df=read.csv("19en.csv", header=T, row.names=1)
head(df)
data.pca <- prcomp(df[,1:19])
summary(data.pca)
names(data.pca)
data.pca$x
输出前三个PC1-PC3
write.csv (data.pca$x, file ="X.csv")
绘图PCA
library(ggplot2)
data =read.csv("PCA.csv", header=T, row.names=1)
data
ggplot(data,aes(x=PC1,y=PC2,color=Code,shape=Code))+
geom_point(size=3)+ theme_bw()
气候因子相关性分析
计算相关系数
heatmap
df=read.csv("19en.csv", header=T, row.names=1)
cor(df,method = 'spearman')
install.packages("Hmisc")
library(Hmisc)
res2<-rcorr(as.matrix(df))
write.csv (res2P, file ="P.csv", row.names =TRUE)
绘制热图
install.packages("pheatmap")
library(pheatmap)
Head=read.csv("R.csv", header=T, row.names=1)
p <- pheatmap(Head,
border_color = "white",
cluster_rows = F,
cluster_cols = F,
cellheight = 20,cellwidth = 30,
color = colorRampPalette(colors = c("blue","yellow","red"))(100)
