Methods for determining breaks (bins) in histograms, as used in the breaks
argument to density_histogram()
.
Supports automatic partial function application.
breaks_fixed(x, weights = NULL, width = 1)
breaks_Sturges(x, weights = NULL)
breaks_Scott(x, weights = NULL)
breaks_FD(x, weights = NULL, digits = 5)
A numeric vector giving a sample.
A numeric vector of length(x)
giving sample weights.
For breaks_fixed()
, the desired bin width.
Number of significant digits to keep when rounding in the Freedman-Diaconis
algorithm (breaks_FD()
). For an explanation of this parameter, see the documentation
of the corresponding parameter in grDevices::nclass.FD()
.
Either a single number (giving the number of bins) or a vector giving the edges between bins.
These functions take a sample and its weights and return a valuable suitable for
the breaks
argument to density_histogram()
that will determine the histogram
breaks.
breaks_fixed()
allows you to manually specify a fixed bin width.
breaks_Sturges()
, breaks_Scott()
, and breaks_FD()
implement weighted
versions of the corresponding base functions. See nclass.Sturges()
,
nclass.scott()
, and nclass.FD()
.
library(ggplot2)
set.seed(1234)
x = rnorm(200, 1, 2)
# Let's compare the different break-selection algorithms on this data:
ggplot(data.frame(x), aes(x)) +
stat_slab(
aes(y = "breaks_fixed(width = 0.5)"),
density = "histogram",
breaks = breaks_fixed(width = 0.5),
outline_bars = TRUE,
color = "black",
) +
stat_slab(
aes(y = "breaks_Sturges()\nor 'Sturges'"),
density = "histogram",
breaks = "Sturges",
outline_bars = TRUE,
color = "black",
) +
stat_slab(
aes(y = "breaks_Scott()\nor 'Scott'"),
density = "histogram",
breaks = "Scott",
outline_bars = TRUE,
color = "black",
) +
stat_slab(
aes(y = "breaks_FD()\nor 'FD'"),
density = "histogram",
breaks = "FD",
outline_bars = TRUE,
color = "black",
) +
geom_point(aes(y = 0.7), alpha = 0.5) +
labs(
subtitle = "ggdist::stat_slab(density = 'histogram', ...)",
y = "breaks =",
x = NULL
)