step_filter creates a specification of a recipe step
that will remove rows using
step_filter( recipe, ..., role = NA, trained = FALSE, inputs = NULL, skip = TRUE, id = rand_id("filter") ) # S3 method for step_filter tidy(x, ...)
A recipe object. The step will be added to the sequence of operations for this recipe.
Logical predicates defined in terms of the variables
in the data. Multiple conditions are combined with
Not used by this step since no new variables are created.
A logical to indicate if the quantities for preprocessing have been estimated.
Quosure of values given by
A logical. Should the step be skipped when the
recipe is baked by
A character string that is unique to this step to identify it.
An updated version of
recipe with the new step
added to the sequence of existing steps (if any). For the
tidy method, a tibble with columns
contains the conditional statements. These
expressions are text representations and are not parsable.
When an object in the user's global environment is
referenced in the expression defining the new variable(s),
it is a good idea to use quasiquotation (e.g.
!!) to embed
the value of the object in the expression (to be portable
between sessions). See the examples.
This step can entirely remove observations (rows of data), which can have
unintended and/or problematic consequences when applying the step to new
data later via
bake.recipe(). Consider whether
skip = TRUE or
skip = FALSE is more appropriate in any given use case. In most instances
that affect the rows of the data being predicted, this step probably should
not be applied at all; instead, execute operations like this outside and
before starting a preprocessing
rec <- recipe( ~ ., data = iris) %>% step_filter(Sepal.Length > 4.5, Species == "setosa") prepped <- prep(rec, training = iris %>% slice(1:75)) library(dplyr) dplyr_train <- iris %>% as_tibble() %>% slice(1:75) %>% dplyr::filter(Sepal.Length > 4.5, Species == "setosa") rec_train <- bake(prepped, new_data = NULL) all.equal(dplyr_train, rec_train)#>  TRUEdplyr_test <- iris %>% as_tibble() %>% slice(76:150) %>% dplyr::filter(Sepal.Length > 4.5, Species != "setosa") rec_test <- bake(prepped, iris %>% slice(76:150)) all.equal(dplyr_test, rec_test)#>  TRUEvalues <- c("versicolor", "virginica") qq_rec <- recipe( ~ ., data = iris) %>% # Embed the `values` object in the call using !! step_filter(Sepal.Length > 4.5, Species %in% !!values) tidy(qq_rec, number = 1)#> # A tibble: 2 x 2 #> terms id #> <chr> <chr> #> 1 "Sepal.Length > 4.5" filter_GyP2S #> 2 "Species %in% c(\"versicolor\", \"virginica\")" filter_GyP2S