跟着Nature Communication学作图：R语言ggpubr包画箱线图并添加显著性P值

跟着Nature Communication学作图：R语言ggpubr包画箱线图并添加显著性P值

论文

Microbiomes in the Challenger Deep slope and bottom-axis sediments

​​ header = TRUE, check.names = FALSE, sep="\t")head(dat)

带有百分号读取进来是字符，我们把它转换成数字

library(tidyverse)dat %>% mutate(`Novel 16s miTags (%)` = dat$`Novel 16s miTags (%)` %>% parse_number()) -> dat01

对表示分组的文本进行处理

dat01 %>% mutate(group=case_when( Group == "Bottom-axis" ~ "Bottom", Group == "Slope" ~ "Slope", Group == "Mariana Water" ~ "Mariana Water", TRUE ~ "Deep sea" )) -> dat02

赋予因子水平

dat02$group <- factor(dat02$group, levels=c("Bottom", "Slope","Deep sea", "Mariana Water"), ordered=TRUE)table(dat02$group)

作图代码

p1<- ggboxplot(dat02, x="group", y="Novel 16s miTags (%)", fill = "group", width = 0.5, xlab = "", palette = c("#F8766D","#00BFC4","#FEFF99","#B14A87"))+ ylab(label = "Novel 16S miTags (%)")+ #scale_y_continuous(labels = scales::percent)+ guides(fill=F)+ scale_x_discrete(labels = c("Bottom\naxis\n(n=17)", "Slope\n(n=20)", "Deep\nsed\n(n=20)", "Mariana\nwater\n(n=7)"))+ theme(axis.text = element_text(size=10,family="serif"))+ stat_compare_means(comparisons=p1_comparisons, label.y = c( 48, 53,40, 58), method = "wilcox.test",size=3) # Add pairwise comparisons p-value p1

image.png

试一下论文中提供的拼图代码

library(cowplot)aligned_plots<- align_plots(p1, p1,align="h")ggdraw( xlim = c(0, 1.1), ylim = c(0, 0.30))+ draw_plot(aligned_plots[[1]], x=0,y=0, width=0.5, height = 0.28)+ draw_plot(aligned_plots[[2]], x=0.50,y=0, width = 0.5, height = 0.28)

image.png

小明的数据分析笔记本

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