Break (bin) selection algorithms for histograms

Usage

breaks_fixed(x, weights = NULL, width = 1)

breaks_Sturges(x, weights = NULL)

breaks_Scott(x, weights = NULL)

breaks_FD(x, weights = NULL, digits = 5)

breaks_quantiles(x, weights = NULL, max_n = "Scott", min_width = 0.5)

Arguments

x

<numeric> Sample values.

weights

<numeric | NULL> Optional weights to apply to x, which will be normalized to sum to 1.

width

<scalar numeric> For breaks_fixed(), the desired bin width.

digits

<scalar numeric> For breaks_FD(), the number of significant digits to keep when rounding in the Freedman-Diaconis algorithm. For an explanation of this parameter, see the documentation of the corresponding parameter in grDevices::nclass.FD().

max_n

<scalar numeric | function | string> For breaks_quantiles(), either a scalar numeric giving the maximum number of bins, or another breaks function (or string giving the suffix of the name of a function prefixed with "breaks_") that will return the maximum number of bins. breaks_quantiles() will construct at most max_n bins.

min_width

<scalar numeric> For breaks_quantiles(), a numeric between 0 and 1 giving the minimum bin width as a proportion of diff(range(x)) / max_n.

Value

Either a single number (giving the number of bins) or a vector giving the edges between bins.

Details

These functions take a sample and its weights and return a value 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 their corresponding base functions. They return a scalar numeric giving the number of bins. See nclass.Sturges(), nclass.scott(), and nclass.FD().

• breaks_quantiles() constructs irregularly-sized bins using max_n + 1 (possibly weighted) quantiles of x. The final number of bins is at most max_n, as small bins (ones whose bin width is less than half the range of the data divided by max_n times min_width) will be merged into adjacent bins.

See also

Examples

library(ggplot2)

set.seed(1234)
x = rnorm(2000, 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",
  ) +
  stat_slab(
    aes(y = "breaks_quantiles()\nor 'quantiles'"),
    density = "histogram",
    breaks = "quantiles",
    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
  )