ggplot2 (version 3.3.2)

aes_eval: Control aesthetic evaluation


Most aesthetics are mapped from variables found in the data. Sometimes, however, you want to delay the mapping until later in the rendering process. ggplot2 has three stages of the data that you can map aesthetics from. The default is to map at the beginning, using the layer data provided by the user. The second stage is after the data has been transformed by the layer stat. The third and last stage is after the data has been transformed and mapped by the plot scales. The most common example of mapping from stat transformed data is the height of bars in geom_histogram(): the height does not come from a variable in the underlying data, but is instead mapped to the count computed by stat_bin(). An example of mapping from scaled data could be to use a desaturated version of the stroke colour for fill. If you want to map directly from the layer data you should not do anything special. In order to map from stat transformed data you should use the after_stat() function to flag that evaluation of the aesthetic mapping should be postponed until after stat transformation. Similarly, you should use after_scale() to flag evaluation of mapping for after data has been scaled. If you want to map the same aesthetic multiple times, e.g. map x to a data column for the stat, but remap it for the geom, you can use the stage() function to collect multiple mappings.




stage(start = NULL, after_stat = NULL, after_scale = NULL)



An aesthetic expression using variables calculated by the stat (after_stat()) or layer aesthetics (after_scale()).


An aesthetic expression using variables from the layer data.


An aesthetic expression using variables calculated by the stat.


An aesthetic expression using layer aesthetics.


after_stat() replaces the old approaches of using either stat() or surrounding the variable names with ...


# Default histogram display
ggplot(mpg, aes(displ)) +
  geom_histogram(aes(y = after_stat(count)))

# Scale tallest bin to 1
ggplot(mpg, aes(displ)) +
  geom_histogram(aes(y = after_stat(count / max(count))))

# Use a transparent version of colour for fill
ggplot(mpg, aes(class, hwy)) +
  geom_boxplot(aes(colour = class, fill = after_scale(alpha(colour, 0.4))))

# Use stage to modify the scaled fill
ggplot(mpg, aes(class, hwy)) +
  geom_boxplot(aes(fill = stage(class, after_scale = alpha(fill, 0.4))))
# }