ggdensity extends ggplot2 providing more interpretable visualizations of density estimates based on highest density regions (HDRs). ggdensity offers drop-in replacements for ggplot2 functions:
- instead of
ggplot2::geom_density_2d_filled()
, useggdensity::geom_hdr()
; - instead of
ggplot2::geom_density_2d()
, useggdensity::geom_hdr_lines()
.
Also included are the functions geom_hdr_fun()
and
geom_hdr_lines_fun()
for plotting HDRs of user-specified bivariate
probability density functions.
ggdensity isn’t on CRAN yet, but you can install its development version from GitHub with:
if (!requireNamespace("remotes")) install.packages("remotes")
remotes::install_github("jamesotto852/ggdensity")
Since it’s not on CRAN yet, the implementation interface may shift a bit, but we feel it’s stable enough to share with you now, so don’t expect big changes.
The standard way to visualize the joint distribution of two continuous
variables in ggplot2 is to use ggplot2::geom_density_2d()
or
geom_density_2d_filled()
. Here’s an example:
library("ggplot2"); theme_set(theme_bw())
library("ggdensity")
df <- data.frame("x" = rnorm(1000), "y" = rnorm(1000))
p <- ggplot(df, aes(x, y)) + coord_equal()
p + geom_density_2d_filled()
While it’s a nice looking plot, it isn’t immediately clear how we should
understand it. That’s because geom_density_2d_filled()
generates its
contours as equidistant level sets of the estimated bivariate density,
i.e. taking horizontal slices of the 3d surface at equally-spaced
heights, and projecting the intersections down into the plane. So you
get a general feel of where the density is high, but not much else. To
interpret a contour, you would need to multiply its height by the area
it bounds, which of course is very challenging to do by just looking at
it.
geom_hdr()
tries to get around this problem by presenting you with
regions of the estimated distribution that are immediately
interpretable:
p + geom_hdr()
level
here tells us the probability bounded by the corresponding
region, and the regions are computed to be the smallest such regions
that bound that level of probability; these are called highest density
regions or HDRs. By default, the plotted regions show the 50%, 80%, 95%,
and 99% HDRs of the estimated density, but this can be changed with the
probs
argument to geom_hdr()
. Notice that your take-away from the
plot made with geom_density_2d_filled()
is subtlely yet significantly
different than that of the plot made by geom_hdr()
.
ggdensity’s functions were designed to be seamlessly consistent with the rest of the ggplot2 framework. As a consequence, pretty much everything you would expect to just work does. (Well, we hope! Let us know if that’s not true.)
For example, because geom_hdr()
maps probability to the alpha
aesthetic, the fill
and color
aesthetics are available for mapping
to variables. You can use them to visualize subpopulations in your data.
For example, in the penguins
data from
palmerpenguins you
may want to look at how the relationship between bill length and flipper
length changes across different species of penguins. Here’s one way you
could look at that:
library("palmerpenguins")
ggplot(penguins, aes(flipper_length_mm, bill_length_mm, fill = species)) +
geom_hdr(xlim = c(160, 240), ylim = c(30, 70)) +
geom_point(shape = 21)
Nice, but a bit overplotted. To alleviate overplotting, we can use
geom_hdr_lines()
:
ggplot(penguins, aes(flipper_length_mm, bill_length_mm, color = species)) +
geom_hdr_lines(xlim = c(160, 240), ylim = c(30, 70)) +
geom_point(size = 1)
Or you could facet the plot:
ggplot(penguins, aes(flipper_length_mm, bill_length_mm, fill = species)) +
geom_hdr(xlim = c(160, 240), ylim = c(30, 70)) +
geom_point(shape = 21) +
facet_wrap(vars(species))
The main point here is that you should really think of geom_hdr()
and
geom_hdr_lines()
as drop-in replacements for functions like
geom_density_2d_filled()
, geom_density2d()
, and so on, and you can
expect all of the rest of the ggplot2 stuff to just work.
The underlying stat used by geom_hdr()
creates the computed variable
level
that can be mapped in the standard way you map computed
variables in ggplot2, with after_stat()
.
For example, geom_hdr()
and geom_hdr_lines()
map level
to the
alpha
aesthetic by default. But you can override it like this, just be
sure to override the alpha
aesthetic by setting alpha = 1
.
ggplot(faithful, aes(eruptions, waiting)) +
geom_hdr(
aes(fill = after_stat(level)),
alpha = 1, xlim = c(0, 8), ylim = c(30, 110)
) +
scale_fill_viridis_d()
ggplot(faithful, aes(eruptions, waiting)) +
geom_hdr_lines(
aes(color = after_stat(level)),
alpha = 1, xlim = c(0, 8), ylim = c(30, 110)
) +
scale_color_viridis_d()
In addition to trying to make the visuals clean and the functions what you would expect as a ggplot2 user, we’ve spent considerable effort in trying to ensure that the graphics you’re getting with ggdensity are statistically rigorous and provide a range of estimation options for more detailed control.
To that end, you can pass a method
argument into geom_hdr()
and
geom_hdr_lines()
that allows you to specify various nonparametric and
parametric ways to estimate the underlying bivariate distribution, and
we have plans for even more. Each of the estimators below offers
advantages in certain contexts. For example, histogram estimators result
in HDRs that obey constrained supports. Normal estimators can be helpful
in providing simplified visuals that give the viewer a sense of where
the distributions are, potentially at the expense of over-simplifying
and removing important features of how the variables (co-)vary.
The above discussion has focused around densities that are estimated
from data. But in some instances, you have the distribution in the form
of a function that encodes the joint
PDF. In
those circumstances, you can use geom_hdr_fun()
and
geom_hdr_lines_fun()
to make the analogous plots. These functions
behave similarly to geom_function()
from
ggplot2, accepting the
argument fun
specifying the pdf to be summarized. Here’s an example:
f <- function(x, y) dnorm(x) * dgamma(y, 5, 3)
ggplot() +
geom_hdr_fun(fun = f, xlim = c(-4, 4), ylim = c(0, 5))
In addition to all of the methods of density estimation available with
geom_hdr()
, one of the perks of having geom_hdr_fun()
is that it
allows you to plot parametric densities that you estimate outside the
ggdensity framework. The basic idea is that you fit your
distribution outside ggdensity calls with your method of choice, say
maximum likelihood, and then plug the maximum likelihood estimate into
the density formula to obtain a function to plug into geom_hdr_fun()
.
Here’s an example of how you can do that that assuming that the underlying data are independent and exponentially distributed with unknown rates.
set.seed(123)
th <- c(3, 5)
df <- data.frame("x" = rexp(1000, th[1]), "y" = rexp(1000, th[2]))
# construct the likelihood function
l <- function(th) {
log_liks <- apply(df, 1, function(xy) {
dexp(xy[1], rate = th[1], log = TRUE) +
dexp(xy[2], rate = th[2], log = TRUE)
})
sum(log_liks)
}
# compute the mle
(th_hat <- optim(c(2, 2), l, control = list(fnscale = -1))$par)
#> [1] 2.912736 5.032125
# construct the parametric density estimate
f <- function(x, y, th) dexp(x, th[1]) * dexp(y, th[2])
# pass estimated density into geom_hdr_fun()
ggplot(df, aes(x, y)) +
geom_hdr_fun(fun = f, args = list(th = th_hat)) +
geom_point(shape = 21, fill = "lightgreen", alpha = .25) +
coord_equal()
We have a number of neat new features cooking. Check back soon!