Filter rows using dplyrSource:
step_filter() creates a specification of a recipe step that will remove
step_filter( recipe, ..., role = NA, trained = FALSE, inputs = NULL, skip = TRUE, id = rand_id("filter") )
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
&. Only rows where the condition evaluates to
TRUEare kept. See
dplyr::filter()for more details.
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
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 = FALSE.
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 any existing operations.
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(). 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
tidy() this step, a tibble with column
terms which contains the conditional statements is returned.
These expressions are text representations and are not parsable.
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) #>  TRUE dplyr_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) #>  TRUE values <- 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 × 2 #> terms id #> <chr> <chr> #> 1 "Sepal.Length > 4.5" filter_zsZ4H #> 2 "Species %in% c(\"versicolor\", \"virginica\")" filter_zsZ4H