R绘图笔记 | 散点分布图与柱形分布图

关于绘图图，前面介绍了一些：

这里介绍散点分布图与柱形分布图，这些图形在文章中是很常见的，也是必须要掌握的。

一.读入数据

如果你想获取该数据用于自己练习，下面是获取数据的地址：

https://docs.qq.com/sheet/DV0dxREV1YkJ0ZmVj

数据格式是这样的。

数据第A列是病人ID，B~E列是临床信息，其他列是病人的RNAseq数据。

你可以保存副本导出，然后自己读入。

library(ggplot2)library(RColorBrewer)library(SuppDists) #提供rJohnson()函数library(ggbeeswarm)

data <- read.csv("BioInfoNotesData1.csv",row.names = 1)

假如我们需要绘制某基因在不同分期的表达情况。

f1.data <- data[,c(1,5)]colnames(f1.data) <- c("Stage","Value")summary(f1.data$Stage)

summary(f1.data$Stage) N Stage I Stage II Stage III Stage IV 11 75 176 128 64

先检查数据是否有缺失值，分期信息不知用N来表示，可以删除这些数据。

f1.data<-f1.data[f1.data$Stage!="N",]head(f1.data)

BioinfoNotes>head(f1.data) Stage ValueTCGA-3L-AA1B-01 Stage I 7.04TCGA-4N-A93T-01 Stage III 7.23TCGA-4T-AA8H-01 Stage II 6.61TCGA-5M-AAT4-01 Stage IV 7.56TCGA-5M-AAT6-01 Stage IV 4.99TCGA-5M-AATE-01 Stage II 7.41

二.绘图

1.散点抖动图

ggplot(f1.data, aes(Stage, Value))+ geom_jitter(aes(fill = Stage),position = position_jitter(0.3),shape=21, size = 2)+ scale_fill_manual(values=c(brewer.pal(7,"Set2")[c(1,2,4,5)]))+ theme_classic()+ theme(panel.background=element_rect(fill="white",colour="black",size=0.25), 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" )

2.蜂群图

#蜂群图ggplot(f1.data, aes(Stage, Value))+ geom_beeswarm(aes(fill = Stage),shape=21,colour="black",size=2,cex=2)+ scale_fill_manual(values= c(brewer.pal(7,"Set2")[c(1,2,4,5)]))+ xlab("Stage")+ ylab("Value")+ theme_classic()+ theme(panel.background=element_rect(fill="white",colour="black",size=0.25), 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" )

3.点阵图

ggplot(f1.data, aes(Stage, Value))+ geom_dotplot(aes(fill = Stage),binaxis='y', stackdir='center', dotsize = 0.6)+ scale_fill_manual(values=c(brewer.pal(7,"Set2")[c(1,2,4,5)]))+ theme_classic()+ theme(panel.background=element_rect(fill="white",colour="black",size=0.25), 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" )


4.带误差线的散点分布图

ggplot(f1.data, aes(Stage, Value))+ geom_jitter(aes(fill = Stage),position = position_jitter(0.3),shape=21, size = 2,color="black")+ scale_fill_manual(values=c(brewer.pal(7,"Set2")[c(1,2,4,5)]))+ stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="pointrange", color = "black",size = 1.2)+ stat_summary(fun.y="mean", fun.args = list(mult=1), geom="point", color = "white",size = 4)+ theme_classic()+ theme(panel.background=element_rect(fill="white",colour="black",size=0.25), 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" )

5.带误差线的柱形分布图

ggplot(f1.data, aes(Stage, Value))+ stat_summary(mapping=aes(fill = Stage),fun.y=mean, fun.args = list(mult=1),geom='bar',colour="black",width=.7) + stat_summary(fun.data = mean_sdl, fun.args = list(mult=1),geom='errorbar', color='black',width=.2) + scale_fill_manual(values=c(brewer.pal(7,"Set2")[c(1,2,4,5)]))+ ylim(0,7.5)+ theme_classic()+ theme(panel.background=element_rect(fill="white",colour="black",size=0.25), 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" )

6.带误差线柱形与抖动图

ggplot(f1.data, aes(Stage, Value))+ stat_summary(fun.y=mean, fun.args = list(mult=1),geom='bar',colour="black",fill="white",width=.7) + stat_summary(fun.data = mean_sdl,fun.args = list(mult=1), geom='errorbar', color='black',width=.2) + geom_jitter(aes(fill = Stage),position = position_jitter(0.2),shape=21, size = 2,alpha=0.9)+ scale_fill_manual(values=c(brewer.pal(7,"Set2")[c(1,2,4,5)]))+ theme_classic()+ theme(panel.background=element_rect(fill="white",colour="black",size=0.25), 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" )

参考资料：

1.R语言数据可视化之美，张杰/著