如何利用R語(yǔ)言的ggplot2包繪制PCA圖

library(ggplot2)#使用經(jīng)典iris數(shù)據(jù)集df <- iris[c(1, 2, 3, 4)]head(df)  Sepal.Length Sepal.Width Petal.Length Petal.Width1          5.1         3.5          1.4         0.22          4.9         3.0          1.4         0.23          4.7         3.2          1.3         0.24          4.6         3.1          1.5         0.25          5.0         3.6          1.4         0.26          5.4         3.9          1.7         0.4

df_pca <- prcomp(df) #計(jì)算主成分df_pcs <-data.frame(df_pca$x, Species = iris$Species)  head(df_pcs,3)  #查看主成分結(jié)果        PC1        PC2         PC3          PC4 Species1 -2.684126 -0.3193972  0.02791483  0.002262437  setosa2 -2.714142  0.1770012  0.21046427  0.099026550  setosa3 -2.888991  0.1449494 -0.01790026  0.019968390  setosa

plot(df_pca$x[,1], df_pca$x[,2])

ggplot(df_pcs,aes(x=PC1,y=PC2,color=Species))+ geom_point()

ggplot(df_pcs,aes(x=PC1,y=PC2,color=Species))+ geom_point()+ theme_bw() +theme(panel.border=element_blank(),panel.grid.major=element_blank(),panel.grid.minor=element_blank(),axis.line= element_line(colour = "black"))

percentage<-round(df_pca$sdev / sum(df_pca$sdev) * 100,2)percentage<-paste(colnames(df_pcs),"(", paste(as.character(percentage), "%", ")", sep=""))ggplot(df_pcs,aes(x=PC1,y=PC2,color=Species))+geom_point()+ xlab(percentage[1]) +ylab(percentage[2])

ggplot(df_pcs,aes(x=PC1,y=PC2,color = Species))+ geom_point()+stat_ellipse(level = 0.95, show.legend = F) + annotate('text', label = 'setosa', x = -2, y = -1.25, size = 5, colour = '#f8766d') +annotate('text', label = 'versicolor', x = 0, y = - 0.5, size = 5, colour = '#00ba38') +annotate('text', label = 'virginica', x = 3, y = 0.5, size = 5, colour = '#619cff')

df_r <- as.data.frame(df_pca$rotation)df_r$feature <- row.names(df_r)df_r                      PC1         PC2         PC3        PC4      featureSepal.Length  0.36138659 -0.65658877  0.58202985  0.3154872 Sepal.LengthSepal.Width  -0.08452251 -0.73016143 -0.59791083 -0.3197231  Sepal.WidthPetal.Length  0.85667061  0.17337266 -0.07623608 -0.4798390 Petal.LengthPetal.Width   0.35828920  0.07548102 -0.54583143  0.7536574  Petal.Width

ggplot(df_r,aes(x=PC1,y=PC2,label=feature,color=feature )) + geom_point()+ geom_text(size=3)

ggplot(df_pcs,aes(x=PC1,y=PC2,color=Species )) + geom_point()+xlab(percentage[1]) + ylab(percentage[2]) + stat_ellipse(level = 0.95, show.legend = F) +annotate('text', label = 'setosa', x = -2, y = -1.25, size = 5, colour = '#f8766d') +annotate('text', label = 'versicolor', x = 0, y = - 0.5, size = 5, colour = '#00ba38') +annotate('text', label = 'virginica', x = 3, y = 0.5, size = 5, colour = '#619cff') + labs(title="Iris PCA Clustering",        subtitle=" PC1 and PC2 principal components ",       caption="Source: Iris") + theme_classic()