Edited at the end to discuss ggnewscale & ggh4x examples.
I played around to see if one can "flatten" the multi-layered scales component of a regular ggplot object, and came up with the following:
flatten.scales <- function(gg) {
# take stock how many different scales are contained within the top-level
# scale list, & sort their names alphabetically for consistency
orig.scales <- gg[["scales"]]
scale.count <- orig.scales$n()
scale.aesthetics <- lapply(seq_len(scale.count),
function(i) orig.scales$scales[[i]]$aesthetics)
names(scale.aesthetics) <- lapply(scale.aesthetics,
function(x) x[[1]])
scale.names.sorted <- sort(names(scale.aesthetics))
# define a new empty scale list ggproto object
new.scales <- ggplot2:::scales_list()
# for each scale, traverse up its inheritance tree until we can't go any
# higher without losing the function call -- i.e. any super's beyond this
# point are inheritances defined in ggproto (e.g. ScaleContinuousPosition
# inherits from ScaleContinuous, which in turn inherits from Scale), not
# inheritances created during cloning of scales within this ggplot object.
# add that scale to the new scale list.
for(i in seq_along(scale.names.sorted)) {
scale.to.add <- orig.scales$get_scales(scale.names.sorted[[i]])
while("super" %in% names(scale.to.add)) {
scale.to.add1 <- scale.to.add$super()
if(!is.null(scale.to.add1$call)) {
scale.to.add <- scale.to.add1
} else {
break
}
}
# added step to handle ggnewscale, as the top-level scale's aesthetic would
# have lost the "XXX_new" format & lead to clash with another scale with
# aesthetic name = "XXX". also keep use of the original guide (which includes
# the renamed aesthetic under available_aes)
if(!scale.names.sorted[[i]] %in% scale.to.add$aesthetics) {
scale.to.add$aesthetics <- scale.names.sorted[[i]]
scale.to.add$guide <- scale.guide
}
new.scales$add(scale.to.add)
}
gg[["scales"]] <- new.scales
return(gg)
}
This won't return the order of the layers, but comparing two ggplot objects with flattened scales would allow one to ignore layer order differences, so that only other, presumably more serious differences are reported for the user's attention.
Demonstration with a variation on the original example
p1 <- ggplot(cars, aes(speed, dist)) + xlim(3, 26) + ylim(25, 100) + geom_point() + geom_line()
p2 <- ggplot(cars, aes(speed, dist)) + ylim(25, 100) + xlim(3, 26) + geom_point() + geom_line()
p3 <- ggplot(cars, aes(speed, dist)) + geom_point() + xlim(3, 26) + geom_line() + ylim(25, 100)
p4 <- ggplot(cars, aes(speed, dist)) + geom_point() + ylim(25, 100) + geom_line() + xlim(3, 26)
length(waldo::compare(p1, p2)) # 22
length(waldo::compare(p1, p3)) # 414
length(waldo::compare(p1, p4)) # 414
length(waldo::compare(p2, p3)) # 414
length(waldo::compare(p2, p4)) # 414
length(waldo::compare(p3, p4)) # 22
waldo::compare(flatten.scales(p1), flatten.scales(p2)) # No differences
waldo::compare(flatten.scales(p1), flatten.scales(p3)) # No differences
waldo::compare(flatten.scales(p1), flatten.scales(p4)) # No differences
waldo::compare(flatten.scales(p2), flatten.scales(p3)) # No differences
waldo::compare(flatten.scales(p2), flatten.scales(p4)) # No differences
waldo::compare(flatten.scales(p3), flatten.scales(p4)) # No differences
library(patchwork)
(p1 | p2 | p3 | p4) / (flatten.scales(p1) | flatten.scales(p2) | flatten.scales(p3) | flatten.scales(p4))
# confirm that the original plots & their variations with flattened scales all
# look identical to one another
A more convoluted example with categorical & non-position scales, plus facets thrown in for good measure:
p1 <- ggplot(mpg, aes(hwy, class, fill = class)) +
ylim(c("suv", "midsize", "compact")) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(width = 0.2) +
xlim(c(0, 35)) +
facet_wrap(vars(fl)) +
scale_fill_brewer(palette = "Set2")
p2 <- ggplot(mpg, aes(hwy, class, fill = class)) +
xlim(c(0, 35)) +
geom_boxplot(outlier.shape = NA) +
scale_fill_brewer(palette = "Set2") +
facet_wrap(vars(fl)) +
ylim(c("suv", "midsize", "compact")) +
geom_jitter(width = 0.2)
length(waldo::compare(p1, p2)) # 623
waldo::compare(flatten.scales(p1), flatten.scales(p2)) # No differences
(p1 | p2) / (flatten.scales(p1) | flatten.scales(p2)) # Identical aside from random jittering
ggnewscale
The ggnewscale package provides functions of the form new_scale_xxx() to allow subsequent layers to follow a different scale definition. (Somewhat confusingly, the original scale's aesthetic is named "XXX_new" while the new version is named "XXX".) I've updated the code for flatten.scales above by inserting a check for any difference in aesthetic name before adding each flattened scale to the scale list. If there's a difference (presumably due to ggnewscale, because so far I'm not aware of other packages that do this), the original scale's aesthetic name & guide are retained.
Demonstration
library(ggnewscale)
library(ggplot2)
# generate data
set.seed(123)
topography <- expand.grid(x = 1:nrow(volcano), y = 1:ncol(volcano))
topography$z <- c(volcano)
measurements <- data.frame(x = runif(30, 1, 80),
y = runif(30, 1, 60),
thing = rnorm(30))
p1 <- ggplot(mapping = aes(x, y)) +
xlim(10, 70) +
geom_contour(data = topography, aes(z = z, color = after_stat(level))) +
scale_color_viridis_c(option = "D") +
new_scale_color() +
geom_point(data = measurements, size = 3, aes(color = thing)) +
scale_color_viridis_c(option = "A") +
labs(x = "HWY", y = "CLASS", colour = "Thing", colour_new = "Level") +
ylim(10, 50)
p2 <- ggplot(mapping = aes(x, y)) +
ylim(10, 50) +
geom_contour(data = topography, aes(z = z, color = after_stat(level))) +
scale_color_viridis_c(option = "D") +
new_scale_color() +
xlim(10, 70) +
geom_point(data = measurements, size = 3, aes(color = thing)) +
scale_color_viridis_c(option = "A") +
labs(x = "HWY", y = "CLASS", colour = "Thing", colour_new = "Level")
length(waldo::compare(p1, p2)) # 409
waldo::compare(flatten.scales(p1), flatten.scales(p2)) # No differences
(p1 | p2) / (flatten.scales(p1) | flatten.scales(p2)) # Identical
ggh4x
The ggh4x package provides functions of the form scale_xxx_multi() to allow multiple scales to be mapped to colour / fill. Like the ggnewscale case, order of layers matter, because scale_xxx_multi() affects all earlier layers that make use of relevant aesthetic mappings, by making changes to their innards.
Hence, the situation is rather more complex because even after flatten.scales() scrubs through the scales, the layers component of the ggplot object contain remnants of the original scales present when scale_xxx_multi() took effect.
Demonstration
library(ggh4x)
p1 <- ggplot(iris, aes(Sepal.Width, Sepal.Length)) +
geom_point(aes(swidth = Sepal.Width),
data = ~ subset(., Species == "setosa")) +
geom_point(aes(pleng = Petal.Length),
data = ~ subset(., Species == "versicolor")) +
scale_y_continuous(name = "Assorted measurements") +
geom_point(aes(pwidth = Petal.Width),
data = ~ subset(., Species == "virginica")) +
facet_wrap(~ Species, scales = "free_x") +
scale_colour_multi(
aesthetics = c("swidth", "pleng", "pwidth"),
colours = list(c("black", "green"), c("gray", "red"), c("white", "blue")),
guide = list(guide_colourbar(barheight = unit(35, "pt")))
)
p2 <- ggplot(iris, aes(Sepal.Width, Sepal.Length)) +
geom_point(aes(swidth = Sepal.Width),
data = ~ subset(., Species == "setosa")) +
geom_point(aes(pleng = Petal.Length),
data = ~ subset(., Species == "versicolor")) +
geom_point(aes(pwidth = Petal.Width),
data = ~ subset(., Species == "virginica")) +
facet_wrap(~ Species, scales = "free_x") +
scale_colour_multi(
aesthetics = c("swidth", "pleng", "pwidth"),
colours = list(c("black", "green"), c("gray", "red"), c("white", "blue")),
guide = list(guide_colourbar(barheight = unit(35, "pt")))
) +
scale_y_continuous(name = "Assorted measurements")
length(waldo::compare(p1, p2)) # 876
for(i in seq_along(p1)) {
cat(i, "(", names(p1)[i], ") :")
res <- waldo::compare(p1[[i]], p2[[i]])
if(length(res) == 0) {
cat("No diff")
} else {
cat(length(res))
}
cat("\n")
}
# 1 ( data ) :No diff
# 2 ( layers ) :232
# 3 ( scales ) :644
# 4 ( mapping ) :No diff
# 5 ( theme ) :No diff
# 6 ( coordinates ) :No diff
# 7 ( facet ) :No diff
# 8 ( plot_env ) :No diff
# 9 ( labels ) :No diff
# p1 & p2 differ in layers & scales
p1.flattened <- flatten.scales(p1)
p2.flattened <- flatten.scales(p2)
for(i in seq_along(p1)) {
cat(i, "(", names(p1)[i], ") :")
res <- waldo::compare(p1.flattened[[i]], p2.flattened[[i]])
if(length(res) == 0) {
cat("No diff")
} else {
cat(length(res))
}
cat("\n")
}
# 1 ( data ) :No diff
# 2 ( layers ) :232
# 3 ( scales ) :No diff
# 4 ( mapping ) :No diff
# 5 ( theme ) :No diff
# 6 ( coordinates ) :No diff
# 7 ( facet ) :No diff
# 8 ( plot_env ) :No diff
# 9 ( labels ) :No diff
# flattened versions of p1 & p2 still differ in layers
cowplot::plot_grid(p1, p1.flattened, p2, p2.flattened, nrow = 2)
# but at least everything still looks identical
# (cowplot used to arrange the plots, as patchwork complained)
If we peer into waldo::compare(p1.flattened[["layers"]], p2.flattened[["layers"]]) (or equivalently waldo::compare(p1[["layers"]], p2[["layers"]]), since the function didn't do anything with the layers component), we get various messages such as:
`parent.env(environment(old[[1]]$geom$draw_key))$.GenericCallEnv$p$scales$scales` is length 4
`parent.env(environment(new[[1]]$geom$draw_key))$.GenericCallEnv$p$scales$scales` is length 3
...
`parent.env(environment(old[[1]]$geom$draw_key))$.GenericCallEnv$p$scales$scales[[1]]` is length 2
`parent.env(environment(new[[1]]$geom$draw_key))$.GenericCallEnv$p$scales$scales[[1]]` is length 19
(omitted)
While it's probably possible to write another function that scrubs through the environments to remove the offending elements (which appears to be copies of the original plot with scales in a particular order), I hesitate to do so for two reasons:
Based on my understanding, these environments are not unique to each ggplot object. I.e. suppose we have a function scrub.layers() that performs the task, whatever happens in p1.new = scrub.layers(p1) will affect the original p1, since they reference the same environments.
I'm not that good with environments & haven't managed to come up with a viable solution after two days of trying. (I did manage to increase my exposure to a fascinating variety of error messages though :P).
That said, waldo::compare does have an option to ignore comparisons within environments of functions, if that's a viable option for you:
length(waldo::compare(p1, p2)) # 876
length(waldo::compare(flatten.scales(p1), flatten.scales(p2))) # 232
length(waldo::compare(p1, p2, ignore_function_env = TRUE)) # 77
length(waldo::compare(flatten.scales(p1), flatten.scales(p2), ignore_function_env = TRUE)) # 0
TL;DR: If it's okay to not compare environments, flatten.scales should work for ggh4x's scale_xxx_multi -- subject to further stress tests based on actual use cases, that is.
