Principal Component Analysis in R

#--- Install following packages 

install.packages("devtools")
library(devtools)
install_github("vqv/ggbiplot")
install_github("vqv/ggbiplot",force = TRUE)
library(ggbiplot)

#-----

mtcars.pca <- prcomp(mtcars[,c(1:7,10,11)], center = TRUE,scale. = TRUE)
summary(mtcars.pca)

Importance of components:
                          PC1    PC2     PC3     PC4     PC5     PC6     PC7    PC8     PC9
Standard deviation     2.3782 1.4429 0.71008 0.51481 0.42797 0.35184 0.32413 0.2419 0.14896
Proportion of Variance 0.6284 0.2313 0.05602 0.02945 0.02035 0.01375 0.01167 0.0065 0.00247
Cumulative Proportion  0.6284 0.8598 0.91581 0.94525 0.96560 0.97936 0.99103 0.9975 1.00000

ggbiplot(mtcars.pca)

ggbiplot(mtcars.pca, labels=rownames(mtcars))

mtcars.country <- c(rep("Japan", 3), rep("US",4), rep("Europe", 7),rep("US",3), "Europe", rep("Japan", 3), rep("US",4), rep("Europe", 3), "US", rep("Europe", 3))

ggbiplot(mtcars.pca,ellipse=TRUE,  labels=rownames(mtcars), groups=mtcars.country)

ggbiplot(mtcars.pca,ellipse=TRUE,choices=c(3,4),   labels=rownames(mtcars), groups=mtcars.country)

Reference: https://www.datacamp.com/tutorial/pca-analysis-r