Histogram density estimator. Supports automatic partial function application.

  weights = NULL,
  breaks = "Sturges",
  align = "none",
  outline_bars = FALSE,
  na.rm = FALSE,
  range_only = FALSE



numeric vector containing a sample to compute a density estimate for.


optional numeric vector of weights to apply to x.


Determines the breakpoints defining bins. Similar to (but not exactly the same as) the breaks argument to graphics::hist(). One of:

  • A scalar (length-1) numeric giving the number of bins

  • A vector numeric giving the breakpoints between histogram bins

  • A function taking x and weights and returning either the number of bins or a vector of breakpoints

  • A string giving the suffix of a function that starts with "breaks_". ggdist provides weighted implementations of the "Sturges", "Scott", and "FD" break-finding algorithms from graphics::hist(), as well as breaks_fixed() for manually setting the bin width. See breaks.

For example, breaks = "Sturges" will use the breaks_Sturges() algorithm, breaks = 9 will create 9 bins, and breaks = breaks_fixed(width = 1) will set the bin width to 1.


Determines how to align the breakpoints defining bins. One of:

  • A scalar (length-1) numeric giving an offset that is subtracted from the breaks. The offset must be between 0 and the bin width.

  • A function taking a sorted vector of breaks (bin edges) and returning an offset to subtract from the breaks.

  • A string giving the suffix of a function that starts with "align_" used to determine the alignment, such as align_none(), align_boundary(), or align_center().

For example, align = "none" will provide no alignment, align = align_center(at = 0) will center a bin on 0, and align = align_boundary(at = 0) will align a bin edge on 0.


Should outlines in between the bars (i.e. density values of 0) be included?


Should missing (NA) values in x be removed?


Additional arguments (ignored).


If TRUE, the range of the output of this density estimator is computed and is returned in the $x element of the result, and c(NA, NA) is returned in $y. This gives a faster way to determine the range of the output than density_XXX(n = 2).


An object of class "density", mimicking the output format of stats::density(), with the following components:

  • x: The grid of points at which the density was estimated.

  • y: The estimated density values.

  • bw: The bandwidth.

  • n: The sample size of the x input argument.

  • call: The call used to produce the result, as a quoted expression.

  • data.name: The deparsed name of the x input argument.

  • has.na: Always FALSE (for compatibility).

  • cdf: Values of the (possibly weighted) empirical cumulative distribution function at x. See weighted_ecdf().

This allows existing methods for density objects, like print() and plot(), to work if desired. This output format (and in particular, the x and y components) is also the format expected by the density argument of the stat_slabinterval()

and the smooth_ family of functions.

See also

Other density estimators: density_bounded(), density_unbounded()



# For compatibility with existing code, the return type of density_unbounded()
# is the same as stats::density(), ...
x = rbeta(5000, 1, 3)
d = density_histogram(x)
#> Call:
#> 	density_histogram(x = x)
#> Data: x (5000 obs.);	Bandwidth 'bw' = 0.07285
#>        x                   y          
#>  Min.   :0.0000338   Min.   :0.02196  
#>  1st Qu.:0.2277000   1st Qu.:0.31845  
#>  Median :0.4735795   Median :0.86475  
#>  Mean   :0.4735795   Mean   :1.05586  
#>  3rd Qu.:0.7194591   3rd Qu.:1.62244  
#>  Max.   :0.9471253   Max.   :2.82486  

# ... thus, while designed for use with the `density` argument of
# stat_slabinterval(), output from density_histogram() can also be used with
# base::plot():

# here we'll use the same data as above with stat_slab():
data.frame(x) %>%
  ggplot() +
    aes(xdist = dist), data = data.frame(dist = dist_beta(1, 3)),
    alpha = 0.25
  ) +
  stat_slab(aes(x), density = "histogram", fill = NA, color = "#d95f02", alpha = 0.5) +
  scale_thickness_shared() +