tidy_pca: Tidy Principal Component Analysis

In tidylearn: A Unified Tidy Interface to R's Machine Learning Ecosystem

Tidy Principal Component Analysis

Description

Performs PCA on a dataset using tidyverse principles. Returns a tidy list containing scores, loadings, variance explained, and the original model.

Usage

tidy_pca(data, cols = NULL, scale = TRUE, center = TRUE, method = "prcomp")

Arguments

data	A data frame or tibble
cols	Columns to include in PCA (tidy select syntax). If NULL, uses all numeric columns.
scale	Logical; should variables be scaled to unit variance? Default TRUE.
center	Logical; should variables be centered? Default TRUE.
method	Character; "prcomp" (default, recommended) or "princomp"

Value

A list of class "tidy_pca" containing:

• scores: tibble of PC scores with observation identifiers

• loadings: tibble of variable loadings in long format

• variance: tibble of variance explained by each PC

• model: the original prcomp/princomp object

• settings: list of scale, center, method used

Examples

# Basic PCA
pca_result <- tidy_pca(USArrests)


# Access components
pca_result$scores
pca_result$loadings
pca_result$variance

tidylearn documentation built on Feb. 6, 2026, 5:07 p.m.