图中标示了箱线图中每条线和点表示的含义，其中应用到了分位数的概念 线的主要包含五个数据节点，将一组数据从大到小排列，分别计算出他的上边缘(Maximum)，上四分位数(Q3)，中位数(Median)，下四分位数(Q1)，下边缘(Minimum) 不在上边缘与下边缘的范围内的为异常值，用点表示。

数据准备

data

Repeat = rep(paste("Repeat", 1:3, sep = "_"), 100),

Condition = rep(c("Control", "Test"), 150))

> head(data)

Value Repeat Condition

1 -1.1395507 Repeat_1 Control

2 0.7319707 Repeat_2 Test

3 -0.2219461 Repeat_3 Control

4 -1.1454664 Repeat_1 Test

5 1.0740937 Repeat_2 Control

6 0.3741845 Repeat_3 Test

boxplot函数(R自带)

最方便的方法就是用boxplot函数，不需要依赖任何包

boxplot(data$Value, ylab="Value")

根据不同的条件，加上颜色

boxplot(Value ~ Condition, data=data, ylab="Value", col=c("darkred", "darkgreen"))

boxplot(Value ~ Condition * Repeat, data=data, ylab="Value", col="darkgreen")

多个分组(condition和repeat)的箱线图

boxplot(Value ~ Condition + Repeat, data=data, ylab="Value", col="darkgreen")

ggplot2

使用ggplot2来画箱线图是现在常用的方法

library(tidyverse)

# 定义一种主题，方便后面重复使用

theme_boxplot

axis.line=element_line(colour="black",size=0.25),

axis.title=element_text(size=13,face="plain",color="black"),

axis.text = element_text(size=12,face="plain",color="black"),

legend.position="none"

# ggplot2画图

ggplot(data, aes(Condition, Value)) +

geom_boxplot(aes(fill = Condition), notch = FALSE) +

scale_fill_brewer(palette = "Set2") +

theme_classic() + theme_boxplot

添加抖动散点

ggplot(data, aes(Condition, Value)) +

geom_boxplot(aes(fill = Condition), notch = FALSE) +

geom_jitter(binaxis = "y", position = position_jitter(0.2), stackdir = "center", dotsize = 0.4) +

scale_fill_brewer(palette = "Set2") +

theme_classic() + theme_boxplot

带凹槽(notched)的箱线图，中位数的置信区用凹槽表示

ggplot(data, aes(Condition, Value)) +

geom_boxplot(aes(fill = Condition), notch = TRUE, varwidth = TRUE) +

geom_jitter(binaxis = "y", position = position_jitter(0.2), stackdir = "center", dotsize = 0.4) +

scale_fill_brewer(palette = "Set2") +

theme_classic() + theme_boxplot

比较流行的小提琴图，内嵌箱线图和扰动散点

ggplot(data, aes(Condition, Value)) +

geom_violin(aes(fill = Condition), trim = FALSE) +

geom_boxplot(width = 0.2) +

geom_jitter(binaxis = "y", position = position_jitter(0.2), stackdir = "center", dotsize = 0.4) +

scale_fill_brewer(palette = "Set2") +

theme_classic() + theme_boxplot

云雨图，它是密度分布图、箱线图、散点图的集合，完美的展示了所有数据信息

library(grid)

# GeomFlatViolin函数的定义见https://github.com/EasyChart/Beautiful-Visualization-with-R

ggplot(data, aes(Condition, Value, fill=Condition)) +

geom_flat_violin(aes(fill = Condition), position = position_nudge(x=.25), color="black") +

geom_jitter(aes(color = Condition), width=0.1) +

geom_boxplot(width=.1, position=position_nudge(x=0.25), fill="white",size=0.5) +

scale_fill_brewer(palette = "Set2") +

coord_flip() + theme_bw() + theme_boxplot

分组画箱线图

根据不同的Condition和Repeat对数据分组画图

ggplot(data, aes(Repeat, Value)) +

geom_boxplot(aes(fill = Condition), notch = FALSE, size = 0.4) +

scale_fill_brewer(palette = "Set2") +

guides(fill=guide_legend(title="Repeat")) +

theme_bw()

同样的，我们可以对箱线图添加抖动点，但是分组之后，并不能直接添加抖动点，需要增加两列信息来辅助画抖动点

# 增加dist_cat和scat_adj ，用于画抖动点

data % mutate(dist_cat = as.numeric(Repeat),

scat_adj = ifelse(Condition == "Control", -0.2, 0.2))

# 增加之后的数据如下

> head(data)

Value Repeat Condition dist_cat scat_adj

1 -1.1395507 Repeat_1 Control 1 -0.2

2 0.7319707 Repeat_2 Test 2 0.2

3 -0.2219461 Repeat_3 Control 3 -0.2

4 -1.1454664 Repeat_1 Test 1 0.2

5 1.0740937 Repeat_2 Control 2 -0.2

6 0.3741845 Repeat_3 Test 3 0.2

ggplot(data, aes(Repeat, Value)) +

geom_boxplot(aes(fill = Condition), notch = FALSE, size = 0.4) +

geom_jitter(aes(scat_adj+dist_cat, Value, fill = factor(Condition)),

position=position_jitter(width=0.1,height=0),

alpha=1,

shape=21, size = 1.2) +

scale_fill_brewer(palette = "Set2") +

guides(fill=guide_legend(title="Condition ")) +

theme_bw()

小提琴图本来是由两个左右对称的密度估计曲线构成，那么对数据分组之后，我们可以只保留两个小提琴图的各一半，这样更能直接的观察出两组之间的差异！

# ggplot2并未提供这样的功能，这里定义了geom_split_violin函数来实现

# geom_split_violin 的定义见 https://github.com/EasyChart/Beautiful-Visualization-with-R

ggplot(data, aes(x = Repeat, y = Value, fill=Condition)) +

geom_split_violin(draw_quantiles = 0.5, trim = FALSE) +

geom_jitter(aes(scat_adj+dist_cat, Value, fill = factor(Condition)),

position=position_jitter(width=0.1,height=0),

alpha=1,

shape=21, size = 1.2) +

scale_fill_brewer(palette = "Set2") +

guides(fill=guide_legend(title="Condition ")) +

theme_bw()

ggpubr (带显著性的箱线图)

生成数据

# 均值为3，标准差为1的正态分布

c1

# Johnson分布的偏斜度2.2和峰度13

c2

# Johnson分布的偏斜度0和峰度20

c3

data

Conditon = rep(c("C_1", "C_2", "C_3"), each = 100),

Value = c(c1, c2, c3)

)

#数据如下

> head(data)

Conditon Value

1 C_1 2.679169

2 C_1 1.699026

3 C_1 5.459568

4 C_1 3.778365

5 C_1 3.689881

6 C_1 1.295534

ggpubr的功能多样，这里只举箱线图的例子

library(ggpubr)

library(RColorBrewer)

# 定义需要两两比较的组

compaired

c("C_2", "C_3"),

c("C_1", "C_3"))

palette

# wilcox.test

ggboxplot(data, x = "Conditon", y = "Value",

fill = "Conditon", palette = palette,

add = "jitter", size=0.5) +

stat_compare_means(comparisons = compaired, method = "wilcox.test") + # 添加每两组变量的显著性

theme_classic() + theme_boxplot

使用ggplot2的语法添加显著性检验，将wilcox.test 换成t.test

# t.test

ggplot(data, aes(Conditon, Value))+

geom_boxplot(aes(fill = Conditon), notch = FALSE, outlier.alpha = 1) +

scale_fill_brewer(palette = "Set2") +

geom_signif(comparisons = compaired,

step_increase = 0.1,

map_signif_level = F,

test = t.test) +

theme_classic() + theme_boxplot

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