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<!DOCTYPE html>
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<title>Chapter 1 Getting Started with Data in R | Statistical Inference via Data Science</title>
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<li class="chapter" data-level="" data-path="index.html"><a href="index.html"><i class="fa fa-check"></i>Welcome to ModernDive</a></li>
<li class="chapter" data-level="" data-path="foreword.html"><a href="foreword.html"><i class="fa fa-check"></i>Foreword</a></li>
<li class="chapter" data-level="" data-path="preface.html"><a href="preface.html"><i class="fa fa-check"></i>Preface</a>
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<li class="chapter" data-level="" data-path="preface.html"><a href="preface.html#introduction-for-students"><i class="fa fa-check"></i>Introduction for students</a>
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<li class="chapter" data-level="" data-path="preface.html"><a href="preface.html#what-we-hope-you-will-learn-from-this-book"><i class="fa fa-check"></i>What we hope you will learn from this book</a></li>
<li class="chapter" data-level="" data-path="preface.html"><a href="preface.html#datascience-pipeline"><i class="fa fa-check"></i>Data/science pipeline</a></li>
<li class="chapter" data-level="" data-path="preface.html"><a href="preface.html#reproducible-research"><i class="fa fa-check"></i>Reproducible research</a></li>
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<li class="chapter" data-level="" data-path="preface.html"><a href="preface.html#resources"><i class="fa fa-check"></i>Resources</a></li>
<li class="chapter" data-level="" data-path="preface.html"><a href="preface.html#why-did-we-write-this-book"><i class="fa fa-check"></i>Why did we write this book?</a></li>
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<li class="chapter" data-level="" data-path="preface.html"><a href="preface.html#connect-and-contribute"><i class="fa fa-check"></i>Connect and contribute</a></li>
<li class="chapter" data-level="" data-path="preface.html"><a href="preface.html#acknowledgements"><i class="fa fa-check"></i>Acknowledgements</a></li>
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<li class="chapter" data-level="" data-path="about-the-authors.html"><a href="about-the-authors.html"><i class="fa fa-check"></i>About the authors</a></li>
<li class="chapter" data-level="1" data-path="1-getting-started.html"><a href="1-getting-started.html"><i class="fa fa-check"></i><b>1</b> Getting Started with Data in R</a>
<ul>
<li class="chapter" data-level="1.1" data-path="1-getting-started.html"><a href="1-getting-started.html#r-rstudio"><i class="fa fa-check"></i><b>1.1</b> What are R and RStudio?</a>
<ul>
<li class="chapter" data-level="1.1.1" data-path="1-getting-started.html"><a href="1-getting-started.html#installing"><i class="fa fa-check"></i><b>1.1.1</b> Installing R and RStudio</a></li>
<li class="chapter" data-level="1.1.2" data-path="1-getting-started.html"><a href="1-getting-started.html#using-r-via-rstudio"><i class="fa fa-check"></i><b>1.1.2</b> Using R via RStudio</a></li>
</ul></li>
<li class="chapter" data-level="1.2" data-path="1-getting-started.html"><a href="1-getting-started.html#code"><i class="fa fa-check"></i><b>1.2</b> How do I code in R?</a>
<ul>
<li class="chapter" data-level="1.2.1" data-path="1-getting-started.html"><a href="1-getting-started.html#programming-concepts"><i class="fa fa-check"></i><b>1.2.1</b> Basic programming concepts and terminology</a></li>
<li class="chapter" data-level="1.2.2" data-path="1-getting-started.html"><a href="1-getting-started.html#messages"><i class="fa fa-check"></i><b>1.2.2</b> Errors, warnings, and messages</a></li>
<li class="chapter" data-level="1.2.3" data-path="1-getting-started.html"><a href="1-getting-started.html#tips-code"><i class="fa fa-check"></i><b>1.2.3</b> Tips on learning to code</a></li>
</ul></li>
<li class="chapter" data-level="1.3" data-path="1-getting-started.html"><a href="1-getting-started.html#packages"><i class="fa fa-check"></i><b>1.3</b> What are R packages?</a>
<ul>
<li class="chapter" data-level="1.3.1" data-path="1-getting-started.html"><a href="1-getting-started.html#package-installation"><i class="fa fa-check"></i><b>1.3.1</b> Package installation</a></li>
<li class="chapter" data-level="1.3.2" data-path="1-getting-started.html"><a href="1-getting-started.html#package-loading"><i class="fa fa-check"></i><b>1.3.2</b> Package loading</a></li>
<li class="chapter" data-level="1.3.3" data-path="1-getting-started.html"><a href="1-getting-started.html#package-use"><i class="fa fa-check"></i><b>1.3.3</b> Package use</a></li>
</ul></li>
<li class="chapter" data-level="1.4" data-path="1-getting-started.html"><a href="1-getting-started.html#rfishbase"><i class="fa fa-check"></i><b>1.4</b> Explore your first datasets</a>
<ul>
<li class="chapter" data-level="1.4.1" data-path="1-getting-started.html"><a href="1-getting-started.html#rfishpackage"><i class="fa fa-check"></i><b>1.4.1</b> <code>rfishbase</code> package</a></li>
<li class="chapter" data-level="1.4.2" data-path="1-getting-started.html"><a href="1-getting-started.html#fishbasedataframe"><i class="fa fa-check"></i><b>1.4.2</b> <code>fishbase</code> data frame</a></li>
<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="getting-started" class="section level1" number="1">
<h1><span class="header-section-number">Chapter 1</span> Getting Started with Data in R</h1>
<p>Before we can start exploring data in R, there are some key concepts to understand first:</p>
<ol style="list-style-type: decimal">
<li>What are R and RStudio?</li>
<li>How do I code in R?</li>
<li>What are R packages?</li>
</ol>
<p>We’ll introduce these concepts in the upcoming Sections <a href="1-getting-started.html#r-rstudio">1.1</a>-<a href="1-getting-started.html#packages">1.3</a>. If you are already somewhat familiar with these concepts, feel free to skip to Section <a href="1-getting-started.html#rfishbase">1.4</a> where we’ll introduce our first dataset, which contains information on the taxonomy, biology, trophic ecology, life history, and uses of >33,000 fish species [see <a href="https://fishbase.org" class="uri">https://fishbase.org</a>]. This is a dataset we will explore in depth for much of the rest of this book.</p>
<div id="r-rstudio" class="section level2" number="1.1">
<h2><span class="header-section-number">1.1</span> What are R and RStudio?</h2>
<p>Throughout this book, we will assume that you are using R via RStudio. First time users often confuse the two. At its simplest, R is like a car’s engine while RStudio is like a car’s dashboard as illustrated in Figure <a href="1-getting-started.html#fig:R-vs-RStudio-1">1.1</a>.</p>
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R: Engine | RStudio: Dashboard
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<div class="figure" style="text-align: center"><span id="fig:R-vs-RStudio-1"></span>
<img src="images/shutterstock/R_vs_RStudio_1.png" alt="Analogy of difference between R and RStudio." width="95%" />
<p class="caption">
FIGURE 1.1: Analogy of difference between R and RStudio.
</p>
</div>
<p>More precisely, R is a programming language that runs computations, while RStudio is an <em>integrated development environment (IDE)</em> that provides an interface by adding many convenient features and tools. So just as the way of having access to a speedometer, rearview mirrors, and a navigation system makes driving much easier, using RStudio’s interface makes using R much easier as well.</p>
<div id="installing" class="section level3" number="1.1.1">
<h3><span class="header-section-number">1.1.1</span> Installing R and RStudio</h3>
<blockquote>
<p><strong>Note about RStudio Server or RStudio Cloud</strong>: If your instructor has provided you with a link and access to RStudio Server or RStudio Cloud, then you can skip this section. We do recommend after a few months of working on RStudio Server/Cloud that you return to these instructions to install this software on your own computer though.</p>
</blockquote>
<p>You will first need to download and install both R and RStudio (Desktop version) on your computer. It is important that you install R first and then install RStudio.</p>
<ol style="list-style-type: decimal">
<li><strong>You must do this first:</strong> Download and install R by going to <a href="https://cloud.r-project.org/" class="uri">https://cloud.r-project.org/</a>.
<ul>
<li>If you are a Windows user: Click on “Download R for Windows,” then click on “base,” then click on the Download link.</li>
<li>If you are macOS user: Click on “Download R for (Mac) OS X,” then under “Latest release:” click on R-X.X.X.pkg, where R-X.X.X is the version number. For example, the latest version of R as of November 25, 2019 was R-3.6.1.</li>
<li>If you are a Linux user: Click on “Download R for Linux” and choose your distribution for more information on installing R for your setup.</li>
</ul></li>
<li><strong>You must do this second:</strong> Download and install RStudio at <a href="https://www.rstudio.com/products/rstudio/download/" class="uri">https://www.rstudio.com/products/rstudio/download/</a>.
<ul>
<li>Scroll down to “Installers for Supported Platforms” near the bottom of the page.</li>
<li>Click on the download link corresponding to your computer’s operating system. </li>
</ul></li>
</ol>
</div>
<div id="using-r-via-rstudio" class="section level3" number="1.1.2">
<h3><span class="header-section-number">1.1.2</span> Using R via RStudio</h3>
<p>Recall our car analogy from earlier. Much as we don’t drive a car by interacting directly with the engine but rather by interacting with elements on the car’s dashboard, we won’t be using R directly but rather we will use RStudio’s interface. After you install R and RStudio on your computer, you’ll have two new <em>programs</em> (also called <em>applications</em>) you can open. We’ll always work in RStudio and not in the R application. Figure <a href="1-getting-started.html#fig:R-vs-RStudio-2">1.2</a> shows what icon you should be clicking on your computer.</p>
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<div class="figure" style="text-align: center"><span id="fig:R-vs-RStudio-2"></span>
<img src="images/logos/R_vs_RStudio.png" alt="Icons of R versus RStudio on your computer." width="90%" />
<p class="caption">
FIGURE 1.2: Icons of R versus RStudio on your computer.
</p>
</div>
<p>After you open RStudio, you should see something similar to Figure <a href="1-getting-started.html#fig:RStudio-interface">1.3</a>. (Note that slight differences might exist if the RStudio interface is updated after 2019 to not be this by default.)</p>
<div class="figure" style="text-align: center"><span id="fig:RStudio-interface"></span>
<img src="images/rstudio_screenshots/rstudio.png" alt="RStudio interface to R." width="93%" />
<p class="caption">
FIGURE 1.3: RStudio interface to R.
</p>
</div>
<p>Note the three <em>panes</em> which are three panels dividing the screen: the <em>console pane</em>, the <em>files pane</em>, and the <em>environment pane</em>. Over the course of this chapter, you’ll come to learn what purpose each of these panes serves.</p>
</div>
</div>
<div id="code" class="section level2" number="1.2">
<h2><span class="header-section-number">1.2</span> How do I code in R?</h2>
<p>Now that you’re set up with R and RStudio, you are probably asking yourself, “OK. Now how do I use R?” The first thing to note is that unlike other statistical software programs like Excel, SPSS, or Minitab that provide <a href="https://en.wikipedia.org/wiki/Point_and_click">point-and-click</a> interfaces, R is an <a href="https://en.wikipedia.org/wiki/Interpreted_language">interpreted language</a>. This means you have to type in commands written in <em>R code</em>. In other words, you have to code/program in R. Note that we’ll use the terms “coding” and “programming” interchangeably in this book.</p>
<p>While it is not required to be a seasoned coder/computer programmer to use R, there is still a set of basic programming concepts that new R users need to understand. Consequently, while this book is not a book on programming, you will still learn just enough of these basic programming concepts needed to explore and analyze data effectively.</p>
<div id="programming-concepts" class="section level3" number="1.2.1">
<h3><span class="header-section-number">1.2.1</span> Basic programming concepts and terminology</h3>
<p>We now introduce some basic programming concepts and terminology. Instead of asking you to memorize all these concepts and terminology right now, we’ll guide you so that you’ll “learn by doing.” To help you learn, we will always use a different font to distinguish regular text from <code>computer_code</code>. The best way to master these topics is, in our opinions, through <a href="https://jamesclear.com/deliberate-practice-theory">deliberate practice</a> with R and lots of repetition.</p>
<ul>
<li>Basics:
<ul>
<li><em>Console pane</em>: where you enter in commands. </li>
<li><em>Running code</em>: the act of telling R to perform an act by giving it commands in the console.</li>
<li><em>Objects</em>: where values are saved in R. We’ll show you how to <em>assign</em> values to objects and how to display the contents of objects. </li>
<li><em>Data types</em>: integers, doubles/numerics, logicals, and characters. Integers are values like -1, 0, 2, 4092. Doubles or numerics are a larger set of values containing both the integers but also fractions and decimal values like -24.932 and 0.8. Logicals are either <code>TRUE</code> or <code>FALSE</code> while characters are text such as “cabbage,” “Hamilton,” “The Wire is the greatest TV show ever,” and “This ramen is delicious.” Note that characters are often denoted with the quotation marks around them.</li>
</ul></li>
<li><em>Vectors</em>: a series of values. These are created using the <code>c()</code> function, where <code>c()</code> stands for “combine” or “concatenate.” For example, <code>c(6, 11, 13, 31, 90, 92)</code> creates a six element series of positive integer values .</li>
<li><em>Factors</em>: <em>categorical data</em> are commonly represented in R as factors. Categorical data can also be represented as <em>strings</em>. We’ll study this difference as we progress through the book.</li>
<li><em>Data frames</em>: rectangular spreadsheets. They are representations of datasets in R where the rows correspond to <em>observations</em> and the columns correspond to <em>variables</em> that describe the observations. We’ll cover data frames later in Section <a href="1-getting-started.html#rfishbase">1.4</a>.</li>
<li><em>Conditionals</em>:
<ul>
<li>Testing for equality in R using <code>==</code> (and not <code>=</code>, which is typically used for assignment). For example, <code>2 + 1 == 3</code> compares <code>2 + 1</code> to <code>3</code> and is correct R code, while <code>2 + 1 = 3</code> will return an error.</li>
<li>Boolean algebra: <code>TRUE/FALSE</code> statements and mathematical operators such as <code><</code> (less than), <code><=</code> (less than or equal), and <code>!=</code> (not equal to). For example, <code>4 + 2 >= 3</code> will return <code>TRUE</code>, but <code>3 + 5 <= 1</code> will return <code>FALSE</code>.</li>
<li>Logical operators: <code>&</code> representing “and” as well as <code>|</code> representing “or.” For example, <code>(2 + 1 == 3) & (2 + 1 == 4)</code> returns <code>FALSE</code> since both clauses are not <code>TRUE</code> (only the first clause is <code>TRUE</code>). On the other hand, <code>(2 + 1 == 3) | (2 + 1 == 4)</code> returns <code>TRUE</code> since at least one of the two clauses is <code>TRUE</code>. </li>
</ul></li>
<li><em>Functions</em>, also called <em>commands</em>: Functions perform tasks in R. They take in inputs called <em>arguments</em> and return outputs. You can either manually specify a function’s arguments or use the function’s <em>default values</em>.
<ul>
<li>For example, the function <code>seq()</code> in R generates a sequence of numbers. If you just run <code>seq()</code> it will return the value 1. That doesn’t seem very useful! This is because the default arguments are set as <code>seq(from = 1, to = 1)</code>. Thus, if you don’t pass in different values for <code>from</code> and <code>to</code> to change this behavior, R just assumes all you want is the number 1. You can change the argument values by updating the values after the <code>=</code> sign. If we try out <code>seq(from = 2, to = 5)</code> we get the result <code>2 3 4 5</code> that we might expect.</li>
<li>We’ll work with functions a lot throughout this book and you’ll get lots of practice in understanding their behaviors. To further assist you in understanding when a function is mentioned in the book, we’ll also include the <code>()</code> after them as we did with <code>seq()</code> above.</li>
</ul></li>
</ul>
<p>This list is by no means an exhaustive list of all the programming concepts and terminology needed to become a savvy R user; such a list would be so large it wouldn’t be very useful, especially for novices. Rather, we feel this is a minimally viable list of programming concepts and terminology you need to know before getting started. We feel that you can learn the rest as you go. Remember that your mastery of all of these concepts and terminology will build as you practice more and more.</p>
</div>
<div id="messages" class="section level3" number="1.2.2">
<h3><span class="header-section-number">1.2.2</span> Errors, warnings, and messages</h3>
<p>One thing that intimidates new R and RStudio users is how it reports <em>errors</em>, <em>warnings</em>, and <em>messages</em>. R reports errors, warnings, and messages in a glaring red font, which makes it seem like it is scolding you. However, seeing red text in the console is not always bad.</p>
<p>R will show red text in the console pane in three different situations:</p>
<ul>
<li><strong>Errors</strong>: When the red text is a legitimate error, it will be prefaced with “Error in…” and will try to explain what went wrong. Generally when there’s an error, the code will not run. For example, we’ll see in Subsection <a href="1-getting-started.html#package-use">1.3.3</a> if you see <code>Error in ggplot(...) : could not find function "ggplot"</code>, it means that the <code>ggplot()</code> function is not accessible because the package that contains the function (<code>ggplot2</code>) was not loaded with <code>library(ggplot2)</code>. Thus you cannot use the <code>ggplot()</code> function without the <code>ggplot2</code> package being loaded first.</li>
<li><strong>Warnings</strong>: When the red text is a warning, it will be prefaced with “Warning:” and R will try to explain why there’s a warning. Generally your code will still work, but with some caveats. For example, you will see in Chapter <a href="2-viz.html#viz">2</a> if you create a scatterplot based on a dataset where two of the rows of data have missing entries that would be needed to create points in the scatterplot, you will see this warning: <code>Warning: Removed 2 rows containing missing values (geom_point)</code>. R will still produce the scatterplot with all the remaining non-missing values, but it is warning you that two of the points aren’t there.</li>
<li><strong>Messages</strong>: When the red text doesn’t start with either “Error” or “Warning,” it’s <em>just a friendly message</em>. You’ll see these messages when you load <em>R packages</em> in the upcoming Subsection <a href="1-getting-started.html#package-loading">1.3.2</a> or when you read data saved in spreadsheet files with the <code>read_csv()</code> function as you’ll see in Chapter <a href="4-tidy.html#tidy">4</a>. These are helpful diagnostic messages and they don’t stop your code from working. Additionally, you’ll see these messages when you install packages too using <code>install.packages()</code> as discussed in Subsection <a href="1-getting-started.html#package-installation">1.3.1</a>.</li>
</ul>
<p>Remember, when you see red text in the console, <em>don’t panic</em>. It doesn’t necessarily mean anything is wrong. Rather:</p>
<ul>
<li>If the text starts with “Error,” figure out what’s causing it. <span style="color:red">Think of errors as a red traffic light: something is wrong!</span></li>
<li>If the text starts with “Warning,” figure out if it’s something to worry about. For instance, if you get a warning about missing values in a scatterplot and you know there are missing values, you’re fine. If that’s surprising, look at your data and see what’s missing. <span style="color:gold">Think of warnings as a yellow traffic light: everything is working fine, but watch out/pay attention.</span></li>
<li>Otherwise, the text is just a message. Read it, wave back at R, and thank it for talking to you. <span style="color:green">Think of messages as a green traffic light: everything is working fine and keep on going!</span></li>
</ul>
</div>
<div id="tips-code" class="section level3" number="1.2.3">
<h3><span class="header-section-number">1.2.3</span> Tips on learning to code</h3>
<p>Learning to code/program is quite similar to learning a foreign language. It can be daunting and frustrating at first. Such frustrations are common and it is normal to feel discouraged as you learn. However, just as with learning a foreign language, if you put in the effort and are not afraid to make mistakes, anybody can learn and improve.</p>
<p>Here are a few useful tips to keep in mind as you learn to program:</p>
<ul>
<li><strong>Remember that computers are not actually that smart</strong>: You may think your computer or smartphone is “smart,” but really people spent a lot of time and energy designing them to appear “smart.” In reality, you have to tell a computer everything it needs to do. Furthermore, the instructions you give your computer can’t have any mistakes in them, nor can they be ambiguous in any way.</li>
<li><strong>Take the “copy, paste, and tweak” approach</strong>: Especially when you learn your first programming language or you need to understand particularly complicated code, it is often much easier to take existing code that you know works and modify it to suit your ends. This is as opposed to trying to type out the code from scratch. We call this the <em>“copy, paste, and tweak”</em> approach. So early on, we suggest not trying to write code from memory, but rather take existing examples we have provided you, then copy, paste, and tweak them to suit your goals. After you start feeling more confident, you can slowly move away from this approach and write code from scratch. Think of the “copy, paste, and tweak” approach as training wheels for a child learning to ride a bike. After getting comfortable, they won’t need them anymore.</li>
<li><strong>The best way to learn to code is by doing</strong>: Rather than learning to code for its own sake, we find that learning to code goes much smoother when you have a goal in mind or when you are working on a particular project, like analyzing data that you are interested in and that is important to you.</li>
<li><strong>Practice is key</strong>: Just as the only method to improve your foreign language skills is through lots of practice and speaking, the only method to improving your coding skills is through lots of practice. Don’t worry, however, we’ll give you plenty of opportunities to do so!</li>
</ul>
</div>
</div>
<div id="packages" class="section level2" number="1.3">
<h2><span class="header-section-number">1.3</span> What are R packages?</h2>
<p>Another point of confusion with many new R users is the idea of an R package. R packages extend the functionality of R by providing additional functions, data, and documentation. They are written by a worldwide community of R users and can be downloaded for free from the internet.</p>
<p>For example, among the many packages we will use in this book are the <code>ggplot2</code> package <span class="citation">(<a href="#ref-R-ggplot2" role="doc-biblioref">Wickham, Chang, et al. 2021</a>)</span> for data visualization in Chapter <a href="2-viz.html#viz">2</a>, the <code>dplyr</code> package <span class="citation">(<a href="#ref-R-dplyr" role="doc-biblioref">Wickham, François, et al. 2021</a>)</span> for data wrangling in Chapter <a href="3-wrangling.html#wrangling">3</a>, the <code>moderndive</code> package <span class="citation">(<a href="#ref-R-moderndive" role="doc-biblioref">Kim and Ismay 2021</a>)</span> that accompanies this book, and the <code>infer</code> package <span class="citation">(<a href="#ref-R-infer" role="doc-biblioref">Bray et al. 2021</a>)</span> for “tidy” and transparent statistical inference in Chapters <a href="8-confidence-intervals.html#confidence-intervals">8</a>, <a href="9-hypothesis-testing.html#hypothesis-testing">9</a>, and <a href="10-inference-for-regression.html#inference-for-regression">10</a>.</p>
<p>A good analogy for R packages is they are like apps you can download onto a mobile phone:</p>
<!--
R: A new phone | R Packages: Apps you can download
:-------------------------:|:-------------------------:
{ height=2.5in } | { height=2.5in }
-->
<div class="figure" style="text-align: center"><span id="fig:R-vs-R-packages"></span>
<img src="images/shutterstock/R_vs_R_packages.png" alt="Analogy of R versus R packages." width="70%" />
<p class="caption">
FIGURE 1.4: Analogy of R versus R packages.
</p>
</div>
<p>So R is like a new mobile phone: while it has a certain amount of features when you use it for the first time, it doesn’t have everything. R packages are like the apps you can download onto your phone from Apple’s App Store or Android’s Google Play.</p>
<p>Let’s continue this analogy by considering the Instagram app for editing and sharing pictures. Say you have purchased a new phone and you would like to share a photo you have just taken with friends on Instagram. You need to:</p>
<ol style="list-style-type: decimal">
<li><em>Install the app</em>: Since your phone is new and does not include the Instagram app, you need to download the app from either the App Store or Google Play. You do this once and you’re set for the time being. You might need to do this again in the future when there is an update to the app.</li>
<li><em>Open the app</em>: After you’ve installed Instagram, you need to open it.</li>
</ol>
<p>Once Instagram is open on your phone, you can then proceed to share your photo with your friends and family. The process is very similar for using an R package. You need to:</p>
<ol style="list-style-type: decimal">
<li><em>Install the package</em>: This is like installing an app on your phone. Most packages are not installed by default when you install R and RStudio. Thus if you want to use a package for the first time, you need to install it first. Once you’ve installed a package, you likely won’t install it again unless you want to update it to a newer version.</li>
<li><em>“Load” the package</em>: “Loading” a package is like opening an app on your phone. Packages are not “loaded” by default when you start RStudio on your computer; you need to “load” each package you want to use every time you start RStudio.</li>
</ol>
<p>Let’s perform these two steps for the <code>ggplot2</code> package for data visualization.</p>
<div id="package-installation" class="section level3" number="1.3.1">
<h3><span class="header-section-number">1.3.1</span> Package installation</h3>
<blockquote>
<p><strong>Note about RStudio Server or RStudio Cloud</strong>: If your instructor has provided you with a link and access to RStudio Server or RStudio Cloud, you might not need to install packages, as they might be preinstalled for you by your instructor. That being said, it is still a good idea to know this process for later on when you are not using RStudio Server or Cloud, but rather RStudio Desktop on your own computer.</p>
</blockquote>
<p>There are two ways to install an R package: an easy way and a more advanced way. Let’s install the <code>ggplot2</code> package the easy way first as shown in Figure <a href="1-getting-started.html#fig:easy-way-install">1.5</a>. In the Files pane of RStudio:</p>
<ol style="list-style-type: lower-alpha">
<li>Click on the “Packages” tab.</li>
<li>Click on “Install” next to Update.</li>
<li>Type the name of the package under “Packages (separate multiple with space or comma):” In this case, type <code>ggplot2</code>.</li>
<li>Click “Install.”</li>
</ol>
<div class="figure" style="text-align: center"><span id="fig:easy-way-install"></span>
<img src="images/rstudio_screenshots/install_packages_easy_way.png" alt="Installing packages in R the easy way." width="55%" height="55%" />
<p class="caption">
FIGURE 1.5: Installing packages in R the easy way.
</p>
</div>
<p>An alternative but slightly less convenient way to install a package is by typing <code>install.packages("ggplot2")</code> in the console pane of RStudio and pressing Return/Enter on your keyboard. Note you must include the quotation marks around the name of the package.</p>
<p>Much like an app on your phone, you only have to install a package once. However, if you want to update a previously installed package to a newer version, you need to reinstall it by repeating the earlier steps.</p>
<div class="learncheck">
<p>
<strong><em>Learning check</em></strong>
</p>
</div>
<p><strong>(LC1.1)</strong> Repeat the earlier installation steps, but for the <code>dplyr</code>, <code>rfishbase</code>, and <code>knitr</code> packages. This will install the earlier mentioned <code>dplyr</code> package for data wrangling, the <code>rfishbase</code> package containing data on >33,000 fish species, and the <code>knitr</code> package for generating easy-to-read tables in R. We’ll use these packages in the next section.</p>
<div class="learncheck">
</div>
<p>Note that if you’d like your output on your computer to match up exactly with the output presented throughout the book, you may want to use the exact versions of the packages that we used. You can find a full listing of these packages and their versions in Appendix <a href="E-appendixE.html#appendixE">E</a>. This likely won’t be relevant for novices, but we included it for reproducibility reasons.</p>
</div>
<div id="package-loading" class="section level3" number="1.3.2">
<h3><span class="header-section-number">1.3.2</span> Package loading</h3>
<p>Recall that after you’ve installed a package, you need to “load it.” In other words, you need to “open it.” We do this by using the <code>library()</code> command. </p>
<p>For example, to load the <code>ggplot2</code> package, run the following code in the console pane. What do we mean by “run the following code?” Either type or copy-and-paste the following code into the console pane and then hit the Enter key.</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="1-getting-started.html#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggplot2)</span></code></pre></div>
<p>If after running the earlier code, a blinking cursor returns next to the <code>></code> “prompt” sign, it means you were successful and the <code>ggplot2</code> package is now loaded and ready to use. If, however, you get a red “error message” that reads <code>...</code> </p>
<pre><code>Error in library(ggplot2) : there is no package called ‘ggplot2’</code></pre>
<p><code>...</code> it means that you didn’t successfully install it. This is an example of an “error message” we discussed in Subsection <a href="1-getting-started.html#messages">1.2.2</a>. If you get this error message, go back to Subsection <a href="1-getting-started.html#package-installation">1.3.1</a> on R package installation and make sure to install the <code>ggplot2</code> package before proceeding.</p>
<div class="learncheck">
<p>
<strong><em>Learning check</em></strong>
</p>
</div>
<p><strong>(LC1.2)</strong> “Load” the <code>dplyr</code>, <code>rfishbase</code>, and <code>knitr</code> packages as well by repeating the earlier steps.</p>
<div class="learncheck">
</div>
<p></p>
</div>
<div id="package-use" class="section level3" number="1.3.3">
<h3><span class="header-section-number">1.3.3</span> Package use</h3>
<p>One very common mistake new R users make when wanting to use particular packages is they forget to “load” them first by using the <code>library()</code> command we just saw. Remember: <em>you have to load each package you want to use every time you start RStudio.</em> If you don’t first “load” a package, but attempt to use one of its features, you’ll see an error message similar to:</p>
<pre><code>Error: could not find function</code></pre>
<p>This is a different error message than the one you just saw on a package not having been installed yet. R is telling you that you are trying to use a function in a package that has not yet been “loaded.” R doesn’t know where to find the function you are using. Almost all new users forget to do this when starting out, and it is a little annoying to get used to doing it. However, you’ll remember with practice and after some time it will become second nature for you.</p>
</div>
</div>
<div id="rfishbase" class="section level2" number="1.4">
<h2><span class="header-section-number">1.4</span> Explore your first datasets</h2>
<p>Let’s put everything we’ve learned so far into practice and start exploring some real data! Data comes to us in a variety of formats, from pictures to text to numbers. Throughout this book, we’ll focus on datasets that are saved in “spreadsheet”-type format. This is probably the most common way data are collected and saved in many fields. Remember from Subsection <a href="1-getting-started.html#programming-concepts">1.2.1</a> that these “spreadsheet”-type datasets are called <em>data frames</em> in R. We’ll focus on working with data saved as data frames throughout this book.</p>
<p>Let’s first load all the packages needed for this chapter, assuming you’ve already installed them. Read Section <a href="1-getting-started.html#packages">1.3</a> for information on how to install and load R packages if you haven’t already.</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="1-getting-started.html#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(rfishbase)</span>
<span id="cb4-2"><a href="1-getting-started.html#cb4-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(dplyr)</span>
<span id="cb4-3"><a href="1-getting-started.html#cb4-3" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(knitr)</span></code></pre></div>
<p>At the beginning of all subsequent chapters in this book, we’ll always have a list of packages that you should have installed and loaded in order to work with that chapter’s R code.</p>
<div id="rfishpackage" class="section level3" number="1.4.1">
<h3><span class="header-section-number">1.4.1</span> <code>rfishbase</code> package</h3>
<p>Daniel Pauly hypothesized that the growth rate of fish was affected by the gill surface area; however, he had difficulty gathering the large amount of data needed to complete his Ph.D. research in the 1970’s. Dr. Pauly went on to become a leader in global fisheries management, working to protect fish populations from unsustainable harvesting. In addition to promoting technology for monitoring fish populations, he helped develop <a href="https://fishbase.org">FishBase</a>, an online database with a wealth of information on over 30,000 species of fish and the fisheries where they live.</p>
<p>Fishbase contains dozens of data tables, with information about individual fish species, including morphological characteristics, food items consumed, population size and age structure, swimming speed, and much, much more. Information about a single species can be readily accessed from the FishBase web site at <a href="https://fishbase.org" class="uri">https://fishbase.org</a>, but this interface becomes limiting for large scale data analysis. The <code>rfishbase</code> R package that we will be using contains many functions for accessing the online data tables available at <a href="https://fishbase.org">fishbase.org</a>.</p>
</div>
<div id="fishbasedataframe" class="section level3" number="1.4.2">
<h3><span class="header-section-number">1.4.2</span> <code>fishbase</code> data frame</h3>
<p>As you will see, there are many ways to access datasets in R. We’ll start with <code>fishbase</code>, a dataset built into the <code>rfishbase</code> R package that contains species information about all of the fish data available online at <a href="https://fishbase.org" class="uri">https://fishbase.org</a>.</p>
<p>We’ll begin by previewing the <code>fishbase</code> data frame. In the Console panel of RStudio, run the following code, either by typing or cutting-and-pasting it. It displays the contents of the <code>fishbase</code> data frame in your console. Note that depending on the size of your monitor, the number of displayed columns may vary.</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="1-getting-started.html#cb5-1" aria-hidden="true" tabindex="-1"></a>fishbase</span></code></pre></div>
<pre><code># A tibble: 33,104 x 12
SpecCode Genus Species SpeciesRefNo FBname SubFamily FamCode GenCode
<int> <chr> <chr> <int> <chr> <chr> <int> <int>
1 48039 Abysso… elochini 26334 <NA> <NA> 584 349
2 48040 Abysso… gibbosus 26334 <NA> <NA> 584 349
3 47208 Abysso… korotnef… 2058 <NA> <NA> 584 349
4 48048 Asproc… abyssalis 26334 <NA> <NA> 584 5868
5 48049 Asproc… herzenst… 2058 Herzenstei… <NA> 584 5868
6 61488 Asproc… intermed… 26334 <NA> <NA> 584 5868
7 60302 Asproc… korjakovi 53210 <NA> <NA> 584 5868
8 62234 Asproc… minor 53210 <NA> <NA> 584 5868
9 48051 Asproc… parmifer… 26334 <NA> <NA> 584 5868
10 48054 Asproc… platycep… 26334 <NA> <NA> 584 5868
# … with 33,094 more rows, and 4 more variables: SubGenCode <int>,
# Family <chr>, Order <chr>, Class <chr></code></pre>
<p>Let’s unpack this output:</p>
<ul>
<li><code>A tibble: 33,104 x 12</code>: A <code>tibble</code> is a specific kind of data frame in R. This particular data frame has
<ul>
<li><code>33,104</code> rows corresponding to different <em>observations</em>. Here, each observation is a fish species</li>
<li><code>12</code> columns corresponding to 12 <em>variables</em> describing each observation.</li>
</ul></li>
<li><code>SpecCode</code>, <code>Genus</code>, <code>Species</code>, <code>SpeciesRefNo</code>, etc. are the different columns, in other words, the different variables of this dataset.</li>
<li>We then have a preview of the first 10 rows of observations corresponding to the first 10 species. R is only showing the first 10 rows, because if it showed all <code>33,104</code> rows, it would overwhelm your screen.</li>
<li><code>... with 33,094 more rows, and 1 more variables:</code> indicating to us that 33,094 more rows of data and 1 more variables could not fit in this screen.</li>
</ul>
<p>In addition to datasets built into packages, we can also import data from external sources using functions. For example, the <code>species()</code> function of the <code>rfishbase</code> package is used to access an online data table at FishBase.org. Run the following two lines of code by typing or copying-and-pasting in your console.</p>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="1-getting-started.html#cb7-1" aria-hidden="true" tabindex="-1"></a>all_fishdata <span class="ot"><-</span> <span class="fu">species</span>()</span>
<span id="cb7-2"><a href="1-getting-started.html#cb7-2" aria-hidden="true" tabindex="-1"></a>all_fishdata</span></code></pre></div>
<pre><code># A tibble: 34,299 x 101
SpecCode Species Genus SpeciesRefNo Author FBname PicPreferredName
<dbl> <chr> <chr> <dbl> <chr> <chr> <chr>
1 64588 Aapticheil… Aaptic… 78622 (Huber, … <NA> Prweb_u0.jpg
2 16239 Aaptosyax … Aaptos… 10431 Rainboth… Giant s… Aagry_u0.gif
3 2347 Abactochro… Abacto… 85491 (Trewava… <NA> Melab_u0.jpg
4 62612 Abalistes … Abalis… 54835 Matsuura… <NA> Abfil_u0.jpg
5 9 Abalistes … Abalis… 9770 (Bloch &… Starry … Abste_u0.jpg
6 58334 Abalistes … Abalis… 11441 (Anonymo… <NA> Abste_u9.jpg
7 62797 Abbottina … Abbott… 84795 Nguyen, … <NA> <NA>
8 65146 Abbottina … Abbott… 81932 Huang & … <NA> <NA>
9 55145 Abbottina … Abbott… 33792 Qin, 1987 <NA> Ablia_u0.jpg
10 55143 Abbottina … Abbott… 33792 (Wu & Wa… <NA> Abobt_u0.jpg
# … with 34,289 more rows, and 94 more variables: PicPreferredNameM <chr>,
# PicPreferredNameF <chr>, PicPreferredNameJ <chr>, FamCode <dbl>,
# Subfamily <chr>, GenCode <dbl>, SubGenCode <dbl>, BodyShapeI <chr>,
# Source <chr>, AuthorRef <int>, Remark <chr>, TaxIssue <dbl>, Fresh <dbl>,
# Brack <dbl>, Saltwater <dbl>, DemersPelag <chr>, Amphibious <int>,
# AmphibiousRef <int>, AnaCat <chr>, MigratRef <dbl>,
# DepthRangeShallow <dbl>, DepthRangeDeep <dbl>, DepthRangeRef <dbl>,
# DepthRangeComShallow <dbl>, DepthRangeComDeep <dbl>, DepthComRef <dbl>,
# LongevityWild <dbl>, LongevityWildRef <dbl>, LongevityCaptive <dbl>,
# LongevityCapRef <dbl>, Vulnerability <dbl>, Length <dbl>, LTypeMaxM <chr>,
# LengthFemale <dbl>, LTypeMaxF <chr>, MaxLengthRef <dbl>,
# CommonLength <dbl>, LTypeComM <chr>, CommonLengthF <dbl>, LTypeComF <chr>,
# CommonLengthRef <dbl>, Weight <dbl>, WeightFemale <dbl>,
# MaxWeightRef <dbl>, Pic <chr>, PictureFemale <chr>, LarvaPic <chr>,
# EggPic <chr>, ImportanceRef <dbl>, Importance <chr>, PriceCateg <chr>,
# PriceReliability <chr>, Remarks7 <chr>, LandingStatistics <chr>,
# Landings <chr>, MainCatchingMethod <chr>, II <chr>, MSeines <dbl>,
# MGillnets <dbl>, MCastnets <dbl>, MTraps <dbl>, MSpears <dbl>,
# MTrawls <dbl>, MDredges <dbl>, MLiftnets <dbl>, MHooksLines <dbl>,
# MOther <dbl>, UsedforAquaculture <chr>, LifeCycle <chr>,
# AquacultureRef <dbl>, UsedasBait <chr>, BaitRef <dbl>, Aquarium <chr>,
# AquariumFishII <chr>, AquariumRef <dbl>, GameFish <dbl>, GameRef <dbl>,
# Dangerous <chr>, DangerousRef <dbl>, Electrogenic <chr>, ElectroRef <dbl>,
# Complete <int>, GoogleImage <dbl>, Comments <chr>, Profile <chr>,
# PD50 <dbl>, Emblematic <dbl>, Entered <dbl>, DateEntered <dbl>,
# Modified <dbl>, DateModified <dbl>, Expert <dbl>, DateChecked <dbl>,
# TS <int></code></pre>
<p>The first line of code <code>all_fishdata <- species()</code> runs the <code>species()</code> function to retrieve data from the FishBase Species table. The left arrow <code><-</code> tells R to save the data by <em>assigning</em> (or putting) it into a data frame named <code>all_fishdata</code>.</p>
<p>Typing the name of the data frame <code>all_fishdata</code> in the second line of code, then displays the data. Unfortunately, this output does not allow us to explore the data very well, but it does give a nice preview. Let’s look at some different ways to explore data frames more closely.</p>
</div>
<div id="exploredataframes" class="section level3" number="1.4.3">
<h3><span class="header-section-number">1.4.3</span> Exploring data frames</h3>
<p>There are many ways to get a feel for the data contained in a complex data frame such as <code>all_fishdata</code>. We present two functions that take as their “argument” (their input) the data frame in question. We also include another method for exploring one particular column of a data frame:</p>
<ol style="list-style-type: decimal">
<li>The <code>View()</code> function brings up RStudio’s built-in data viewer.</li>
<li>The <code>glimpse()</code> function is included in the <code>dplyr</code> package.</li>
<li>The <code>$</code> “extraction operator” is used to view a single variable/column in a data frame.</li>
</ol>
<p><strong>1. <code>View()</code></strong>:</p>
<p>Run <code>View(all_fishdata)</code> in your console in RStudio, either by typing it or cutting-and-pasting it into the console pane. (Note the uppercase <code>V</code> in <code>View()</code>. R is case-sensitive, so you’ll get an error message if you run <code>view(all_fishdata)</code> instead.)</p>
<p>Explore this data frame in the resulting pop up viewer. You should get into the habit of viewing any data frames you encounter.</p>
<div class="learncheck">
<p>
<strong><em>Learning check</em></strong>
</p>
</div>
<p><strong>(LC1.3)</strong> What does any <em>ONE</em> row in this <code>all_fishdata</code> dataset refer to?</p>
<ul>
<li>A. Data on a species</li>
<li>B. Data on a family</li>
<li>C. Data on a genus</li>
<li>D. Data on fish weight</li>