geom_violin¤

A violin plot is a compact display of a continuous distribution. It is a mirrored density plot displayed in the same way as a boxplot. Violin plots are useful for comparing distributions across groups, as they show more detail about the shape of the distribution than boxplots while still being compact.

Note on apppropriate use¤

The violin plot is somewhat controversial and there is usually a better way to display your data. For a discussion of this topic, see this video by Angela Collier.

Arguments¤

plot::GGPlot (optional): a plot object to add this geom to. This is typically used to facilitate creating your ggplot as part of a @chain.
data (DataFrame): Data to use for this geom. If not provided, the geom will inherit the data from ggplot.
aes(...): the names of the columns in the DataFrame that will be used in the mapping
inherit_aes: should the geom inherit aes from the ggplot?
...: options that are not mapped to a column (passed to Makie.Violin)

Violin vs Boxplot¤

Both violin plots and boxplots display distributions, but they show different information:

Violin plots show the full shape of the distribution (including multimodality)
Boxplots show summary statistics (median, quartiles, outliers)
Use violin plots when the shape of the distribution matters
Use boxplots when you want to compare specific statistics

Required Aesthetics¤

x (categorical or integer)
y (numeric)

Optional Aesthetics (see `aes`)¤

fill (maps to violin interior color)
color / colour (maps to violin outline color)
dodge (grouping variable for side-by-side violins)

Optional Arguments¤

orientation: :vertical (default) or :horizontal
width: width of the violins (default: 1)
gap: gap between violins (default: 0.2)
show_notch: whether to show notches (default: false)
notchwidth: width of the notches (default: 0.5)
show_median: whether to show the median line (default: true)
dodge_gap: gap between dodged violins (default: 0.03)
strokecolor / strokecolour
strokewidth
alpha

using TidierPlots
using DataFrames

penguins = penguins_data();

Basic Violin Plot¤

Create a basic violin plot showing the distribution of bill length for each species:

ggplot(penguins, @aes(x = species, y = bill_length_mm)) +
    geom_violin()

Horizontal Violins¤

Create horizontal violin plots using the orientation argument:

ggplot(penguins, @aes(x = species, y = bill_length_mm)) +
    geom_violin(orientation = :horizontal)

Grouped Violins¤

Use the fill aesthetic to compare distributions across an additional categorical variable:

ggplot(penguins, @aes(x = species, y = bill_length_mm, fill = sex)) +
    geom_violin()

Use dodge to place violins side-by-side:

ggplot(penguins, @aes(x = species, y = bill_length_mm, fill = sex, dodge = sex)) +
    geom_violin()

Customizing Appearance¤

Add stroke color and adjust transparency:

ggplot(penguins, @aes(x = species, y = bill_length_mm)) +
    geom_violin(color = :steelblue, strokecolor = :black, strokewidth = 2)

Different fill colors:

ggplot(penguins, @aes(x = species, y = bill_length_mm, fill = species)) +
    geom_violin(strokecolor = :black, strokewidth = 1)

Comparing Multiple Variables¤

Compare distributions across different measurements:

ggplot(penguins, @aes(x = species, y = flipper_length_mm)) +
    geom_violin() +
    labs(title = "Flipper Length by Species")

ggplot(penguins, @aes(x = species, y = body_mass_g)) +
    geom_violin() +
    labs(title = "Body Mass by Species")

Use faceting to compare distributions across multiple categorical variables:

ggplot(penguins, @aes(x = species, y = bill_length_mm)) +
    geom_violin() +
    facet_wrap(:sex)

Violin vs Density Comparison¤

Violin plots are essentially mirrored density plots. Compare:

ggplot(penguins, @aes(x = bill_length_mm, fill = species)) +
    geom_density()

The violin plot shows the same information but in a compact, comparative format:

ggplot(penguins, @aes(x = species, y = bill_length_mm, fill = species)) +
    geom_violin()

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