Creating Custom Y-Scales for ggplot2 Facet Plots with Ggh4x

In this article, we will explore how to create custom y-scales for ggplot2 facet plots using the ggh4x package. We will cover the process of generating a named list of scales, evaluating arguments at creation time, and applying these scales to our facet plot.

Introduction to ggplot2 Facet Plots

ggplot2 is a popular data visualization library in R that provides a high-level interface for creating beautiful and informative plots. One of its key features is the ability to create facet plots, which allow us to visualize multiple datasets or subsets of data on the same plot.

The ggh4x package is a collection of ggplot2 extensions that provide additional functionality for faceted plots, including support for custom scales and axis transformations. In this article, we will focus on creating custom y-scales using the ggh4x::facetted_pos_scales function.

Creating Custom Y-Scales

To create a custom y-scale, we need to generate a list of scales that can be used with the facetted_pos_scales function. The scale is defined by its transformation function, which takes in an x-value and returns a transformed value.

Let’s start with an example dataset:

library(ggplot2)
library(scales)
library(ggh4x)

df = tibble(Name = c('A','B'), 
       min_from = c(0,0), 
       max_from = c(1,1), 
       min_to = c(100,100), 
       max_to = c(1000,1000))

In this example, we have a dataset with two rows and two columns. We can create a custom y-scale by using the split function to split the data into separate scales for each row, and then applying the transformation function to each scale.

scale_y <- df |> 
  split(~Name) |> 
  lapply(\(sc\) {
    scale_y_continuous(
      sec.axis = sec_axis(transform = \(x) rescale(x,
        to = c(sc$min_to, sc$max_to),
        from = c(sc$min_from, sc$max_from)
      ))
    )
  })

In this code, we use the split function to split the data into separate scales for each row. We then apply the transformation function to each scale using the sec_axis function.

Applying Custom Scales to a Facet Plot

Now that we have created our custom y-scales, we can apply them to a facet plot using the facetted_pos_scales function from the ggh4x package.

data.frame(
  x = 1:10, y = 1:10, Name = rep(c("A", "B"), each = 10)
) |> 
  ggplot(aes(x, y)) +
  geom_point() +
  facet_wrap(~Name, scales = "free_y") +
  ggh4x::facetted_pos_scales(y = scale_y)

In this code, we create a sample dataset with two rows and ten points each. We then create a ggplot object using the ggplot function, specifying the aesthetic mappings for the x and y variables.

We use the facet_wrap function to create a facet plot with free y-scales for each row. Finally, we apply our custom y-scales to the facet plot using the ggh4x::facetted_pos_scales function.

Evaluating Arguments at Creation Time

In the previous example, we applied the transformation function to the scale after it had been created. However, this can lead to issues if the arguments are evaluated too late in the pipeline.

To evaluate the arguments at creation time, we can use a technique called “currying”. Currying is a process of transforming a function that takes multiple arguments into a sequence of functions, each taking one argument.

scale_y <- df |> 
  split(~Name) |> 
  lapply(\(sc\) {
    scale_y_continuous(
      sec.axis = sec_axis(transform = curried_rescale(x),
        to = c(sc$min_to, sc$max_to),
        from = c(sc$min_from, sc$max_from)
      ))
    )
  })

In this code, we use the curried_rescale function from the scales package to create a curried transformation function. This allows us to evaluate the arguments at creation time, rather than after the scale has been created.

Conclusion

Creating custom y-scales for ggplot2 facet plots is a powerful way to customize your visualizations and improve their overall appearance. In this article, we covered the process of generating a named list of scales, evaluating arguments at creation time, and applying these scales to our facet plot using the ggh4x package.

By following the techniques outlined in this article, you can create beautiful and informative custom y-scales for your ggplot2 facet plots.

Last modified on 2024-05-28