Performs PCA on a dataset using tidyverse principles. Returns a tidy list containing scores, loadings, variance explained, and the original model.
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$scores
#> # A tibble: 50 × 5
#> .obs_id PC1 PC2 PC3 PC4
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Alabama -0.976 -1.12 0.440 0.155
#> 2 Alaska -1.93 -1.06 -2.02 -0.434
#> 3 Arizona -1.75 0.738 -0.0542 -0.826
#> 4 Arkansas 0.140 -1.11 -0.113 -0.181
#> 5 California -2.50 1.53 -0.593 -0.339
#> 6 Colorado -1.50 0.978 -1.08 0.00145
#> 7 Connecticut 1.34 1.08 0.637 -0.117
#> 8 Delaware -0.0472 0.322 0.711 -0.873
#> 9 Florida -2.98 -0.0388 0.571 -0.0953
#> 10 Georgia -1.62 -1.27 0.339 1.07
#> # ℹ 40 more rows
pca_result$loadings
#> # A tibble: 16 × 3
#> variable component loading
#> <chr> <chr> <dbl>
#> 1 Murder PC1 -0.536
#> 2 Murder PC2 -0.418
#> 3 Murder PC3 0.341
#> 4 Murder PC4 0.649
#> 5 Assault PC1 -0.583
#> 6 Assault PC2 -0.188
#> 7 Assault PC3 0.268
#> 8 Assault PC4 -0.743
#> 9 UrbanPop PC1 -0.278
#> 10 UrbanPop PC2 0.873
#> 11 UrbanPop PC3 0.378
#> 12 UrbanPop PC4 0.134
#> 13 Rape PC1 -0.543
#> 14 Rape PC2 0.167
#> 15 Rape PC3 -0.818
#> 16 Rape PC4 0.0890
pca_result$variance
#> # A tibble: 4 × 5
#> component sdev variance prop_variance cum_variance
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 PC1 1.57 2.48 0.620 0.620
#> 2 PC2 0.995 0.990 0.247 0.868
#> 3 PC3 0.597 0.357 0.0891 0.957
#> 4 PC4 0.416 0.173 0.0434 1