分面图（Julia）

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分面图把一个数据集按类别变量切分为面板网格，并让所有面板共享相同坐标轴——组间差异因此表现为形状差异而非刻度差异。Julia 中的语法图形层 AlgebraOfGraphics 用一个 mapping 组合分面规格，是最接近 ggplot 分面/seaborn relplot 的方案。

示例

环境配置

• 系统要求：跨平台（Linux/MacOS/Windows）
• 编程语言：Julia
• 依赖包：CairoMakie、AlgebraOfGraphics、DataFrames、Statistics

using CairoMakie
using AlgebraOfGraphics
using DataFrames
using Statistics
using Random

数据准备

我们模拟一项纵向治疗研究的长格式数据——4 基因型 × 2 处理臂 × 6 周测量，每行一个观测。

Random.seed!(201)

genotypes = ["WT", "HET", "KO", "RES"]
rates = Dict(g => 0.10 + 0.08 * (3 - i) for (i, g) in enumerate(genotypes))
rows = []
for geno in genotypes
    for arm in ["Vehicle", "Treatment"]
        rate = rates[geno] + (arm == "Treatment" ? 0.0 : -0.05)
        for mouse in 1:8, week in 0:5
            push!(rows, (Genotype = geno, Arm = arm, Week = week,
                         Score = 1 + 2.4 * rate * week / 5 + 0.12 * randn()))
        end
    end
end
long = DataFrame(rows)

384×4 DataFrame

359 rows omitted

Row	Genotype	Arm	Week	Score
	String	String	Int64	Float64
1	WT	Vehicle	0	0.996337
2	WT	Vehicle	1	1.09882
3	WT	Vehicle	2	1.30855
4	WT	Vehicle	3	1.17022
5	WT	Vehicle	4	1.34779
6	WT	Vehicle	5	1.55968
7	WT	Vehicle	0	1.15025
8	WT	Vehicle	1	1.13707
9	WT	Vehicle	2	1.11064
10	WT	Vehicle	3	1.38315
11	WT	Vehicle	4	1.42207
12	WT	Vehicle	5	1.76371
13	WT	Vehicle	0	1.11001
⋮	⋮	⋮	⋮	⋮
373	RES	Treatment	0	1.18669
374	RES	Treatment	1	0.97793
375	RES	Treatment	2	1.11329
376	RES	Treatment	3	0.927396
377	RES	Treatment	4	0.995892
378	RES	Treatment	5	0.856548
379	RES	Treatment	0	1.15491
380	RES	Treatment	1	0.97661
381	RES	Treatment	2	1.25163
382	RES	Treatment	3	1.17638
383	RES	Treatment	4	1.04982
384	RES	Treatment	5	0.966681

可视化

分面轨迹

mapping 承载美学映射（layout 切分网格）；与平滑 visual 线层组合即可用一条流水线构建面板。

spec = data(long) * mapping(:Week, :Score, color = :Arm,
                            layout = :Genotype) *
       visual(Scatter) +
       data(long) * mapping(:Week, :Score, color = :Arm,
                            layout = :Genotype) * linear()

fg = draw(spec; figure = (size = (560, 400),))
fg

图 1: 按基因型分面、按处理臂着色的响应轨迹

分布分面

同一语法更换图层即可得到直方图 + 密度分析；mapping 决定「用什么」，分析层决定「怎么画」。注意 density 需以限定名 AlgebraOfGraphics.density() 调用。

final = long[long.Week .== 5, :]

hist_spec = data(final) * mapping(:Score, layout = :Genotype, color = :Genotype) *
            histogram(bins = 14) +
            data(final) * mapping(:Score, layout = :Genotype, color = :Genotype) *
            AlgebraOfGraphics.density()

fg = draw(hist_spec; figure = (size = (560, 380),))
fg

图 2: 末周评分分布按基因型分面

参考文献

1. AlgebraOfGraphics development team. AlgebraOfGraphics.jl documentation. https://aog.makie.org/
2. Danisch, S., & Krumbiegel, J. (2021). Makie.jl: Flexible high-performance data visualization for Julia. Journal of Open Source Software, 6(65), 3349.