tests/testthat/_snaps/dummy.md

In tidymodels/recipes: Preprocessing and Feature Engineering Steps for Modeling

dummy variables errors with character inputs

Code
  prep(dummy, training = sacr, verbose = FALSE)
Condition
  Error in `step_dummy()`:
  Caused by error in `prep()`:
  x All columns selected for the step should be factor or ordered.
  * 2 string variables found: `city` and `zip`

check_type() is used

Code
  recipe(sqft ~ zip + price + city, data = sacr) %>% step_dummy(city, zip, price) %>%
    prep()
Condition
  Error in `step_dummy()`:
  Caused by error in `prep()`:
  x All columns selected for the step should be factor or ordered.
  * 1 integer variable found: `price`

make sure contrasts argument is checked

Code
  recipe(~Species, iris) %>% step_dummy(Species, contrasts = TRUE) %>% prep()
Condition
  Error in `step_dummy()`:
  Caused by error in `prep()`:
  ! The ordered element of `contracts` must be a string, not `TRUE`.

Code
  recipe(~Species, iris) %>% step_dummy(Species, contrasts = list()) %>% prep()
Condition
  Error in `step_dummy()`:
  Caused by error in `prep()`:
  ! The list passed to `contrasts` must have the names "ordered" and "unordered".

Code
  recipe(~Species, iris) %>% step_dummy(Species, contrasts = list(ordered = "contr.treatment")) %>%
    prep()
Condition
  Error in `step_dummy()`:
  Caused by error in `prep()`:
  ! The ordered element of `contracts` must be a string, not a list.

Code
  recipe(~Species, iris) %>% step_dummy(Species, contrasts = list(ordered = 1,
    unordered = "contr.treatment")) %>% prep()
Condition
  Error in `step_dummy()`:
  Caused by error in `prep()`:
  ! The ordered element of `contracts` must be a string, not a number.

Code
  recipe(~Species, iris) %>% step_dummy(Species, contrasts = list(ordered = "contr.treatment",
    unordered = 1)) %>% prep()
Condition
  Error in `step_dummy()`:
  Caused by error in `prep()`:
  ! The unordered element of `contracts` must be a string, not a number.

getOption('contrasts') gives deprecation warning in step_dummy

Code
  tmp <- recipe(~., data = iris) %>% step_dummy(Species) %>% prep()
Condition
  Warning:
  options(contrasts) with step_dummy() was deprecated in recipes 1.3.0.
  i Please use step_dummy(contrasts) instead.

tests for NA values in factor

Code
  factors <- prep(factors, training = sacr_missing)
Condition
  Warning:
  ! There are new levels in `city`: NA.
  i Consider using step_unknown() (`?recipes::step_unknown()`) before `step_dummy()` to handle missing values.

Code
  factors_data_1 <- bake(factors, new_data = sacr_missing)
Condition
  Warning:
  ! There are new levels in `city`: NA.
  i Consider using step_unknown() (`?recipes::step_unknown()`) before `step_dummy()` to handle missing values.

tests for NA values in ordered factor

Code
  factors <- prep(factors, training = sacr_ordered)
Condition
  Warning:
  ! There are new levels in `city`: NA.
  i Consider using step_unknown() (`?recipes::step_unknown()`) before `step_dummy()` to handle missing values.

Code
  factors_data_1 <- bake(factors, new_data = sacr_ordered)
Condition
  Warning:
  ! There are new levels in `city`: NA.
  i Consider using step_unknown() (`?recipes::step_unknown()`) before `step_dummy()` to handle missing values.

new levels

Code
  recipes:::warn_new_levels(testing$x1, levels(training$x1), "column",
  "step_dummy")
Condition
  Warning:
  ! There are new levels in `column`: C.
  i Consider using step_novel() (`?recipes::step_novel()`) before `step_dummy()` to handle unseen values.

Code
  bake(rec, new_data = testing)
Condition
  Warning:
  ! There are new levels in `x1`: C.
  i Consider using step_novel() (`?recipes::step_novel()`) before `step_dummy()` to handle unseen values.
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

warns about NA in column (#450)

Code
  tmp <- recipe(~a, data = data) %>% step_dummy(a) %>% prep()
Condition
  Warning:
  ! There are new levels in `a`: NA.
  i Consider using step_unknown() (`?recipes::step_unknown()`) before `step_dummy()` to handle missing values.

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.

no columns selected

Code
  print(rec)
Message

  -- Recipe ----------------------------------------------------------------------

  -- Inputs 
  Number of variables by role
  outcome:   1
  predictor: 2

  -- Training information 
  Training data contained 3 data points and no incomplete rows.

  -- Operations 
  * Zero variance filter removed: x | Trained
  * Dummy variables from: <none> | Trained

check_name() is used

Code
  prep(rec, training = dat)
Condition
  Error in `step_dummy()`:
  Caused by error in `bake()`:
  ! Name collision occurred. The following variable names already exist:
  * `Species_versicolor`

throws a informative error for too many levels (#828)

Code
  prep(rec)
Condition
  Error in `step_dummy()`:
  Caused by error:
  ! `x` contains too many levels (123456), which would result in a data.frame too large to fit in memory.

throws an informative error for single level

Code
  recipe(~., data = data.frame(x = "only-level")) %>% step_dummy(x) %>% prep()
Condition
  Error in `step_dummy()`:
  Caused by error in `bake()`:
  ! Only one factor level in `x`: "only-level".

bake method errors when needed non-standard role columns are missing

Code
  bake(dummy_trained, new_data = sacr_fac[, 3:4], all_predictors())
Condition
  Error in `step_dummy()`:
  ! The following required columns are missing from `new_data`: city and zip.

empty printing

Code
  rec
Message

  -- Recipe ----------------------------------------------------------------------

  -- Inputs 
  Number of variables by role
  outcome:    1
  predictor: 10

  -- Operations 
  * Dummy variables from: <none>

Code
  rec
Message

  -- Recipe ----------------------------------------------------------------------

  -- Inputs 
  Number of variables by role
  outcome:    1
  predictor: 10

  -- Training information 
  Training data contained 32 data points and no incomplete rows.

  -- Operations 
  * Dummy variables from: <none> | Trained

keep_original_cols - can prep recipes with it missing

Code
  rec <- prep(rec)
Condition
  Warning:
  `keep_original_cols` was added to `step_dummy()` after this recipe was created.
  i Regenerate your recipe to avoid this warning.

printing

Code
  print(rec)
Message

  -- Recipe ----------------------------------------------------------------------

  -- Inputs 
  Number of variables by role
  outcome:   1
  predictor: 7

  -- Operations 
  * Dummy variables from: city zip

Code
  prep(rec)
Message

  -- Recipe ----------------------------------------------------------------------

  -- Inputs 
  Number of variables by role
  outcome:   1
  predictor: 7

  -- Training information 
  Training data contained 932 data points and no incomplete rows.

  -- Operations 
  * Dummy variables from: city zip | Trained

bad args

Code
  recipe(~ city + sqft + price, data = Sacramento) %>% step_dummy(city, one_hot = 2) %>%
    prep()
Condition
  Error in `step_dummy()`:
  Caused by error in `prep()`:
  ! `one_hot` must be `TRUE` or `FALSE`, not the number 2.

Code
  recipe(~ city + sqft + price, data = Sacramento) %>% step_dummy(city, naming = NULL) %>%
    prep()
Condition
  Error in `step_dummy()`:
  Caused by error in `prep()`:
  ! `naming` must be a function, not `NULL`.

tidymodels/recipes documentation built on April 17, 2025, 6:17 p.m.