PCA Tutorial in R

Eigenvalues: Measure variance explained by each component
Scree Plot: Helps determine optimal components to retain
Biplot: Combines variable and observation plots

💡 Principal Component Analysis (PCA) is a powerful technique for dimensionality reduction. Here's how to implement it in R:

Steps to Perform PCA

Install & Load Libraries
```
install.packages("FactoMineR")
library(FactoMineR)
```
📌 Download R here if you need setup guidance.
Prepare Your Data
- Ensure data is numeric
- Check for missing values
```
data <- read.csv("your_dataset.csv")
summary(data)
```
Standardize Variables
```
data_std <- scale(data)
```
📊
Calculate PCA
```
pca_result <- PCA(data_std, graph = FALSE)
```
📈 Explore PCA results for advanced analysis.
Visualize Components
```
plot(pca_result, choix = "cos2")
```
📌 Learn about PCA plots to interpret patterns.

📊

For hands-on practice, try the PCA in R workshop next!