Tailoring plots to particular data types

There are three functions to make plotting particular data types easier: autoplot(), autolayer() and fortify(). These are S3 generics for which other packages can write methods to display classes of data. The three functions are complementary and allow different levels of customisation. Below we'll explore implementing this series of methods to automate plotting of some class.

Let's suppose we are writing a packages that has a class called 'my_heatmap', that wraps a matrix and we'd like users to easily plot this heatmap.

my_heatmap <- function(...) {
  m <- matrix(...)
  class(m) <- c("my_heatmap", class(m))
  m
}

my_data <- my_heatmap(volcano)

Automatic data shaping

One of the things we have to do is ensure that the data is shaped in the long format so that it is compatible with ggplot2. This is the job of the fortify() function. Because 'my_heatmap' wraps a matrix, we can let the fortify method 'melt' the matrix to a long format. If your data is already based on a long-format <data.frame>, you can skip implementing a fortify() method.

fortify.my_heatmap <- function(model, ...) {
  data.frame(
    row = as.vector(row(model)),
    col = as.vector(col(model)),
    value = as.vector(model)
  )
}

fortify(my_data)

When you have implemented the fortify() method, it should be easier to construct a plot with the data than with the matrix.

ggplot(my_data, aes(x = col, y = row, fill = value)) +
  geom_raster()

Automatic layers

A next step in automating plotting of your data type is to write an autolayer() method. These are typically wrappers around geoms or stats that automatically set aesthetics or other parameters. If you haven't implemented a fortify() method for your data type, you might have to reshape the data in autolayer().

If you require multiple layers to display your data type, you can use an autolayer() method that constructs a list of layers, which can be added to a plot.

autolayer.my_heatmap <- function(object, ...) {
  geom_raster(
    mapping = aes(x = col, y = row, fill = value),
    data = object,
    ...,
    inherit.aes = FALSE
  )
}

ggplot() + autolayer(my_data)

As a quick tip: if you define a mapping in autolayer(), you might want to set inherit.aes = FALSE to not have aesthetics set in other layers interfere with your layer.

Automatic plots

The last step in automating plotting is to write an autoplot() method for your data type. The expectation is that these return a complete plot. In the example below, we're exploiting the autolayer() method that we have already written to make a complete plot.

autoplot.my_heatmap <- function(object, ..., option = "magma") {
  ggplot() +
    autolayer(my_data) +
    scale_fill_viridis_c(option = option) +
    theme_void()
}

autoplot(my_data)

If you don't have a wish to implement a base R plotting method, you can set the plot method for your class to the autoplot method.

plot.my_heatmap <- autoplot.my_heatmap
plot(my_data)

Automatic data shaping

Automatic layers

Automatic plots

See also