# Sample data creation
set.seed(0)
data <- matrix(runif(300), nrow=100, ncol=3)  # 100 samples with 3 features
# Standardize the data
data_scaled <- scale(data)
# Perform PCA
pca <- prcomp(data_scaled, center = TRUE, scale. = TRUE)
# Create a data frame with the principal components
principal_components <- data.frame(pca$x[,1:2])
colnames(principal_components) <- c("PC1", "PC2")
# Plot the principal components
library(ggplot2)
ggplot(principal_components, aes(x = PC1, y = PC2)) +
  geom_point() +
  ggtitle("2 component PCA") +
  xlab("Principal Component 1") +
  ylab("Principal Component 2")
# Explained variance
explained_variance <- pca$sdev^2 / sum(pca$sdev^2)
print(explained_variance)