Installation
You can install the development version from GitHub with:
# install.packages("remotes")
remotes::install_github("yjunechoe/ggtrace")
library(ggtrace)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.
ggtrace now also has a paper! Sub-layer modularity in the Grammar of Graphics
Example usage
library(ggplot2)
packageVersion("ggplot2")
#> [1] '3.4.1'1) Inspect sub-layer data
A bar plot of counts with geom_bar() with stat = "count" default:
bar_plot <- ggplot(mpg, aes(class, fill = class)) +
geom_bar() +
theme(legend.position = "none")State of bar layer’s data after the statistical transformation step:
ggtrace::layer_after_stat(bar_plot)
#> ✔ Executed `ggtrace_inspect_return(bar_plot, ggplot2:::Layer$compute_statistic)`
#> # A tibble: 7 × 8
#> count prop x width flipped_aes fill PANEL group
#> <dbl> <dbl> <mppd_dsc> <dbl> <lgl> <chr> <fct> <int>
#> 1 5 1 1 0.9 FALSE 2seater 1 1
#> 2 47 1 2 0.9 FALSE compact 1 2
#> 3 41 1 3 0.9 FALSE midsize 1 3
#> 4 11 1 4 0.9 FALSE minivan 1 4
#> 5 33 1 5 0.9 FALSE pickup 1 5
#> 6 35 1 6 0.9 FALSE subcompact 1 6
#> 7 62 1 7 0.9 FALSE suv 1 7We can map aesthetics to variables from the after-stat data using after_stat():
bar_plot +
geom_text(
aes(label = after_stat(count)),
stat = "count",
position = position_nudge(y = 1), vjust = 0
)
Same idea with after_scale():
scatter_plot <- ggplot(mpg, aes(displ, hwy, fill = class)) +
scale_fill_viridis_d(option = "magma")
scatter_plot +
geom_point(shape = 21, size = 4, stroke = 1)
# `fill` column available for `after_scale(fill)`
ggtrace::layer_after_scale(scatter_plot)
#> ✔ Executed `ggtrace_inspect_return(scatter_plot, ggplot2:::Layer$compute_geom_2)`
#> # A tibble: 234 × 5
#> fill x y PANEL group
#> <chr> <dbl> <dbl> <fct> <int>
#> 1 #2D1160FF 1.8 29 1 2
#> 2 #2D1160FF 1.8 29 1 2
#> 3 #2D1160FF 2 31 1 2
#> 4 #2D1160FF 2 30 1 2
#> 5 #2D1160FF 2.8 26 1 2
#> 6 #2D1160FF 2.8 26 1 2
#> 7 #2D1160FF 3.1 27 1 2
#> 8 #2D1160FF 1.8 26 1 2
#> 9 #2D1160FF 1.8 25 1 2
#> 10 #2D1160FF 2 28 1 2
#> # … with 224 more rows
scatter_plot +
geom_point(
aes(color = after_scale(prismatic::best_contrast(fill))),
shape = 21, size = 4, stroke = 1
)
2) Highjack ggproto (remove boxplot outliers)
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 27One 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.
Note that this is also possible in “vanilla” ggplot. Following our earlier discussion of after_stat():
# Suppress warning from mapping to `outliers` aesthetic
update_geom_defaults("boxplot", list(outliers = NULL))
ggplot(mpg, aes(hwy, class)) +
geom_boxplot(
# Equivalent effect of modifying the after-stat data
aes(outliers = after_stat(list(NULL)))
)
3) Not just ggproto
The method argument of ggtrace_*() workflow functions can be (almost) any function-like object called during the rendering of a ggplot.
set.seed(2023)
# Example from `?stat_summary`
summary_plot <- ggplot(mtcars, aes(mpg, factor(cyl))) +
geom_point() +
stat_summary(fun.data = "mean_cl_boot", colour = "red", linewidth = 2, size = 3)
summary_plot
ggtrace_inspect_args(x = summary_plot, method = mean_cl_boot)
#> $x
#> [1] 22.8 24.4 22.8 32.4 30.4 33.9 21.5 27.3 26.0 30.4 21.4
ggtrace_inspect_return(x = summary_plot, method = mean_cl_boot)
#> y ymin ymax
#> 1 26.66364 24.11727 29.19159
ggtrace_highjack_return(
x = summary_plot, method = mean_cl_boot,
value = quote({
data.frame(y = 50, ymin = 25, ymax = 75)
})
)
4) Visually crop polar coordinate plots
Here’s a plot in polar coordinates:
polar_plot <- ggplot(mtcars, aes(hp, mpg)) +
geom_point() +
geom_smooth(method = "lm", formula = y ~ x) +
expand_limits(y = c(0, 60)) +
coord_polar(start = 0, theta = "y")
polar_plot
We can clip the plot panel by highjacking the Layout$render() method using the generic workflow function with_ggtrace():
with_ggtrace(
x = polar_plot + theme(aspect.ratio = 1/.48),
method = Layout$render,
trace_steps = 5L,
trace_expr = quote({
panels[[1]] <- editGrob(panels[[1]], vp = viewport(xscale = c(.48, 1)))
}),
out = "g"
)
Inspired by a twitter thread. See implementation in MSBMisc::crop_coord_polar().
5) Highjack the drawing context
Flashy example adopted from my UseR! talk:
library(palmerpenguins)
flashy_plot <- na.omit(palmerpenguins::penguins) |>
ggplot(aes(x = species, y = flipper_length_mm)) +
geom_boxplot(aes(fill = species), width = .7) +
facet_wrap(~ year)
flashy_plot
ggtrace_highjack_return(
flashy_plot, Geom$draw_panel, cond = TRUE,
value = quote({
circ <- circleGrob(y = .25 * ._counter_)
grobTree( editGrob(circ, gp = gpar(fill = linearGradient())),
editGrob(returnValue(), vp = viewport(clip = circ)) )
}))
Note the use of the special variable ._counter_, which increments every time a function/method has been called. See the reference section on tracing context for more details.