ggtrace • ggtrace

Programmatically explore, debug, and manipulate ggplot internals

Installation

You can install the development version from GitHub with:

# install.packages("remotes")
remotes::install_github("yjunechoe/ggtrace")

library(ggtrace) # v0.5.3.9000

More on the 📦 package website: https://yjunechoe.github.io/ggtrace

Description

ggtrace embodies an opinionated approach to learning about ggplot internals. The internals is a difficult topic, so I recommend watching the following presentations on ggtrace before getting started on any kind of code:

Read more about the philosophy behind ggtrace in the Getting Started vignette, and see examples in the Overview vignette.

Example usage

library(ggplot2)
packageVersion("ggplot2")
#> [1] '3.4.0'

1) Remove boxplot outliers from data (vs. just hiding it visually)

You can hide outliers in geom_boxplot(), but they’ll still be in the layer’s underlying dataframe representation. This makes the plot look stretched:

boxplot_plot <- ggplot(mpg, aes(hwy, class)) +
  geom_boxplot(outlier.shape = NA)
boxplot_plot

This is because the scales are re-trained after the calculation of the boxplot statistics. In other words, the “final” min/max value of the x-scale are derived from the calculated outliers, even if they’re not drawn.

layer_data(boxplot_plot)[, c("xmin", "xmax", "outliers", "xmin_final", "xmax_final")]
#>   xmin xmax                       outliers xmin_final xmax_final
#> 1   23   26                                        23         26
#> 2   23   33                 35, 37, 35, 44         23         44
#> 3   23   32                                        23         32
#> 4   21   24                             17         17         24
#> 5   15   20                 12, 12, 12, 22         12         22
#> 6   20   36                         44, 41         20         44
#> 7   14   22 12, 12, 25, 24, 27, 25, 26, 23         12         27

One solution is to highjack the calculation of the boxplot layer’s statistics such that values of the outliers column is set to NULL. In ggtrace_highjack_return(), we pass an expression that modifies returnValue() to the value argument, which evaluates to the value about to be returned by the method.

ggtrace_highjack_return(
  x = boxplot_plot,
  method = Stat$compute_layer,
  cond = 1L,
  value = quote({
    transform(returnValue(), outliers = NULL)
  })
)

Problem inspired by https://github.com/tidyverse/ggplot2/issues/4892.

2) Crop polar coordinate plots

Plot in polar coordinates:

polar_plot <- ggplot(mtcars, aes(hp, mpg)) +
  geom_point() +
  geom_smooth(method = "lm", formula = y ~ x) +
  expand_limits(y = c(0, 60)) +
  coord_polar(start = 0, theta = "y")

polar_plot

Clipping the plot panel with ggtrace by highjacking the Layout$render() method:

ggtrace::with_ggtrace(
  x = polar_plot + theme(aspect.ratio = 1/.48),
  method = Layout$render,
  trace_steps = 5L,
  trace_expr = quote({
    panels[[1]] <- editGrob(panels[[1]], vp = viewport(xscale = c(0.48, 1)))
  }),
  out = "g"
)

See implementation in MSBMisc::crop_coord_polar().

3) Highjack the drawing context

Flashy example adopted from my UseR! talk:

library(palmerpenguins)
p <- na.omit(palmerpenguins::penguins) |> 
  ggplot(aes(x = species, y = flipper_length_mm)) +
  geom_boxplot(aes(fill = species), width = .7) +
  facet_wrap(~ year)

ggtrace_highjack_return(
  p, Geom$draw_panel, cond = TRUE,
  value = quote({
    y_pos <- .25 * ._counter_
    grobTree( circleGrob(y = y_pos, gp = gpar(fill = linearGradient())), # R >= 4.1
              editGrob(returnValue(), vp = viewport(clip = circleGrob(y = y_pos))) )
  }))

{ggtrace}