大家好，我是邓飞，下面介绍一下箱线图实现显著性添加的方法，类似这种：

「单因素二水平T检验箱线图可视化」

「单因素三水平T检验箱线图可视化」

「单因素三水平柱形图」

「单因素三水平折线图」「二因素柱形图」

「二因素折线图」

1. 单因素二水平

这种试验，比如有两个品种，株高的差异，每个品种调查了10株，就构成了这样的试验数据。

「模拟数据：」

set.seed(123)
y1 = rnorm(10) + 5
y2 = rnorm(10) + 15
dd = data.frame(Group = rep(c("A","B"),each=10),y = c(y1,y2))
dd
str(dd)
dd$Group = as.factor(dd$Group)

「数据：」

> dd
   Group         y
1      A  4.439524
2      A  4.769823
3      A  6.558708
4      A  5.070508
5      A  5.129288
6      A  6.715065
7      A  5.460916
8      A  3.734939
9      A  4.313147
10     A  4.554338
11     B 16.224082
12     B 15.359814
13     B 15.400771
14     B 15.110683
15     B 14.444159
16     B 16.786913
17     B 15.497850
18     B 13.033383
19     B 15.701356
20     B 14.527209

这里，使用的是ggpubr包进行绘图：

1.1 绘制箱线图

library(ggplot2)
library(ggpubr)
ggboxplot(dd,x = "Group",y = "y")

1.2 箱线图添加不同颜色

ggboxplot(dd,x = "Group",y = "y",color = "Group")

1.3 箱线图添加散点图

ggboxplot(dd,x = "Group",y = "y",color = "Group",add = "jitter")

1.4 箱线图+散点图+显著性水平

这里，默认的统计方法是非参数统计Wilcoxon，如果想用t.test，见下面操作

ggboxplot(dd,x = "Group",y = "y",color = "Group",add = "jitter") + 
  stat_compare_means()

1.5 用t.test作为统计方法

ggboxplot(dd,x = "Group",y = "y",color = "Group",add = "jitter") + 
  stat_compare_means(method = "t.test")

1.6 直接输出显著性

ggboxplot(dd,x = "Group",y = "y",color = "Group",add = "jitter") + 
  stat_compare_means(method = "t.test",label = "p.signif")

2. 单因素三水平

二个水平可以用T检验，三个水平或者多个水平的数据，如何检验呢？

「模拟数据：」

# 构建三个水平 ANOVA
set.seed(123)
y1 = rnorm(10) + 5
y2 = rnorm(10) + 15
y3 = rnorm(10) + 15

dd = data.frame(Group = rep(c("A","B","C"),each=10),y = c(y1,y2,y3))
dd
str(dd)
dd$Group = as.factor(dd$Group)

「数据如下：」

> dd
   Group         y
1      A  4.439524
2      A  4.769823
3      A  6.558708
4      A  5.070508
5      A  5.129288
6      A  6.715065
7      A  5.460916
8      A  3.734939
9      A  4.313147
10     A  4.554338
11     B 16.224082
12     B 15.359814
13     B 15.400771
14     B 15.110683
15     B 14.444159
16     B 16.786913
17     B 15.497850
18     B 13.033383
19     B 15.701356
20     B 14.527209
21     C 13.932176
22     C 14.782025
23     C 13.973996
24     C 14.271109
25     C 14.374961
26     C 13.313307
27     C 15.837787
28     C 15.153373
29     C 13.861863
30     C 16.253815

2.1 箱线图+散点图

p = ggboxplot(dd,x = "Group",y = "y",color = "Group",add = "jitter") 
p

2.2 箱线图+散点图+显著性

p + stat_compare_means(method = "anova")

2.3 两两之间显著性绘制

my_comparisons = list( c("A", "B"), c("A", "C"), c("B", "C") )
p + stat_compare_means(comparisons = my_comparisons,
                       # label = "p.signif",
                       method = "t.test")

2.4 显示显著性

p + stat_compare_means(comparisons = my_comparisons,
                       label = "p.signif",
                       method = "t.test")

3. 两因素数据

「模拟数据：」

# 两个因素的数据
set.seed(123)
y1 = rnorm(10) + 5
y2 = rnorm(10) + 8
y3 = rnorm(10) + 7
y4 = rnorm(10) + 15
y5 = rnorm(10) + 18
y6 = rnorm(10) + 17

dd = data.frame(Group1 = rep(c("A","B","C"),each=10),
                Group2 = rep(c("X","Y"),each=30),
                y = c(y1,y2,y3,y4,y5,y6))
dd
str(dd)
dd$Group1 = as.factor(dd$Group1)
dd$Group2 = as.factor(dd$Group2)
str(dd)

「数据预览：」

> dd
   Group1 Group2         y
1       A      X  4.439524
2       A      X  4.769823
3       A      X  6.558708
4       A      X  5.070508
5       A      X  5.129288
6       A      X  6.715065
7       A      X  5.460916
8       A      X  3.734939
9       A      X  4.313147
10      A      X  4.554338
11      B      X  9.224082
12      B      X  8.359814
13      B      X  8.400771
14      B      X  8.110683
15      B      X  7.444159
16      B      X  9.786913
17      B      X  8.497850
18      B      X  6.033383
19      B      X  8.701356
20      B      X  7.527209
21      C      X  5.932176
22      C      X  6.782025
23      C      X  5.973996
24      C      X  6.271109
25      C      X  6.374961
26      C      X  5.313307
27      C      X  7.837787
28      C      X  7.153373
29      C      X  5.861863
30      C      X  8.253815
31      A      Y 15.426464
32      A      Y 14.704929
33      A      Y 15.895126
34      A      Y 15.878133
35      A      Y 15.821581
36      A      Y 15.688640
37      A      Y 15.553918
38      A      Y 14.938088
39      A      Y 14.694037
40      A      Y 14.619529
41      B      Y 17.305293
42      B      Y 17.792083
43      B      Y 16.734604
44      B      Y 20.168956
45      B      Y 19.207962
46      B      Y 16.876891
47      B      Y 17.597115
48      B      Y 17.533345
49      B      Y 18.779965
50      B      Y 17.916631
51      C      Y 17.253319
52      C      Y 16.971453
53      C      Y 16.957130
54      C      Y 18.368602
55      C      Y 16.774229
56      C      Y 18.516471
57      C      Y 15.451247
58      C      Y 17.584614
59      C      Y 17.123854
60      C      Y 17.215942

3.1 绘制分组箱线图

p = ggboxplot(dd,x = "Group1",y="y",color = "Group2",
              add = "jitter")
p

3.2 增加P值

p + stat_compare_means(aes(group = Group2),method = "t.test")

3.3 修改为显著性结果

p + stat_compare_means(aes(group = Group2),method = "t.test",label = "p.signif")

3.4 将分组数据分开绘制

p = ggboxplot(dd,x = "Group2",y="y",color = "Group1",
              add = "jitter",facet.by = "Group1")
p

3.5 分组显示统计检验

p + stat_compare_means(method = "t.test")

3.6 分组显示显著性结果

p + stat_compare_means(method = "t.test",label = "p.signif",label.y = 17)

4. 单因素直方图绘制

直方图+标准误，之前用ggplot2需要很长的代码，这里有更好的方案。

4.1 直方图+标准误

p = ggbarplot(dd,x = "Group1",y = "y",add = "mean_se",color = "Group1")
p

4.2 直方图+标准误+显著性

p + stat_compare_means(method = "anova",,label.y = 15)+ 
  stat_compare_means(comparisons = my_comparisons)

5. 单因素折线图绘制

5.1 折线图+标准误

p = ggline(dd,x = "Group1",y = "y",add = "mean_se")
p

5.2 折线图+标准误+显著性

p + stat_compare_means(method = "anova",,label.y = 15)+ 
  stat_compare_means(comparisons = my_comparisons)

6. 二因素直方图绘制

6.1 直方图+标准误

p = ggbarplot(dd,x = "Group1",y = "y",add = "mean_se",color = "Group2", position = position_dodge(0.8))
p

6.2 直方图+标准误+显著性

p + stat_compare_means(aes(group=Group2), label = "p.signif")

7. 二因素折线图绘制

7.1 折线图+标准误

p = ggline(dd,x = "Group1",y = "y",add = "mean_se",color = "Group2", position = position_dodge(0.8))
p

7.2 折线图+标准误+显著性

p + stat_compare_means(aes(group=Group2), label = "p.signif")

8. 代码汇总

下面代码是所有代码的汇总，里面包括生成数据，做不同类型的图。只需要将数据整理为这种格式，就可以出图了，对于初学者而言，是最简单最快捷的方法。show you the code！

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# 构建两个水平 T-test
set.seed(123)
y1 = rnorm(10) + 5
y2 = rnorm(10) + 15
dd = data.frame(Group = rep(c("A","B"),each=10),y = c(y1,y2))
dd
str(dd)
dd$Group = as.factor(dd$Group)

library(ggplot2)
library(ggpubr)
ggboxplot(dd,x = "Group",y = "y")
ggboxplot(dd,x = "Group",y = "y",color = "Group")
ggboxplot(dd,x = "Group",y = "y",color = "Group",add = "jitter")
ggboxplot(dd,x = "Group",y = "y",color = "Group",add = "jitter") + 
  stat_compare_means()

ggboxplot(dd,x = "Group",y = "y",color = "Group",add = "jitter") + 
  stat_compare_means(method = "t.test")

ggboxplot(dd,x = "Group",y = "y",color = "Group",add = "jitter") + 
  stat_compare_means(method = "t.test",label = "p.signif")




# 构建三个水平 ANOVA
set.seed(123)
y1 = rnorm(10) + 5
y2 = rnorm(10) + 15
y3 = rnorm(10) + 15

dd = data.frame(Group = rep(c("A","B","C"),each=10),y = c(y1,y2,y3))
dd
str(dd)
dd$Group = as.factor(dd$Group)

p = ggboxplot(dd,x = "Group",y = "y",color = "Group",add = "jitter") 
p
p + stat_compare_means(method = "anova")


# Perorm pairwise comparisons
# compare_means(y ~ Group,  data = dd,method = "anova")

my_comparisons = list( c("A", "B"), c("A", "C"), c("B", "C") )
p + stat_compare_means(comparisons = my_comparisons,
                       # label = "p.signif",
                       method = "t.test")

p + stat_compare_means(comparisons = my_comparisons,
                       label = "p.signif",
                       method = "t.test")

# 两个因素的数据
set.seed(123)
y1 = rnorm(10) + 5
y2 = rnorm(10) + 8
y3 = rnorm(10) + 7
y4 = rnorm(10) + 15
y5 = rnorm(10) + 18
y6 = rnorm(10) + 17

dd = data.frame(Group1 = rep(c("A","B","C"),each=10),
                Group2 = rep(c("X","Y"),each=30),
                y = c(y1,y2,y3,y4,y5,y6))
dd
str(dd)
dd$Group1 = as.factor(dd$Group1)
dd$Group2 = as.factor(dd$Group2)
str(dd)

## 分组查看
p = ggboxplot(dd,x = "Group1",y="y",color = "Group2",
              add = "jitter")
p
p + stat_compare_means(aes(group = Group2),method = "t.test")
p + stat_compare_means(aes(group = Group2),method = "t.test",label = "p.signif")

## 分组查看
p = ggboxplot(dd,x = "Group2",y="y",color = "Group1",
              add = "jitter",facet.by = "Group1")
p
p + stat_compare_means(method = "t.test")
p + stat_compare_means(method = "t.test",label = "p.signif",label.y = 17)


# 单分组
# 三水平直方图
p = ggbarplot(dd,x = "Group1",y = "y",add = "mean_se",color = "Group1")
p
p + stat_compare_means(method = "anova",,label.y = 15)+ 
  stat_compare_means(comparisons = my_comparisons)

# 有误差的折线图
p = ggline(dd,x = "Group1",y = "y",add = "mean_se")
p
p + stat_compare_means(method = "anova",,label.y = 15)+ 
  stat_compare_means(comparisons = my_comparisons)


# 二分组
p = ggbarplot(dd,x = "Group1",y = "y",add = "mean_se",color = "Group2", position = position_dodge(0.8))
p
p + stat_compare_means(aes(group=Group2), label = "p.signif")


# 有误差的折线图
p = ggline(dd,x = "Group1",y = "y",add = "mean_se",color = "Group2", position = position_dodge(0.8))
p
p + stat_compare_means(aes(group=Group2), label = "p.signif")

❝

欢迎关注我的公众号：育种数据分析之放飞自我。主要分享R语言，Python，育种数据分析，生物统计，数量遗传学，混合线性模型，GWAS和GS相关的知识。

❞