Violin Plot (Julia)

Author

Modified

2026-04-04

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A violin plot combines box plot statistics with kernel density estimation to show data distributions. Julia’s CairoMakie makes it straightforward to create violin plots for comparing gene expression, biomarker levels, or clinical measurements across groups.

Example

Setup

System Requirements: Cross-platform (Linux/MacOS/Windows)
Programming Language: Julia
Dependencies: CairoMakie, DataFrames, Statistics

using CairoMakie
using DataFrames
using Statistics
using Random

Data Preparation

Random.seed!(42)
n_per = 80
groups = vcat(fill("Tumor", n_per), fill("Normal", n_per), fill("Adjacent", n_per))
expression = vcat(
    randn(n_per) .* 1.5 .+ 8,
    randn(n_per) .* 1.2 .+ 5,
    randn(n_per) .* 1.8 .+ 6.5
)
group_idx = vcat(fill(1, n_per), fill(2, n_per), fill(3, n_per))
df = DataFrame(Group=groups, Expression=expression, GroupIdx=group_idx)

240×3 DataFrame

215 rows omitted

Row	Group	Expression	GroupIdx
	String	Float64	Int64
1	Tumor	7.45496	1
2	Tumor	8.37761	1
3	Tumor	7.52752	1
4	Tumor	7.53312	1
5	Tumor	9.22446	1
6	Tumor	8.71511	1
7	Tumor	6.71067	1
8	Tumor	5.79607	1
9	Tumor	4.8285	1
10	Tumor	8.06567	1
11	Tumor	6.762	1
12	Tumor	9.26043	1
13	Tumor	8.65083	1
⋮	⋮	⋮	⋮
229	Adjacent	8.67822	3
230	Adjacent	5.35136	3
231	Adjacent	6.22416	3
232	Adjacent	3.53355	3
233	Adjacent	8.33217	3
234	Adjacent	6.73736	3
235	Adjacent	11.0713	3
236	Adjacent	6.77344	3
237	Adjacent	7.34419	3
238	Adjacent	5.31451	3
239	Adjacent	5.72756	3
240	Adjacent	3.46341	3

Visualization

Basic Violin Plot

fig = Figure(size=(700, 500))
ax = Axis(fig[1,1], xlabel="Group", ylabel="Expression Level",
          title="Gene Expression Distribution",
          xticks=(1:3, ["Tumor", "Normal", "Adjacent"]))
violin!(ax, df.GroupIdx, df.Expression, color=(:steelblue, 0.7))
fig

Grouped Violin with Box Plot Overlay

fig2 = Figure(size=(700, 500))
ax2 = Axis(fig2[1,1], xlabel="Group", ylabel="Expression Level",
           title="Violin + Box Plot",
           xticks=(1:3, ["Tumor", "Normal", "Adjacent"]))
colors = [:red, :steelblue, :green]
for (i, g) in enumerate(["Tumor", "Normal", "Adjacent"])
    mask = df.Group .== g
    vals = df.Expression[mask]
    violin!(ax2, fill(i, sum(mask)), vals, color=(colors[i], 0.4), side=:left)
    boxplot!(ax2, fill(i, sum(mask)), vals, color=(colors[i], 0.7), width=0.3)
end
fig2

Figure 2: Violin Plot with Box Plot Overlay

Half Violin (Raincloud) Plot

fig3 = Figure(size=(800, 500))
ax3 = Axis(fig3[1,1], xlabel="Group", ylabel="Expression Level",
           title="Raincloud Plot",
           xticks=(1:3, ["Tumor", "Normal", "Adjacent"]))
for (i, g) in enumerate(["Tumor", "Normal", "Adjacent"])
    mask = df.Group .== g
    vals = df.Expression[mask]
    violin!(ax3, fill(i, sum(mask)), vals, color=(colors[i], 0.5), side=:right)
    scatter!(ax3, fill(i, sum(mask)) .- 0.15 .+ randn(sum(mask)) .* 0.03,
             vals, color=(colors[i], 0.3), markersize=5)
end
fig3

Figure 3: Raincloud-style Half Violin Plot

References

Danisch, S., & Krumbiegel, J. (2021). Makie.jl: Flexible high-performance data visualization for Julia. JOSS, 6(65), 3349.

📋 AI Skill: Violin Plot (Julia)