R中的刻面或分组相关和相关图

Posted 2023-02-18

【中文标题】R中的刻面或分组相关和相关图

【英文标题】：facet or grouped correlation and correlogram plots in R

【发布时间】：2017-07-21 01:02:42

【问题描述】：

我正在尝试从数据框中按组/方面绘制相关图。如果我对每个变量的数据进行子集化，我就可以做到这一点。如何一次对所有变量执行此操作以根据每个变量生成分面图？

###Load libraries
library(gdata)
library(corrplot)
library(ggplot2)
library(gtable)
library(ggpmisc)
library(grid)
library(reshape2)
library(plotly)
packageVersion('plotly')

##Subset ample data from the "iris" data set in R
B<-iris[iris$Species == "virginica", ]

##calculate correlation for numeric columns only
M<-cor(B[,1:4])
head(round(M,2))

###calculate significance
cor.mtest <- function(mat, ...) 
mat <- as.matrix(mat)
n <- ncol(mat)
p.mat<- matrix(NA, n, n)
diag(p.mat) <- 0
for (i in 1:(n - 1)) 
    for (j in (i + 1):n) 
        tmp <- cor.test(mat[, i], mat[, j], ...)
        p.mat[i, j] <- p.mat[j, i] <- tmp$p.value
    

colnames(p.mat) <- rownames(p.mat) <- colnames(mat)
p.mat

# matrix of the p-value of the correlation
p.mat <- cor.mtest(B[,1:4])

###plot
#color ramp
col<- colorRampPalette(c("red","white","blue"))(40)
corrplot(M, type="upper",tl.col="black", tl.cex=0.7,tl.srt=45, col=col,
p.mat = p.mat, insig = "blank", sig.level = 0.01)

这很有效，因为我只从数据框中取出了一个变量“virginica”。如何自动执行此操作以进行唯一的相关性计算，然后将所有单个变量作为单个方面进行 corrplot？

【参考方案1】：

@Jimbou，感谢您的代码。我对其进行了一些编辑，以在一个代码中添加相关性分析、唯一 R 和绘图，并为每个绘图添加唯一名称。

library(ggplot2)
library(Hmisc) 
library(corrplot)
# split the data 
B <- split(iris[,1:4], iris$Species)
##extract names
nam<-names(B)
# Plot three pictures
par(mfrow=c(1,3))
col<- colorRampPalette(c("red","white","blue"))(40)
for (i in seq_along(B))
# Calculate the correlation in all data.frames using lapply 
M<-rcorr(as.matrix(B[[i]]))
corrplot(M$r, type="upper",tl.col="black", tl.cex=0.7,tl.srt=45, col=col,
 addCoef.col = "black", p.mat = M$P, insig = "blank",sig.level = 0.01)
mtext(paste(nam[i]),line=1,side=3)

【参考方案2】：

据我了解，您希望每个 Species 级别都有一个 corrplot。 所以，你可以试试：

library(Hmisc) # this package has implemented a cor function calculating both r and p.  
library(corrplot)
# split the data 
B <- split(iris[,1:4], iris$Species)
# Calculate the correlation in all data.frames using lapply 
M <- lapply(B, function(x) rcorr(as.matrix(x)))

# Plot three pictures
par(mfrow=c(1,3))
col<- colorRampPalette(c("red","white","blue"))(40)
lapply(M, function(x)
corrplot(x$r, type="upper",tl.col="black", tl.cex=0.7,tl.srt=45, col=col,
         p.mat = x$P, insig = "blank", sig.level = 0.01)
)