# dummy variables with non-factor inputs
Code
prep(dummy, training = sacr, verbose = FALSE, strings_as_factors = FALSE)
Condition
Warning:
The following variables are not factor vectors and will be ignored: `city`, `zip`
Error in `step_dummy()`:
Caused by error in `prep()`:
! The `terms` argument in `step_dummy` did not select any factor columns.
---
Code
recipe(sqft ~ zip + price + city, data = sacr_fac_ish) %>% step_dummy(city, zip,
price) %>% prep(training = sacr_fac_ish, verbose = FALSE, strings_as_factors = FALSE)
Condition
Error in `step_dummy()`:
Caused by error in `prep()`:
! All columns selected for the step should be string, factor, or ordered.
# tests for NA values in factor
Code
factors <- prep(factors, training = sacr_missing)
Condition
Warning:
There are new levels in a factor: NA
---
Code
factors_data_1 <- bake(factors, new_data = sacr_missing)
Condition
Warning:
There are new levels in a factor: NA
# tests for NA values in ordered factor
Code
factors <- prep(factors, training = sacr_ordered)
Condition
Warning:
There are new levels in a factor: NA
---
Code
factors_data_1 <- bake(factors, new_data = sacr_ordered)
Condition
Warning:
There are new levels in a factor: NA
# new levels
Code
recipes:::warn_new_levels(testing$x1, levels(training$x1))
Condition
Warning:
There are new levels in a factor: C
---
Code
bake(rec, new_data = testing)
Condition
Warning:
There are new levels in a factor: C
Output
# A tibble: 10 x 2
y x1_B
<fct> <dbl>
1 0 0
2 0 1
3 1 0
4 0 NA
5 1 NA
6 0 1
7 0 0
8 0 1
9 1 NA
10 0 0
# Deprecation warning
Code
recipe(~., data = mtcars) %>% step_dummy(preserve = TRUE)
Condition
Error:
! The `preserve` argument of `step_dummy()` was deprecated in recipes 0.1.16 and is now defunct.
i Please use the `keep_original_cols` argument instead.
# printing
Code
print(dummy)
Output
Recipe
Inputs:
role #variables
outcome 1
predictor 7
Operations:
Dummy variables from city, zip
---
Code
prep(dummy)
Output
Recipe
Inputs:
role #variables
outcome 1
predictor 7
Training data contained 932 data points and no missing data.
Operations:
Dummy variables from city, zip [trained]
# no columns selected
Code
print(rec)
Output
Recipe
Inputs:
role #variables
outcome 1
predictor 2
Training data contained 3 data points and no missing data.
Operations:
Zero variance filter removed x [trained]
Dummy variables from <none> [trained]
# can prep recipes with no keep_original_cols
Code
dummy_trained <- prep(dummy, training = sacr_fac, verbose = FALSE)
Condition
Warning:
'keep_original_cols' was added to `step_dummy()` after this recipe was created.
Regenerate your recipe to avoid this warning.
# empty printing
Code
rec
Output
Recipe
Inputs:
role #variables
outcome 1
predictor 10
Operations:
Dummy variables from <none>
---
Code
rec
Output
Recipe
Inputs:
role #variables
outcome 1
predictor 10
Training data contained 32 data points and no missing data.
Operations:
Dummy variables from <none> [trained]
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