9.2 Examples

9.2.1 PCA

An excellent answer on tidyverse solutions to PCA: https://community.rstudio.com/t/tidyverse-solutions-for-factor-analysis-principal-component-analysis/4504

library(ggfortify) # for plotting pca

iris_pca <- iris %>% 
  nest() %>% 
  mutate(pca = map(data, ~ prcomp(.x %>% select(-Species), 
                                  center = TRUE, 
                                  scale = TRUE)), 
         pca_tidy = map2(pca, data, ~ broom::augment(.x, data = .y)))

iris_pca
#> # A tibble: 1 x 3
#>   data               pca      pca_tidy           
#>   <list>             <list>   <list>             
#> 1 <tibble [150 x 5]> <prcomp> <tibble [150 x 10]>

iris_pca %>%
  unnest(pca_tidy) %>% 
  summarize_at(.vars = vars(contains(".fittedPC")), list(variance = var)) %>% 
  pivot_longer(everything(), names_to = "pc", values_to = "variance") %>% 
  mutate(var_explained = variance / sum(variance))
#> # A tibble: 4 x 3
#>   pc                  variance var_explained
#>   <chr>                  <dbl>         <dbl>
#> 1 .fittedPC1_variance   2.92         0.730  
#> 2 .fittedPC2_variance   0.914        0.229  
#> 3 .fittedPC3_variance   0.147        0.0367 
#> 4 .fittedPC4_variance   0.0207       0.00518

iris_pca %>% 
  mutate(
    pca_graph = map2(
      pca, 
      data,
      ~ autoplot(.x, loadings = TRUE, loadings.label = TRUE, 
                 data = .y, colour = "Species")
    )
  ) %>% 
  pull(pca_graph)
#> [[1]]