CODEWITHSUNDEEP
rr-recipestidymodels
Zafar
Zafar

Reputation: 2016

Can't seem to remove variables in recipes

I'm new to recipes and having some issues with the API. Why can't I bake or juice my recipe steps when I've removed certain features that I'm not interested in?

set.seed(999)
train_test_split <- initial_split(mtcars)

mtcars_train <- training(train_test_split)
mtcars_test <- testing(train_test_split)

mtcars_train %>%
    recipe(mpg ~ cyl + disp + hp + gear) %>% 
    step_rm(qsec, vs, carb) %>% 
    step_center(all_numeric())  %>%
    step_scale(all_numeric()) %>%
    prep(training = mtcars_train)

results in:

Error in .f(.x[[i]], ...) : object 'qsec' not found

Which is pretty annoying because that means that I'll need to remove rows manually on both the test and train sets after steps have been applied:

rec_scale <- mtcars %>%
    recipe(mpg ~ cyl + disp + hp + gear) %>% 
    step_center(all_numeric())  %>%
    step_scale(all_numeric()) %>%
    prep(training = mtcars_train)
train <- juice(rec_scale) %>%
  select(-qsec, -vs, -carb)
test <- bake(rec_scale, mtcars_test) %>%
  select(-qsec, -vs, -carb)

Am I thinking about this wrong? I could alternatively filter beforehand, but I would think that my recipe should include things like that.

Upvotes: 3

Views: 2158

Answers (1)

topepo
topepo

Reputation: 14316

You should include all columns used in a recipe steps inside the recipe() call. They can't be removed if they are not in the recipe.

library(tidymodels)
#> ── Attaching packages ────────────────────────────── tidymodels 0.0.2 ──
#> ✔ broom     0.5.2       ✔ purrr     0.3.2  
#> ✔ dials     0.0.2       ✔ recipes   0.1.6  
#> ✔ dplyr     0.8.3       ✔ rsample   0.0.5  
#> ✔ ggplot2   3.2.0       ✔ tibble    2.1.3  
#> ✔ infer     0.4.0.1     ✔ yardstick 0.0.3  
#> ✔ parsnip   0.0.3
#> ── Conflicts ───────────────────────────────── tidymodels_conflicts() ──
#> ✖ purrr::discard() masks scales::discard()
#> ✖ dplyr::filter()  masks stats::filter()
#> ✖ dplyr::lag()     masks stats::lag()
#> ✖ recipes::step()  masks stats::step()

set.seed(999)
train_test_split <- initial_split(mtcars)

mtcars_train <- training(train_test_split)
mtcars_test <- testing(train_test_split)

rec <- 
  mtcars_train %>%
  recipe(mpg ~ cyl + disp + hp + gear) %>% 
  step_center(all_numeric())  %>%
  step_scale(all_numeric()) %>%
  prep(training = mtcars_train)

summary(rec)
#> # A tibble: 5 x 4
#>   variable type    role      source  
#>   <chr>    <chr>   <chr>     <chr>   
#> 1 cyl      numeric predictor original
#> 2 disp     numeric predictor original
#> 3 hp       numeric predictor original
#> 4 gear     numeric predictor original
#> 5 mpg      numeric outcome   original

Created on 2019-08-04 by the reprex package (v0.2.1)

Upvotes: 3

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