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<!DOCTYPE html>
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<title> 7 Tutorial: Data management with tidyverse | R for Conditional Process Analysis</title>
<meta name="description" content="Using Conditional Process Analysis to evaluate communication theories - B.A. Seminar at IKMZ, FS 2022" />
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<meta property="og:title" content=" 7 Tutorial: Data management with tidyverse | R for Conditional Process Analysis" />
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<meta name="author" content="Lara Kobilke, IKMZ, University of Zurich" />
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<li><a href="./">CPA Seminar</a></li>
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<li class="chapter" data-level="" data-path="index.html"><a href="index.html"><i class="fa fa-check"></i>General information on the course</a>
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<li class="chapter" data-level="1" data-path="tutorial-installing-understanding-rr-studio.html"><a href="tutorial-installing-understanding-rr-studio.html"><i class="fa fa-check"></i><b>1</b> Tutorial: Installing & Understanding R/R Studio</a>
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<li class="chapter" data-level="1.1" data-path="tutorial-installing-understanding-rr-studio.html"><a href="tutorial-installing-understanding-rr-studio.html#installing-r"><i class="fa fa-check"></i><b>1.1</b> Installing R</a></li>
<li class="chapter" data-level="1.2" data-path="tutorial-installing-understanding-rr-studio.html"><a href="tutorial-installing-understanding-rr-studio.html#installing-r-studio"><i class="fa fa-check"></i><b>1.2</b> Installing R Studio</a></li>
<li class="chapter" data-level="1.3" data-path="tutorial-installing-understanding-rr-studio.html"><a href="tutorial-installing-understanding-rr-studio.html#updating-r-and-r-studio"><i class="fa fa-check"></i><b>1.3</b> Updating R and R Studio</a>
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<li class="chapter" data-level="1.3.1" data-path="tutorial-installing-understanding-rr-studio.html"><a href="tutorial-installing-understanding-rr-studio.html#on-windows"><i class="fa fa-check"></i><b>1.3.1</b> On Windows</a></li>
<li class="chapter" data-level="1.3.2" data-path="tutorial-installing-understanding-rr-studio.html"><a href="tutorial-installing-understanding-rr-studio.html#on-mac"><i class="fa fa-check"></i><b>1.3.2</b> On MAC</a></li>
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<li class="chapter" data-level="1.4" data-path="tutorial-installing-understanding-rr-studio.html"><a href="tutorial-installing-understanding-rr-studio.html#how-does-r-work"><i class="fa fa-check"></i><b>1.4</b> How does R work?</a></li>
<li class="chapter" data-level="1.5" data-path="tutorial-installing-understanding-rr-studio.html"><a href="tutorial-installing-understanding-rr-studio.html#how-does-r-studio-work"><i class="fa fa-check"></i><b>1.5</b> How does R Studio work?</a>
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<li class="chapter" data-level="1.5.1" data-path="tutorial-installing-understanding-rr-studio.html"><a href="tutorial-installing-understanding-rr-studio.html#source-writing-your-own-code"><i class="fa fa-check"></i><b>1.5.1</b> Source: Writing your own code</a></li>
<li class="chapter" data-level="1.5.2" data-path="tutorial-installing-understanding-rr-studio.html"><a href="tutorial-installing-understanding-rr-studio.html#console-printing-results"><i class="fa fa-check"></i><b>1.5.2</b> Console: Printing results</a></li>
<li class="chapter" data-level="1.5.3" data-path="tutorial-installing-understanding-rr-studio.html"><a href="tutorial-installing-understanding-rr-studio.html#environment-overview-of-objects"><i class="fa fa-check"></i><b>1.5.3</b> Environment: Overview of objects</a></li>
<li class="chapter" data-level="1.5.4" data-path="tutorial-installing-understanding-rr-studio.html"><a href="tutorial-installing-understanding-rr-studio.html#plotshelppackages-do-everything-else"><i class="fa fa-check"></i><b>1.5.4</b> Plots/Help/Packages: Do everything else</a></li>
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<li class="chapter" data-level="1.6" data-path="tutorial-installing-understanding-rr-studio.html"><a href="tutorial-installing-understanding-rr-studio.html#take-aways"><i class="fa fa-check"></i><b>1.6</b> Take-Aways</a></li>
<li class="chapter" data-level="1.7" data-path="tutorial-installing-understanding-rr-studio.html"><a href="tutorial-installing-understanding-rr-studio.html#additional-tutorials"><i class="fa fa-check"></i><b>1.7</b> Additional tutorials</a></li>
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<li class="chapter" data-level="2" data-path="tutorial-workflow-in-r.html"><a href="tutorial-workflow-in-r.html"><i class="fa fa-check"></i><b>2</b> Tutorial: Workflow in R</a>
<ul>
<li class="chapter" data-level="2.1" data-path="tutorial-workflow-in-r.html"><a href="tutorial-workflow-in-r.html#defining-your-working-directory"><i class="fa fa-check"></i><b>2.1</b> Defining your working directory</a></li>
<li class="chapter" data-level="2.2" data-path="tutorial-workflow-in-r.html"><a href="tutorial-workflow-in-r.html#packages"><i class="fa fa-check"></i><b>2.2</b> Packages</a>
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<li class="chapter" data-level="2.2.1" data-path="tutorial-workflow-in-r.html"><a href="tutorial-workflow-in-r.html#installing-packages"><i class="fa fa-check"></i><b>2.2.1</b> Installing packages</a></li>
<li class="chapter" data-level="2.2.2" data-path="tutorial-workflow-in-r.html"><a href="tutorial-workflow-in-r.html#activating-packages"><i class="fa fa-check"></i><b>2.2.2</b> Activating packages</a></li>
<li class="chapter" data-level="2.2.3" data-path="tutorial-workflow-in-r.html"><a href="tutorial-workflow-in-r.html#getting-information-about-packages"><i class="fa fa-check"></i><b>2.2.3</b> Getting information about packages</a></li>
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<li class="chapter" data-level="2.3" data-path="tutorial-workflow-in-r.html"><a href="tutorial-workflow-in-r.html#help"><i class="fa fa-check"></i><b>2.3</b> Help?!</a>
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<li class="chapter" data-level="2.3.1" data-path="tutorial-workflow-in-r.html"><a href="tutorial-workflow-in-r.html#finding-information-about-packages"><i class="fa fa-check"></i><b>2.3.1</b> Finding information about packages</a></li>
<li class="chapter" data-level="2.3.2" data-path="tutorial-workflow-in-r.html"><a href="tutorial-workflow-in-r.html#finding-information-about-functions"><i class="fa fa-check"></i><b>2.3.2</b> Finding information about functions</a></li>
<li class="chapter" data-level="2.3.3" data-path="tutorial-workflow-in-r.html"><a href="tutorial-workflow-in-r.html#searching-for-help-online"><i class="fa fa-check"></i><b>2.3.3</b> Searching for help online</a></li>
<li class="chapter" data-level="2.3.4" data-path="tutorial-workflow-in-r.html"><a href="tutorial-workflow-in-r.html#interrupting-r"><i class="fa fa-check"></i><b>2.3.4</b> Interrupting R</a></li>
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<li class="chapter" data-level="2.4" data-path="tutorial-workflow-in-r.html"><a href="tutorial-workflow-in-r.html#saving-loading-cleaning-coderesults"><i class="fa fa-check"></i><b>2.4</b> Saving, loading & cleaning code/results</a>
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<li class="chapter" data-level="2.4.1" data-path="tutorial-workflow-in-r.html"><a href="tutorial-workflow-in-r.html#saving-your-code"><i class="fa fa-check"></i><b>2.4.1</b> Saving your code</a></li>
<li class="chapter" data-level="2.4.2" data-path="tutorial-workflow-in-r.html"><a href="tutorial-workflow-in-r.html#saving-your-results"><i class="fa fa-check"></i><b>2.4.2</b> Saving your results</a></li>
<li class="chapter" data-level="2.4.3" data-path="tutorial-workflow-in-r.html"><a href="tutorial-workflow-in-r.html#loading-working-spaces"><i class="fa fa-check"></i><b>2.4.3</b> Loading working spaces</a></li>
<li class="chapter" data-level="2.4.4" data-path="tutorial-workflow-in-r.html"><a href="tutorial-workflow-in-r.html#clean-your-working-space"><i class="fa fa-check"></i><b>2.4.4</b> Clean your working space</a></li>
<li class="chapter" data-level="2.4.5" data-path="tutorial-workflow-in-r.html"><a href="tutorial-workflow-in-r.html#take-aways-1"><i class="fa fa-check"></i><b>2.4.5</b> Take Aways</a></li>
</ul></li>
<li class="chapter" data-level="2.5" data-path="tutorial-workflow-in-r.html"><a href="tutorial-workflow-in-r.html#additional-tutorials-1"><i class="fa fa-check"></i><b>2.5</b> Additional tutorials</a></li>
</ul></li>
<li class="chapter" data-level="3" data-path="tutorial-using-r-as-a-calculator.html"><a href="tutorial-using-r-as-a-calculator.html"><i class="fa fa-check"></i><b>3</b> Tutorial: Using R as a calculator</a>
<ul>
<li class="chapter" data-level="3.1" data-path="tutorial-using-r-as-a-calculator.html"><a href="tutorial-using-r-as-a-calculator.html#using-variables-for-calculation"><i class="fa fa-check"></i><b>3.1</b> Using variables for calculation</a></li>
<li class="chapter" data-level="3.2" data-path="tutorial-using-r-as-a-calculator.html"><a href="tutorial-using-r-as-a-calculator.html#using-vectors-for-calculation"><i class="fa fa-check"></i><b>3.2</b> Using vectors for calculation</a></li>
<li class="chapter" data-level="3.3" data-path="tutorial-using-r-as-a-calculator.html"><a href="tutorial-using-r-as-a-calculator.html#take-aways-2"><i class="fa fa-check"></i><b>3.3</b> Take-Aways</a></li>
</ul></li>
<li class="chapter" data-level="4" data-path="tutorial-objects-structures-in-r.html"><a href="tutorial-objects-structures-in-r.html"><i class="fa fa-check"></i><b>4</b> Tutorial: Objects & structures in R</a>
<ul>
<li class="chapter" data-level="4.1" data-path="tutorial-objects-structures-in-r.html"><a href="tutorial-objects-structures-in-r.html#types-of-data"><i class="fa fa-check"></i><b>4.1</b> Types of data</a>
<ul>
<li class="chapter" data-level="4.1.1" data-path="tutorial-objects-structures-in-r.html"><a href="tutorial-objects-structures-in-r.html#accessing-variables-in-data-sets"><i class="fa fa-check"></i><b>4.1.1</b> Accessing variables in data sets</a></li>
<li class="chapter" data-level="4.1.2" data-path="tutorial-objects-structures-in-r.html"><a href="tutorial-objects-structures-in-r.html#numbers"><i class="fa fa-check"></i><b>4.1.2</b> Numbers</a></li>
<li class="chapter" data-level="4.1.3" data-path="tutorial-objects-structures-in-r.html"><a href="tutorial-objects-structures-in-r.html#text"><i class="fa fa-check"></i><b>4.1.3</b> Text</a></li>
<li class="chapter" data-level="4.1.4" data-path="tutorial-objects-structures-in-r.html"><a href="tutorial-objects-structures-in-r.html#factors"><i class="fa fa-check"></i><b>4.1.4</b> Factors</a></li>
<li class="chapter" data-level="4.1.5" data-path="tutorial-objects-structures-in-r.html"><a href="tutorial-objects-structures-in-r.html#dates"><i class="fa fa-check"></i><b>4.1.5</b> Dates</a></li>
<li class="chapter" data-level="4.1.6" data-path="tutorial-objects-structures-in-r.html"><a href="tutorial-objects-structures-in-r.html#missing-datanas"><i class="fa fa-check"></i><b>4.1.6</b> Missing data/NAs</a></li>
<li class="chapter" data-level="4.1.7" data-path="tutorial-objects-structures-in-r.html"><a href="tutorial-objects-structures-in-r.html#logicalother-operators"><i class="fa fa-check"></i><b>4.1.7</b> Logical/other operators</a></li>
</ul></li>
<li class="chapter" data-level="4.2" data-path="tutorial-objects-structures-in-r.html"><a href="tutorial-objects-structures-in-r.html#types-of-objects"><i class="fa fa-check"></i><b>4.2</b> Types of objects</a>
<ul>
<li class="chapter" data-level="4.2.1" data-path="tutorial-objects-structures-in-r.html"><a href="tutorial-objects-structures-in-r.html#scalars"><i class="fa fa-check"></i><b>4.2.1</b> Scalars</a></li>
<li class="chapter" data-level="4.2.2" data-path="tutorial-objects-structures-in-r.html"><a href="tutorial-objects-structures-in-r.html#vectors"><i class="fa fa-check"></i><b>4.2.2</b> Vectors</a></li>
<li class="chapter" data-level="4.2.3" data-path="tutorial-objects-structures-in-r.html"><a href="tutorial-objects-structures-in-r.html#data-frames-matrices"><i class="fa fa-check"></i><b>4.2.3</b> Data frames & matrices</a></li>
<li class="chapter" data-level="4.2.4" data-path="tutorial-objects-structures-in-r.html"><a href="tutorial-objects-structures-in-r.html#lists"><i class="fa fa-check"></i><b>4.2.4</b> Lists</a></li>
<li class="chapter" data-level="4.2.5" data-path="tutorial-objects-structures-in-r.html"><a href="tutorial-objects-structures-in-r.html#other-types-of-objects"><i class="fa fa-check"></i><b>4.2.5</b> Other types of objects</a></li>
</ul></li>
<li class="chapter" data-level="4.3" data-path="tutorial-objects-structures-in-r.html"><a href="tutorial-objects-structures-in-r.html#take-aways-3"><i class="fa fa-check"></i><b>4.3</b> Take Aways</a></li>
<li class="chapter" data-level="4.4" data-path="tutorial-objects-structures-in-r.html"><a href="tutorial-objects-structures-in-r.html#additional-tutorials-2"><i class="fa fa-check"></i><b>4.4</b> Additional tutorials</a></li>
</ul></li>
<li class="chapter" data-level="5" data-path="tutorial-reading-data-inout.html"><a href="tutorial-reading-data-inout.html"><i class="fa fa-check"></i><b>5</b> Tutorial: Reading data in/out</a>
<ul>
<li class="chapter" data-level="5.1" data-path="tutorial-reading-data-inout.html"><a href="tutorial-reading-data-inout.html#getting-data-into-r"><i class="fa fa-check"></i><b>5.1</b> Getting data into R</a></li>
<li class="chapter" data-level="5.2" data-path="tutorial-reading-data-inout.html"><a href="tutorial-reading-data-inout.html#getting-data-out-of-r"><i class="fa fa-check"></i><b>5.2</b> Getting data out of R</a></li>
<li class="chapter" data-level="5.3" data-path="tutorial-reading-data-inout.html"><a href="tutorial-reading-data-inout.html#other-packages-for-getting-data-intoout-of-r"><i class="fa fa-check"></i><b>5.3</b> Other packages for getting data into/out of R</a></li>
<li class="chapter" data-level="5.4" data-path="tutorial-reading-data-inout.html"><a href="tutorial-reading-data-inout.html#take-aways-4"><i class="fa fa-check"></i><b>5.4</b> Take Aways</a></li>
<li class="chapter" data-level="5.5" data-path="tutorial-reading-data-inout.html"><a href="tutorial-reading-data-inout.html#additional-tutorials-3"><i class="fa fa-check"></i><b>5.5</b> Additional tutorials</a></li>
</ul></li>
<li class="chapter" data-level="6" data-path="exercise-1-test-your-knowledge.html"><a href="exercise-1-test-your-knowledge.html"><i class="fa fa-check"></i><b>6</b> Exercise 1: Test your knowledge</a>
<ul>
<li class="chapter" data-level="6.1" data-path="exercise-1-test-your-knowledge.html"><a href="exercise-1-test-your-knowledge.html#task-1"><i class="fa fa-check"></i><b>6.1</b> Task 1</a></li>
<li class="chapter" data-level="6.2" data-path="exercise-1-test-your-knowledge.html"><a href="exercise-1-test-your-knowledge.html#task-2"><i class="fa fa-check"></i><b>6.2</b> Task 2</a></li>
<li class="chapter" data-level="6.3" data-path="exercise-1-test-your-knowledge.html"><a href="exercise-1-test-your-knowledge.html#task-3"><i class="fa fa-check"></i><b>6.3</b> Task 3</a></li>
<li class="chapter" data-level="6.4" data-path="exercise-1-test-your-knowledge.html"><a href="exercise-1-test-your-knowledge.html#task-4"><i class="fa fa-check"></i><b>6.4</b> Task 4</a></li>
<li class="chapter" data-level="6.5" data-path="exercise-1-test-your-knowledge.html"><a href="exercise-1-test-your-knowledge.html#task-5"><i class="fa fa-check"></i><b>6.5</b> Task 5</a></li>
</ul></li>
<li class="chapter" data-level="7" data-path="tutorial-data-management-with-tidyverse.html"><a href="tutorial-data-management-with-tidyverse.html"><i class="fa fa-check"></i><b>7</b> Tutorial: Data management with tidyverse</a>
<ul>
<li class="chapter" data-level="7.1" data-path="tutorial-data-management-with-tidyverse.html"><a href="tutorial-data-management-with-tidyverse.html#why-not-stick-with-base-r"><i class="fa fa-check"></i><b>7.1</b> Why not stick with Base R?</a></li>
<li class="chapter" data-level="7.2" data-path="tutorial-data-management-with-tidyverse.html"><a href="tutorial-data-management-with-tidyverse.html#tidyverse-packages"><i class="fa fa-check"></i><b>7.2</b> Tidyverse packages</a></li>
<li class="chapter" data-level="7.3" data-path="tutorial-data-management-with-tidyverse.html"><a href="tutorial-data-management-with-tidyverse.html#tidy-data"><i class="fa fa-check"></i><b>7.3</b> Tidy data</a></li>
<li class="chapter" data-level="7.4" data-path="tutorial-data-management-with-tidyverse.html"><a href="tutorial-data-management-with-tidyverse.html#the-pipe-operator"><i class="fa fa-check"></i><b>7.4</b> The pipe operator</a></li>
<li class="chapter" data-level="7.5" data-path="tutorial-data-management-with-tidyverse.html"><a href="tutorial-data-management-with-tidyverse.html#data-transformation-with-dplyr"><i class="fa fa-check"></i><b>7.5</b> Data transformation with dplyr</a>
<ul>
<li class="chapter" data-level="7.5.1" data-path="tutorial-data-management-with-tidyverse.html"><a href="tutorial-data-management-with-tidyverse.html#select"><i class="fa fa-check"></i><b>7.5.1</b> select()</a></li>
<li class="chapter" data-level="7.5.2" data-path="tutorial-data-management-with-tidyverse.html"><a href="tutorial-data-management-with-tidyverse.html#filter"><i class="fa fa-check"></i><b>7.5.2</b> filter()</a></li>
<li class="chapter" data-level="7.5.3" data-path="tutorial-data-management-with-tidyverse.html"><a href="tutorial-data-management-with-tidyverse.html#arrange"><i class="fa fa-check"></i><b>7.5.3</b> arrange()</a></li>
<li class="chapter" data-level="7.5.4" data-path="tutorial-data-management-with-tidyverse.html"><a href="tutorial-data-management-with-tidyverse.html#mutate"><i class="fa fa-check"></i><b>7.5.4</b> mutate()</a></li>
<li class="chapter" data-level="7.5.5" data-path="tutorial-data-management-with-tidyverse.html"><a href="tutorial-data-management-with-tidyverse.html#simmarize-group_by"><i class="fa fa-check"></i><b>7.5.5</b> simmarize() [+ group_by()]</a></li>
<li class="chapter" data-level="7.5.6" data-path="tutorial-data-management-with-tidyverse.html"><a href="tutorial-data-management-with-tidyverse.html#chaining-functions-in-a-pipe"><i class="fa fa-check"></i><b>7.5.6</b> Chaining functions in a pipe</a></li>
</ul></li>
<li class="chapter" data-level="7.6" data-path="tutorial-data-management-with-tidyverse.html"><a href="tutorial-data-management-with-tidyverse.html#take-aways-5"><i class="fa fa-check"></i><b>7.6</b> Take-Aways</a></li>
<li class="chapter" data-level="7.7" data-path="tutorial-data-management-with-tidyverse.html"><a href="tutorial-data-management-with-tidyverse.html#additional-tutorials-4"><i class="fa fa-check"></i><b>7.7</b> Additional tutorials</a></li>
</ul></li>
<li class="chapter" data-level="8" data-path="exercise-2-test-your-knowledge.html"><a href="exercise-2-test-your-knowledge.html"><i class="fa fa-check"></i><b>8</b> Exercise 2: Test your knowledge</a>
<ul>
<li class="chapter" data-level="8.1" data-path="exercise-2-test-your-knowledge.html"><a href="exercise-2-test-your-knowledge.html#task-1-1"><i class="fa fa-check"></i><b>8.1</b> Task 1</a></li>
<li class="chapter" data-level="8.2" data-path="exercise-2-test-your-knowledge.html"><a href="exercise-2-test-your-knowledge.html#task-2-1"><i class="fa fa-check"></i><b>8.2</b> Task 2</a></li>
<li class="chapter" data-level="8.3" data-path="exercise-2-test-your-knowledge.html"><a href="exercise-2-test-your-knowledge.html#task-3-1"><i class="fa fa-check"></i><b>8.3</b> Task 3</a></li>
<li class="chapter" data-level="8.4" data-path="exercise-2-test-your-knowledge.html"><a href="exercise-2-test-your-knowledge.html#task-4-1"><i class="fa fa-check"></i><b>8.4</b> Task 4</a></li>
<li class="chapter" data-level="8.5" data-path="exercise-2-test-your-knowledge.html"><a href="exercise-2-test-your-knowledge.html#task-5-1"><i class="fa fa-check"></i><b>8.5</b> Task 5</a></li>
<li class="chapter" data-level="8.6" data-path="exercise-2-test-your-knowledge.html"><a href="exercise-2-test-your-knowledge.html#task-6"><i class="fa fa-check"></i><b>8.6</b> Task 6</a></li>
<li class="chapter" data-level="8.7" data-path="exercise-2-test-your-knowledge.html"><a href="exercise-2-test-your-knowledge.html#task-7"><i class="fa fa-check"></i><b>8.7</b> Task 7</a></li>
</ul></li>
<li class="chapter" data-level="9" data-path="tutorial-data-visualization-with-ggplot.html"><a href="tutorial-data-visualization-with-ggplot.html"><i class="fa fa-check"></i><b>9</b> Tutorial: Data visualization with ggplot</a>
<ul>
<li class="chapter" data-level="9.1" data-path="tutorial-data-visualization-with-ggplot.html"><a href="tutorial-data-visualization-with-ggplot.html#why-not-stick-with-base-r-1"><i class="fa fa-check"></i><b>9.1</b> Why not stick with Base R?</a></li>
<li class="chapter" data-level="9.2" data-path="tutorial-data-visualization-with-ggplot.html"><a href="tutorial-data-visualization-with-ggplot.html#components-of-a-ggplot-graph"><i class="fa fa-check"></i><b>9.2</b> Components of a ggplot graph</a></li>
<li class="chapter" data-level="9.3" data-path="tutorial-data-visualization-with-ggplot.html"><a href="tutorial-data-visualization-with-ggplot.html#installing-activating-ggplot"><i class="fa fa-check"></i><b>9.3</b> Installing & activating ggplot</a></li>
<li class="chapter" data-level="9.4" data-path="tutorial-data-visualization-with-ggplot.html"><a href="tutorial-data-visualization-with-ggplot.html#building-your-first-plot"><i class="fa fa-check"></i><b>9.4</b> Building your first plot</a>
<ul>
<li class="chapter" data-level="9.4.1" data-path="tutorial-data-visualization-with-ggplot.html"><a href="tutorial-data-visualization-with-ggplot.html#data"><i class="fa fa-check"></i><b>9.4.1</b> Data</a></li>
<li class="chapter" data-level="9.4.2" data-path="tutorial-data-visualization-with-ggplot.html"><a href="tutorial-data-visualization-with-ggplot.html#aesthetics"><i class="fa fa-check"></i><b>9.4.2</b> Aesthetics</a></li>
<li class="chapter" data-level="9.4.3" data-path="tutorial-data-visualization-with-ggplot.html"><a href="tutorial-data-visualization-with-ggplot.html#geometrics"><i class="fa fa-check"></i><b>9.4.3</b> Geometrics</a></li>
<li class="chapter" data-level="9.4.4" data-path="tutorial-data-visualization-with-ggplot.html"><a href="tutorial-data-visualization-with-ggplot.html#scales"><i class="fa fa-check"></i><b>9.4.4</b> Scales</a></li>
<li class="chapter" data-level="9.4.5" data-path="tutorial-data-visualization-with-ggplot.html"><a href="tutorial-data-visualization-with-ggplot.html#themes"><i class="fa fa-check"></i><b>9.4.5</b> Themes</a></li>
<li class="chapter" data-level="9.4.6" data-path="tutorial-data-visualization-with-ggplot.html"><a href="tutorial-data-visualization-with-ggplot.html#labs"><i class="fa fa-check"></i><b>9.4.6</b> Labs</a></li>
<li class="chapter" data-level="9.4.7" data-path="tutorial-data-visualization-with-ggplot.html"><a href="tutorial-data-visualization-with-ggplot.html#facets"><i class="fa fa-check"></i><b>9.4.7</b> Facets</a></li>
<li class="chapter" data-level="9.4.8" data-path="tutorial-data-visualization-with-ggplot.html"><a href="tutorial-data-visualization-with-ggplot.html#saving-graphs"><i class="fa fa-check"></i><b>9.4.8</b> Saving graphs</a></li>
</ul></li>
<li class="chapter" data-level="9.5" data-path="tutorial-data-visualization-with-ggplot.html"><a href="tutorial-data-visualization-with-ggplot.html#other-common-plot-types"><i class="fa fa-check"></i><b>9.5</b> Other common plot types</a>
<ul>
<li class="chapter" data-level="9.5.1" data-path="tutorial-data-visualization-with-ggplot.html"><a href="tutorial-data-visualization-with-ggplot.html#bar-plots"><i class="fa fa-check"></i><b>9.5.1</b> bar plots</a></li>
<li class="chapter" data-level="9.5.2" data-path="tutorial-data-visualization-with-ggplot.html"><a href="tutorial-data-visualization-with-ggplot.html#box-plots"><i class="fa fa-check"></i><b>9.5.2</b> box plots</a></li>
</ul></li>
<li class="chapter" data-level="9.6" data-path="tutorial-data-visualization-with-ggplot.html"><a href="tutorial-data-visualization-with-ggplot.html#take-aways-6"><i class="fa fa-check"></i><b>9.6</b> Take Aways</a></li>
<li class="chapter" data-level="9.7" data-path="tutorial-data-visualization-with-ggplot.html"><a href="tutorial-data-visualization-with-ggplot.html#additional-tutorials-5"><i class="fa fa-check"></i><b>9.7</b> Additional tutorials</a></li>
</ul></li>
<li class="chapter" data-level="10" data-path="exercise-3-test-your-knowledge.html"><a href="exercise-3-test-your-knowledge.html"><i class="fa fa-check"></i><b>10</b> Exercise 3: Test your knowledge</a>
<ul>
<li class="chapter" data-level="10.1" data-path="exercise-3-test-your-knowledge.html"><a href="exercise-3-test-your-knowledge.html#task-1-2"><i class="fa fa-check"></i><b>10.1</b> Task 1</a></li>
<li class="chapter" data-level="10.2" data-path="exercise-3-test-your-knowledge.html"><a href="exercise-3-test-your-knowledge.html#task-2-2"><i class="fa fa-check"></i><b>10.2</b> Task 2</a></li>
<li class="chapter" data-level="10.3" data-path="exercise-3-test-your-knowledge.html"><a href="exercise-3-test-your-knowledge.html#task-3-2"><i class="fa fa-check"></i><b>10.3</b> Task 3</a></li>
</ul></li>
<li class="chapter" data-level="11" data-path="tutorial-linear-regression.html"><a href="tutorial-linear-regression.html"><i class="fa fa-check"></i><b>11</b> Tutorial: Linear regression</a>
<ul>
<li class="chapter" data-level="11.1" data-path="tutorial-linear-regression.html"><a href="tutorial-linear-regression.html#knowing-your-data"><i class="fa fa-check"></i><b>11.1</b> Knowing your data</a></li>
<li class="chapter" data-level="11.2" data-path="tutorial-linear-regression.html"><a href="tutorial-linear-regression.html#visual-inspection-of-linear-trends"><i class="fa fa-check"></i><b>11.2</b> Visual inspection of linear trends</a></li>
<li class="chapter" data-level="11.3" data-path="tutorial-linear-regression.html"><a href="tutorial-linear-regression.html#pearsons-r"><i class="fa fa-check"></i><b>11.3</b> Pearson´s r</a></li>
<li class="chapter" data-level="11.4" data-path="tutorial-linear-regression.html"><a href="tutorial-linear-regression.html#ols-regression"><i class="fa fa-check"></i><b>11.4</b> OLS regression</a></li>
<li class="chapter" data-level="11.5" data-path="tutorial-linear-regression.html"><a href="tutorial-linear-regression.html#standardizing-coefficients"><i class="fa fa-check"></i><b>11.5</b> Standardizing coefficients</a></li>
<li class="chapter" data-level="11.6" data-path="tutorial-linear-regression.html"><a href="tutorial-linear-regression.html#multiple-regression"><i class="fa fa-check"></i><b>11.6</b> Multiple regression</a></li>
<li class="chapter" data-level="11.7" data-path="tutorial-linear-regression.html"><a href="tutorial-linear-regression.html#standardized-multiple-regression"><i class="fa fa-check"></i><b>11.7</b> Standardized multiple regression</a></li>
<li class="chapter" data-level="11.8" data-path="tutorial-linear-regression.html"><a href="tutorial-linear-regression.html#take-aways-7"><i class="fa fa-check"></i><b>11.8</b> Take Aways</a></li>
<li class="chapter" data-level="11.9" data-path="tutorial-linear-regression.html"><a href="tutorial-linear-regression.html#additional-tutorials-6"><i class="fa fa-check"></i><b>11.9</b> Additional tutorials</a></li>
</ul></li>
<li class="chapter" data-level="12" data-path="exercise-4-test-your-knowledge.html"><a href="exercise-4-test-your-knowledge.html"><i class="fa fa-check"></i><b>12</b> Exercise 4: Test your knowledge</a>
<ul>
<li class="chapter" data-level="12.1" data-path="exercise-4-test-your-knowledge.html"><a href="exercise-4-test-your-knowledge.html#task-1-3"><i class="fa fa-check"></i><b>12.1</b> Task 1</a></li>
<li class="chapter" data-level="12.2" data-path="exercise-4-test-your-knowledge.html"><a href="exercise-4-test-your-knowledge.html#task-2-3"><i class="fa fa-check"></i><b>12.2</b> Task 2</a></li>
<li class="chapter" data-level="12.3" data-path="exercise-4-test-your-knowledge.html"><a href="exercise-4-test-your-knowledge.html#task-3-3"><i class="fa fa-check"></i><b>12.3</b> Task 3</a></li>
<li class="chapter" data-level="12.4" data-path="exercise-4-test-your-knowledge.html"><a href="exercise-4-test-your-knowledge.html#task-4-2"><i class="fa fa-check"></i><b>12.4</b> Task 4</a></li>
<li class="chapter" data-level="12.5" data-path="exercise-4-test-your-knowledge.html"><a href="exercise-4-test-your-knowledge.html#task-5-2"><i class="fa fa-check"></i><b>12.5</b> Task 5</a></li>
<li class="chapter" data-level="12.6" data-path="exercise-4-test-your-knowledge.html"><a href="exercise-4-test-your-knowledge.html#task-6-1"><i class="fa fa-check"></i><b>12.6</b> Task 6</a></li>
</ul></li>
<li class="chapter" data-level="13" data-path="tutorial-mediation-analysis.html"><a href="tutorial-mediation-analysis.html"><i class="fa fa-check"></i><b>13</b> Tutorial: Mediation analysis</a>
<ul>
<li class="chapter" data-level="13.1" data-path="tutorial-mediation-analysis.html"><a href="tutorial-mediation-analysis.html#introduction-to-mediation"><i class="fa fa-check"></i><b>13.1</b> Introduction to mediation</a></li>
<li class="chapter" data-level="13.2" data-path="tutorial-mediation-analysis.html"><a href="tutorial-mediation-analysis.html#difference-between-mediation-and-moderation"><i class="fa fa-check"></i><b>13.2</b> Difference between mediation and moderation</a></li>
<li class="chapter" data-level="13.3" data-path="tutorial-mediation-analysis.html"><a href="tutorial-mediation-analysis.html#two-step-process-of-mediation"><i class="fa fa-check"></i><b>13.3</b> Two-step process of mediation</a></li>
<li class="chapter" data-level="13.4" data-path="tutorial-mediation-analysis.html"><a href="tutorial-mediation-analysis.html#statistical-representation-and-equations"><i class="fa fa-check"></i><b>13.4</b> Statistical representation and equations</a>
<ul>
<li class="chapter" data-level="13.4.1" data-path="tutorial-mediation-analysis.html"><a href="tutorial-mediation-analysis.html#direct-effect-c"><i class="fa fa-check"></i><b>13.4.1</b> Direct effect <em>c′</em></a></li>
<li class="chapter" data-level="13.4.2" data-path="tutorial-mediation-analysis.html"><a href="tutorial-mediation-analysis.html#indirect-effect-ab"><i class="fa fa-check"></i><b>13.4.2</b> Indirect effect <em>ab</em></a></li>
<li class="chapter" data-level="13.4.3" data-path="tutorial-mediation-analysis.html"><a href="tutorial-mediation-analysis.html#total-effect"><i class="fa fa-check"></i><b>13.4.3</b> Total effect</a></li>
</ul></li>
<li class="chapter" data-level="13.5" data-path="tutorial-mediation-analysis.html"><a href="tutorial-mediation-analysis.html#example"><i class="fa fa-check"></i><b>13.5</b> Example</a>
<ul>
<li class="chapter" data-level="13.5.1" data-path="tutorial-mediation-analysis.html"><a href="tutorial-mediation-analysis.html#knowing-your-data-1"><i class="fa fa-check"></i><b>13.5.1</b> Knowing your data</a></li>
<li class="chapter" data-level="13.5.2" data-path="tutorial-mediation-analysis.html"><a href="tutorial-mediation-analysis.html#visual-inspection-of-linear-trends-1"><i class="fa fa-check"></i><b>13.5.2</b> Visual inspection of linear trends</a></li>
<li class="chapter" data-level="13.5.3" data-path="tutorial-mediation-analysis.html"><a href="tutorial-mediation-analysis.html#pearsons-r-1"><i class="fa fa-check"></i><b>13.5.3</b> Pearson’s r</a></li>
<li class="chapter" data-level="13.5.4" data-path="tutorial-mediation-analysis.html"><a href="tutorial-mediation-analysis.html#fit-models"><i class="fa fa-check"></i><b>13.5.4</b> Fit models</a></li>
<li class="chapter" data-level="13.5.5" data-path="tutorial-mediation-analysis.html"><a href="tutorial-mediation-analysis.html#using-processr"><i class="fa fa-check"></i><b>13.5.5</b> Using processR</a></li>
</ul></li>
<li class="chapter" data-level="13.6" data-path="tutorial-mediation-analysis.html"><a href="tutorial-mediation-analysis.html#take-aways-8"><i class="fa fa-check"></i><b>13.6</b> Take Aways</a></li>
<li class="chapter" data-level="13.7" data-path="tutorial-mediation-analysis.html"><a href="tutorial-mediation-analysis.html#additional-tutorials-7"><i class="fa fa-check"></i><b>13.7</b> Additional tutorials</a></li>
</ul></li>
<li class="chapter" data-level="14" data-path="exercise-5-test-your-knowledge.html"><a href="exercise-5-test-your-knowledge.html"><i class="fa fa-check"></i><b>14</b> Exercise 5: Test your knowledge</a>
<ul>
<li class="chapter" data-level="14.1" data-path="exercise-5-test-your-knowledge.html"><a href="exercise-5-test-your-knowledge.html#task-1-4"><i class="fa fa-check"></i><b>14.1</b> Task 1</a></li>
<li class="chapter" data-level="14.2" data-path="exercise-5-test-your-knowledge.html"><a href="exercise-5-test-your-knowledge.html#task-2-4"><i class="fa fa-check"></i><b>14.2</b> Task 2</a></li>
<li class="chapter" data-level="14.3" data-path="exercise-5-test-your-knowledge.html"><a href="exercise-5-test-your-knowledge.html#task-3-4"><i class="fa fa-check"></i><b>14.3</b> Task 3</a></li>
<li class="chapter" data-level="14.4" data-path="exercise-5-test-your-knowledge.html"><a href="exercise-5-test-your-knowledge.html#task-4-3"><i class="fa fa-check"></i><b>14.4</b> Task 4</a></li>
<li class="chapter" data-level="14.5" data-path="exercise-5-test-your-knowledge.html"><a href="exercise-5-test-your-knowledge.html#task-5-3"><i class="fa fa-check"></i><b>14.5</b> Task 5</a></li>
<li class="chapter" data-level="14.6" data-path="exercise-5-test-your-knowledge.html"><a href="exercise-5-test-your-knowledge.html#task-6-2"><i class="fa fa-check"></i><b>14.6</b> Task 6</a></li>
<li class="chapter" data-level="14.7" data-path="exercise-5-test-your-knowledge.html"><a href="exercise-5-test-your-knowledge.html#task-7-1"><i class="fa fa-check"></i><b>14.7</b> Task 7</a></li>
</ul></li>
<li class="chapter" data-level="15" data-path="tutorial-moderation-analysis.html"><a href="tutorial-moderation-analysis.html"><i class="fa fa-check"></i><b>15</b> Tutorial: Moderation analysis</a>
<ul>
<li class="chapter" data-level="15.1" data-path="tutorial-moderation-analysis.html"><a href="tutorial-moderation-analysis.html#introduction-to-moderation"><i class="fa fa-check"></i><b>15.1</b> Introduction to moderation</a></li>
<li class="chapter" data-level="15.2" data-path="tutorial-moderation-analysis.html"><a href="tutorial-moderation-analysis.html#one-step-process-of-moderation"><i class="fa fa-check"></i><b>15.2</b> One-step process of moderation</a></li>
<li class="chapter" data-level="15.3" data-path="tutorial-moderation-analysis.html"><a href="tutorial-moderation-analysis.html#statistical-representation-and-equations-1"><i class="fa fa-check"></i><b>15.3</b> Statistical representation and equations</a></li>
<li class="chapter" data-level="15.4" data-path="tutorial-moderation-analysis.html"><a href="tutorial-moderation-analysis.html#example-1"><i class="fa fa-check"></i><b>15.4</b> Example</a>
<ul>
<li class="chapter" data-level="15.4.1" data-path="tutorial-moderation-analysis.html"><a href="tutorial-moderation-analysis.html#knowing-your-data-2"><i class="fa fa-check"></i><b>15.4.1</b> Knowing your data</a></li>
<li class="chapter" data-level="15.4.2" data-path="tutorial-moderation-analysis.html"><a href="tutorial-moderation-analysis.html#visual-inspection-of-linear-trends-2"><i class="fa fa-check"></i><b>15.4.2</b> Visual inspection of linear trends</a></li>
<li class="chapter" data-level="15.4.3" data-path="tutorial-moderation-analysis.html"><a href="tutorial-moderation-analysis.html#fit-models-1"><i class="fa fa-check"></i><b>15.4.3</b> Fit models</a></li>
<li class="chapter" data-level="15.4.4" data-path="tutorial-moderation-analysis.html"><a href="tutorial-moderation-analysis.html#standardization-and-mean-centering"><i class="fa fa-check"></i><b>15.4.4</b> Standardization and mean-centering</a></li>
<li class="chapter" data-level="15.4.5" data-path="tutorial-moderation-analysis.html"><a href="tutorial-moderation-analysis.html#follow-up-analysis-pobing-the-interaction"><i class="fa fa-check"></i><b>15.4.5</b> Follow-up analysis: Pobing the interaction</a></li>
<li class="chapter" data-level="15.4.6" data-path="tutorial-moderation-analysis.html"><a href="tutorial-moderation-analysis.html#using-processr-1"><i class="fa fa-check"></i><b>15.4.6</b> Using processR</a></li>
</ul></li>
<li class="chapter" data-level="15.5" data-path="tutorial-moderation-analysis.html"><a href="tutorial-moderation-analysis.html#take-aways-9"><i class="fa fa-check"></i><b>15.5</b> Take Aways</a></li>
<li class="chapter" data-level="15.6" data-path="tutorial-moderation-analysis.html"><a href="tutorial-moderation-analysis.html#additional-tutorials-8"><i class="fa fa-check"></i><b>15.6</b> Additional tutorials</a></li>
</ul></li>
<li class="chapter" data-level="16" data-path="exercise-6-test-your-knowledge.html"><a href="exercise-6-test-your-knowledge.html"><i class="fa fa-check"></i><b>16</b> Exercise 6: Test your knowledge</a>
<ul>
<li class="chapter" data-level="16.1" data-path="exercise-6-test-your-knowledge.html"><a href="exercise-6-test-your-knowledge.html#task-1-5"><i class="fa fa-check"></i><b>16.1</b> Task 1</a></li>
<li class="chapter" data-level="16.2" data-path="exercise-6-test-your-knowledge.html"><a href="exercise-6-test-your-knowledge.html#task-2-5"><i class="fa fa-check"></i><b>16.2</b> Task 2</a></li>
<li class="chapter" data-level="16.3" data-path="exercise-6-test-your-knowledge.html"><a href="exercise-6-test-your-knowledge.html#task-3-5"><i class="fa fa-check"></i><b>16.3</b> Task 3</a></li>
<li class="chapter" data-level="16.4" data-path="exercise-6-test-your-knowledge.html"><a href="exercise-6-test-your-knowledge.html#task-4-4"><i class="fa fa-check"></i><b>16.4</b> Task 4</a></li>
<li class="chapter" data-level="16.5" data-path="exercise-6-test-your-knowledge.html"><a href="exercise-6-test-your-knowledge.html#task-5-4"><i class="fa fa-check"></i><b>16.5</b> Task 5</a></li>
<li class="chapter" data-level="16.6" data-path="exercise-6-test-your-knowledge.html"><a href="exercise-6-test-your-knowledge.html#task-6-3"><i class="fa fa-check"></i><b>16.6</b> Task 6</a></li>
</ul></li>
<li class="chapter" data-level="17" data-path="tutorial-cpa-and-model-fit.html"><a href="tutorial-cpa-and-model-fit.html"><i class="fa fa-check"></i><b>17</b> Tutorial: CPA and model fit</a>
<ul>
<li class="chapter" data-level="17.1" data-path="tutorial-cpa-and-model-fit.html"><a href="tutorial-cpa-and-model-fit.html#what-is-cpa"><i class="fa fa-check"></i><b>17.1</b> What is CPA?</a></li>
<li class="chapter" data-level="17.2" data-path="tutorial-cpa-and-model-fit.html"><a href="tutorial-cpa-and-model-fit.html#fitting-models-with-lavaan"><i class="fa fa-check"></i><b>17.2</b> Fitting models with lavaan</a></li>
<li class="chapter" data-level="17.3" data-path="tutorial-cpa-and-model-fit.html"><a href="tutorial-cpa-and-model-fit.html#example-evaluate-model-fit-for-complex-models"><i class="fa fa-check"></i><b>17.3</b> Example: Evaluate model fit for complex models</a>
<ul>
<li class="chapter" data-level="17.3.1" data-path="tutorial-cpa-and-model-fit.html"><a href="tutorial-cpa-and-model-fit.html#theory-driven-hypotheses"><i class="fa fa-check"></i><b>17.3.1</b> Theory-driven hypotheses</a></li>
<li class="chapter" data-level="17.3.2" data-path="tutorial-cpa-and-model-fit.html"><a href="tutorial-cpa-and-model-fit.html#model-fit-and-evaluation"><i class="fa fa-check"></i><b>17.3.2</b> Model fit and evaluation</a></li>
</ul></li>
<li class="chapter" data-level="17.4" data-path="tutorial-cpa-and-model-fit.html"><a href="tutorial-cpa-and-model-fit.html#take-aways-10"><i class="fa fa-check"></i><b>17.4</b> Take-Aways</a></li>
<li class="chapter" data-level="17.5" data-path="tutorial-cpa-and-model-fit.html"><a href="tutorial-cpa-and-model-fit.html#additional-tutorials-9"><i class="fa fa-check"></i><b>17.5</b> Additional tutorials</a></li>
</ul></li>
<li class="chapter" data-level="18" data-path="exercise-7-test-your-knowledge.html"><a href="exercise-7-test-your-knowledge.html"><i class="fa fa-check"></i><b>18</b> Exercise 7: Test your knowledge</a>
<ul>
<li class="chapter" data-level="18.1" data-path="exercise-7-test-your-knowledge.html"><a href="exercise-7-test-your-knowledge.html#task-1-6"><i class="fa fa-check"></i><b>18.1</b> Task 1</a></li>
<li class="chapter" data-level="18.2" data-path="exercise-7-test-your-knowledge.html"><a href="exercise-7-test-your-knowledge.html#task-2-6"><i class="fa fa-check"></i><b>18.2</b> Task 2</a></li>
</ul></li>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html"><i class="fa fa-check"></i>Solutions</a>
<ul>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html#solutions-for-exercise-1"><i class="fa fa-check"></i>Solutions for Exercise 1</a>
<ul>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html#task-1-7"><i class="fa fa-check"></i>Task 1</a></li>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html#task-2-7"><i class="fa fa-check"></i>Task 2</a></li>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html#task-3-6"><i class="fa fa-check"></i>Task 3</a></li>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html#task-4-5"><i class="fa fa-check"></i>Task 4</a></li>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html#task-5-5"><i class="fa fa-check"></i>Task 5</a></li>
</ul></li>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html#solutions-for-exercise-2"><i class="fa fa-check"></i>Solutions for Exercise 2</a>
<ul>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html#task-1-8"><i class="fa fa-check"></i>Task 1</a></li>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html#task-2-8"><i class="fa fa-check"></i>Task 2</a></li>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html#task-3-7"><i class="fa fa-check"></i>Task 3</a></li>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html#task-4-6"><i class="fa fa-check"></i>Task 4</a></li>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html#task-5-6"><i class="fa fa-check"></i>Task 5</a></li>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html#task-6-4"><i class="fa fa-check"></i>Task 6</a></li>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html#task-7-2"><i class="fa fa-check"></i>Task 7</a></li>
</ul></li>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html#solutions-for-exercise-3"><i class="fa fa-check"></i>Solutions for Exercise 3</a>
<ul>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html#task-1-9"><i class="fa fa-check"></i>Task 1</a></li>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html#task-2-9"><i class="fa fa-check"></i>Task 2</a></li>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html#task-3-8"><i class="fa fa-check"></i>Task 3</a></li>
</ul></li>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html#solutions-for-exercise-4"><i class="fa fa-check"></i>Solutions for Exercise 4</a>
<ul>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html#task-1-10"><i class="fa fa-check"></i>Task 1</a></li>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html#task-2-10"><i class="fa fa-check"></i>Task 2</a></li>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html#task-3-9"><i class="fa fa-check"></i>Task 3</a></li>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html#task-4-7"><i class="fa fa-check"></i>Task 4</a></li>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html#task-5-7"><i class="fa fa-check"></i>Task 5</a></li>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html#task-6-5"><i class="fa fa-check"></i>Task 6</a></li>
</ul></li>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html#solutions-for-exercise-5"><i class="fa fa-check"></i>Solutions for Exercise 5</a>
<ul>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html#task-1-11"><i class="fa fa-check"></i>Task 1</a></li>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html#task-2-11"><i class="fa fa-check"></i>Task 2</a></li>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html#task-3-10"><i class="fa fa-check"></i>Task 3</a></li>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html#task-4-8"><i class="fa fa-check"></i>Task 4</a></li>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html#task-5-8"><i class="fa fa-check"></i>Task 5</a></li>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html#task-6-6"><i class="fa fa-check"></i>Task 6</a></li>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html#task-7-3"><i class="fa fa-check"></i>Task 7</a></li>
</ul></li>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html#solutions-for-exercise-6"><i class="fa fa-check"></i>Solutions for Exercise 6</a>
<ul>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html#task-1-12"><i class="fa fa-check"></i>Task 1</a></li>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html#task-2-12"><i class="fa fa-check"></i>Task 2</a></li>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html#task-3-11"><i class="fa fa-check"></i>Task 3</a></li>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html#task-4-9"><i class="fa fa-check"></i>Task 4</a></li>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html#task-5-9"><i class="fa fa-check"></i>Task 5</a></li>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html#task-6-7"><i class="fa fa-check"></i>Task 6</a></li>
</ul></li>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html#solutions-for-exercise-7"><i class="fa fa-check"></i>Solutions for Exercise 7</a>
<ul>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html#task-1-13"><i class="fa fa-check"></i>Task 1</a></li>
<li class="chapter" data-level="" data-path="solutions.html"><a href="solutions.html#task-2-13"><i class="fa fa-check"></i>Task 2</a></li>
</ul></li>
</ul></li>
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<li><a href="https://github.com/LKobilke/CPA-Seminar" target="blank">Published with bookdown</a></li>
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<div id="tutorial-data-management-with-tidyverse" class="section level1" number="7">
<h1><span class="header-section-number"> 7</span> Tutorial: Data management with tidyverse</h1>
<p><strong>After working through Tutorial 7, you’ll…</strong></p>
<ul>
<li>know the advantages of the <code>tidyverse</code> vs. <code>Base R</code></li>
<li>know about different formats of tabular data</li>
<li>understand what packages are included in the <code>tidyverse</code> meta-package</li>
<li>know how to do data modifications and transformations with <code>dplyr</code></li>
</ul>
<div id="why-not-stick-with-base-r" class="section level2" number="7.1">
<h2><span class="header-section-number">7.1</span> Why not stick with Base R?</h2>
<p>You might wonder why we’ve spent so much time exploring functions in <code>Base R</code> to now learn data management with <code>tidyverse</code>. After all, data management can also be done in <code>Base R</code>, can’t it? I personally recommend that all R beginners should work with the <code>tidyverse</code> as early as possible. There are three reasons supporting my argument:</p>
<ol style="list-style-type: decimal">
<li><strong>Ease of use:</strong> The <code>tidyverse</code> is very accessible for R “beginners”, i.e. its syntax is very easy to understand. It allows you to set goals (i.e. what you want to do with your data) and get you working on these goals very quickly. Definitely more quickly than in <code>Base R</code>!</li>
<li><strong>Standard for data management:</strong> A few years ago, the <code>tidyverse</code> has become the de facto standard for data management in R. It is a meta-package, which means that it is a collection of distinct packages that all follow the same design principles to make code reading and writing as simple as possible. For example, all functions are named after verbs that indicate exactly what they perform (e.g. filter or summarize).</li>
<li><strong>Beautiful graphs:</strong> With the <code>tidyverse</code>, all data management steps can be swiftly transferred into beautiful graphs. This is because the most popular graph package in R, <code>ggplot2</code>, is part of the <code>tidyverse</code>.</li>
</ol>
<p>Are you excited now? Then let’s get started!</p>
</div>
<div id="tidyverse-packages" class="section level2" number="7.2">
<h2><span class="header-section-number">7.2</span> Tidyverse packages</h2>
<p>The <code>tidyverse</code> comes with a great arsenal of topic-specific packages and their respective functions. It includes packages for:</p>
<ul>
<li><code>tibble</code>: creating data structures like tibbles, which is an enhanced type of data frame</li>
<li><code>readr, haven, readxl</code>: reading data (e.g. readr for CSV, haven for SPSS, Stata and SAS, readxl for Excel)</li>
<li><code>tidyr, dplyr</code>: data transformation, modification, and summary statistics</li>
<li><code>stringr, forcats, lubridate</code>: create special, powerful object types (e.g. stringr for working with text objects, forcats for factors, lubridate for time data)</li>
<li><code>purrr</code>: programming with R</li>
<li><code>ggplot2</code>: graphing/charting</li>
</ul>
<p>The most frequently used packages of the <code>tidyverse</code> can be installed and activated in one go (less frequently used packages like <code>haven</code> still need to be installed and activated separately):</p>
<div class="sourceCode" id="cb141"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb141-1"><a href="tutorial-data-management-with-tidyverse.html#cb141-1" aria-hidden="true" tabindex="-1"></a><span class="fu">install.packages</span>(<span class="st">"tidyverse"</span>) <span class="co"># install the package (only on the first time)</span></span>
<span id="cb141-2"><a href="tutorial-data-management-with-tidyverse.html#cb141-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(tidyverse) <span class="co"># active the package</span></span></code></pre></div>
</div>
<div id="tidy-data" class="section level2" number="7.3">
<h2><span class="header-section-number">7.3</span> Tidy data</h2>
<p>Dataframes, which we learned about in <a href="tutorial-objects-structures-in-r.html#types-of-objects">Types of objects</a>, are tabular data. However, data can also have other formats, for example as nested, i.e. hierarchical, lists. In communication research, these other data formats are mainly used by social media and their respective APIs (perhaps you have heard of the “JSON” format before).</p>
<p>In our course, however, we’ll focus on tabular data.The same data can be represented differently in tables. We perceive some of these representations as tidy, others as messy. While tidy data principles establish a standard for organizing data values inside a data frame and thus all tidy data look the same, every messy dataset is messy in its own way.</p>
<p><strong>Take a look at the table below. It shows a Starwars data set that comes pre-installed with the dplyr package. Do you feel the tabled data is messy? Why (not)?</strong></p>
<pre><code>## # A tibble: 10 x 3
## name body_feature value
## <chr> <chr> <dbl>
## 1 Anakin Skywalker height 188
## 2 Anakin Skywalker mass 84
## 3 Chewbacca height 228
## 4 Chewbacca mass 112
## 5 Darth Vader height 202
## 6 Darth Vader mass 136
## 7 Jabba Desilijic Tiure height 175
## 8 Jabba Desilijic Tiure mass 1358
## 9 Leia Organa height 150
## 10 Leia Organa mass 49</code></pre>
<p>Overall, this data is messy. It comes with three messy problems:</p>
<ol style="list-style-type: decimal">
<li>This <em>body_feature</em> column comprises information relating to both height and weight, i.e. both variables are stored in a single column.</li>
<li>As a result, the <em>value</em> column is reliant on the <em>body_feature</em> column; we can’t tell the stored values apart by merely looking at the value column. We always need to check the <em>body_feature</em> column.</li>
<li>Consequently, we have issues with vectorized functions (remember, in R, columns in data sets are vectors): We can’t, for example, use the mean() function on the <em>value</em> column to determine the average weight of the Star Wars characters since the height values are also stored there.</li>
</ol>
<p><strong>What do you think of this table? Is it messy?</strong></p>
<pre><code>## # A tibble: 5 x 3
## name height mass
## <chr> <int> <dbl>
## 1 Anakin Skywalker 188 84
## 2 Chewbacca 228 112
## 3 Darth Vader 202 136
## 4 Jabba Desilijic Tiure 175 1358
## 5 Leia Organa 150 49</code></pre>
<p>This table looks tidy! Tidy data is a standard way of mapping the meaning of a dataset to its structure. We determine whether a dataset is messy or tidy depending on how rows, columns and tables are matched up with observations, variables and types. We consider a table tidy when it follows the following <strong>golden rules</strong>:</p>
<ol style="list-style-type: decimal">
<li><strong>Columns:</strong> Every column is one variable.</li>
<li><strong>Rows</strong>: Every row is one observation.</li>
<li><strong>Cells:</strong> Every cell contains one single value.</li>
</ol>
<table>
<colgroup>
<col width="100%" />
</colgroup>
<tbody>
<tr class="odd">
<td><em>Image: The tidy data principle <a href="https://r4ds.had.co.nz/tidy-data.html">(Source: R for Data Science)</a></em></td>
</tr>
<tr class="even">
<td><img src="images/Tut7_tidy_data.png" /></td>
</tr>
</tbody>
</table>
<p>In messy data sets, on the other hand…</p>
<ol style="list-style-type: decimal">
<li>Column headers are values, not variable names.</li>
<li>Multiple variables are stored in one column.</li>
<li>Variables are stored in both rows and columns.</li>
<li>Multiple types of observational units are stored in the same table.</li>
<li>A single observational unit is stored in multiple tables.</li>
</ol>
<p><strong>Why should you be concerned about tidy data organization?</strong></p>
<p>There are two major advantages:</p>
<ol style="list-style-type: decimal">
<li>When you have a consistent data structure, it is easier to learn the respective tools that work well with this data structure. <code>dplyr</code>, <code>ggplot2</code>, and all the other <code>tidyverse</code> packages are designed for working with tidy data.</li>
<li>Putting variables in columns makes R’s vectorized nature shine. The majority of built-in R-functions (like the mean() function) works with vectors of values. As a result, the tidy reorganization of data seems only natural for a good work flow in R.</li>
</ol>
<p>If you have been working mainly with survey data, then you will already be familiar with these basic rules, as data export from survey software usually follows these principles. However, “real-world” data from databases or social media often does not follow these principles. That’s why it’s sometimes true to say that 80% of data analysis is spent on cleaning and transforming data.</p>
</div>
<div id="the-pipe-operator" class="section level2" number="7.4">
<h2><span class="header-section-number">7.4</span> The pipe operator</h2>
<p>Truly, <code>dplyr</code> is my favorite <code>tidyverse</code> package (even more so than <code>ggplot2</code>, which we’ll cover later!). It allows you to perform powerful data transformations in just a few simple steps.</p>
<p>To this end, <code>dplyr</code> relies on the pipe operator (<em>%>%</em>).<a href="#fn4" class="footnote-ref" id="fnref4"><sup>4</sup></a> The <em>%>%</em> operator allows functions to be applied sequentially to the same source object in a concise manner, so that step-by-step transformations can be applied to the data. Therefore, we always call the source object first and then add each transformation step separated by the <em>%>%</em> operator. Let’s illustrate this concept with an example. We’ll use the Starwars data set that you are already familiar with.</p>
<div class="sourceCode" id="cb144"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb144-1"><a href="tutorial-data-management-with-tidyverse.html#cb144-1" aria-hidden="true" tabindex="-1"></a>starwars_data <span class="sc">%>%</span> <span class="co"># First, we define the source object, i.e. the data frame that we want to transform, followed by the pipe operator </span></span>
<span id="cb144-2"><a href="tutorial-data-management-with-tidyverse.html#cb144-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">plot</span>() <span class="co"># Second, we specify which function should be performed on the source object, here: scatterplot</span></span></code></pre></div>
<p><img src="CPA-Seminar_files/figure-html/unnamed-chunk-17-1.png" width="672" /></p>
<p>Now, that’s not very impressive. We could do the same in <code>Base R</code> like this:</p>
<div class="sourceCode" id="cb145"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb145-1"><a href="tutorial-data-management-with-tidyverse.html#cb145-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(starwars_data)</span></code></pre></div>
<p><img src="CPA-Seminar_files/figure-html/unnamed-chunk-18-1.png" width="672" /></p>
<p>However, <code>dplyr</code> gets really impressive when you chain functions sequentially. You can apply certain selection criteria to your data and plot it in one go. For example, we might exclude the variable <em>name</em> from our scatter plot, since it’s not a metric variable anyway. Also, we might want to look only at those Star Wars characters taller than 170 cm. Let’s try it in a single run!</p>
<div class="sourceCode" id="cb146"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb146-1"><a href="tutorial-data-management-with-tidyverse.html#cb146-1" aria-hidden="true" tabindex="-1"></a>starwars_data <span class="sc">%>%</span> <span class="co"># Define the source object</span></span>
<span id="cb146-2"><a href="tutorial-data-management-with-tidyverse.html#cb146-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(height, mass) <span class="sc">%>%</span> <span class="co"># Keep only the height and mass column</span></span>
<span id="cb146-3"><a href="tutorial-data-management-with-tidyverse.html#cb146-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(height <span class="sc">></span> <span class="dv">170</span>) <span class="sc">%>%</span> <span class="co"># Filter all observations that are taller than 170cm </span></span>
<span id="cb146-4"><a href="tutorial-data-management-with-tidyverse.html#cb146-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">plot</span>() <span class="co"># Plot!</span></span></code></pre></div>
<p><img src="CPA-Seminar_files/figure-html/unnamed-chunk-19-1.png" width="672" /></p>
<p>Now try to do the same in <code>Base R</code>:</p>
<div class="sourceCode" id="cb147"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb147-1"><a href="tutorial-data-management-with-tidyverse.html#cb147-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(starwars_data[starwars_data<span class="sc">$</span>height<span class="sc">></span><span class="dv">170</span>,]<span class="sc">$</span>mass<span class="sc">~</span>starwars_data[starwars_data<span class="sc">$</span>height<span class="sc">></span><span class="dv">170</span>,]<span class="sc">$</span>height, <span class="at">xlab=</span><span class="st">"height"</span>, <span class="at">ylab=</span><span class="st">"mass"</span>)</span></code></pre></div>
<p><img src="CPA-Seminar_files/figure-html/unnamed-chunk-20-1.png" width="672" /></p>
<p>The <code>Base R</code> code is longer, more nested, and not as readable as the code written in <code>dplyr</code>. And the more selection criteria and functions you need to implement, the worse it gets. For example, imagine you would also want to exclude Star Wars characters with a mass bigger than 1200kg. Peace of cake with <code>dplyr</code>:</p>
<div class="sourceCode" id="cb148"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb148-1"><a href="tutorial-data-management-with-tidyverse.html#cb148-1" aria-hidden="true" tabindex="-1"></a>starwars_data <span class="sc">%>%</span> </span>
<span id="cb148-2"><a href="tutorial-data-management-with-tidyverse.html#cb148-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(height, mass) <span class="sc">%>%</span> </span>
<span id="cb148-3"><a href="tutorial-data-management-with-tidyverse.html#cb148-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(height <span class="sc">></span> <span class="dv">170</span>) <span class="sc">%>%</span></span>
<span id="cb148-4"><a href="tutorial-data-management-with-tidyverse.html#cb148-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(mass <span class="sc"><</span> <span class="dv">1200</span>) <span class="sc">%>%</span> </span>
<span id="cb148-5"><a href="tutorial-data-management-with-tidyverse.html#cb148-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">plot</span>() </span></code></pre></div>
<p><img src="CPA-Seminar_files/figure-html/unnamed-chunk-21-1.png" width="672" /></p>
</div>
<div id="data-transformation-with-dplyr" class="section level2" number="7.5">
<h2><span class="header-section-number">7.5</span> Data transformation with dplyr</h2>
<p><code>dplyr</code>comes with five main functions:</p>
<ol style="list-style-type: decimal">
<li><code>select()</code>: select variables column by column, i.e. pick columns / variables by their names</li>
<li><code>filter()</code>: filter observations row by row, i.e. pick observations by their values</li>
<li><code>arrange()</code>: sort / reorder data in ascending or descending order</li>
<li><code>mutate()</code>: calculate new variables or transform existing ones</li>
<li><code>summarize()</code>: summarize variables (e.g. mean, standard deviation, etc.), best combined with <code>group_by()</code></li>
</ol>
<iframe src="https://www.youtube.com/embed/qj7poscNpis" width="672" height="400px" data-external="1">
</iframe>
<div id="select" class="section level3" number="7.5.1">
<h3><span class="header-section-number">7.5.1</span> select()</h3>
<p>Scientists will frequently provide you with large data sets including hundreds of variables (often even more!). The first problem in this scenario is narrowing down the variables you are truly interested in. <code>select()</code> helps you to easily choose a suitable subset of variables. In this selection process, the name of the data frame is the source object, followed by the pipe <em>%>%</em> operator. The expression that selects the columns that you are interested in comes after that.</p>
<p>Take the Star Wars data, for example. The original data set has 87 observations (Star Wars characters) and 14 columns / variables (traits of these characters, e.g., <em>birth_year</em>, <em>gender</em>, and <em>species</em>). Yes, 14 columns is not a lot and you could get an overview of this data without subsetting columns. Let’s take a look at the original data frame:</p>
<div class="sourceCode" id="cb149"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb149-1"><a href="tutorial-data-management-with-tidyverse.html#cb149-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(dplyr) <span class="co"># load dplyr</span></span>
<span id="cb149-2"><a href="tutorial-data-management-with-tidyverse.html#cb149-2" aria-hidden="true" tabindex="-1"></a>starwars_data <span class="ot"><-</span> starwars <span class="co"># assign the pre-installed starwars data from dplyr to a source object / variable</span></span>
<span id="cb149-3"><a href="tutorial-data-management-with-tidyverse.html#cb149-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb149-4"><a href="tutorial-data-management-with-tidyverse.html#cb149-4" aria-hidden="true" tabindex="-1"></a>starwars_data <span class="co"># print the content of the data frame to the console</span></span></code></pre></div>
<pre><code>## # A tibble: 87 x 14
## name height mass hair_color skin_color eye_color birth_year sex gender homeworld species films vehicles starships
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <list> <list> <list>
## 1 Luke Skywalker 172 77 blond fair blue 19 male masculine Tatooine Human <chr> <chr [2]> <chr [2]>
## 2 C-3PO 167 75 <NA> gold yellow 112 none masculine Tatooine Droid <chr> <chr [0]> <chr [0]>
## 3 R2-D2 96 32 <NA> white, blue red 33 none masculine Naboo Droid <chr> <chr [0]> <chr [0]>
## 4 Darth Vader 202 136 none white yellow 41.9 male masculine Tatooine Human <chr> <chr [0]> <chr [1]>
## 5 Leia Organa 150 49 brown light brown 19 female feminine Alderaan Human <chr> <chr [1]> <chr [0]>
## 6 Owen Lars 178 120 brown, grey light blue 52 male masculine Tatooine Human <chr> <chr [0]> <chr [0]>
## 7 Beru Whitesun lars 165 75 brown light blue 47 female feminine Tatooine Human <chr> <chr [0]> <chr [0]>
## 8 R5-D4 97 32 <NA> white, red red NA none masculine Tatooine Droid <chr> <chr [0]> <chr [0]>
## 9 Biggs Darklighter 183 84 black light brown 24 male masculine Tatooine Human <chr> <chr [0]> <chr [1]>
## 10 Obi-Wan Kenobi 182 77 auburn, white fair blue-gray 57 male masculine Stewjon Human <chr> <chr [1]> <chr [5]>
## # ... with 77 more rows</code></pre>
<p>For the sake of practice, let’s say we only want to analyze the <em>species</em>, <em>birth_year</em>, <em>mass</em>, and <em>height</em> of these characters. To simplify data handling, we want to keep only the respective columns.</p>
<div class="sourceCode" id="cb151"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb151-1"><a href="tutorial-data-management-with-tidyverse.html#cb151-1" aria-hidden="true" tabindex="-1"></a>starwars_data <span class="sc">%>%</span> <span class="co"># define the source object</span></span>
<span id="cb151-2"><a href="tutorial-data-management-with-tidyverse.html#cb151-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(name, species, birth_year, mass, height) <span class="co"># keep only the name, species, birth_year, mass and height column</span></span></code></pre></div>
<pre><code>## # A tibble: 87 x 5
## name species birth_year mass height
## <chr> <chr> <dbl> <dbl> <int>
## 1 Luke Skywalker Human 19 77 172
## 2 C-3PO Droid 112 75 167
## 3 R2-D2 Droid 33 32 96
## 4 Darth Vader Human 41.9 136 202
## 5 Leia Organa Human 19 49 150
## 6 Owen Lars Human 52 120 178
## 7 Beru Whitesun lars Human 47 75 165
## 8 R5-D4 Droid NA 32 97
## 9 Biggs Darklighter Human 24 84 183
## 10 Obi-Wan Kenobi Human 57 77 182
## # ... with 77 more rows</code></pre>
<p>At the moment you have only printed the transformed data to the console. However, most of the time we want to keep the transformed data ready for further calculations. In this case we should assign the transformed data into a new source object, which we can access later.</p>
<div class="sourceCode" id="cb153"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb153-1"><a href="tutorial-data-management-with-tidyverse.html#cb153-1" aria-hidden="true" tabindex="-1"></a>starwars_short <span class="ot"><-</span> starwars_data <span class="sc">%>%</span> <span class="co"># assign a new source object and define the old source object</span></span>
<span id="cb153-2"><a href="tutorial-data-management-with-tidyverse.html#cb153-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(name, species, birth_year, mass, height) <span class="co"># keep only the name, species, birth_year, mass and height column</span></span></code></pre></div>
<p>Let’s print the new source object, <em>starwars_short</em>, to the console.</p>
<div class="sourceCode" id="cb154"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb154-1"><a href="tutorial-data-management-with-tidyverse.html#cb154-1" aria-hidden="true" tabindex="-1"></a>starwars_short</span></code></pre></div>
<pre><code>## # A tibble: 87 x 5
## name species birth_year mass height
## <chr> <chr> <dbl> <dbl> <int>
## 1 Luke Skywalker Human 19 77 172
## 2 C-3PO Droid 112 75 167
## 3 R2-D2 Droid 33 32 96
## 4 Darth Vader Human 41.9 136 202
## 5 Leia Organa Human 19 49 150
## 6 Owen Lars Human 52 120 178
## 7 Beru Whitesun lars Human 47 75 165
## 8 R5-D4 Droid NA 32 97
## 9 Biggs Darklighter Human 24 84 183
## 10 Obi-Wan Kenobi Human 57 77 182
## # ... with 77 more rows</code></pre>
<p>You can also delete columns by making a reverse selection with the <em>- symbol</em>. This means that you select all columns except the one whose name you specify.</p>
<div class="sourceCode" id="cb156"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb156-1"><a href="tutorial-data-management-with-tidyverse.html#cb156-1" aria-hidden="true" tabindex="-1"></a>starwars_short <span class="sc">%>%</span> </span>
<span id="cb156-2"><a href="tutorial-data-management-with-tidyverse.html#cb156-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(<span class="sc">-</span>name) <span class="co"># keep all columns except the name column (i.e. delete name column)</span></span></code></pre></div>
<pre><code>## # A tibble: 87 x 4
## species birth_year mass height
## <chr> <dbl> <dbl> <int>
## 1 Human 19 77 172
## 2 Droid 112 75 167
## 3 Droid 33 32 96
## 4 Human 41.9 136 202
## 5 Human 19 49 150
## 6 Human 52 120 178
## 7 Human 47 75 165
## 8 Droid NA 32 97
## 9 Human 24 84 183
## 10 Human 57 77 182
## # ... with 77 more rows</code></pre>
<p>You can delete more than one column in one go:</p>
<div class="sourceCode" id="cb158"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb158-1"><a href="tutorial-data-management-with-tidyverse.html#cb158-1" aria-hidden="true" tabindex="-1"></a>starwars_short <span class="sc">%>%</span> </span>
<span id="cb158-2"><a href="tutorial-data-management-with-tidyverse.html#cb158-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(<span class="sc">-</span><span class="fu">c</span>(name,species)) <span class="co"># keep all columns except the name & species column (i.e. delete these columns)</span></span></code></pre></div>
<pre><code>## # A tibble: 87 x 3
## birth_year mass height
## <dbl> <dbl> <int>
## 1 19 77 172
## 2 112 75 167
## 3 33 32 96
## 4 41.9 136 202
## 5 19 49 150
## 6 52 120 178
## 7 47 75 165
## 8 NA 32 97
## 9 24 84 183
## 10 57 77 182
## # ... with 77 more rows</code></pre>
<p><strong>Tip for advanced users:</strong> You can select columns and rename them at the same time.</p>
<div class="sourceCode" id="cb160"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb160-1"><a href="tutorial-data-management-with-tidyverse.html#cb160-1" aria-hidden="true" tabindex="-1"></a>starwars_short <span class="sc">%>%</span> </span>
<span id="cb160-2"><a href="tutorial-data-management-with-tidyverse.html#cb160-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(<span class="st">"character"</span><span class="ot">=</span>name, <span class="st">"age"</span><span class="ot">=</span>birth_year) <span class="co"># select columns that you want to keep & rename them</span></span></code></pre></div>
<pre><code>## # A tibble: 87 x 2
## character age
## <chr> <dbl>
## 1 Luke Skywalker 19
## 2 C-3PO 112
## 3 R2-D2 33
## 4 Darth Vader 41.9
## 5 Leia Organa 19
## 6 Owen Lars 52
## 7 Beru Whitesun lars 47
## 8 R5-D4 NA
## 9 Biggs Darklighter 24
## 10 Obi-Wan Kenobi 57
## # ... with 77 more rows</code></pre>
</div>
<div id="filter" class="section level3" number="7.5.2">
<h3><span class="header-section-number">7.5.2</span> filter()</h3>
<p><code>filter()</code> divides observations into groups depending on their values. The name of the data frame is the source object, followed by the pipe <em>%>%</em> operator. Then follow the expressions that filter the data.</p>
<p>Let’s only select human Star Wars characters in our transformed data set <em>starwars_short</em>:</p>
<div class="sourceCode" id="cb162"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb162-1"><a href="tutorial-data-management-with-tidyverse.html#cb162-1" aria-hidden="true" tabindex="-1"></a>starwars_short <span class="sc">%>%</span></span>
<span id="cb162-2"><a href="tutorial-data-management-with-tidyverse.html#cb162-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(species<span class="sc">==</span><span class="st">"Human"</span>)</span></code></pre></div>
<pre><code>## # A tibble: 35 x 5
## name species birth_year mass height
## <chr> <chr> <dbl> <dbl> <int>
## 1 Luke Skywalker Human 19 77 172
## 2 Darth Vader Human 41.9 136 202
## 3 Leia Organa Human 19 49 150
## 4 Owen Lars Human 52 120 178
## 5 Beru Whitesun lars Human 47 75 165
## 6 Biggs Darklighter Human 24 84 183
## 7 Obi-Wan Kenobi Human 57 77 182
## 8 Anakin Skywalker Human 41.9 84 188
## 9 Wilhuff Tarkin Human 64 NA 180
## 10 Han Solo Human 29 80 180
## # ... with 25 more rows</code></pre>
<p>And now let’s only select Star Wars character who are younger than 24 or exactly 24 years old.</p>
<div class="sourceCode" id="cb164"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb164-1"><a href="tutorial-data-management-with-tidyverse.html#cb164-1" aria-hidden="true" tabindex="-1"></a>starwars_short <span class="sc">%>%</span></span>
<span id="cb164-2"><a href="tutorial-data-management-with-tidyverse.html#cb164-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(birth_year<span class="sc"><=</span><span class="dv">24</span>)</span></code></pre></div>
<pre><code>## # A tibble: 7 x 5
## name species birth_year mass height
## <chr> <chr> <dbl> <dbl> <int>
## 1 Luke Skywalker Human 19 77 172
## 2 Leia Organa Human 19 49 150
## 3 Biggs Darklighter Human 24 84 183
## 4 Wedge Antilles Human 21 77 170
## 5 IG-88 Droid 15 140 200
## 6 Wicket Systri Warrick Ewok 8 20 88
## 7 Plo Koon Kel Dor 22 80 188</code></pre>
<p>Chaining some functions, let’s look at Star Wars character who are a <em>Droid</em> and older than 24.</p>
<div class="sourceCode" id="cb166"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb166-1"><a href="tutorial-data-management-with-tidyverse.html#cb166-1" aria-hidden="true" tabindex="-1"></a>starwars_short <span class="sc">%>%</span></span>
<span id="cb166-2"><a href="tutorial-data-management-with-tidyverse.html#cb166-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(species<span class="sc">==</span><span class="st">"Droid"</span> <span class="sc">&</span> birth_year <span class="sc">></span> <span class="dv">24</span>) <span class="co"># & --> filter all observations to which both logical statements apply</span></span></code></pre></div>
<pre><code>## # A tibble: 2 x 5
## name species birth_year mass height
## <chr> <chr> <dbl> <dbl> <int>
## 1 C-3PO Droid 112 75 167
## 2 R2-D2 Droid 33 32 96</code></pre>
<p>Alternatively, you can also write these filters like this:</p>
<div class="sourceCode" id="cb168"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb168-1"><a href="tutorial-data-management-with-tidyverse.html#cb168-1" aria-hidden="true" tabindex="-1"></a>starwars_short <span class="sc">%>%</span></span>
<span id="cb168-2"><a href="tutorial-data-management-with-tidyverse.html#cb168-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(species<span class="sc">==</span><span class="st">"Droid"</span>) <span class="sc">%>%</span></span>
<span id="cb168-3"><a href="tutorial-data-management-with-tidyverse.html#cb168-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(birth_year <span class="sc">></span> <span class="dv">24</span>)</span></code></pre></div>
<pre><code>## # A tibble: 2 x 5
## name species birth_year mass height
## <chr> <chr> <dbl> <dbl> <int>
## 1 C-3PO Droid 112 75 167
## 2 R2-D2 Droid 33 32 96</code></pre>
<p>Besides the <em>&</em> operator, there are many more logical operators that you can choose from to optimize your filter choices. Here is an overview:</p>
<hr />
<p><em>Image: Logical, i.e. boolean, operators (Source: <a href="https://r4ds.had.co.nz/transform.html?q=filter#logical-operators">R for Data Science</a></em>)</p>
<p>[]!(C:/Users/LaraK/Desktop/To-Do/Verwendung von Conditional Process Analysis zur Bewertung von Kommunikationstheorien/LaraKobilke/images/Tut7_logical_operators.JPG)</p>
<hr />
<p><strong>Tip for advanced users 1:</strong> You can negate filters. This means that you keep all observations except the one that you have specified with the <em>!=</em> operator (read <em>!=</em> as: <em>is not</em> or <em>is unequal to</em>). For example, you can choose to include only non-human Star Wars characters.</p>
<div class="sourceCode" id="cb170"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb170-1"><a href="tutorial-data-management-with-tidyverse.html#cb170-1" aria-hidden="true" tabindex="-1"></a>starwars_short <span class="sc">%>%</span></span>
<span id="cb170-2"><a href="tutorial-data-management-with-tidyverse.html#cb170-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(species<span class="sc">!=</span><span class="st">"Human"</span>)</span></code></pre></div>
<pre><code>## # A tibble: 48 x 5
## name species birth_year mass height
## <chr> <chr> <dbl> <dbl> <int>
## 1 C-3PO Droid 112 75 167
## 2 R2-D2 Droid 33 32 96
## 3 R5-D4 Droid NA 32 97
## 4 Chewbacca Wookiee 200 112 228
## 5 Greedo Rodian 44 74 173
## 6 Jabba Desilijic Tiure Hutt 600 1358 175
## 7 Yoda Yoda's species 896 17 66
## 8 IG-88 Droid 15 140 200
## 9 Bossk Trandoshan 53 113 190
## 10 Ackbar Mon Calamari 41 83 180
## # ... with 38 more rows</code></pre>
<p>Alternatively, you achieve the same goal by negating the entire function call. Negating the entire function call can be handy at times.</p>
<div class="sourceCode" id="cb172"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb172-1"><a href="tutorial-data-management-with-tidyverse.html#cb172-1" aria-hidden="true" tabindex="-1"></a>starwars_short <span class="sc">%>%</span></span>
<span id="cb172-2"><a href="tutorial-data-management-with-tidyverse.html#cb172-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(<span class="sc">!</span>(species<span class="sc">==</span><span class="st">"Human"</span>))</span></code></pre></div>
<pre><code>## # A tibble: 48 x 5
## name species birth_year mass height
## <chr> <chr> <dbl> <dbl> <int>
## 1 C-3PO Droid 112 75 167
## 2 R2-D2 Droid 33 32 96
## 3 R5-D4 Droid NA 32 97
## 4 Chewbacca Wookiee 200 112 228
## 5 Greedo Rodian 44 74 173
## 6 Jabba Desilijic Tiure Hutt 600 1358 175
## 7 Yoda Yoda's species 896 17 66
## 8 IG-88 Droid 15 140 200
## 9 Bossk Trandoshan 53 113 190
## 10 Ackbar Mon Calamari 41 83 180
## # ... with 38 more rows</code></pre>
<p><strong>Tip for advanced users 2:</strong> You can filter for missing values (NAs) with the <code>is.na()</code> function.</p>
<div class="sourceCode" id="cb174"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb174-1"><a href="tutorial-data-management-with-tidyverse.html#cb174-1" aria-hidden="true" tabindex="-1"></a>starwars_short <span class="sc">%>%</span></span>
<span id="cb174-2"><a href="tutorial-data-management-with-tidyverse.html#cb174-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(<span class="fu">is.na</span>(birth_year))</span></code></pre></div>
<pre><code>## # A tibble: 44 x 5
## name species birth_year mass height
## <chr> <chr> <dbl> <dbl> <int>
## 1 R5-D4 Droid NA 32 97
## 2 Jek Tono Porkins Human NA 110 180
## 3 Arvel Crynyd Human NA NA NA
## 4 Nien Nunb Sullustan NA 68 160
## 5 Nute Gunray Neimodian NA 90 191
## 6 Roos Tarpals Gungan NA 82 224
## 7 Rugor Nass Gungan NA NA 206
## 8 Ric Olié <NA> NA NA 183
## 9 Watto Toydarian NA NA 137
## 10 Sebulba Dug NA 40 112
## # ... with 34 more rows</code></pre>
<p>And you can negate that filter to get rid of all observation that have missing values (NAs).</p>
<div class="sourceCode" id="cb176"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb176-1"><a href="tutorial-data-management-with-tidyverse.html#cb176-1" aria-hidden="true" tabindex="-1"></a>starwars_short <span class="sc">%>%</span></span>
<span id="cb176-2"><a href="tutorial-data-management-with-tidyverse.html#cb176-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(<span class="sc">!</span><span class="fu">is.na</span>(birth_year))</span></code></pre></div>
<pre><code>## # A tibble: 43 x 5
## name species birth_year mass height
## <chr> <chr> <dbl> <dbl> <int>
## 1 Luke Skywalker Human 19 77 172
## 2 C-3PO Droid 112 75 167
## 3 R2-D2 Droid 33 32 96
## 4 Darth Vader Human 41.9 136 202
## 5 Leia Organa Human 19 49 150
## 6 Owen Lars Human 52 120 178
## 7 Beru Whitesun lars Human 47 75 165
## 8 Biggs Darklighter Human 24 84 183
## 9 Obi-Wan Kenobi Human 57 77 182
## 10 Anakin Skywalker Human 41.9 84 188
## # ... with 33 more rows</code></pre>
<p><strong>Tip for advanced users 3:</strong> Watch out for the <em>| operator</em> (read: <em>or</em>). This one can be tricky to negate!</p>
<p>For example, with this code you get all characters that are NEITHER human NOR older than 33 years. I.e. you get all non-human characters who are younger than 33 or exactly 33 years old.</p>
<div class="sourceCode" id="cb178"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb178-1"><a href="tutorial-data-management-with-tidyverse.html#cb178-1" aria-hidden="true" tabindex="-1"></a>starwars_short <span class="sc">%>%</span></span>
<span id="cb178-2"><a href="tutorial-data-management-with-tidyverse.html#cb178-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(<span class="sc">!</span>((species <span class="sc">==</span> <span class="st">"Human"</span>) <span class="sc">|</span> (birth_year <span class="sc">></span> <span class="dv">33</span>)))</span></code></pre></div>
<pre><code>## # A tibble: 4 x 5
## name species birth_year mass height
## <chr> <chr> <dbl> <dbl> <int>
## 1 R2-D2 Droid 33 32 96
## 2 IG-88 Droid 15 140 200
## 3 Wicket Systri Warrick Ewok 8 20 88
## 4 Plo Koon Kel Dor 22 80 188</code></pre>
<p>But with this code, you’ll get all observations that are either non-human (regardless of their age) OR humans who are older than 33 years old.</p>
<div class="sourceCode" id="cb180"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb180-1"><a href="tutorial-data-management-with-tidyverse.html#cb180-1" aria-hidden="true" tabindex="-1"></a>starwars_short <span class="sc">%>%</span></span>
<span id="cb180-2"><a href="tutorial-data-management-with-tidyverse.html#cb180-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>((species <span class="sc">!=</span> <span class="st">"Human"</span>) <span class="sc">|</span> (birth_year <span class="sc">></span> <span class="dv">33</span>))</span></code></pre></div>
<pre><code>## # A tibble: 67 x 5
## name species birth_year mass height
## <chr> <chr> <dbl> <dbl> <int>
## 1 C-3PO Droid 112 75 167
## 2 R2-D2 Droid 33 32 96
## 3 Darth Vader Human 41.9 136 202
## 4 Owen Lars Human 52 120 178
## 5 Beru Whitesun lars Human 47 75 165
## 6 R5-D4 Droid NA 32 97
## 7 Obi-Wan Kenobi Human 57 77 182
## 8 Anakin Skywalker Human 41.9 84 188
## 9 Wilhuff Tarkin Human 64 NA 180
## 10 Chewbacca Wookiee 200 112 228
## # ... with 57 more rows</code></pre>
</div>
<div id="arrange" class="section level3" number="7.5.3">
<h3><span class="header-section-number">7.5.3</span> arrange()</h3>
<p><code>arrange()</code> and <code>filter()</code> are like two brothers: both look similar, but they also differ in at least one essential aspect. Both functions change the rows of the data frame, but unlike <code>filter()</code>, <code>arrange()</code> does not select or delete rows, it only changes their order (either ascending or descending). By default, <code>arrange()</code> will sort in ascending order, i.e. from 1:100 (numeric vector) and from A:Z (character vector). <code>arrange()</code> must always be applied to at least one column that is to be sorted.</p>
<div class="sourceCode" id="cb182"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb182-1"><a href="tutorial-data-management-with-tidyverse.html#cb182-1" aria-hidden="true" tabindex="-1"></a>starwars_short <span class="sc">%>%</span></span>
<span id="cb182-2"><a href="tutorial-data-management-with-tidyverse.html#cb182-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">arrange</span>(birth_year)</span></code></pre></div>
<pre><code>## # A tibble: 87 x 5
## name species birth_year mass height
## <chr> <chr> <dbl> <dbl> <int>
## 1 Wicket Systri Warrick Ewok 8 20 88
## 2 IG-88 Droid 15 140 200
## 3 Luke Skywalker Human 19 77 172
## 4 Leia Organa Human 19 49 150
## 5 Wedge Antilles Human 21 77 170
## 6 Plo Koon Kel Dor 22 80 188
## 7 Biggs Darklighter Human 24 84 183
## 8 Han Solo Human 29 80 180
## 9 Lando Calrissian Human 31 79 177
## 10 Boba Fett Human 31.5 78.2 183
## # ... with 77 more rows</code></pre>
<p>To get a descending order:</p>
<div class="sourceCode" id="cb184"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb184-1"><a href="tutorial-data-management-with-tidyverse.html#cb184-1" aria-hidden="true" tabindex="-1"></a>starwars_short <span class="sc">%>%</span></span>
<span id="cb184-2"><a href="tutorial-data-management-with-tidyverse.html#cb184-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">arrange</span>(<span class="fu">desc</span>(birth_year))</span></code></pre></div>
<pre><code>## # A tibble: 87 x 5
## name species birth_year mass height
## <chr> <chr> <dbl> <dbl> <int>
## 1 Yoda Yoda's species 896 17 66
## 2 Jabba Desilijic Tiure Hutt 600 1358 175
## 3 Chewbacca Wookiee 200 112 228
## 4 C-3PO Droid 112 75 167
## 5 Dooku Human 102 80 193
## 6 Qui-Gon Jinn Human 92 89 193
## 7 Ki-Adi-Mundi Cerean 92 82 198
## 8 Finis Valorum Human 91 NA 170
## 9 Palpatine Human 82 75 170
## 10 Cliegg Lars Human 82 NA 183
## # ... with 77 more rows</code></pre>
<p>If you specify more than one column, then subsequent columns are used to break ties. Also note that missing values are always displayed last:</p>
<div class="sourceCode" id="cb186"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb186-1"><a href="tutorial-data-management-with-tidyverse.html#cb186-1" aria-hidden="true" tabindex="-1"></a>starwars_short <span class="sc">%>%</span></span>
<span id="cb186-2"><a href="tutorial-data-management-with-tidyverse.html#cb186-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">arrange</span>(species, birth_year)</span></code></pre></div>
<pre><code>## # A tibble: 87 x 5
## name species birth_year mass height
## <chr> <chr> <dbl> <dbl> <int>
## 1 Ratts Tyerell Aleena NA 15 79
## 2 Dexter Jettster Besalisk NA 102 198
## 3 Ki-Adi-Mundi Cerean 92 82 198
## 4 Mas Amedda Chagrian NA NA 196
## 5 Zam Wesell Clawdite NA 55 168
## 6 IG-88 Droid 15 140 200
## 7 R2-D2 Droid 33 32 96
## 8 C-3PO Droid 112 75 167
## 9 R5-D4 Droid NA 32 97
## 10 R4-P17 Droid NA NA 96
## # ... with 77 more rows</code></pre>
</div>
<div id="mutate" class="section level3" number="7.5.4">
<h3><span class="header-section-number">7.5.4</span> mutate()</h3>
<p>Often you want to add new columns to a data set, e.g. when you calculate new variables or when you want to store re-coded values of other variables. With <code>mutate()</code>, new columns will be added to the end of you data frame.</p>
<p>For example, we can resize the height column to provide the body height in m instead of cm. Let’s call that variable <em>m_height</em>. We’ll assign our transformed data (with the newly created <em>m_height</em> column) back into our source object (<em>starwars_short</em>) to keep the changes for the future (and not just print it to the console).</p>
<div class="sourceCode" id="cb188"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb188-1"><a href="tutorial-data-management-with-tidyverse.html#cb188-1" aria-hidden="true" tabindex="-1"></a>starwars_short <span class="ot"><-</span> starwars_short <span class="sc">%>%</span> <span class="co"># assigns your source object, i.e. data, back to itself to save changes</span></span>
<span id="cb188-2"><a href="tutorial-data-management-with-tidyverse.html#cb188-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">m_height=</span>height<span class="sc">/</span><span class="dv">100</span>) <span class="co"># creates the new variable "m_height" and adds it to the end of the data frame</span></span>
<span id="cb188-3"><a href="tutorial-data-management-with-tidyverse.html#cb188-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb188-4"><a href="tutorial-data-management-with-tidyverse.html#cb188-4" aria-hidden="true" tabindex="-1"></a>starwars_short <span class="co"># print the data to your console to inspect the new column</span></span></code></pre></div>
<pre><code>## # A tibble: 87 x 6
## name species birth_year mass height m_height
## <chr> <chr> <dbl> <dbl> <int> <dbl>
## 1 Luke Skywalker Human 19 77 172 1.72
## 2 C-3PO Droid 112 75 167 1.67
## 3 R2-D2 Droid 33 32 96 0.96
## 4 Darth Vader Human 41.9 136 202 2.02
## 5 Leia Organa Human 19 49 150 1.5
## 6 Owen Lars Human 52 120 178 1.78
## 7 Beru Whitesun lars Human 47 75 165 1.65
## 8 R5-D4 Droid NA 32 97 0.97
## 9 Biggs Darklighter Human 24 84 183 1.83
## 10 Obi-Wan Kenobi Human 57 77 182 1.82
## # ... with 77 more rows</code></pre>
<p>Let’s calculate the BMI of the Star Wars characters with the BMI formula and the newly created <em>m_height</em> variable. Save the changes to your data frame by assigning the source object back to itself.</p>
<div class="sourceCode" id="cb190"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb190-1"><a href="tutorial-data-management-with-tidyverse.html#cb190-1" aria-hidden="true" tabindex="-1"></a>starwars_short <span class="ot"><-</span> starwars_short <span class="sc">%>%</span> <span class="co"># assigns your source object, i.e. data, back to itself to save changes</span></span>
<span id="cb190-2"><a href="tutorial-data-management-with-tidyverse.html#cb190-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">BMI=</span> mass<span class="sc">/</span>m_height<span class="sc">^</span><span class="dv">2</span>) <span class="co"># creates the new variable "BMI" and adds it to the end of the data frame</span></span>
<span id="cb190-3"><a href="tutorial-data-management-with-tidyverse.html#cb190-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb190-4"><a href="tutorial-data-management-with-tidyverse.html#cb190-4" aria-hidden="true" tabindex="-1"></a>starwars_short <span class="co"># print the data to your console to inspect the new column</span></span></code></pre></div>
<pre><code>## # A tibble: 87 x 7
## name species birth_year mass height m_height BMI
## <chr> <chr> <dbl> <dbl> <int> <dbl> <dbl>
## 1 Luke Skywalker Human 19 77 172 1.72 26.0
## 2 C-3PO Droid 112 75 167 1.67 26.9
## 3 R2-D2 Droid 33 32 96 0.96 34.7
## 4 Darth Vader Human 41.9 136 202 2.02 33.3
## 5 Leia Organa Human 19 49 150 1.5 21.8
## 6 Owen Lars Human 52 120 178 1.78 37.9
## 7 Beru Whitesun lars Human 47 75 165 1.65 27.5
## 8 R5-D4 Droid NA 32 97 0.97 34.0
## 9 Biggs Darklighter Human 24 84 183 1.83 25.1
## 10 Obi-Wan Kenobi Human 57 77 182 1.82 23.2
## # ... with 77 more rows</code></pre>
<p><code>mutate()</code> does not merely work with mathematical operators. You can also categorize numeric variables with the <code>case_when</code> function, which is part of the <code>mutate()</code> function.</p>
<div class="sourceCode" id="cb192"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb192-1"><a href="tutorial-data-management-with-tidyverse.html#cb192-1" aria-hidden="true" tabindex="-1"></a>starwars_short <span class="ot"><-</span> starwars_short <span class="sc">%>%</span></span>
<span id="cb192-2"><a href="tutorial-data-management-with-tidyverse.html#cb192-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">age_cat=</span> <span class="fu">case_when</span>( <span class="co"># "cat" is short for "categorized"</span></span>
<span id="cb192-3"><a href="tutorial-data-management-with-tidyverse.html#cb192-3" aria-hidden="true" tabindex="-1"></a> birth_year <span class="sc"><</span> <span class="dv">20</span> <span class="sc">~</span> <span class="st">"very young"</span>,</span>
<span id="cb192-4"><a href="tutorial-data-management-with-tidyverse.html#cb192-4" aria-hidden="true" tabindex="-1"></a> birth_year <span class="sc"><</span> <span class="dv">40</span> <span class="sc">~</span> <span class="st">"young"</span>,</span>
<span id="cb192-5"><a href="tutorial-data-management-with-tidyverse.html#cb192-5" aria-hidden="true" tabindex="-1"></a> birth_year <span class="sc"><</span> <span class="dv">70</span> <span class="sc">~</span> <span class="st">"mid-aged"</span>,</span>
<span id="cb192-6"><a href="tutorial-data-management-with-tidyverse.html#cb192-6" aria-hidden="true" tabindex="-1"></a> birth_year <span class="sc"><=</span> <span class="dv">100</span> <span class="sc">~</span> <span class="st">"old"</span>,</span>
<span id="cb192-7"><a href="tutorial-data-management-with-tidyverse.html#cb192-7" aria-hidden="true" tabindex="-1"></a> birth_year <span class="sc">></span> <span class="dv">100</span> <span class="sc">~</span> <span class="st">"very old"</span>)</span>
<span id="cb192-8"><a href="tutorial-data-management-with-tidyverse.html#cb192-8" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb192-9"><a href="tutorial-data-management-with-tidyverse.html#cb192-9" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb192-10"><a href="tutorial-data-management-with-tidyverse.html#cb192-10" aria-hidden="true" tabindex="-1"></a>starwars_short</span></code></pre></div>
<pre><code>## # A tibble: 87 x 8
## name species birth_year mass height m_height BMI age_cat
## <chr> <chr> <dbl> <dbl> <int> <dbl> <dbl> <chr>
## 1 Luke Skywalker Human 19 77 172 1.72 26.0 very young
## 2 C-3PO Droid 112 75 167 1.67 26.9 very old
## 3 R2-D2 Droid 33 32 96 0.96 34.7 young
## 4 Darth Vader Human 41.9 136 202 2.02 33.3 mid-aged
## 5 Leia Organa Human 19 49 150 1.5 21.8 very young
## 6 Owen Lars Human 52 120 178 1.78 37.9 mid-aged
## 7 Beru Whitesun lars Human 47 75 165 1.65 27.5 mid-aged
## 8 R5-D4 Droid NA 32 97 0.97 34.0 <NA>
## 9 Biggs Darklighter Human 24 84 183 1.83 25.1 young
## 10 Obi-Wan Kenobi Human 57 77 182 1.82 23.2 mid-aged
## # ... with 77 more rows</code></pre>
<p>Finally, you can recode variables by using the <code>recode()</code> function, which is part of the <code>mutate()</code> function. Let’s be crazy and recode all droids as robots<a href="#fn5" class="footnote-ref" id="fnref5"><sup>5</sup></a> and save the result in a new variable called <em>crazy_species</em>! Please note that <code>recode()</code> has an unusual syntax because it follows the order of <code>old_var = new_var</code> instead of the usual order: <code>new_var = old_var</code>. Therefore, <code>recode()</code> is likely to be retired in the future (use <code>case_when</code> instead).</p>
<div class="sourceCode" id="cb194"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb194-1"><a href="tutorial-data-management-with-tidyverse.html#cb194-1" aria-hidden="true" tabindex="-1"></a>starwars_short <span class="ot"><-</span> starwars_short <span class="sc">%>%</span> </span>
<span id="cb194-2"><a href="tutorial-data-management-with-tidyverse.html#cb194-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">crazy_species=</span><span class="fu">recode</span>( <span class="co"># alternatively, you could also recode directly back into the species variable</span></span>