recipe-grow.r

library(recipes)
library(butcher)
library(rlang)

my_function2 <- function() {
  a <- iris
  b <- iris
  c <- iris
  
  rec <- 
    recipe(Species ~ ., data = iris) %>% 
    step_normalize(all_numeric()) %>% 
    step_pca(all_numeric()) %>% 
    prep()
  
  rec <- butcher(rec)
  rec
}

x <- my_function2()
x
#> Data Recipe
#> 
#> Inputs:
#> 
#>       role #variables
#>    outcome          1
#>  predictor          4
#> 
#> Training data contained 150 data points and no missing data.
#> 
#> Operations:
#> 
#> Centering and scaling for Sepal.Length, Sepal.Width, ... [trained]
#> PCA extraction with Sepal.Length, Sepal.Width, ... [trained]

quo <- x$steps[[1]]$terms[[1]]
quo
#> <quosure>
#> expr: ^all_numeric()
#> env:  0x7fd13a3ebe60

env <- quo_get_env(quo)

env_parent <- rlang::env_parent(env)

head(env_parent$a)
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1          5.1         3.5          1.4         0.2  setosa
#> 2          4.9         3.0          1.4         0.2  setosa
#> 3          4.7         3.2          1.3         0.2  setosa
#> 4          4.6         3.1          1.5         0.2  setosa
#> 5          5.0         3.6          1.4         0.2  setosa
#> 6          5.4         3.9          1.7         0.4  setosa