Filter rows by position using dplyr

step_slice creates a specification of a recipe step that will filter rows using dplyr::slice().

step_slice(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  inputs = NULL,
  skip = TRUE,
  id = rand_id("slice")
)

Arguments

recipe	A recipe object. The step will be added to the sequence of operations for this recipe.
...	Integer row values. See dplyr::slice() 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.
inputs	Quosure of values given by ....
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.
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

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.

When you tidy() this step, a tibble with column terms which contains the filtering indices is returned.

Row Filtering

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().

See also

Other row operation steps: step_arrange(), step_filter(), step_impute_roll(), step_lag(), step_naomit(), step_sample(), step_shuffle()

Other dplyr steps: step_arrange(), step_filter(), step_mutate_at(), step_mutate(), step_rename_at(), step_rename(), step_sample(), step_select()

Examples

rec <- recipe( ~ ., data = iris) %>%
  step_slice(1:3)

prepped <- prep(rec, training = iris %>% slice(1:75))
tidy(prepped, number = 1)
#> # A tibble: 1 × 2
#>   terms id         
#>   <chr> <chr>      
#> 1 1:3   slice_FPmAy

library(dplyr)

dplyr_train <-
  iris %>%
  as_tibble() %>%
  slice(1:75) %>%
  slice(1:3)

rec_train <- bake(prepped, new_data = NULL)
all.equal(dplyr_train, rec_train)
#> [1] TRUE

dplyr_test <-
  iris %>%
  as_tibble() %>%
  slice(76:150) %>%
  slice(1:3)
rec_test <- bake(prepped, iris %>% slice(76:150))
all.equal(dplyr_test, rec_test)
#> [1] "Attributes: < Component “row.names”: Numeric: lengths (3, 75) differ >" 
#> [2] "Component “Sepal.Length”: Numeric: lengths (3, 75) differ"              
#> [3] "Component “Sepal.Width”: Numeric: lengths (3, 75) differ"               
#> [4] "Component “Petal.Length”: Numeric: lengths (3, 75) differ"              
#> [5] "Component “Petal.Width”: Numeric: lengths (3, 75) differ"               
#> [6] "Component “Species”: Lengths: 3, 75"                                    
#> [7] "Component “Species”: Lengths (3, 75) differ (string compare on first 3)"

# Embedding the integer expression (or vector) into the
# recipe:

keep_rows <- 1:6

qq_rec <-
  recipe( ~ ., data = iris) %>%
  # Embed `keep_rows` in the call using !!
  step_slice(!!keep_rows) %>%
  prep(training = iris)

tidy(qq_rec, number = 1)
#> # A tibble: 1 × 2
#>   terms id         
#>   <chr> <chr>      
#> 1 1:6   slice_xhh4Y