Load “BreastCancer” dataset from “mlbench” package, perform PCA analysis on columns 2 to 10 of the dataset as features and use PC1 and PC2 to plot a PCA result graph (indicating tumor types with different colors), recommended to use the “ggplot2”package for plotting.

Sure, here's how you can do it:

# Load required packages
library(mlbench)
library(ggplot2)

# Load BreastCancer dataset
data(BreastCancer)

# Perform PCA analysis on columns 2 to 10 of the dataset as features
pca <- prcomp(BreastCancer[, 2:10], scale. = TRUE)

# Extract PC1 and PC2 scores
pc_scores <- data.frame(PC1 = pca$x[, 1], PC2 = pca$x[, 2], Type = BreastCancer$Type)

# Plot PCA result graph using ggplot2
ggplot(pc_scores, aes(x = PC1, y = PC2, color = Type)) +
  geom_point() +
  ggtitle("PCA analysis of BreastCancer dataset") +
  xlab("PC1") +
  ylab("PC2")

This code will load the BreastCancer dataset from the mlbench package, perform PCA analysis on columns 2 to 10 of the dataset as features, and use PC1 and PC2 to plot a PCA result graph. The tumor types will be indicated with different colors. Finally, ggplot2 package is used for plotting.