Getting started
Using ggh4x is not that different from using ggplot2, as most of the function following their API conventions. Below is an example of the types of things you could do, with as much ggh4x functions as could be reasonably fit into a single plot.
library(ggplot2)
library(ggh4x)
# Setting up some random data
n <- 200
df <- data.frame(
x = c(rpois(n, 25),
rnbinom(n, 5, 0.2),
rgamma(n, 30, 1.5),
rchisq(n, 15)),
distribution = rep(c("Poisson", "Negative Binomial",
"Gamma", "Chi-squared"), each = n),
type = rep(c("Discrete", "Continuous"), each = 2 * n)
)
ggplot(df, aes(x, y = after_stat(count),
fill = distribution, colour = distribution)) +
geom_histogram(position = "identity", binwidth = 1,
alpha = 0.3, colour = NA) +
# One type of theoretical densities for discrete distributions with steps
stat_theodensity(data = ggsubset(type == "Discrete"),
distri = "nbinom", geom = "step",
position = position_nudge(x = -0.5)) +
# Another type for the continuous ones with lines
stat_theodensity(data = ggsubset(type == "Continuous"),
distri = "gamma") +
scale_colour_discrete(aesthetics = c("colour", "fill"), guide = "none") +
# Have the facet strips span categories
facet_nested(~ type + distribution, scales = "free_x") +
# Precisely control aspect ratio of panels
force_panelsizes(rows = 1.618, cols = 1, respect = TRUE) +
# Tweak the scales of individual panels
facetted_pos_scales(list(
scale_x_continuous(labels = scales::number_format(0.1)),
# Give the 2nd panel minor ticks
scale_x_continuous(guide = guide_axis_minor()),
scale_x_continuous(limits = c(0, 40), oob = scales::oob_keep),
scale_x_continuous(expand = c(1, 0))
))
#> Warning: `ggsubset()` was deprecated in ggh4x 0.2.0.
#> ℹ This is best replaced by using `data = ~ subset(.x, ...)` instead.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.