Relevel factors to a desired level

step_relevel() creates a specification of a recipe step that will reorder the provided factor columns so that the level specified by ref_level is first. This is useful for contr.treatment() contrasts which take the first level as the reference.

Usage

step_relevel(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  ref_level,
  objects = NULL,
  skip = FALSE,
  id = rand_id("relevel")
)

Arguments

recipe

A recipe object. The step will be added to the sequence of operations for this recipe.

...

One or more selector functions to choose variables for this step. See selections() for more details.

role

Not used by this step since no new variables are created.

trained

A logical to indicate if the quantities for preprocessing have been estimated.

ref_level

A single character value that will be used to relevel the factor column(s) (if the level is present).

objects

A list of objects that contain the information on factor levels that will be determined by prep().

skip

A logical. Should the step be skipped when the recipe is baked by bake()? While all operations are baked when prep() is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using skip = TRUE as it may affect the computations for subsequent operations.

id

A character string that is unique to this step to identify it.

Value

An updated version of recipe with the new step added to the sequence of any existing operations.

Details

The selected variables are releveled to a level (given by ref_level), placing the ref_level in the first position.

Note that if the original columns are character, they will be converted to factors by this step.

Tidying

When you tidy() this step, a tibble is returned with columns terms, value , and id:

terms

character, the selectors or variables selected

value

character, the value of ref_level

id

character, id of this step

Case weights

The underlying operation does not allow for case weights.

See also

Other dummy variable and encoding steps: step_bin2factor(), step_count(), step_date(), step_dummy(), step_dummy_extract(), step_dummy_multi_choice(), step_factor2string(), step_holiday(), step_indicate_na(), step_integer(), step_novel(), step_num2factor(), step_ordinalscore(), step_other(), step_regex(), step_string2factor(), step_time(), step_unknown(), step_unorder()

Examples

data(Sacramento, package = "modeldata")
rec <- recipe(~ city + zip, data = Sacramento) %>%
  step_unknown(city, new_level = "UNKNOWN") %>%
  step_relevel(city, ref_level = "UNKNOWN") %>%
  prep()

data <- bake(rec, Sacramento)
levels(data$city)
#>  [1] "UNKNOWN"         "ANTELOPE"        "AUBURN"         
#>  [4] "CAMERON_PARK"    "CARMICHAEL"      "CITRUS_HEIGHTS" 
#>  [7] "COOL"            "DIAMOND_SPRINGS" "EL_DORADO"      
#> [10] "EL_DORADO_HILLS" "ELK_GROVE"       "ELVERTA"        
#> [13] "FAIR_OAKS"       "FOLSOM"          "FORESTHILL"     
#> [16] "GALT"            "GARDEN_VALLEY"   "GOLD_RIVER"     
#> [19] "GRANITE_BAY"     "GREENWOOD"       "LINCOLN"        
#> [22] "LOOMIS"          "MATHER"          "MEADOW_VISTA"   
#> [25] "NORTH_HIGHLANDS" "ORANGEVALE"      "PENRYN"         
#> [28] "PLACERVILLE"     "POLLOCK_PINES"   "RANCHO_CORDOVA" 
#> [31] "RANCHO_MURIETA"  "RIO_LINDA"       "ROCKLIN"        
#> [34] "ROSEVILLE"       "SACRAMENTO"      "WALNUT_GROVE"   
#> [37] "WEST_SACRAMENTO" "WILTON"