Module 7: PCA & Dimension Reduction

Topic: Principal component analysis, interpretation of loadings and scores, other dimension reduction methods.

Learning Objectives

Compute PCA with prcomp()
Interpret biplots, scree plots, and loadings
Understand variance explained by components
Survey other dimension reduction approaches (t-SNE, UMAP)

Slides

R Code

Data

Key Packages

library(reshape2)
library(ggplot2)
library(data.table)

Assignment

HW7: Principal Component Analysis — due Module 8 Wednesday. Submit on Canvas.

---
title: "Module 7: PCA & Dimension Reduction"
---

::: {.week-header}
**Topic:** Principal component analysis, interpretation of loadings and scores, other dimension reduction methods.
:::

## Learning Objectives

- Compute PCA with `prcomp()`
- Interpret biplots, scree plots, and loadings
- Understand variance explained by components
- Survey other dimension reduction approaches (t-SNE, UMAP)

## Slides

::: {.file-list}
- [L12 PCA Intro Slides (PDF)](../Week%207/L12%20PCA%20intro%20slides.pdf)
- [L13 More Dimension Reduction (PDF)](../Week%207/L13%20more%20dimension%20reduction.pdf)
:::

## R Code

::: {.file-list}
- [L12 PCA intro.R](../Week%207/L12%20PCA%20intro.R)
- [L13 more dimension reduction.R](../Week%207/L13%20more%20dimension%20reduction.R)
:::

## Data

::: {.file-list}
- [cassava.csv](../Week%207/cassava.csv)
- [marmot.csv](../Week%207/marmot.csv)
:::

## Key Packages

```r
library(reshape2)
library(ggplot2)
library(data.table)
```

## Assignment

**HW7: Principal Component Analysis** — due Module 8 Wednesday. Submit on Canvas.