收集了几张漂亮的组间比较图，调整了一些细节，分享给大家。使用的数据时R语言内置数据iris，以下代码都可以直接运行。
1.ggstatplot
难点是这个R包安装时通常会遇到一些依赖包安装不成功的问题，需要多折腾几下，安好了使用起来就非常轻松咯
library(ggstatsplot)
ggbetweenstats(iris,x = "Species",y = "Sepal.Length")
2.经典箱线图叠加点图
library(ggplot2)
library(ggpubr)
library(paletteer)
ggplot(iris,aes(x = Species,y=Sepal.Length))+
geom_boxplot(aes(fill = Species))+
geom_jitter(shape = 21,size = 2,color = "black",aes(fill = Species),stroke = 1.5)+
scale_fill_paletteer_d("basetheme::minimal")+
stat_compare_means(method = "wilcox.test",comparisons = list(c("setosa","virginica"),c("setosa","versicolor"),c("virginica","versicolor")))+
theme_bw()+
theme(legend.position = c("top"),panel.grid = element_blank())
ggplot(iris,aes(x = Species,y=Sepal.Length))+
geom_boxplot(aes(fill = Species))+
geom_dotplot(binaxis = "y",binwidth = 0.12,stackdir = "center",stroke = 1.5,aes(fill = Species))+
scale_fill_paletteer_d("basetheme::minimal")+
stat_compare_means(method = "wilcox.test",comparisons = list(c("setosa","virginica"),c("setosa","versicolor"),c("virginica","versicolor")))+
theme_bw()+
theme(legend.position = c("top"),panel.grid = element_blank())
jitter太跳脱了，dotplot又比较呆板，有个折中的图：蜜蜂图
3.箱线图叠加蜜蜂图
library(ggbeeswarm)
ggplot(iris,aes(x = Species,y = Sepal.Length,fill = Species))+
geom_boxplot()+
geom_beeswarm(size = 3,cex = 3,shape = 21,stroke = 1.5)+
theme_bw()+
theme(legend.position = c("top"),panel.grid = element_blank())+
stat_compare_means(method = "wilcox.test",comparisons = list(c("setosa","virginica"),c("setosa","versicolor"),c("virginica","versicolor")))+
scale_fill_paletteer_d("basetheme::minimal")
4.微笑版密蜂图加分位数线
有些纠结到底应该是叠加分位数线，还是叠加误差棒，发现其实两个都说的过去，干脆都画一下咯。
ggplot(iris,aes(x = Species,y = Sepal.Length,fill = Species))+
geom_quasirandom(method = "smiley",size = 3,width = 0.25,shape = 21,stroke = 1.5)+
theme_bw()+
theme(legend.position = c("top"),panel.grid = element_blank())+
stat_compare_means(method = "wilcox.test",comparisons = list(c("setosa","virginica"),c("setosa","versicolor"),c("virginica","versicolor")))+
stat_summary(fun = median, fun.min = median, fun.max = median,
geom = 'crossbar', width = 0.3, size = 0.4,color = 'black') +
stat_summary(fun.data = function(x) median_hilow(x, 0.5),
geom = 'errorbar', width = 0.25, size = 1,color = 'black')+
scale_fill_paletteer_d("basetheme::minimal")
5.微笑版蜜蜂图叠加误差棒
library(dplyr)
df2 <- group_by(iris,Species)%>%summarise(sd = sd(Sepal.Length),
Sepal.Length=mean(Sepal.Length))
head(df2)
## # A tibble: 3 x 3
## Species sd Sepal.Length
## <fct> <dbl> <dbl>
## 1 setosa 0.352 5.01
## 2 versicolor 0.516 5.94
## 3 virginica 0.636 6.59
ggplot(iris,aes(x = Species,y = Sepal.Length,fill = Species))+
geom_quasirandom(method = "smiley",size = 3,width = 0.25,shape = 21,stroke = 1.5)+
theme_bw()+
theme(legend.position = c("top"),panel.grid = element_blank())+
stat_compare_means(method = "wilcox.test",comparisons = list(c("setosa","virginica"),c("setosa","versicolor"),c("virginica","versicolor")))+
stat_summary(fun = median, fun.min = median, fun.max = median,
geom = 'crossbar', width = 0.3, size = 0.4,color = 'black')+
geom_errorbar(dat = df2,aes(ymin=Sepal.Length-sd, ymax=Sepal.Length+sd), width=.2)+
scale_fill_paletteer_d("basetheme::minimal")
参考代码：http://www.sthda.com/english/wiki/ggplot2-error-bars-quick-start-guide-r-software-and-data-visualization
https://paulvanderlaken.com/2019/01/25/visualization-innovation-waffleplots-and-swarmplots-aka-beeplots/
https://mp.weixin.qq.com/s/8LwTRKTlOR0CsQDUc15sBA
