论文图表复现|多重柱状注释图详细教程

之前看论文发现一张很有意思的图表，下面来介绍如何通过ggplot2来进行图表复现

论文标题：Bacterial Community Dynamics in an Oyster Hatchery in Response to Probiotic Treatment

加载R包

pacman::p_load(tidyverse,scales,ggh4x,magrittr,patchwork)

数据过滤

computed_persent <- function(path) {
  data <- path %>%read.delim(check.names = F,sep="\t",row.names = 1) %>% 
    t() %>% as.data.frame()
  data2 <- data %>%mutate(sum=rowSums(.),persent = sum / sum(sum) * 100, 
           sum = NULL,) %>% rbind(filter(., persent < 0.1) %>% colSums()) %>%
    mutate(Taxa = c(data %>% rownames(), "others"))
  filter(data2[1:(nrow(data2) - 1),], persent > 0.1) %>%
    rbind(data2[nrow(data2),]) %>%select(ncol(.), 1:(ncol(.) - 2)) %>%
    set_rownames(seq_len(nrow(.))) %>%return()
}

数据整合

otu_taxa <- computed_persent("otu.xls") %>% 
  pivot_longer(cols = !Taxa,names_to = "Samples",
               values_to = "number") %>% arrange(desc(number))
meta_taxa <- read.delim("taxa.xls",check.names = F,sep="\t") %>% 
  inner_join(.,otu_taxa,by="Samples")
meta_taxa$Taxa <- factor(meta_taxa$Taxa,levels = unique(meta_taxa$Taxa))

定义调色板

palette <- c("#709AE1FF","#8A9197FF","#D2AF81FF","#FD7446FF","#D5E4A2FF","#197EC0FF","#F05C3BFF",
             "#46732EFF","#71D0F5FF","#370335FF","#075149FF","#C80813FF","#91331FFF","#1A9993FF")

pal <- c("#E64B35FF","#4DBBD5FF","#00A087FF","#3C5488FF","#F39B7FFF","#3C5488FF","#F39B7FFF",
         "#3C5488FF","#F39B7FFF","#8491B4FF","#91D1C2FF","#FF0000","#4DBBD5FF","#00A087FF",
         "#91D1C2FF","#FF0000","#4DBBD5FF","#00A087FF","#3C5488FF","#FF0000","#3C5488FF",
         "#00A087FF", "#91D1C2FF","#868686FF","#FF0000")

绘制堆砌条形图

通过ggh4x中的facet_nested函数来对分面进行加强，background_x来根据调色板来填充分面背景

p2 <- ggplot(meta_taxa,aes(Samples,number,fill=Taxa))+
  geom_col(position="stack") +
  facet_nested(.~Type+Trial+Day,drop=T,scale="free",space="free",switch="x",
               strip =strip_nested(background_x = elem_list_rect(fill =pal),by_layer_x = F))+
  scale_fill_manual(values=palette)+
  labs(x=NULL, y="Percent Phyla Abundance")+
  scale_y_continuous(expand = c(0,0),labels=scales::percent)+
  theme(strip.background = element_rect(fill="white",color="black"),
        panel.spacing = unit(0,"lines"),
        strip.text.x = element_text(size=8,color="black"),
        axis.text.y=element_text(size=8,color="black"),
        axis.title.y = element_text(size=10,color="black"),
        axis.text.x = element_blank(),axis.ticks.x = element_blank(),
        legend.key=element_blank(), legend.text = element_text(color="black",size=10),
        legend.spacing.x=unit(0.1,'cm'),legend.key.width=unit(0.5,'cm'),
        legend.key.height=unit(0.5,'cm'), legend.background=element_blank(),
        panel.grid.major=element_blank(),panel.grid.minor=element_blank())+
  labs(fill="Phylum")

p2

绘制条形图

p1 <- ggplot(meta_taxa,aes(Samples,ReadCount,fill=Group))+
  geom_col(width = 0.9)+theme_grey()+
  labs(y="Read Abundance",x=NULL)+
  scale_fill_manual(values=c("light blue","dark red"))+
  facet_nested(.~Type,drop=TRUE,scale="free",space="free")+
  scale_x_discrete(expand=c(0,0)) +
  scale_y_continuous(expand = c(0,0),labels=scales::scientific_format(digits=1))+
  theme(strip.text = element_blank(),axis.ticks.x = element_blank(),
        panel.background = element_rect(fill='white'),panel.spacing = unit(0.01,"lines"),
        axis.text.y=element_text(size=8,color="black"),axis.title.y =element_blank(),
        axis.text.x = element_blank(),legend.key=element_blank(),
        legend.text = element_text(color="black",size=10),
        legend.spacing.x=unit(0.1,'cm'),legend.key.width=unit(0.5,'cm'),
        legend.key.height=unit(0.5,'cm'),legend.background=element_blank(),
        panel.grid.major=element_blank(),panel.grid.minor=element_blank())

拼图

p1/p2+plot_layout(ncol = 1, heights = c(1,3))

数据获取

上面简单介绍了如何对柱状图进行多重注释，喜欢的朋友欢迎分享，转发；后台回复关键词20220519获取对应的数据及代码

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