#导入数据
data = read.table(“cor.txt”,header=T,sep="\t")
data1<-data[,-1]
#输入数据的格式
#相关性
cor_matr = cor(data1)
cor_matr
library(corrplot)
corrplot(cor_matr, method = “circle”)
corrplot(cor_matr, method = “square”)
corrplot(cor_matr, method = “ellipse”)
corrplot(cor_matr, method = “shade”)
corrplot(cor_matr, method = “color”)
corrplot(cor_matr, method = “pie”)
method 参数的设置不同，相关性热图的表现形式也就不一样了。
#layout type
corrplot(cor_matr, method = “pie”,type=‘upper’)
corrplot(cor_matr, method = “pie”,type=‘upper’)
corrplot.mixed(cor_matr)
corrplot(cor_matr, method = “pie”,type=‘upper’)
corrplot.mixed(cor_matr, lower.col = “black”, number.cex = .7)
corrplot(cor_matr, method = “pie”,type=‘upper’)
corrplot.mixed(cor_matr, lower = “ellipse”, upper = “circle”)
corrplot.mixed(cor_matr, lower = “square”, upper = “circle”, tl.col = “black”)
corrplot(cor_matr, order = “AOE”)
corrplot(cor_matr, order = “hclust”)
corrplot(cor_matr, order = “FPC”)
corrplot(cor_matr, order = “alphabet”)
corrplot(cor_matr, order = “hclust”, addrect = 3)
corrplot(cor_matr, type = “upper”, order = “hclust”,col = c(“black”, “white”), bg = “lightblue”)
col1 <- colorRampPalette(c("#7F0000", “red”, “#FF7F00”, “yellow”, “white”,
“cyan”, “#007FFF”, “blue”, “#00007F”))
col2 <- colorRampPalette(c("#67001F", “#B2182B”, “#D6604D”, “#F4A582”,
“#FDDBC7”, “#FFFFFF”, “#D1E5F0”, “#92C5DE”,
“#4393C3”, “#2166AC”, “#053061”))
col3 <- colorRampPalette(c(“red”, “white”, “blue”))
col4 <- colorRampPalette(c("#7F0000", “red”, “#FF7F00”, “yellow”, “#7FFF7F”,
“cyan”, “#007FFF”, “blue”, “#00007F”))
whiteblack <- c(“white”, “black”)
corrplot(cor_matr, order = “hclust”, addrect = 2, col = col1(100))
corrplot(cor_matr, order = “hclust”, addrect = 2, col = whiteblack, bg = “gold2”)
corrplot(cor_matr, order = “hclust”, addrect = 2, col = heat.colors(100))
corrplot(cor_matr, type = “lower”, order = “hclust”, tl.col = “black”, tl.srt = 45)
corrplot(abs(cor_matr),order = “AOE”, col = col3(200), cl.lim = c(0, 1))
https://cran.r-project.org/web/packages/corrplot/vignettes/corrplot-intro.html
