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.
时间: 2023-11-27 22:55:39 浏览: 32
Sure, here's the code to perform PCA analysis on the "BreastCancer" dataset from the "mlbench" package and plot the results using PC1 and PC2 with different colors indicating different tumor types using the "ggplot2" package:
```{r}
# Load required packages
library(mlbench)
library(ggplot2)
# Load the BreastCancer dataset
data(BreastCancer)
# Subset columns 2 to 10 as features
features <- BreastCancer[, 2:10]
# Perform PCA analysis
pca <- prcomp(features, scale. = TRUE)
# Extract PC1 and PC2 scores
scores <- as.data.frame(pca$x[, 1:2])
# Add tumor type as a column to scores dataframe
scores$tumor_type <- as.factor(BreastCancer$Class)
# Plot PCA result graph with different colors indicating tumor types
ggplot(scores, aes(x = PC1, y = PC2, color = tumor_type)) +
geom_point() +
labs(title = "PCA Analysis of Breast Cancer Dataset",
x = "PC1",
y = "PC2",
color = "Tumor Type")
```
This will generate a PCA result graph with PC1 on the x-axis, PC2 on the y-axis, and different colors indicating different tumor types.