Aesthetics for categorical & continuous variables

Many of the aesthetics can accept either continuous or categorical variables, but some are restricted to categorical data. Which aesthetics are only applicable to categorical data?

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Introduction to Data Visualization with ggplot2

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Introduction to Data Visualization with ggplot2

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In this chapter we’ll get you into the right frame of mind for developing meaningful visualizations with R. You’ll understand that as a communications tool, visualizations require you to think about your audience first. You’ll also be introduced to the basics of ggplot2 - the 7 different grammatical elements (layers) and aesthetic mappings.

Exercise 1: Introduction Exercise 2: Explore and explain Exercise 3: Drawing your first plot Exercise 4: Data columns types affect plot types Exercise 5: The grammar of graphics Exercise 6: Mapping data columns to aesthetics Exercise 7: Understanding variables Exercise 8: ggplot2 layers Exercise 9: Adding geometries Exercise 10: Changing one geom or every geom Exercise 11: Saving plots as variables

Aesthetic mappings are the cornerstone of the grammar of graphics plotting concept. This is where the magic happens - converting continuous and categorical data into visual scales that provide access to a large amount of information in a very short time. In this chapter you’ll understand how to choose the best aesthetic mappings for your data.

Exercise 1: Visible aesthetics Exercise 2: All about aesthetics: color, shape and size Exercise 3: All about aesthetics: color vs. fill Exercise 4: All about aesthetics: comparing aesthetics Exercise 5: Aesthetics for categorical & continuous variables

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Exercise 6: Using attributes Exercise 7: All about attributes: color, shape, size and alpha Exercise 8: All about attributes: conflicts with aesthetics Exercise 9: Going all out Exercise 10: Modifying aesthetics Exercise 11: Updating aesthetic labels Exercise 12: Setting a dummy aesthetic Exercise 13: Aesthetics best practices Exercise 14: Appropriate mappings

A plot’s geometry dictates what visual elements will be used. In this chapter, we’ll familiarize you with the geometries used in the three most common plot types you’ll encounter - scatter plots, bar charts and line plots. We’ll look at a variety of different ways to construct these plots.

Exercise 1: Scatter plots Exercise 2: Overplotting 1: large datasets Exercise 3: Overplotting 2: Aligned values Exercise 4: Overplotting 3: Low-precision data Exercise 5: Overplotting 4: Integer data Exercise 6: Histograms Exercise 7: Drawing histograms Exercise 8: Positions in histograms Exercise 9: Bar plots Exercise 10: Position in bar and col plots Exercise 11: Overlapping bar plots Exercise 12: Bar plots: sequential color palette Exercise 13: Line plots Exercise 14: Basic line plots Exercise 15: Multiple time series

In this chapter, we’ll explore how understanding the structure of your data makes data visualization much easier. Plus, it’s time to make our plots pretty. This is the last step in the data viz process. The Themes layer will enable you to make publication quality plots directly in R. In the next course we'll look at some extra layers to add more variables to your plots.

Exercise 1: Themes from scratch Exercise 2: Moving the legend Exercise 3: Modifying theme elements Exercise 4: Modifying whitespace Exercise 5: Theme flexibility Exercise 6: Built-in themes Exercise 7: Exploring ggthemes Exercise 8: Setting themes Exercise 9: Publication-quality plots Exercise 10: Effective explanatory plots Exercise 11: Using geoms for explanatory plots Exercise 12: Using annotate() for embellishments