Public Interest::Data Ethics & Practice

ggplot2: Mapping Aesthetics

The Grammar of Graphcis: All data visualizations map data to aesthetic attributes (location, shape, color) of geometric objects (lines, points, bars)

• Quantitative (continuous, discrete, time) mapped to position, shape size/line width, color, transparency, …
• Qualitiative (ordered, unordered, text) mapped to position, shape/line type, color

Scales control the mapping from data to aesthetics and provide tools to read the plot (axes, legends). Geometric objects are drawn in a specific coordinate system.

A plot can contains statistical transformations of the data (counts, means, medians) and faceting can be used to generate the same plot for different subsets of the data.

• ggplot2 webiste
• ggplot2 cheat sheet
• Hadley Wickham, ggplot2: Elegant Graphics for Data Analysis

Basic Syntax

ggplot(data, aes(x=, y=, color=, shape=, size=)) +
  geom_point() # or geom_histogram() or geom_boxplot(), etc.

• The data must be a data frame.
• The aes() function maps columns of the data frame to aesthetic properties of geometric shapes to be plotted.
• ggplot() defines the plot; the geoms show the data
• layers are added with +

For example:

ggplot(data, aes(x = var1, y = var2)) +
  geom_point(aes(color = var3)) +
  geom_smooth(color = "red") +
  labs(title = "Helpful Title", 
       x = "x-axis label")

# geom_histogram(), geom_boxplot(), geom_bar(), etc.

• Building layers (data, geoms, scales, labels, etc.)
• Mapping versus setting aesthetics
• Global versus geom-specific aesthetics

Beyond ggplot + geoms

• Create small multiples based on subsets of data:
  • facet_wrap()
  • facet_grid()
• Modify the coordinate system:
  • coord_cartesian allows us to zoom in on a plot
  • coord_flip allows us to flip the x and y axis

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To the Script!!!