step_naomit creates a specification of a recipe step that will remove observations (rows of data) if they contain NA or NaN values.

step_naomit(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  columns = NULL,
  skip = FALSE,
  id = rand_id("naomit")
)

# S3 method for step_naomit
tidy(x, ...)

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 which variables will be used to remove observations containing NA or NaN values. See selections() for more details.

role

Unused, include for consistency with other steps.

trained

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

columns

A character string of variable names that will be populated (eventually) by the terms argument.

skip

A logical. Should the step be skipped when the recipe is baked by bake.recipe()? While all operations are baked when prep.recipe() 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 = FALSE; in most instances that affect the rows of the data being predicted, this step probably should not be applied.

id

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

x

A step_naomit object.

Value

An updated version of recipe with the new step added to the sequence of existing steps (if any).

See also

Examples

recipe(Ozone ~ ., data = airquality) %>% step_naomit(Solar.R) %>% prep(airquality, verbose = FALSE) %>% bake(new_data = NULL)
#> # A tibble: 146 x 6 #> Solar.R Wind Temp Month Day Ozone #> <int> <dbl> <int> <int> <int> <int> #> 1 190 7.4 67 5 1 41 #> 2 118 8 72 5 2 36 #> 3 149 12.6 74 5 3 12 #> 4 313 11.5 62 5 4 18 #> 5 299 8.6 65 5 7 23 #> 6 99 13.8 59 5 8 19 #> 7 19 20.1 61 5 9 8 #> 8 194 8.6 69 5 10 NA #> 9 256 9.7 69 5 12 16 #> 10 290 9.2 66 5 13 11 #> # … with 136 more rows