step_arrange: Sort rows using dplyr

In recipes: Preprocessing and Feature Engineering Steps for Modeling

Sort rows using dplyr

Description

step_arrange() creates a specification of a recipe step that will sort rows using dplyr::arrange().

Usage

step_arrange(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  inputs = NULL,
  skip = FALSE,
  id = rand_id("arrange")
)

Arguments

recipe	A recipe object. The step will be added to the sequence of operations for this recipe.
...	Comma separated list of unquoted variable names. Use 'desc()“ to sort a variable in descending order. See dplyr::arrange() 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()? 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 = TRUE as it may affect the computations for subsequent operations.
id	A character string that is unique to this step to identify it.

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.

Value

An updated version of recipe with the new step added to the sequence of any existing operations.

Tidying

When you tidy() this step, a tibble is returned with columns terms and id:

terms

character, the selectors or variables selected

id

character, id of this step

Sparse data

This step can be applied to sparse_data such that it is preserved. Nothing needs to be done for this to happen as it is done automatically.

Case weights

The underlying operation does not allow for case weights.

See Also

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

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

Examples

rec <- recipe(~., data = iris) %>%
  step_arrange(desc(Sepal.Length), 1 / Petal.Length)

prepped <- prep(rec, training = iris %>% slice(1:75))
tidy(prepped, number = 1)

library(dplyr)

dplyr_train <-
  iris %>%
  as_tibble() %>%
  slice(1:75) %>%
  dplyr::arrange(desc(Sepal.Length), 1 / Petal.Length)

rec_train <- bake(prepped, new_data = NULL)
all.equal(dplyr_train, rec_train)

dplyr_test <-
  iris %>%
  as_tibble() %>%
  slice(76:150) %>%
  dplyr::arrange(desc(Sepal.Length), 1 / Petal.Length)
rec_test <- bake(prepped, iris %>% slice(76:150))
all.equal(dplyr_test, rec_test)

# When you have variables/expressions, you can create a
# list of symbols with `rlang::syms()`` and splice them in
# the call with `!!!`. See https://tidyeval.tidyverse.org

sort_vars <- c("Sepal.Length", "Petal.Length")

qq_rec <-
  recipe(~., data = iris) %>%
  # Embed the `values` object in the call using !!!
  step_arrange(!!!syms(sort_vars)) %>%
  prep(training = iris)

tidy(qq_rec, number = 1)

recipes documentation built on April 13, 2025, 5:10 p.m.