Filter rows using dplyr
step_filter creates a specification of a recipe step
that will remove rows using dplyr::filter().
step_filter(
recipe,
...,
role = NA,
trained = FALSE,
inputs = NULL,
skip = TRUE,
id = rand_id("filter")
)
## S3 method for class 'step_filter'
tidy(x, ...)recipe |
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 |
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. |
inputs |
Quosure of values given by |
skip |
A logical. Should the step be skipped when the
recipe is baked by |
id |
A character string that is unique to this step to identify it. |
x |
A |
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.
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 terms which
contains the conditional statements. These
expressions are text representations and are not parsable.
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 recipe().
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)
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)
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.