Sort rows using dplyr
step_arrange creates a specification of a recipe step
that will sort rows using dplyr::arrange().
step_arrange(
recipe,
...,
role = NA,
trained = FALSE,
inputs = NULL,
skip = FALSE,
id = rand_id("arrange")
)
## S3 method for class 'step_arrange'
tidy(x, ...)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 |
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 sorting variable(s) or expression(s). The
expressions are text representations and are not parsable.
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.