ggplot2绘制多图层相关性热图

R语言数据分析指南

发布于 2023-10-24 14:02:22

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结果图

图形解读

❝此图作为一张基础的相关性热图，通过代码将r值与p值进行结合直接展示在图中，同时对色块通过透明度来区分数据是否显著，其余方面没做过多的调整，其主要思路是筛选数据进行分类添加图层。 ❞

加载R包

library(tidyverse)
library(RColorBrewer)
library(ggtext)
library(magrittr)
library(reshape)
library(psych)

加载数据

table1 <- read_tsv("varechem.tsv")
table2 <- read_tsv("varespec.tsv") %>% select(1:20)

数据清洗

pp <- corr.test(table1,table2,method="pearson",adjust = "fdr")

df <- melt(pp$r) %>% mutate(pvalue=melt(pp$p)[,3],
                            p_signif=symnum(pvalue, corr = FALSE, na = FALSE,  
                                            cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), 
                                            symbols = c("***", "**", "*", "", " "))) %>% 
  set_colnames(c("env","genus","r","p","p_signif")) %>% 
  mutate(p=round(p,digits = 2)) %>% 
  unite(.,col="p_value",p:p_signif,sep="",remove = T,na.rm = F)

数据可视化

ggplot()+
  geom_tile(data= df %>% filter(!str_detect(p_value, "\\*")),
            aes(genus,env,fill=r),alpha = 0.5)+
  geom_text(data= df %>% filter(!str_detect(p_value, "\\*")),
            aes(genus,env,col=r,fill=r,label=p_value),
            size=3,color="grey70",hjust=0.5,vjust=0.7)+
  geom_tile(data= df %>% filter(str_detect(p_value, "\\*")),
            aes(genus,env,col=r,fill=r),alpha=1)+
  geom_text(data= df %>% filter(str_detect(p_value, "\\*")),
            aes(genus,env,col=r,fill=r,label=p_value),
            size=3,color="black",hjust=0.5,vjust=0.7)+
  labs(x = NULL,y = NULL,color=NULL,fill=NULL)+
  scale_color_gradientn(colours = rev(RColorBrewer::brewer.pal(11,"RdBu")))+
  scale_fill_gradientn(colours = rev(RColorBrewer::brewer.pal(11,"RdBu")))+
  scale_y_discrete(expand=c(0,0),position = 'right',
                   labels=c("N"=expression(NH[4]^+""),
                            "Ca"=expression(CaSO[4])))+
  scale_x_discrete(expand=c(0,0)) +
  theme(axis.text.x=element_text(angle =90,hjust =1,vjust =0.5,
                                 color="black",face = "bold",size = 10),
        axis.text.y=element_text(color="black",face = "bold",size =10),
        axis.ticks= element_blank(),
        panel.spacing.y = unit(0,"cm"),
        plot.background = element_blank(),
        panel.background = element_blank())+
  scale_size(range=c(1,10),guide=NULL)+
  guides(color=guide_colorbar(direction="vertical",reverse=F,barwidth=unit(.5,"cm"),
                              barheight=unit(11,"cm")))

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原始发表：2023-10-14，如有侵权请联系 cloudcommunity@tencent.com 删除

数据分析