跟着Nature学作图：R语言ggplot2箱线图/堆积柱形图完整示例

论文

Graph pangenome captures missing heritability and empowers tomato breeding

https://www.nature.com/articles/s41586-022-04808-9#MOESM8

没有找到论文里的作图的代码，但是找到了部分组图数据，我们可以用论文中提供的原始数据模仿出论文中的图

今天的推文重复一下论文中的 Extended Data Fig7b Extended Data Fig7c 箱线图和堆积柱形图

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箱线图下方的8个矩形可以用拼图的方式来实现

箱线图的部分示例数据

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这里并没有提供箱线图的分组数据，这里我自己随便构造一列分组数据了，所以最终结果可能和原图不一致

整理数据

library(readxl) dat01<-read_excel("data/20220711/41586_2022_4808_MOESM10_ESM.xlsx", sheet = "Extend Fig7b", skip = 1) head(dat01) library(tidyverse) dat01 %>% mutate(group_info=sample(c(rep("A",313), rep("B",5), rep("C",8), rep("D",6)), 332, replace = FALSE)) -> efig7c

箱线图代码

library(ggplot2) library(latex2exp) help(package="latex2exp") dat<-data.frame(x=c(0.5,1:4), y=-Inf, label=c("n=",313,5,8,5)) ggplot(data=efig7c, aes(x=group_info,y=BLUP))+ geom_boxplot(aes(fill=group_info))+ scale_fill_manual(values = c("#feb2a9","#fdd79d", "#dbcde4","#c993c7"))+ geom_jitter(width = 0.4)+ theme_bw()+ theme(panel.grid = element_blank())+ annotate(geom = "text", x=4,y=Inf, label=TeX(r"(Kruskal Wallis, \textit{P} = 5 \times 10${^-}$${^7}$)"), vjust=1,hjust=1)+ geom_text(data=dat,aes(x=x,y=y,label=label), inherit.aes = FALSE, vjust=-0.8)+ ylim(-40,NA)+ labs(y="BLUP value of expression")+ theme(axis.title.x = element_blank(), axis.text.x = element_blank(), legend.position = "none") -> p1 p1

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箱线图下方的矩形点

dat.2<-data.frame(x=rep(LETTERS[1:4],2), y=rep(c(1,2),each=4), group=c("A","A","A","D","A","B","D","A")) dat.2 ggplot(data=dat.2,aes(x=x,y=y,fill=group,color=group))+ geom_point(shape=22,size=5)+ scale_fill_manual(values = c("#fc8072","#a1d99b", "#4192c6"), labels=c("Reference homozygous", "Heterozygous", "Alternate homozygous"))+ scale_color_manual(values = c("#fc8072","#a1d99b", "#4192c6"), labels=c("Reference homozygous", "Heterozygous", "Alternate homozygous"))+ theme_void() + theme(legend.title = element_blank())+ annotate(geom = "text", x=1,y=2,label="SV3_42936717", hjust=1.2,size=3,vjust=1)+ annotate(geom = "text", x=1,y=1,label="SV3_42954617", hjust=1.2,size=3,vjust=0) -> p2 p2

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将两个图组合到一起

library(ggpubr) as_ggplot(get_legend(p2)) library(patchwork) p1+ annotation_custom(grob = get_legend(p2), xmin=3.5,xmax=3.5,ymin=-28,ymax=-28)+ p2+ theme(legend.position ="none")+ plot_layout(ncol = 1,heights = c(10,1)) -> p3 p3

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最后是堆积柱形图的代码

数据集

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dat02<-read_excel("data/20220711/41586_2022_4808_MOESM10_ESM.xlsx", sheet = "Extend Fig7c") dat02 dat02$x<-factor(dat02$x, levels = c("SNPs","Indels","SVs")) dat02 dat02 %>% group_by(x) %>% mutate(new_col=cumsum(y)) -> dat02 ggplot(data=dat02,aes(x=x,y=y,fill=group))+ geom_bar(stat="identity", position = "stack")+ scale_fill_manual(values = c("Non-module"="#e99e9c", "Module"="#98c0d7"))+ geom_text(aes(x=x,y=new_col,label=y), vjust=1)+ labs(x=NULL,y=TeX(r"(\textit{h}${^2}$)"))+ theme_classic()+ scale_y_continuous(expand = expansion(mult = c(0,0)))+ theme(legend.position = c(0.2,0.8), legend.title = element_blank(), axis.title.y = element_text(angle = 0,vjust = 0.5)) -> p4 p4