Consider the following data table:
| country | continent | population | life expectancy | income |
|---|---|---|---|---|
| China | Asia | 1.420.000.000 | 76,9 | 16.000 |
| India | Asia | 1.350.000.000 | 69,1 | 6.890 |
| United States | Americas | 327.000.000 | 79,1 | 54.900 |
| Indonesia | Asia | 267.000.000 | 72 | 11.700 |
| Brazil | Americas | 211.000.000 | 75.7 | 14.300 |
| … |
This data set (which is a tidy data set, see the Tidy data module) can be visualised as follows:
Source: Maarten Lambrechts, CC BY SA 4.0
In chart type vocabulary, this plot is called a bubble chart. But in the Grammar of Graphics, this plot
point geometriesshape aestheticincome variable mapped to the x aesthetic (in fact, it is the logarithm of the GDP/capita)life expectancy variable mapped to the y aestheticpopulation variable mapped to the size aestheticcontinent variable mapped to the fill colour aestheticSo shape, x, y, size and fill colour are all aesthetics of the point geometry that can be used to map variables in a data set.
In this example, the shape aesthetic is fixed, and no data is mapped to it. The shape aesthetic can be used to map categorical data on the point geometry. In the chart below, the continent variable is mapped to the shape aesthetic instead of to the fill colour aesthetic.
Source: Maarten Lambrechts, CC BY SA 4.0
You’ll notice that processing and understanding this chart is a bit harder than the original one above: it is easier to distinguish between colours than it is to distinguish between the different shapes. So some aesthetics are more powerful than others: fill colour is stronger than shape, for example.
But nothing prevents us from mapping a single variable in the data to multiple aesthetics. In the next plot, the continent variable is mapped to both the shape and the fill colour aesthetics of the point geometry.
Source: Maarten Lambrechts, CC BY SA 4.0
Mapping the same variable to multiple aesthetics is called double encoding: one variable in the data is “encoded” into multiple visual properties of the geometries. Double encoding is especially important when colour aesthetics are used: with double encoding you can ensure that people who cannot fully perceive all colours can still read your visualisation correctly. See the Colours, colour blindness and data visualisation module of the Accessibility for data visualisation training for more information.