箱线图 | 小提琴图，如何用合适的图形来展示数据,下面通过2个小例子来系统介绍一下
原文链接:箱线图|小提琴图，你需要的他都有
- R包的安装与加载
package.list=c("tidyverse","ggsci","ggsignif")
for (package in package.list) {
if (!require(package,character.only=T, quietly=T)) {
install.packages(package)
library(package, character.only=T)
}
}
准备数据集，在此我们使用ToothGrowth数据集
ToothGrowth %>% as_tibble()
# A tibble: 60 x 3
len supp dose
<dbl> <fct> <dbl>
1 4.2 VC 0.5
2 11.5 VC 0.5
3 7.3 VC 0.5
4 5.8 VC 0.5
5 6.4 VC 0.5
6 10 VC 0.5
7 11.2 VC 0.5
8 11.2 VC 0.5
9 5.2 VC 0.5
10 7 VC 0.5
# ... with 50 more rows
注：使用 ctrl+shift+M可快速打出%>%
下面我们先来做一个简单的箱线图
ToothGrowth %>% mutate(dose=as.factor(dose)) %>%
ggplot(aes(dose,len,fill=supp))+
geom_boxplot()
通常绘制箱线图最好添加上误差线，可通过stat_boxplot添加
ToothGrowth %>% mutate(dose=as.factor(dose)) %>%
ggplot(aes(dose,len,fill=supp))+
geom_boxplot(position = position_dodge(0.7),
width = 0.5,show.legend = T,alpha=0.8) +
stat_boxplot(geom="errorbar",position=position_dodge(width=0.7),width=0.1,alpha=0.8)
也可以根据does | supp类型进行分面展示
ToothGrowth %>% mutate(dose=as.factor(dose)) %>%
ggplot(aes(dose,len,fill=supp))+
geom_boxplot(position = position_dodge(0.7),
width = 0.5,show.legend = T,alpha=0.8) +
stat_boxplot(geom="errorbar",position=position_dodge(width=0.7),width=0.1,alpha=0.8)+
facet_wrap(.~supp,scales = "free")+
scale_fill_jco()
后续就是一些对图例和主题的调整，可以参考之前的文档 ggplot2修饰图例的那些事
下面让我们通过小提琴的形式来重新展示数据
ToothGrowth %>% mutate(dose=as.factor(dose)) %>%
ggplot(aes(dose,len,fill=supp))+
geom_violin(position = position_dodge(0.7),trim = FALSE,alpha=0.8) +
geom_boxplot(position = position_dodge(0.7),
width = 0.15,show.legend = F,alpha=0.8,color="white") +
stat_boxplot(geom="errorbar",position=position_dodge(width=0.7),width=0.1,alpha=0.8,color="white")+
facet_wrap(.~supp,scales = "free")+
scale_fill_jco()+
theme_bw()
可以看到小提琴图明显美观很多，同时我们也可以通过ggsignif添加上分组统计信息
ToothGrowth %>% mutate(dose=as.factor(dose)) %>%
ggplot(aes(dose,len,fill=supp))+
geom_violin(position = position_dodge(0.7),trim = FALSE,alpha=0.8) +
geom_boxplot(position = position_dodge(0.7),
width = 0.15,show.legend = F,alpha=0.8,color="white") +
stat_boxplot(geom="errorbar",position=position_dodge(width=0.7),width=0.1,alpha=0.8,color="white")+
geom_signif(comparisons = list(c("0.5","1"),
c("0.5","2"),
c("1","2")),
map_signif_level=T,vjust=0.5,color="black",
textsize=5,test=wilcox.test,step_increase=0.1)+
facet_wrap(.~supp,scales = "free")+
scale_fill_jco()+
theme_bw()+
theme(panel.spacing.x = unit(0.2,"cm"),
panel.spacing.y = unit(0.1, "cm"),
axis.title = element_blank(),
strip.text.x = element_text(size=9,color="black"),
strip.background.x = element_blank(),
axis.text = element_text(color="black"),
axis.ticks.x=element_blank(),
legend.text = element_text(color="black",size=9),
legend.title=element_blank(),
legend.spacing.x=unit(0.1,'cm'),
legend.key=element_blank(),
legend.key.width=unit(0.5,'cm'),
legend.key.height=unit(0.5,'cm'),
legend.position = "non",
plot.margin=unit(c(0.3,0.3,0.3,0.3),units=,"cm"))
通过一系列主题调整图形之后，终于有了一定的美感；但是这还远远不够，针对多组数据我们还有更好的数据可视化形式，下一节带大家继续探索，喜欢的小伙伴欢迎分享转发
关注下方公众号下回更新不迷路，如需要加入微信交流群，请在菜单栏处添加作者微信，备注单位+方向+姓名即可邀您进群
R语言数据分析指南，持续分享数据可视化的经典案例及一些生信知识，希望对大家有所帮助
