1. Load data

Using iris data for analysis.

head(iris)
df <- iris
df <- as.data.frame(iris)

Dimension conversion for futher analysis:

row.names(df) <- paste(df$Species, row.names(df), sep="_") 
df$Species <- NULL

head(df)

2. PCA analysis using prcomp

df_pca <- prcomp(df)

2.1 plot directly

plot(df_pca$x[,1], df_pca$x[,2])

png

3. Using ggplot2 to revise this plot:

First, a new dataframe should be created, with the information of sample-group.

df_out <- as.data.frame(df_pca$x)
df_out$group <- sapply( strsplit(as.character(row.names(df)), "_"), "[[", 1 )
head(df_out)

Second, some prepartions for ggplot2:

library(ggplot2)
library(grid)
library(gridExtra)

Try:

p<-ggplot(df_out,aes(x=PC1,y=PC2,color=group ))
p<-p+geom_point()
p

png

3.2 plot with a theme

For format of picture.

theme<-theme(panel.background = element_blank(),panel.border=element_rect(fill=NA),panel.grid.major = element_blank(),panel.grid.minor = element_blank(),strip.background=element_blank(),axis.text.x=element_text(colour="black"),axis.text.y=element_text(colour="black"),axis.ticks=element_line(colour="black"),plot.margin=unit(c(1,1,1,1),"line"))

For plot:

p<-ggplot(df_out,aes(x=PC1,y=PC2,color=group ))
p<-p+geom_point()+theme
p

png

3.3 Put the words on the figure:

p<-ggplot(df_out,aes(x=PC1,y=PC2,color=group, label=row.names(df) ))
p<-p+geom_point()+ geom_text(size=3)+theme
p

png

3.4 The percentage:

percentage <- round(df_pca$sdev / sum(df_pca$sdev) * 100, 2)
percentage <- paste( colnames(df_out), "(", paste( as.character(percentage), "%", ")", sep="") )

p<-ggplot(df_out,aes(x=PC1,y=PC2,color=group ))
p<-p+geom_point()+theme + xlab(percentage[1]) + ylab(percentage[2])
p

png

3.5 Change the order for Sample group:

df_out$group <- factor(df_out$group, levels = c("virginica", "setosa", "versicolor"))

p<-ggplot(df_out,aes(x=PC1,y=PC2,color=group ))
p<-p+geom_point()+theme + xlab(percentage[1]) + ylab(percentage[2]) + scale_color_manual(values=c("#FFFF00", "#00FFFF", "#FF00FF"))
p

png

3.5 Save in PDF file

pdf(  "file_out.pdf",width = 10,height = 10)
library(gridExtra)
yy <- grid.arrange(p,nrow=1)
op <- par(no.readonly=TRUE)
par(op)
dev.off()

3.6 Plot features that contribute to the classification

df_out_r <- as.data.frame(df_pca$rotation)
df_out_r$feature <- row.names(df_out_r)

df_out_r

p<-ggplot(df_out_r,aes(x=PC1,y=PC2,label=feature,color=feature ))
p<-p+geom_point()+theme + geom_text(size=3)
p

png

本文作者Boqiang Hu, 欢迎评论、交流。

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R plot PCA using ggplot2

r plot pca using ggplot2 1. Load data 2. PCA analysis using prcomp 3. Using ggplot2 to revise this plot: