-path="B-appendixB.html"><a href="B-appendixB.html#two-means-paired-samples"><i class="fa fa-check"></i><b>B.6</b> Two means (paired samples)</a>
<ul>
<li class="chapter" data-level="" data-path="B-appendixB.html"><a href="B-appendixB.html#problem-statement-4"><i class="fa fa-check"></i>Problem statement</a></li>
<li class="chapter" data-level="B.6.1" data-path="B-appendixB.html"><a href="B-appendixB.html#competing-hypotheses-4"><i class="fa fa-check"></i><b>B.6.1</b> Competing hypotheses</a></li>
<li class="chapter" data-level="B.6.2" data-path="B-appendixB.html"><a href="B-appendixB.html#exploring-the-sample-data-4"><i class="fa fa-check"></i><b>B.6.2</b> Exploring the sample data</a></li>
<li class="chapter" data-level="B.6.3" data-path="B-appendixB.html"><a href="B-appendixB.html#non-traditional-methods-4"><i class="fa fa-check"></i><b>B.6.3</b> Non-traditional methods</a></li>
<li class="chapter" data-level="B.6.4" data-path="B-appendixB.html"><a href="B-appendixB.html#traditional-methods-4"><i class="fa fa-check"></i><b>B.6.4</b> Traditional methods</a></li>
<li class="chapter" data-level="B.6.5" data-path="B-appendixB.html"><a href="B-appendixB.html#comparing-results-4"><i class="fa fa-check"></i><b>B.6.5</b> Comparing results</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="C" data-path="C-appendixC.html"><a href="C-appendixC.html"><i class="fa fa-check"></i><b>C</b> Tips and Tricks</a>
<ul>
<li class="chapter" data-level="" data-path="C-appendixC.html"><a href="C-appendixC.html#needed-packages-2"><i class="fa fa-check"></i>Needed packages</a></li>
<li class="chapter" data-level="C.1" data-path="C-appendixC.html"><a href="C-appendixC.html#data-wrangling"><i class="fa fa-check"></i><b>C.1</b> Data wrangling</a>
<ul>
<li class="chapter" data-level="C.1.1" data-path="C-appendixC.html"><a href="C-appendixC.html#appendix-missing-values"><i class="fa fa-check"></i><b>C.1.1</b> Dealing with missing values</a></li>
<li class="chapter" data-level="C.1.2" data-path="C-appendixC.html"><a href="C-appendixC.html#appendix-reordering-bars"><i class="fa fa-check"></i><b>C.1.2</b> Reordering bars in a barplot</a></li>
<li class="chapter" data-level="C.1.3" data-path="C-appendixC.html"><a href="C-appendixC.html#appendix-money-on-axis"><i class="fa fa-check"></i><b>C.1.3</b> Showing money on an axis</a></li>
<li class="chapter" data-level="C.1.4" data-path="C-appendixC.html"><a href="C-appendixC.html#appendix-changing-values"><i class="fa fa-check"></i><b>C.1.4</b> Changing values inside cells</a></li>
<li class="chapter" data-level="C.1.5" data-path="C-appendixC.html"><a href="C-appendixC.html#appendix-convert-numerical-categorical"><i class="fa fa-check"></i><b>C.1.5</b> Converting a numerical variable to a categorical one</a></li>
<li class="chapter" data-level="C.1.6" data-path="C-appendixC.html"><a href="C-appendixC.html#appendix-prop"><i class="fa fa-check"></i><b>C.1.6</b> Computing proportions</a></li>
<li class="chapter" data-level="C.1.7" data-path="C-appendixC.html"><a href="C-appendixC.html#appendix-commas"><i class="fa fa-check"></i><b>C.1.7</b> Dealing with %, commas, and $</a></li>
</ul></li>
<li class="chapter" data-level="C.2" data-path="C-appendixC.html"><a href="C-appendixC.html#interactive-graphics"><i class="fa fa-check"></i><b>C.2</b> Interactive graphics</a>
<ul>
<li class="chapter" data-level="C.2.1" data-path="C-appendixC.html"><a href="C-appendixC.html#interactive-linegraphs"><i class="fa fa-check"></i><b>C.2.1</b> Interactive linegraphs</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="D" data-path="D-appendixD.html"><a href="D-appendixD.html"><i class="fa fa-check"></i><b>D</b> Learning Check Solutions</a>
<ul>
<li class="chapter" data-level="D.1" data-path="D-appendixD.html"><a href="D-appendixD.html#chapter-1-solutions"><i class="fa fa-check"></i><b>D.1</b> Chapter 1 Solutions</a></li>
</ul></li>
<li class="chapter" data-level="E" data-path="E-appendixE.html"><a href="E-appendixE.html"><i class="fa fa-check"></i><b>E</b> Versions of R Packages Used</a></li>
<li class="chapter" data-level="" data-path="references.html"><a href="references.html"><i class="fa fa-check"></i>References</a></li>
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<div id="appendixC" class="section level1" number="14">
<h1><span class="header-section-number">C</span> Tips and Tricks</h1>
<div id="needed-packages-2" class="section level3 unnumbered">
<h3>Needed packages</h3>
<p>Let’s load all the packages needed for this chapter (this assumes you’ve already installed them). Recall from our discussion in Section <a href="4-tidy.html#tidyverse-package">4.4</a> that loading the <code>tidyverse</code> package by running <code>library(tidyverse)</code> loads the following commonly used data science packages all at once:</p>
<ul>
<li><code>ggplot2</code> for data visualization</li>
<li><code>dplyr</code> for data wrangling</li>
<li><code>tidyr</code> for converting data to “tidy” format</li>
<li><code>readr</code> for importing spreadsheet data into R</li>
<li>As well as the more advanced <code>purrr</code>, <code>tibble</code>, <code>stringr</code>, and <code>forcats</code> packages</li>
</ul>
<p>If needed, read Section <a href="1-getting-started.html#packages">1.3</a> for information on how to install and load R packages.</p>
<div class="sourceCode" id="cb555"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb555-1"><a href="C-appendixC.html#cb555-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(tidyverse)</span>
<span id="cb555-2"><a href="C-appendixC.html#cb555-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(scales)</span>
<span id="cb555-3"><a href="C-appendixC.html#cb555-3" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(janitor)</span>
<span id="cb555-4"><a href="C-appendixC.html#cb555-4" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(dygraphs)</span>
<span id="cb555-5"><a href="C-appendixC.html#cb555-5" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(nycflights13)</span></code></pre></div>
</div>
<div id="data-wrangling" class="section level2" number="14.1">
<h2><span class="header-section-number">C.1</span> Data wrangling</h2>
<p>In this Section, we address some of the most common data wrangling questions we’ve encountered in student projects (shout out to <a href="https://www.scsparkscience.org/fellow/jennifer-smetzer/">Dr. Jenny Smetzer</a> for her work setting this up!):</p>
<ul>
<li><a href="C-appendixC.html#appendix-missing-values">C.1.1</a>: Dealing with missing values</li>
<li><a href="C-appendixC.html#appendix-reordering-bars">C.1.2</a>: Reordering bars in a barplot</li>
<li><a href="C-appendixC.html#appendix-money-on-axis">C.1.3</a>: Showing money on an axis</li>
<li><a href="C-appendixC.html#appendix-changing-values">C.1.4</a>: Changing values inside cells</li>
<li><a href="C-appendixC.html#appendix-convert-numerical-categorical">C.1.5</a>: Converting a numerical variable to a categorical one</li>
<li><a href="C-appendixC.html#appendix-prop">C.1.6</a>: Computing proportions</li>
<li><a href="C-appendixC.html#appendix-commas">C.1.7</a>: Dealing with %, commas, and $</li>
</ul>
<p><img src="images/data_ninja1.png" style="width:30.0%" /></p>
<p>Let’s load an example movies dataset, pare down the rows and columns a bit, and then show the first 10 rows using <code>slice()</code>.</p>
<div class="sourceCode" id="cb556"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb556-1"><a href="C-appendixC.html#cb556-1" aria-hidden="true" tabindex="-1"></a>movies_ex <span class="ot"><-</span> <span class="fu">read_csv</span>(<span class="st">"https://moderndive.com/data/movies.csv"</span>) <span class="sc">%>%</span></span>
<span id="cb556-2"><a href="C-appendixC.html#cb556-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(type <span class="sc">%in%</span> <span class="fu">c</span>(<span class="st">"action"</span>, <span class="st">"comedy"</span>, <span class="st">"drama"</span>, <span class="st">"animated"</span>, <span class="st">"fantasy"</span>, <span class="st">"rom comedy"</span>)) <span class="sc">%>%</span></span>
<span id="cb556-3"><a href="C-appendixC.html#cb556-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(<span class="sc">-</span>over200)</span>
<span id="cb556-4"><a href="C-appendixC.html#cb556-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb556-5"><a href="C-appendixC.html#cb556-5" aria-hidden="true" tabindex="-1"></a>movies_ex <span class="sc">%>%</span></span>
<span id="cb556-6"><a href="C-appendixC.html#cb556-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">slice</span>(<span class="dv">1</span><span class="sc">:</span><span class="dv">10</span>)</span></code></pre></div>
<pre><code># A tibble: 10 x 5
name score rating type millions
<chr> <dbl> <chr> <chr> <dbl>
1 2 Fast 2 Furious 48.9000 PG-13 action NA
2 A Guy Thing 39.5 PG-13 rom comedy 15.545
3 A Man Apart 42.9000 R action 26.2480
4 A Mighty Wind 79.9000 PG-13 comedy 17.781
5 Agent Cody Banks 57.9000 PG action 47.8110
6 Alex & Emma 35.1000 PG-13 rom comedy 14.219
7 American Wedding 50.7000 R comedy 104.441
8 Anger Management 62.6000 PG-13 comedy 134.404
9 Anything Else 63.3000 R rom comedy 3.21200
10 Bad Boys II 38.1000 R action 138.397 </code></pre>
<div id="appendix-missing-values" class="section level3" number="14.1.1">
<h3><span class="header-section-number">C.1.1</span> Dealing with missing values</h3>
<p>You see the revenue in <code>million</code>s value for the movie “2 Fast 2 Furious” is <code>NA</code> (missing). So the following occurs when computing the median revenue:</p>
<div class="sourceCode" id="cb558"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb558-1"><a href="C-appendixC.html#cb558-1" aria-hidden="true" tabindex="-1"></a>movies_ex <span class="sc">%>%</span></span>
<span id="cb558-2"><a href="C-appendixC.html#cb558-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">summarize</span>(<span class="at">mean_profit =</span> <span class="fu">median</span>(millions))</span></code></pre></div>
<pre><code># A tibble: 1 x 1
mean_profit
<dbl>
1 NA</code></pre>
<p>You should always think about why a data value might be missing and what that missingness may mean. For example, imagine you are conducting a study on the effects of smoking on lung cancer and a lot of your patients’ data is missing because they died of lung cancer. If you just “sweep these patients under the rug” and ignore them, you are clearly biasing the results.</p>
<p>While there are statistical methods to deal with missing data they are beyond the reach of this class. The easiest thing to do is to remove all missing cases, but <strong>you should always at the very least report to the reader if you do so, as by removing the missing values you may be biasing your results.</strong></p>
<p>You can do this with a <code>na.rm = TRUE</code> argument like so:</p>
<div class="sourceCode" id="cb560"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb560-1"><a href="C-appendixC.html#cb560-1" aria-hidden="true" tabindex="-1"></a>movies_ex <span class="sc">%>%</span></span>
<span id="cb560-2"><a href="C-appendixC.html#cb560-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">summarize</span>(<span class="at">mean_profit =</span> <span class="fu">median</span>(millions, <span class="at">na.rm =</span> <span class="cn">TRUE</span>))</span></code></pre></div>
<pre><code># A tibble: 1 x 1
mean_profit
<dbl>
1 43.4270</code></pre>
<p>If you decide you want to remove the row with the missing data, you can use the filter function like so:</p>
<div class="sourceCode" id="cb562"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb562-1"><a href="C-appendixC.html#cb562-1" aria-hidden="true" tabindex="-1"></a>movies_no_missing <span class="ot"><-</span> movies_ex <span class="sc">%>%</span></span>
<span id="cb562-2"><a href="C-appendixC.html#cb562-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(<span class="sc">!</span><span class="fu">is.na</span>(millions))</span>
<span id="cb562-3"><a href="C-appendixC.html#cb562-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb562-4"><a href="C-appendixC.html#cb562-4" aria-hidden="true" tabindex="-1"></a>movies_no_missing <span class="sc">%>%</span></span>
<span id="cb562-5"><a href="C-appendixC.html#cb562-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">slice</span>(<span class="dv">1</span><span class="sc">:</span><span class="dv">10</span>)</span></code></pre></div>
<pre><code># A tibble: 10 x 5
name score rating type millions
<chr> <dbl> <chr> <chr> <dbl>
1 A Guy Thing 39.5 PG-13 rom comedy 15.545
2 A Man Apart 42.9000 R action 26.2480
3 A Mighty Wind 79.9000 PG-13 comedy 17.781
4 Agent Cody Banks 57.9000 PG action 47.8110
5 Alex & Emma 35.1000 PG-13 rom comedy 14.219
6 American Wedding 50.7000 R comedy 104.441
7 Anger Management 62.6000 PG-13 comedy 134.404
8 Anything Else 63.3000 R rom comedy 3.21200
9 Bad Boys II 38.1000 R action 138.397
10 Bad Santa 75.8000 R comedy 59.5230 </code></pre>
<p>We see “2 Fast 2 Furious” is now gone.</p>
</div>
<div id="appendix-reordering-bars" class="section level3" number="14.1.2">
<h3><span class="header-section-number">C.1.2</span> Reordering bars in a barplot</h3>
<p>Let’s compute the total revenue for each movie type and plot a barplot.</p>
<div class="sourceCode" id="cb564"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb564-1"><a href="C-appendixC.html#cb564-1" aria-hidden="true" tabindex="-1"></a>revenue_by_type <span class="ot"><-</span> movies_ex <span class="sc">%>%</span></span>
<span id="cb564-2"><a href="C-appendixC.html#cb564-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">group_by</span>(type) <span class="sc">%>%</span></span>
<span id="cb564-3"><a href="C-appendixC.html#cb564-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">summarize</span>(<span class="at">total_revenue =</span> <span class="fu">sum</span>(millions))</span>
<span id="cb564-4"><a href="C-appendixC.html#cb564-4" aria-hidden="true" tabindex="-1"></a>revenue_by_type</span></code></pre></div>
<pre><code># A tibble: 6 x 2
type total_revenue
<chr> <dbl>
1 action NA
2 animated 561.306
3 comedy 2286.81
4 drama 840.038
5 fantasy 508.580
6 rom comedy 492.282</code></pre>
<div class="sourceCode" id="cb566"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb566-1"><a href="C-appendixC.html#cb566-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(revenue_by_type, <span class="fu">aes</span>(<span class="at">x =</span> type, <span class="at">y =</span> total_revenue)) <span class="sc">+</span></span>
<span id="cb566-2"><a href="C-appendixC.html#cb566-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_col</span>() <span class="sc">+</span></span>
<span id="cb566-3"><a href="C-appendixC.html#cb566-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(<span class="at">x =</span> <span class="st">"Movie genre"</span>, <span class="at">y =</span> <span class="st">"Total box office revenue (in millions of $)"</span>)</span></code></pre></div>
<p><img src="ModernDive_files/figure-html/unnamed-chunk-577-1.png" width="\textwidth" style="display: block; margin: auto;" /></p>
<p>Say we want to reorder the categorical variable <code>type</code> so that the bars show in a different order. We can reorder the bars by manually defining the order of the <code>levels</code> in the <code>factor()</code> command:</p>
<div class="sourceCode" id="cb567"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb567-1"><a href="C-appendixC.html#cb567-1" aria-hidden="true" tabindex="-1"></a>type_levels <span class="ot"><-</span> <span class="fu">c</span>(<span class="st">"rom comedy"</span>, <span class="st">"action"</span>, <span class="st">"drama"</span>, <span class="st">"animated"</span>, <span class="st">"comedy"</span>, <span class="st">"fantasy"</span>)</span>
<span id="cb567-2"><a href="C-appendixC.html#cb567-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb567-3"><a href="C-appendixC.html#cb567-3" aria-hidden="true" tabindex="-1"></a>revenue_by_type <span class="ot"><-</span> revenue_by_type <span class="sc">%>%</span></span>
<span id="cb567-4"><a href="C-appendixC.html#cb567-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">type =</span> <span class="fu">factor</span>(type, <span class="at">levels =</span> type_levels))</span>
<span id="cb567-5"><a href="C-appendixC.html#cb567-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb567-6"><a href="C-appendixC.html#cb567-6" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(revenue_by_type, <span class="fu">aes</span>(<span class="at">x =</span> type, <span class="at">y =</span> total_revenue)) <span class="sc">+</span></span>
<span id="cb567-7"><a href="C-appendixC.html#cb567-7" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_col</span>() <span class="sc">+</span></span>
<span id="cb567-8"><a href="C-appendixC.html#cb567-8" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(<span class="at">x =</span> <span class="st">"Movie genre"</span>, <span class="at">y =</span> <span class="st">"Total boxoffice revenue (in millions of $)"</span>)</span></code></pre></div>
<p><img src="ModernDive_files/figure-html/unnamed-chunk-578-1.png" width="\textwidth" style="display: block; margin: auto;" /></p>
<p>Or if you want to reorder <code>type</code> in ascending order of <code>total_revenue</code>, we use <code>reorder()</code></p>
<div class="sourceCode" id="cb568"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb568-1"><a href="C-appendixC.html#cb568-1" aria-hidden="true" tabindex="-1"></a>revenue_by_type <span class="ot"><-</span> revenue_by_type <span class="sc">%>%</span></span>
<span id="cb568-2"><a href="C-appendixC.html#cb568-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">type =</span> <span class="fu">reorder</span>(type, total_revenue))</span>
<span id="cb568-3"><a href="C-appendixC.html#cb568-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb568-4"><a href="C-appendixC.html#cb568-4" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(revenue_by_type, <span class="fu">aes</span>(<span class="at">x =</span> type, <span class="at">y =</span> total_revenue)) <span class="sc">+</span></span>
<span id="cb568-5"><a href="C-appendixC.html#cb568-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_col</span>() <span class="sc">+</span></span>
<span id="cb568-6"><a href="C-appendixC.html#cb568-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span>
<span id="cb568-7"><a href="C-appendixC.html#cb568-7" aria-hidden="true" tabindex="-1"></a> <span class="at">x =</span> <span class="st">"Movie genre"</span>, <span class="at">y =</span> <span class="st">"Total boxoffice revenue (in millions of $)"</span></span>
<span id="cb568-8"><a href="C-appendixC.html#cb568-8" aria-hidden="true" tabindex="-1"></a> )</span></code></pre></div>
<p><img src="ModernDive_files/figure-html/unnamed-chunk-579-1.png" width="\textwidth" style="display: block; margin: auto;" /></p>
<p>Or if you want to reorder <code>type</code> in descending order of <code>total_revenue</code>, just put
a <code>-</code> sign in front of <code>-total_revenue</code> in <code>reorder()</code>:</p>
<div class="sourceCode" id="cb569"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb569-1"><a href="C-appendixC.html#cb569-1" aria-hidden="true" tabindex="-1"></a>revenue_by_type <span class="ot"><-</span> revenue_by_type <span class="sc">%>%</span></span>
<span id="cb569-2"><a href="C-appendixC.html#cb569-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">type =</span> <span class="fu">reorder</span>(type, <span class="sc">-</span>total_revenue))</span>
<span id="cb569-3"><a href="C-appendixC.html#cb569-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb569-4"><a href="C-appendixC.html#cb569-4" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(revenue_by_type, <span class="fu">aes</span>(<span class="at">x =</span> type, <span class="at">y =</span> total_revenue)) <span class="sc">+</span></span>
<span id="cb569-5"><a href="C-appendixC.html#cb569-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_col</span>() <span class="sc">+</span></span>
<span id="cb569-6"><a href="C-appendixC.html#cb569-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span>
<span id="cb569-7"><a href="C-appendixC.html#cb569-7" aria-hidden="true" tabindex="-1"></a> <span class="at">x =</span> <span class="st">"Movie genre"</span>, <span class="at">y =</span> <span class="st">"Total boxoffice revenue (in millions of $)"</span></span>
<span id="cb569-8"><a href="C-appendixC.html#cb569-8" aria-hidden="true" tabindex="-1"></a> )</span></code></pre></div>
<p><img src="ModernDive_files/figure-html/unnamed-chunk-580-1.png" width="\textwidth" style="display: block; margin: auto;" /></p>
<p>For more advanced categorical variable (i.e. factor) manipulations, check out
the <a href="https://forcats.tidyverse.org/" target="_blank"><code>forcats</code> package</a>. Note: <code>forcats</code> is an anagram of <code>factors</code></p>
<p><img src="https://github.com/tidyverse/forcats/raw/master/man/figures/logo.png" style="width:20.0%" /></p>
</div>
<div id="appendix-money-on-axis" class="section level3" number="14.1.3">
<h3><span class="header-section-number">C.1.3</span> Showing money on an axis</h3>
<div class="sourceCode" id="cb570"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb570-1"><a href="C-appendixC.html#cb570-1" aria-hidden="true" tabindex="-1"></a>movies_ex <span class="ot"><-</span> movies_ex <span class="sc">%>%</span></span>
<span id="cb570-2"><a href="C-appendixC.html#cb570-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">revenue =</span> millions <span class="sc">*</span> <span class="dv">10</span><span class="sc">^</span><span class="dv">6</span>)</span>
<span id="cb570-3"><a href="C-appendixC.html#cb570-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb570-4"><a href="C-appendixC.html#cb570-4" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> movies_ex, <span class="fu">aes</span>(<span class="at">x =</span> rating, <span class="at">y =</span> revenue)) <span class="sc">+</span></span>
<span id="cb570-5"><a href="C-appendixC.html#cb570-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_boxplot</span>() <span class="sc">+</span></span>
<span id="cb570-6"><a href="C-appendixC.html#cb570-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(<span class="at">x =</span> <span class="st">"rating"</span>, <span class="at">y =</span> <span class="st">"Revenue in $"</span>, <span class="at">title =</span> <span class="st">"Profits for different movie ratings"</span>)</span></code></pre></div>
<p><img src="ModernDive_files/figure-html/unnamed-chunk-581-1.png" width="\textwidth" style="display: block; margin: auto;" /></p>
<p>Google “ggplot2 axis scale dollars” and click on the <a href="http://www.sthda.com/english/wiki/ggplot2-axis-scales-and-transformations">first link</a> and search for the word “dollars.” You’ll find:</p>
<div class="sourceCode" id="cb571"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb571-1"><a href="C-appendixC.html#cb571-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Don't forget to load the scales package first!</span></span>
<span id="cb571-2"><a href="C-appendixC.html#cb571-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(scales)</span>
<span id="cb571-3"><a href="C-appendixC.html#cb571-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb571-4"><a href="C-appendixC.html#cb571-4" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> movies_ex, <span class="fu">aes</span>(<span class="at">x =</span> rating, <span class="at">y =</span> revenue)) <span class="sc">+</span></span>
<span id="cb571-5"><a href="C-appendixC.html#cb571-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_boxplot</span>() <span class="sc">+</span></span>
<span id="cb571-6"><a href="C-appendixC.html#cb571-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(<span class="at">x =</span> <span class="st">"rating"</span>, <span class="at">y =</span> <span class="st">"Revenue in $"</span>, <span class="at">title =</span> <span class="st">"Profits for different movie ratings"</span>) <span class="sc">+</span></span>
<span id="cb571-7"><a href="C-appendixC.html#cb571-7" aria-hidden="true" tabindex="-1"></a> <span class="fu">scale_y_continuous</span>(<span class="at">labels =</span> dollar)</span></code></pre></div>
<p><img src="ModernDive_files/figure-html/unnamed-chunk-582-1.png" width="\textwidth" style="display: block; margin: auto;" /></p>
</div>
<div id="appendix-changing-values" class="section level3" number="14.1.4">
<h3><span class="header-section-number">C.1.4</span> Changing values inside cells</h3>
<p>The <code>rename()</code> function in the <code>dplyr</code> package renames column/variable names. To “rename” values inside cells of a particular column, you need to <code>mutate()</code> the column using one of the three functions below. There might be other ones too, but these are the three we’ve seen the most. In these examples, we’ll change values in the variable <code>type</code>.</p>
<ol style="list-style-type: decimal">
<li><code>if_else()</code></li>
<li><code>recode()</code></li>
<li><code>case_when()</code></li>
</ol>
<div id="if_else" class="section level4 unnumbered">
<h4><code>if_else()</code></h4>
<p>Switch all instances of <code>rom comedy</code> with <code>romantic comedy</code> using <code>if_else()</code> from the <code>dplyr</code> package. If a particular row has <code>type == "rom comedy"</code>, then return <code>"romantic comedy"</code>, else return whatever was originally in <code>type</code>. Save everything in a new variable <code>type_new</code>:</p>
<div class="sourceCode" id="cb572"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb572-1"><a href="C-appendixC.html#cb572-1" aria-hidden="true" tabindex="-1"></a>movies_ex <span class="sc">%>%</span></span>
<span id="cb572-2"><a href="C-appendixC.html#cb572-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">type_new =</span> <span class="fu">if_else</span>(type <span class="sc">==</span> <span class="st">"rom comedy"</span>, <span class="st">"romantic comedy"</span>, type)) <span class="sc">%>%</span></span>
<span id="cb572-3"><a href="C-appendixC.html#cb572-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">slice</span>(<span class="dv">1</span><span class="sc">:</span><span class="dv">10</span>)</span></code></pre></div>
<pre><code># A tibble: 10 x 7
name score rating type millions revenue type_new
<chr> <dbl> <chr> <chr> <dbl> <dbl> <chr>
1 2 Fast 2 Furious 48.9000 PG-13 action NA NA action
2 A Guy Thing 39.5 PG-13 rom come… 15.545 15545000 romantic come…
3 A Man Apart 42.9000 R action 26.2480 26247999 action
4 A Mighty Wind 79.9000 PG-13 comedy 17.781 17781000 comedy
5 Agent Cody Banks 57.9000 PG action 47.8110 47811001 action
6 Alex & Emma 35.1000 PG-13 rom come… 14.219 14219000 romantic come…
7 American Wedding 50.7000 R comedy 104.441 104441000 comedy
8 Anger Management 62.6000 PG-13 comedy 134.404 134404010 comedy
9 Anything Else 63.3000 R rom come… 3.21200 3212000. romantic come…
10 Bad Boys II 38.1000 R action 138.397 138397000 action </code></pre>
<p>Do the same here, but return <code>"not romantic comedy"</code> if <code>type</code> is not <code>"rom comedy"</code> and this time overwrite the original <code>type</code> variable</p>
<div class="sourceCode" id="cb574"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb574-1"><a href="C-appendixC.html#cb574-1" aria-hidden="true" tabindex="-1"></a>movies_ex <span class="sc">%>%</span></span>
<span id="cb574-2"><a href="C-appendixC.html#cb574-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">type =</span> <span class="fu">if_else</span>(type <span class="sc">==</span> <span class="st">"rom comedy"</span>, <span class="st">"romantic comedy"</span>, <span class="st">"not romantic comedy"</span>)) <span class="sc">%>%</span></span>
<span id="cb574-3"><a href="C-appendixC.html#cb574-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">slice</span>(<span class="dv">1</span><span class="sc">:</span><span class="dv">10</span>)</span></code></pre></div>
<pre><code># A tibble: 10 x 6
name score rating type millions revenue
<chr> <dbl> <chr> <chr> <dbl> <dbl>
1 2 Fast 2 Furious 48.9000 PG-13 not romantic comedy NA NA
2 A Guy Thing 39.5 PG-13 romantic comedy 15.545 15545000
3 A Man Apart 42.9000 R not romantic comedy 26.2480 26247999
4 A Mighty Wind 79.9000 PG-13 not romantic comedy 17.781 17781000
5 Agent Cody Banks 57.9000 PG not romantic comedy 47.8110 47811001
6 Alex & Emma 35.1000 PG-13 romantic comedy 14.219 14219000
7 American Wedding 50.7000 R not romantic comedy 104.441 104441000
8 Anger Management 62.6000 PG-13 not romantic comedy 134.404 134404010
9 Anything Else 63.3000 R romantic comedy 3.21200 3212000.
10 Bad Boys II 38.1000 R not romantic comedy 138.397 138397000 </code></pre>
</div>
<div id="recode" class="section level4 unnumbered">
<h4><code>recode()</code></h4>
<p><code>if_else()</code> is rather limited however. What if we want to “rename” all <code>type</code> so that they start with uppercase? Use <code>recode()</code>:</p>
<div class="sourceCode" id="cb576"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb576-1"><a href="C-appendixC.html#cb576-1" aria-hidden="true" tabindex="-1"></a>movies_ex <span class="sc">%>%</span></span>
<span id="cb576-2"><a href="C-appendixC.html#cb576-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">type_new =</span> <span class="fu">recode</span>(type,</span>
<span id="cb576-3"><a href="C-appendixC.html#cb576-3" aria-hidden="true" tabindex="-1"></a> <span class="st">"action"</span> <span class="ot">=</span> <span class="st">"Action"</span>,</span>
<span id="cb576-4"><a href="C-appendixC.html#cb576-4" aria-hidden="true" tabindex="-1"></a> <span class="st">"animated"</span> <span class="ot">=</span> <span class="st">"Animated"</span>,</span>
<span id="cb576-5"><a href="C-appendixC.html#cb576-5" aria-hidden="true" tabindex="-1"></a> <span class="st">"comedy"</span> <span class="ot">=</span> <span class="st">"Comedy"</span>,</span>
<span id="cb576-6"><a href="C-appendixC.html#cb576-6" aria-hidden="true" tabindex="-1"></a> <span class="st">"drama"</span> <span class="ot">=</span> <span class="st">"Drama"</span>,</span>
<span id="cb576-7"><a href="C-appendixC.html#cb576-7" aria-hidden="true" tabindex="-1"></a> <span class="st">"fantasy"</span> <span class="ot">=</span> <span class="st">"Fantasy"</span>,</span>
<span id="cb576-8"><a href="C-appendixC.html#cb576-8" aria-hidden="true" tabindex="-1"></a> <span class="st">"rom comedy"</span> <span class="ot">=</span> <span class="st">"Romantic Comedy"</span></span>
<span id="cb576-9"><a href="C-appendixC.html#cb576-9" aria-hidden="true" tabindex="-1"></a> )) <span class="sc">%>%</span></span>
<span id="cb576-10"><a href="C-appendixC.html#cb576-10" aria-hidden="true" tabindex="-1"></a> <span class="fu">slice</span>(<span class="dv">1</span><span class="sc">:</span><span class="dv">10</span>)</span></code></pre></div>
<pre><code># A tibble: 10 x 7
name score rating type millions revenue type_new
<chr> <dbl> <chr> <chr> <dbl> <dbl> <chr>
1 2 Fast 2 Furious 48.9000 PG-13 action NA NA Action
2 A Guy Thing 39.5 PG-13 rom come… 15.545 15545000 Romantic Come…
3 A Man Apart 42.9000 R action 26.2480 26247999 Action
4 A Mighty Wind 79.9000 PG-13 comedy 17.781 17781000 Comedy
5 Agent Cody Banks 57.9000 PG action 47.8110 47811001 Action
6 Alex & Emma 35.1000 PG-13 rom come… 14.219 14219000 Romantic Come…
7 American Wedding 50.7000 R comedy 104.441 104441000 Comedy
8 Anger Management 62.6000 PG-13 comedy 134.404 134404010 Comedy
9 Anything Else 63.3000 R rom come… 3.21200 3212000. Romantic Come…
10 Bad Boys II 38.1000 R action 138.397 138397000 Action </code></pre>
</div>
<div id="case_when" class="section level4 unnumbered">
<h4><code>case_when()</code></h4>
<p><code>case_when()</code> is a little trickier, but allows you to evaluate boolean operations using <code>==</code>, <code>></code>, <code>>=</code>, <code>&</code>, <code>|</code>, etc:</p>
<div class="sourceCode" id="cb578"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb578-1"><a href="C-appendixC.html#cb578-1" aria-hidden="true" tabindex="-1"></a>movies_ex <span class="sc">%>%</span></span>
<span id="cb578-2"><a href="C-appendixC.html#cb578-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(</span>
<span id="cb578-3"><a href="C-appendixC.html#cb578-3" aria-hidden="true" tabindex="-1"></a> <span class="at">type_new =</span></span>
<span id="cb578-4"><a href="C-appendixC.html#cb578-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">case_when</span>(</span>
<span id="cb578-5"><a href="C-appendixC.html#cb578-5" aria-hidden="true" tabindex="-1"></a> type <span class="sc">==</span> <span class="st">"action"</span> <span class="sc">&</span> millions <span class="sc">></span> <span class="dv">40</span> <span class="sc">~</span> <span class="st">"Big budget action"</span>,</span>
<span id="cb578-6"><a href="C-appendixC.html#cb578-6" aria-hidden="true" tabindex="-1"></a> type <span class="sc">==</span> <span class="st">"rom comedy"</span> <span class="sc">&</span> millions <span class="sc"><</span> <span class="dv">40</span> <span class="sc">~</span> <span class="st">"Small budget romcom"</span>,</span>
<span id="cb578-7"><a href="C-appendixC.html#cb578-7" aria-hidden="true" tabindex="-1"></a> <span class="co"># Need this for everything else that aren't the two cases above:</span></span>
<span id="cb578-8"><a href="C-appendixC.html#cb578-8" aria-hidden="true" tabindex="-1"></a> <span class="cn">TRUE</span> <span class="sc">~</span> <span class="st">"Rest"</span></span>
<span id="cb578-9"><a href="C-appendixC.html#cb578-9" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb578-10"><a href="C-appendixC.html#cb578-10" aria-hidden="true" tabindex="-1"></a> )</span></code></pre></div>
<pre><code># A tibble: 108 x 7
name score rating type millions revenue type_new
<chr> <dbl> <chr> <chr> <dbl> <dbl> <chr>
1 2 Fast 2 Furi… 48.9000 PG-13 action NA NA Rest
2 A Guy Thing 39.5 PG-13 rom come… 15.545 15545000 Small budget ro…
3 A Man Apart 42.9000 R action 26.2480 26247999 Rest
4 A Mighty Wind 79.9000 PG-13 comedy 17.781 17781000 Rest
5 Agent Cody Ba… 57.9000 PG action 47.8110 47811001 Big budget acti…
6 Alex & Emma 35.1000 PG-13 rom come… 14.219 14219000 Small budget ro…
7 American Wedd… 50.7000 R comedy 104.441 104441000 Rest
8 Anger Managem… 62.6000 PG-13 comedy 134.404 134404010 Rest
9 Anything Else 63.3000 R rom come… 3.21200 3212000. Small budget ro…
10 Bad Boys II 38.1000 R action 138.397 138397000 Big budget acti…
# … with 98 more rows</code></pre>
</div>
</div>
<div id="appendix-convert-numerical-categorical" class="section level3" number="14.1.5">
<h3><span class="header-section-number">C.1.5</span> Converting a numerical variable to a categorical one</h3>
<p>Sometimes we want to turn a numerical, continuous variable into a categorical variable. For instance, what if we wanted to have a variable that tells us if a movie made one hundred million dollars or more. That is to say, we can create a binary variable, which is the same thing as a categorical variable with 2 levels. We can again use the <code>mutate()</code> function:</p>
<div class="sourceCode" id="cb580"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb580-1"><a href="C-appendixC.html#cb580-1" aria-hidden="true" tabindex="-1"></a>movies_ex <span class="sc">%>%</span></span>
<span id="cb580-2"><a href="C-appendixC.html#cb580-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">big_budget =</span> millions <span class="sc">></span> <span class="dv">100</span>) <span class="sc">%>%</span></span>
<span id="cb580-3"><a href="C-appendixC.html#cb580-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">slice</span>(<span class="dv">1</span><span class="sc">:</span><span class="dv">10</span>)</span></code></pre></div>
<pre><code># A tibble: 10 x 7
name score rating type millions revenue big_budget
<chr> <dbl> <chr> <chr> <dbl> <dbl> <lgl>
1 2 Fast 2 Furious 48.9000 PG-13 action NA NA NA
2 A Guy Thing 39.5 PG-13 rom comedy 15.545 15545000 FALSE
3 A Man Apart 42.9000 R action 26.2480 26247999 FALSE
4 A Mighty Wind 79.9000 PG-13 comedy 17.781 17781000 FALSE
5 Agent Cody Banks 57.9000 PG action 47.8110 47811001 FALSE
6 Alex & Emma 35.1000 PG-13 rom comedy 14.219 14219000 FALSE
7 American Wedding 50.7000 R comedy 104.441 104441000 TRUE
8 Anger Management 62.6000 PG-13 comedy 134.404 134404010 TRUE
9 Anything Else 63.3000 R rom comedy 3.21200 3212000. FALSE
10 Bad Boys II 38.1000 R action 138.397 138397000 TRUE </code></pre>
<p>What if you want to convert a numerical variable into a categorical variable with more than 2 levels? One way is to use the <code>cut()</code> command. For instance, below, we <code>cut()</code> the <code>score</code> variable, to recode it into 4 categories:</p>
<ol style="list-style-type: decimal">
<li>0 - 40 = bad</li>
<li>40.1 - 60 = so-so</li>
<li>60.1 - 80 = good</li>
<li>80.1+ = great</li>
</ol>
<p>We set the breaking points for cutting the numerical variable with the <code>c(0, 40, 60, 80, 100)</code> part, and set the labels for each of these bins with the <code>labels = c("bad", "so-so", "good", "great")</code> part. All this action happens inside the <code>mutate()</code> command, so the new categorical variable <code>score_categ</code> is added to the data frame.</p>
<div class="sourceCode" id="cb582"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb582-1"><a href="C-appendixC.html#cb582-1" aria-hidden="true" tabindex="-1"></a>movies_ex <span class="sc">%>%</span></span>
<span id="cb582-2"><a href="C-appendixC.html#cb582-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">score_categ =</span> <span class="fu">cut</span>(score,</span>
<span id="cb582-3"><a href="C-appendixC.html#cb582-3" aria-hidden="true" tabindex="-1"></a> <span class="at">breaks =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">40</span>, <span class="dv">60</span>, <span class="dv">80</span>, <span class="dv">100</span>),</span>
<span id="cb582-4"><a href="C-appendixC.html#cb582-4" aria-hidden="true" tabindex="-1"></a> <span class="at">labels =</span> <span class="fu">c</span>(<span class="st">"bad"</span>, <span class="st">"so-so"</span>, <span class="st">"good"</span>, <span class="st">"great"</span>)</span>
<span id="cb582-5"><a href="C-appendixC.html#cb582-5" aria-hidden="true" tabindex="-1"></a> )) <span class="sc">%>%</span></span>
<span id="cb582-6"><a href="C-appendixC.html#cb582-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">slice</span>(<span class="dv">1</span><span class="sc">:</span><span class="dv">10</span>)</span></code></pre></div>
<pre><code># A tibble: 10 x 7
name score rating type millions revenue score_categ
<chr> <dbl> <chr> <chr> <dbl> <dbl> <fct>
1 2 Fast 2 Furious 48.9000 PG-13 action NA NA so-so
2 A Guy Thing 39.5 PG-13 rom comedy 15.545 15545000 bad
3 A Man Apart 42.9000 R action 26.2480 26247999 so-so
4 A Mighty Wind 79.9000 PG-13 comedy 17.781 17781000 good
5 Agent Cody Banks 57.9000 PG action 47.8110 47811001 so-so
6 Alex & Emma 35.1000 PG-13 rom comedy 14.219 14219000 bad
7 American Wedding 50.7000 R comedy 104.441 104441000 so-so
8 Anger Management 62.6000 PG-13 comedy 134.404 134404010 good
9 Anything Else 63.3000 R rom comedy 3.21200 3212000. good
10 Bad Boys II 38.1000 R action 138.397 138397000 bad </code></pre>
<p>Other options with the <code>cut</code> function:</p>
<ul>
<li>By default, if the value is exactly the upper bound of an interval, it’s
included in the lessor category (e.g. 60.0 is ‘so-so’ not ‘good’), to
flip this, include the argument <code>right = FALSE</code>.</li>
<li>You could also have R equally divide the variable into a balanced
number of groups. For example, specifying <code>breaks = 3</code> would create 3 groups with
approximately the same number of values in each group.</li>
</ul>
</div>
<div id="appendix-prop" class="section level3" number="14.1.6">
<h3><span class="header-section-number">C.1.6</span> Computing proportions</h3>
<p>By using a <code>group_by()</code> followed not by a <code>summarize()</code> as is often the case, but rather a <code>mutate()</code>. So say we compute the total revenue millions for each movie rating and type:</p>