Constrict ggplot ellips to realistic/possible values - r

Question

When plotting an ellips with ggplot is it possible to constrain the ellips to values that are actually possible?
For example, the following reproducible code and data plots Ele vs. Var for two species. Var is a positive variable and cannot be negative. Nonetheless, negative values are included in the resulting ellips. Is it possible to bound the ellips by 0 on the x-axis (using ggplot)?
More specifically, I am picturing a flat edge with the ellipsoids truncated at 0 on the x-axis.
library(ggplot2)
set.seed(123)
df <- data.frame(Species = rep(c("BHS", "MTG"), each = 100),
Ele = c(sample(1500:3000, 100), sample(2500:3500, 100)),
Var = abs(rnorm(200)))
ggplot(df, aes(Var, Ele, color = Species)) +
geom_point() +
stat_ellipse(aes(fill = Species), geom="polygon",level=0.95,alpha=0.2)

Answer 1

You could edit the default stat to clip points to a particular value. Here we change the basic stat to trim x values less than 0 to 0
StatClipEllipse <- ggproto("StatClipEllipse", Stat,
required_aes = c("x", "y"),
compute_group = function(data, scales, type = "t", level = 0.95,
segments = 51, na.rm = FALSE) {
xx <- ggplot2:::calculate_ellipse(data = data, vars = c("x", "y"), type = type,
level = level, segments = segments)
xx %>% mutate(x=pmax(x, 0))
}
)
Then we have to wrap it in a ggplot stat that is identical to stat_ellipe except that it uses our custom Stat object
stat_clip_ellipse <- function(mapping = NULL, data = NULL,
geom = "path", position = "identity",
...,
type = "t",
level = 0.95,
segments = 51,
na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE) {
layer(
data = data,
mapping = mapping,
stat = StatClipEllipse,
geom = geom,
position = position,
show.legend = show.legend,
inherit.aes = inherit.aes,
params = list(
type = type,
level = level,
segments = segments,
na.rm = na.rm,
...
)
)
}
then you can use it to make your plot
ggplot(df, aes(Var, Ele, color = Species)) +
geom_point() +
stat_clip_ellipse(aes(fill = Species), geom="polygon",level=0.95,alpha=0.2)
This was inspired by the source code for stat_ellipse.

Answer 2

Based on my comment above, I created a less-misleading option for visualization. This is ignoring the problem with y being uniformly distributed, since that's a somewhat less egregious problem than the heavily skewed x variable.
Both these options use the ggforce package, which is an extension of ggplot2, but just in case, I've also included the source for the particular function I used.
library(ggforce)
library(scales)
# power_trans <- function (n)
# {
# scales::trans_new(name = paste0("power of ", fractions(n)), transform = function(x) {
# x^n
# }, inverse = function(x) {
# x^(1/n)
# }, breaks = scales::extended_breaks(), format = scales::format_format(),
# domain = c(0, Inf))
# }
Option 1:
ggplot(df, aes(Var, Ele, color = Species)) +
geom_point() +
stat_ellipse(aes(fill = Species), geom="polygon",level=0.95,alpha=0.2) +
scale_x_sqrt(limits = c(-0.1,3.5),
breaks = c(0.0001,1:4),
labels = 0:4,
expand = c(0.00,0))
This option stretches the x-axis along a square-root transform, spreading out the points clustered near zero. Then it computes an ellipse over this new space.
Advantage: looks like an ellipse still.
Disadvantage: in order to get it to play nice and label the Var=0 point on the x axis, you have to use expand = c(0,0), which clips the limits exactly, and so requires a bit more fiddling with manual limits/breaks/labels, including choosing a very small value (0.0001) to be represented as 0.
Disadvantage: the x values aren't linearly distributed along the axis, which requires a bit more cognitive load when reading the figure.
Option 2:
ggplot(df, aes(sqrt(Var), Ele, color = Species)) +
geom_point() +
stat_ellipse() +
coord_trans(x = ggforce::power_trans(2)) +
scale_x_continuous(breaks = sqrt(0:4), labels = 0:4,
name = "Var")
This option plots the pre-transformed sqrt(Var) (notice the aes(...)). It then calculates the ellipses based on this new approximately normal value. Then it stretches out the x-axis so that the values of Var are once again linearly spaced, which distorts the ellipse in the same transformation.
Advantage: looks cool.
Advantage: values of Var are easy to interpret on the x-axis.
Advantage: you can see the density near Var=0 with the points and the wide flat end of the "egg" easily.
Advantage: the pointy end shows you how low the density is at those values.
Disadvantage: looks unfamiliar and requires explanation and additional cognitive load to interpret.