library(tidymodels)
car_rec <- recipe(~ ., data = mtcars) %>%
step_normalize(disp, qsec)
car_prep <- prep(car_rec)
juice(car_prep)
#> # A tibble: 32 x 11
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 21 6 -0.571 110 3.9 2.62 -0.777 0 1 4 4
#> 2 21 6 -0.571 110 3.9 2.88 -0.464 0 1 4 4
#> 3 22.8 4 -0.990 93 3.85 2.32 0.426 1 1 4 1
#> 4 21.4 6 0.220 110 3.08 3.22 0.890 1 0 3 1
#> 5 18.7 8 1.04 175 3.15 3.44 -0.464 0 0 3 2
#> 6 18.1 6 -0.0462 105 2.76 3.46 1.33 1 0 3 1
#> 7 14.3 8 1.04 245 3.21 3.57 -1.12 0 0 3 4
#> 8 24.4 4 -0.678 62 3.69 3.19 1.20 1 0 4 2
#> 9 22.8 4 -0.726 95 3.92 3.15 2.83 1 0 4 2
#> 10 19.2 6 -0.509 123 3.92 3.44 0.253 1 0 4 4
#> # … with 22 more rows
car_folds <- vfold_cv(mtcars)
car_folds %>%
mutate(juiced = map(splits, ~ bake(car_prep, new_data = analysis(.))))
#> # 10-fold cross-validation
#> # A tibble: 10 x 3
#> splits id juiced
#> <list> <chr> <list>
#> 1 <split [28/4]> Fold01 <tibble [28 × 11]>
#> 2 <split [28/4]> Fold02 <tibble [28 × 11]>
#> 3 <split [29/3]> Fold03 <tibble [29 × 11]>
#> 4 <split [29/3]> Fold04 <tibble [29 × 11]>
#> 5 <split [29/3]> Fold05 <tibble [29 × 11]>
#> 6 <split [29/3]> Fold06 <tibble [29 × 11]>
#> 7 <split [29/3]> Fold07 <tibble [29 × 11]>
#> 8 <split [29/3]> Fold08 <tibble [29 × 11]>
#> 9 <split [29/3]> Fold09 <tibble [29 × 11]>
#> 10 <split [29/3]> Fold10 <tibble [29 × 11]>Created on 2020-08-17 by the reprex package (v0.3.0.9001)