boxplot

boxplot(x, y; kwargs...)

Draw a Tukey style boxplot. The boxplot has 3 components:

a crossbar spanning the interquartile (IQR) range with a midline marking the median
an errorbar whose whiskers span range * iqr
points marking outliers, that is, data outside the whiskers

Arguments

x: positions of the categories
y: variables within the boxes

Keywords

weights: vector of statistical weights (length of data). By default, each observation has weight 1.
orientation=:vertical: orientation of box (:vertical or :horizontal)
width=1: width of the box before shrinking
gap=0.2: shrinking factor, width -> width * (1 - gap)
show_notch=false: draw the notch
notchwidth=0.5: multiplier of width for narrowest width of notch
show_median=true: show median as midline
range: multiple of IQR controlling whisker length
whiskerwidth: multiplier of width for width of T's on whiskers, or :match to match width
show_outliers: show outliers as points
dodge: vector of Integer (length of data) of grouping variable to create multiple side-by-side boxes at the same x position
dodge_gap = 0.03: spacing between dodged boxes

Examples

using CairoMakie


categories = rand(1:3, 1000)
values = randn(1000)

boxplot(categories, values)

using CairoMakie


categories = rand(1:3, 1000)
values = randn(1000)
dodge = rand(1:2, 1000)

boxplot(categories, values, dodge = dodge, show_notch = true, color = dodge)

Colors are customizable. The color attribute refers to the color of the boxes, whereas outliercolor refers to the color of the outliers. If not scalars (e.g. :red), these attributes must have the length of the data. If outliercolor is not provided, outliers will have the same color as their box, as shown above.

Note

For all indices corresponding to points within the same box, color (but not outliercolor) must have the same value.

using CairoMakie


categories = rand(1:3, 1000)
values = randn(1000)
dodge = rand(1:2, 1000)

boxplot(categories, values, dodge = dodge, show_notch = true, color = map(d->d==1 ? :blue : :red, dodge) , outliercolor = rand([:red, :green, :blue, :black, :yellow], 1000))

Using statistical weights

using CairoMakie, Distributions


N = 100_000
x = rand(1:3, N)
y = rand(Uniform(-1, 5), N)

w = pdf.(Normal(), x .- y)

fig = Figure()

boxplot(fig[1,1], x, y)
boxplot(fig[1,2], x, y, weights = w)

fig

Horizontal axis

using CairoMakie

fig = Figure()

categories = rand(1:3, 1000)
values = randn(1000)

ax_vert = Axis(fig[1,1];
    xlabel = "categories",
    ylabel = "values",
    xticks = (1:3, ["one", "two", "three"])
)
ax_horiz = Axis(fig[1,2];
    xlabel="values", # note that x/y still correspond to horizontal/vertical axes respectively
    ylabel="categories",
    yticks=(1:3, ["one", "two", "three"])
)

# Note: same order of category/value, despite different axes
boxplot!(ax_vert, categories, values) # `orientation=:vertical` is default
boxplot!(ax_horiz, categories, values; orientation=:horizontal)

fig

These docs were autogenerated using Makie: v0.20.10, GLMakie: v0.9.11, CairoMakie: v0.11.12, WGLMakie: v0.9.10