Abstract
Are you sick of those drab-looking graphs and plots in Stata and SPSS? We are too! So, this chapter covers data visualization in R. Specifically, you will be working with ggplot2, a package within the tidyverse set of packages, for making high-quality, reproducible graphics. Data visualization is an accessible, aesthetically pleasing, and powerful way to explore, analyze, and convey complex information. It is an integral part of investigating data and disseminating findings to wider audiences. Learning the basics of data visualization in R can improve your workflow and make your findings easier to interpret and more impactful. This chapter reviews how to visually represent nominal and ordinal data using bar charts and how to visualize ratio and interval data using histograms, scatterplots, line graphs, and boxplots in R. Crime data from Greater Manchester, England, will be used to demonstrate these visualization techniques.
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Reference
Wilkinson, L. (1999). The Grammar of Graphics. Springer. ISBN 978-0-387-98774-3.
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Key Terms
- Aesthetics
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Describe visual characteristics that represent the data.
- Data
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Information used to answer a research question; typically will be stored in a data frame. Data (plural) are made up of numerous datum (singular).
- Geom
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Abbreviation for geometries from the ggplot2 package.
- Geometries
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Describe the objects that represent the data.
- Themes
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Customizations that can alter the general appearance of a plot.
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Wooditch, A., Johnson, N.J., Solymosi, R., Medina Ariza, J., Langton, S. (2021). Data Visualization. In: A Beginner’s Guide to Statistics for Criminology and Criminal Justice Using R. Springer, Cham. https://doi.org/10.1007/978-3-030-50625-4_3
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DOI: https://doi.org/10.1007/978-3-030-50625-4_3
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Publisher Name: Springer, Cham
Print ISBN: 978-3-030-50624-7
Online ISBN: 978-3-030-50625-4
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