SImple PCA in R using iris dataset

pca.r

# Running a principal components analysis (PCA) in R

# Load data
data(iris)

# Remove factors
data <- iris

# Scale data
data_scaled <- scale(data[-5])

# Perform PCA
pca_results <- prcomp(data_scaled, scale = TRUE)

# View results
summary(pca_results)

# Create biplot
# library(devtools)
# install_github("vqv/ggbiplot")
library(ggbiplot)
g <- ggbiplot(pca_results, choices=c(1,2), obs.scale = 1, var.scale = 1, groups = iris$Species, ellipse = TRUE)
g <- g + geom_point(aes(color = iris$Species), size = 3)
g <- g + theme_classic()
g <- g + scale_color_discrete(name = 'Species', labels = c("I. setosa", "I. versicolor", "I. virginica"))
g <- g + theme(legend.direction = 'horizontal', legend.position = 'top')
print(g)