Is there a function that returns the list/vector/etc. of variables that "survive" the step functions? I have a simple reproducible example below. I'd like to be able to get a list of variables that are left at the end - in this case, only x2. This seems somewhat related to this post.
library(tidymodels)
set.seed(123)
samp_size <- 1000
# Creating sample data where x2 is highly correlated with x1 and x3 has near-zero variance
sample_data <- tibble(x = rnorm(samp_size)) %>%
mutate(
y = 3 + 2*x + rnorm(samp_size, 0, .5),
x2 = x + rnorm(samp_size, 0,.1),
x3 = c(rep(0, samp_size - 1), 1)
)
# Didn't break out training and testing since it's not needed for this simple example.
simple_recipe <- recipe(y ~ ., sample_data) %>%
step_nzv(all_numeric_predictors()) %>%
step_corr(all_numeric_predictors())
# When I run the steps, I'm only left with y and x2
simple_recipe %>%
prep() %>%
juice()
Good question!
for now you can use tidy() to see which variables are removed from each step.
library(tidymodels)
set.seed(123)
samp_size <- 1000
# Creating sample data where x2 is highly correlated with x1 and x3 has near-zero variance
sample_data <- tibble(x = rnorm(samp_size)) %>%
mutate(
y = 3 + 2*x + rnorm(samp_size, 0, .5),
x2 = x + rnorm(samp_size, 0,.1),
x3 = c(rep(0, samp_size - 1), 1)
)
# Didn't break out training and testing since it's not needed for this simple example.
simple_recipe <- recipe(y ~ ., sample_data) %>%
step_nzv(all_numeric_predictors()) %>%
step_corr(all_numeric_predictors())
# When I run the steps, I'm only left with y and x2
prepped_recipe <- simple_recipe %>%
prep()
prepped_recipe %>%
tidy(1)
#> # A tibble: 1 × 2
#> terms id
#> <chr> <chr>
#> 1 x3 nzv_dOXnH
prepped_recipe %>%
tidy(2)
#> # A tibble: 1 × 2
#> terms id
#> <chr> <chr>
#> 1 x corr_07DFJ
prepped_recipe %>%
bake(new_data = NULL)
#> # A tibble: 1,000 × 2
#> x2 y
#> <dbl> <dbl>
#> 1 -0.612 1.38
#> 2 -0.206 2.02
#> 3 1.50 6.11
#> 4 0.192 3.07
#> 5 0.147 1.98
#> 6 1.65 6.95
#> 7 0.280 4.05
#> 8 -1.33 1.68
#> 9 -0.482 1.97
#> 10 -0.502 1.89
#> # ℹ 990 more rows
I have been thinking more deeply about this type of problems, and have some ideas outlined here Feature: Extracting names of variable input and output · Issue #1137 · tidymodels/recipes · GitHub
I'll watch your development closely 
. I knew the tidymodels team would be on top of this! And, just to give a use case, I'm often asked how the predictor variables differ across certain groups. If I have maybe 500 variables to start but only 300 make it past the step_XXX() phase, it would be nice only to report on the 300, rather than 500.
I appreciate you answering the question so quickly.