How can I get PCA cumulative proportion with Tidymodels?

I can apply PCA on the classic Iris dataset to obtain the cumulative proportion per dimension:

library(tidyverse)
x <- iris[,1:4] %>% as.matrix()
pca <- prcomp(x)
summary(pca)

But I don't know how can I do that with tidymodels. My code so far is:

library(tidymodels)
iris_vars <- iris %>% select(-Species)
iris_rec <- recipe(~., iris_vars) %>%
  step_pca(all_predictors())
iris_prep <- prep(iris_rec)
iris_tidy <- tidy(iris_prep,1)
iris_tidy
summary(iris_tidy)

I would like to obtain this with tidymodels:

Importance of components:
                          PC1     PC2    PC3     PC4
Standard deviation     2.0563 0.49262 0.2797 0.15439
Proportion of Variance 0.9246 0.05307 0.0171 0.00521
Cumulative Proportion  0.9246 0.97769 0.9948 1.00000

Any help will be greatly appreciated.

Solution

You can get the same results, if you use the same model. prcomp() defaults to center = TRUE, whereas step_pca() defaults to center = FALSE. In the following, I use centering and scaling for both (since this is often recommended).

library("tidymodels")

x <- iris[,1:4] %>% as.matrix()
pca <- prcomp(x, scale. = TRUE)
summary(pca)
#> Importance of components:
#>                           PC1    PC2     PC3     PC4
#> Standard deviation     1.7084 0.9560 0.38309 0.14393
#> Proportion of Variance 0.7296 0.2285 0.03669 0.00518
#> Cumulative Proportion  0.7296 0.9581 0.99482 1.00000

iris_rec <- recipe(Species ~ ., iris) %>%
    step_normalize(all_predictors()) %>% 
    step_pca(all_predictors())
iris_prep <- prep(iris_rec)

summary(iris_prep$steps[[2]]$res)
#> Importance of components:
#>                           PC1    PC2     PC3     PC4
#> Standard deviation     1.7084 0.9560 0.38309 0.14393
#> Proportion of Variance 0.7296 0.2285 0.03669 0.00518
#> Cumulative Proportion  0.7296 0.9581 0.99482 1.00000

^{Created on 2020-05-29 by the reprex package (v0.3.0)}