In recipes: Preprocessing and Feature Engineering Steps for Modeling

Tips for saving recipes and filtering columns

When creating variable selections:

If you are using column filtering steps, such as step_corr(), try to avoid hardcoding specific variable names in downstream steps in case those columns are removed by the filter. Instead, use [dplyr::any_of()] and [dplyr::all_of()].
[dplyr::any_of()] will be tolerant if a column has been removed.
[dplyr::all_of()] will fail unless all of the columns are present in the data.
For both of these functions, if you are going to save the recipe as a binary object to use in another R session, try to avoid referring to a vector in your workspace.
Preferred: any_of(!!var_names)
Avoid: any_of(var_names)

Some examples:

some_vars <- names(mtcars)[4:6]

# No filter steps, OK for not saving the recipe
rec_1 <-
  recipe(mpg ~ ., data = mtcars) %>% 
  step_log(all_of(some_vars)) %>% 
  prep()

# No filter steps, saving the recipe
rec_2 <-
  recipe(mpg ~ ., data = mtcars) %>% 
  step_log(!!!some_vars) %>% 
  prep()

# This fails since `wt` is not in the data
recipe(mpg ~ ., data = mtcars)  %>% 
  step_rm(wt) %>% 
  step_log(!!!some_vars) %>% 
  prep()

# Best for filters (using any_of()) and when
# saving the recipe
rec_4 <- 
  recipe(mpg ~ ., data = mtcars) %>% 
  step_rm(wt) %>% 
  step_log(any_of(!!some_vars)) %>% 
  # equal to step_log(any_of(c("hp", "drat", "wt")))
  prep()