Package 'extrasteps' reference manual

Title:	More Miscellaneous Steps for the 'recipes' Package
Description:	Contains additional miscellaneous steps for the 'recipes' package. These steps are useful, but doesn't have a good home in other 'recipes' packages or its extensions.
Authors:	Emil Hvitfeldt [aut, cre]
Maintainer:	Emil Hvitfeldt <[email protected]>
License:	MIT + file LICENSE
Version:	0.1.0.9000
Built:	2024-11-03 06:44:34 UTC
Source:	https://github.com/emilhvitfeldt/extrasteps

Time after Recurrent Date Time Event

Description

step_date_after() creates a specification of a recipe step that will create new columns indicating the time after an recurrent event.

Usage

step_date_after(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  rules = list(),
  transform = "identity",
  columns = NULL,
  skip = FALSE,
  id = rand_id("date_after")
)
step_date_after(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  rules = list(),
  transform = "identity",
  columns = NULL,
  skip = FALSE,
  id = rand_id("date_after")
)

Arguments

`recipe`	A recipe object. The step will be added to the sequence of operations for this recipe.
`...`	One or more selector functions to choose variables for this step. See `selections()` 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.
`rules`	Named list of `almanac` rules.
`transform`	A function or character indication a function used oon the resulting variables. See details for allowed names and their functions.
`columns`	A character string of variables that will be used as inputs. This field is a placeholder and will be populated once `recipes::prep.recipe()` is used.
`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

The transform argument can be function that takes a numeric vector and returns a numeric vector of the same length. It can also be a character vector, below is the supported vector names. Some functions come with offset to avoid Inf.

"identity"
function(x) x

"inverse"
function(x) 1 / (x + 0.5)

"exp"
function(x) exp(x)

"log"
function(x) log(x + 0.5)

The effect of transform is illustrated below.

The naming of the resulting variables will be on the form

{variable name}_after_{name of rule}

Value

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

Examples

library(recipes)
library(extrasteps)
library(almanac)
library(modeldata)

data(Chicago)

on_easter <- yearly() %>% recur_on_easter()
on_weekend <- weekly() %>% recur_on_weekends()

rules <- list(easter = on_easter, weekend = on_weekend)

rec_spec <- recipe(ridership ~ date, data = Chicago) %>%
  step_date_after(date, rules = rules)

rec_spec_preped <- prep(rec_spec)

bake(rec_spec_preped, new_data = NULL)
library(recipes)
library(extrasteps)
library(almanac)
library(modeldata)

data(Chicago)

on_easter <- yearly() %>% recur_on_easter()
on_weekend <- weekly() %>% recur_on_weekends()

rules <- list(easter = on_easter, weekend = on_weekend)

rec_spec <- recipe(ridership ~ date, data = Chicago) %>%
  step_date_after(date, rules = rules)

rec_spec_preped <- prep(rec_spec)

bake(rec_spec_preped, new_data = NULL)

Time before Recurrent Date Time Event

Description

step_date_before() creates a specification of a recipe step that will create new columns indicating the time before an recurrent event.

Usage

step_date_before(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  rules = list(),
  transform = "identity",
  columns = NULL,
  skip = FALSE,
  id = rand_id("date_before")
)
step_date_before(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  rules = list(),
  transform = "identity",
  columns = NULL,
  skip = FALSE,
  id = rand_id("date_before")
)

Arguments

`recipe`	A recipe object. The step will be added to the sequence of operations for this recipe.
`...`	One or more selector functions to choose variables for this step. See `selections()` 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.
`rules`	Named list of `almanac` rules.
`transform`	A function or character indication a function used oon the resulting variables. See details for allowed names and their functions.
`columns`	A character string of variables that will be used as inputs. This field is a placeholder and will be populated once `recipes::prep.recipe()` is used.
`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

"identity"
function(x) x

"inverse"
function(x) 1 / (x + 0.5)

"exp"
function(x) exp(x)

"log"
function(x) log(x + 0.5)

The effect of transform is illustrated below.

The naming of the resulting variables will be on the form

{variable name}_before_{name of rule}

Value

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

Examples

library(recipes)
library(extrasteps)
library(almanac)
library(modeldata)

data(Chicago)

on_easter <- yearly() %>% recur_on_easter()
on_weekend <- weekly() %>% recur_on_weekends()

rules <- list(easter = on_easter, weekend = on_weekend)

rec_spec <- recipe(ridership ~ date, data = Chicago) %>%
  step_date_before(date, rules = rules)

rec_spec_preped <- prep(rec_spec)

bake(rec_spec_preped, new_data = NULL)
library(recipes)
library(extrasteps)
library(almanac)
library(modeldata)

data(Chicago)

on_easter <- yearly() %>% recur_on_easter()
on_weekend <- weekly() %>% recur_on_weekends()

rules <- list(easter = on_easter, weekend = on_weekend)

rec_spec <- recipe(ridership ~ date, data = Chicago) %>%
  step_date_before(date, rules = rules)

rec_spec_preped <- prep(rec_spec)

bake(rec_spec_preped, new_data = NULL)

Time to Nearest Recurrent Date Time Event

Description

step_date_nearest() creates a specification of a recipe step that will create new columns indicating the time to nearest recurrent event.

Usage

step_date_nearest(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  rules = list(),
  transform = "identity",
  columns = NULL,
  skip = FALSE,
  id = rand_id("date_nearest")
)
step_date_nearest(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  rules = list(),
  transform = "identity",
  columns = NULL,
  skip = FALSE,
  id = rand_id("date_nearest")
)

Arguments

`recipe`	A recipe object. The step will be added to the sequence of operations for this recipe.
`...`	One or more selector functions to choose variables for this step. See `selections()` 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.
`rules`	Named list of `almanac` rules.
`transform`	A function or character indication a function used oon the resulting variables. See details for allowed names and their functions.
`columns`	A character string of variables that will be used as inputs. This field is a placeholder and will be populated once `recipes::prep.recipe()` is used.
`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

"identity"
function(x) x

"inverse"
function(x) 1 / (x + 0.5)

"exp"
function(x) exp(x)

"log"
function(x) log(x + 0.5)

The effect of transform is illustrated below.

The naming of the resulting variables will be on the form

{variable name}_nearest_{name of rule}

Value

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

Examples

library(recipes)
library(extrasteps)
library(almanac)
library(modeldata)

data(Chicago)

on_easter <- yearly() %>% recur_on_easter()
on_weekend <- weekly() %>% recur_on_weekends()

rules <- list(easter = on_easter, weekend = on_weekend)

rec_spec <- recipe(ridership ~ date, data = Chicago) %>%
  step_date_nearest(date, rules = rules)

rec_spec_preped <- prep(rec_spec)

bake(rec_spec_preped, new_data = NULL)
library(recipes)
library(extrasteps)
library(almanac)
library(modeldata)

data(Chicago)

on_easter <- yearly() %>% recur_on_easter()
on_weekend <- weekly() %>% recur_on_weekends()

rules <- list(easter = on_easter, weekend = on_weekend)

rec_spec <- recipe(ridership ~ date, data = Chicago) %>%
  step_date_nearest(date, rules = rules)

rec_spec_preped <- prep(rec_spec)

bake(rec_spec_preped, new_data = NULL)

difftimearithmic Transformation

Description

step_difftime() creates a specification of a recipe step that will calculate difftimes of the data.

Usage

step_difftime(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  time = NULL,
  tz = NULL,
  unit = "auto",
  columns = NULL,
  skip = FALSE,
  id = rand_id("difftime")
)
step_difftime(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  time = NULL,
  tz = NULL,
  unit = "auto",
  columns = NULL,
  skip = FALSE,
  id = rand_id("difftime")
)

Arguments

`recipe`	A recipe object. The step will be added to the sequence of operations for this recipe.
`...`	One or more selector functions to choose which variables are affected by the step. See `recipes::selections()` for more details. For the `tidy` method, these are not currently used.
`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.
`time`	date-time or date objects. Used for reference. Must match the type of variable.
`tz`	an optional time zone specification to be used for the conversion, mainly for "POSIXlt" objects.
`unit`	character string. Units in which the results are desired. Must be one of "auto", "secs", "mins", "hours","days", and "weeks" Defaults to "auto".
`columns`	A character string of variable names that will be populated (eventually) by the `terms` argument.
`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.

Value

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 (the columns that will be affected) and base.

Examples

library(recipes)
example_date <- data.frame(
  dates = seq(as.Date("2010/1/1"), as.Date("2016/1/1"), by = "quarter")
 )

 example_datetime <- data.frame(
  datetimes = seq(ISOdate(1993,1,1), ISOdate(1993,1,2), by = "hour")
 )

rec <- recipe(~ dates, data = example_date) %>%
  step_difftime(dates, time = as.Date("2010/1/1"))

difftime_obj <- prep(rec)

bake(difftime_obj, new_data = NULL)

recipe(~ dates, data = example_date) %>%
  step_difftime(dates, time = as.Date("2010/1/1"), unit = "weeks") %>%
  prep() %>%
  bake(new_data = NULL)

recipe(~ datetimes, data = example_datetime) %>%
  step_difftime(datetimes, time = ISOdate(1993,1,1), unit = "secs") %>%
  prep() %>%
  bake(new_data = NULL)
library(recipes)
example_date <- data.frame(
  dates = seq(as.Date("2010/1/1"), as.Date("2016/1/1"), by = "quarter")
 )

 example_datetime <- data.frame(
  datetimes = seq(ISOdate(1993,1,1), ISOdate(1993,1,2), by = "hour")
 )

rec <- recipe(~ dates, data = example_date) %>%
  step_difftime(dates, time = as.Date("2010/1/1"))

difftime_obj <- prep(rec)

bake(difftime_obj, new_data = NULL)

recipe(~ dates, data = example_date) %>%
  step_difftime(dates, time = as.Date("2010/1/1"), unit = "weeks") %>%
  prep() %>%
  bake(new_data = NULL)

recipe(~ datetimes, data = example_datetime) %>%
  step_difftime(datetimes, time = ISOdate(1993,1,1), unit = "secs") %>%
  prep() %>%
  bake(new_data = NULL)

Perform binary encoding of factor variables

Description

step_encoding_binary() creates a specification of a recipe step that will perform binary encoding of factor variables.

Usage

step_encoding_binary(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  res = NULL,
  columns = NULL,
  keep_original_cols = FALSE,
  skip = FALSE,
  id = rand_id("encoding_binary")
)
step_encoding_binary(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  res = NULL,
  columns = NULL,
  keep_original_cols = FALSE,
  skip = FALSE,
  id = rand_id("encoding_binary")
)

Arguments

`recipe`	A recipe object. The step will be added to the sequence of operations for this recipe.
`...`	One or more selector functions to choose which variables are affected by the step. See `recipes::selections()` for more details. For the `tidy` method, these are not currently used.
`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.
`res`	A list containing levels of training variables is stored here once this preprocessing step has be trained by `recipes::prep()`.
`columns`	A character string of variable names that will be populated (eventually) by the `terms` argument.
`keep_original_cols`	A logical to keep the original variables in the output. Defaults to `FALSE`.
`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.

Value

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 (the columns that will be affected) and base.

Examples

library(recipes)
library(modeldata)

data(ames)

rec <- recipe(~ Land_Contour + Neighborhood, data = ames) %>%
  step_encoding_binary(all_nominal_predictors()) %>%
  prep()

rec %>%
  bake(new_data = NULL)

tidy(rec, 1)
library(recipes)
library(modeldata)

data(ames)

rec <- recipe(~ Land_Contour + Neighborhood, data = ames) %>%
  step_encoding_binary(all_nominal_predictors()) %>%
  prep()

rec %>%
  bake(new_data = NULL)

tidy(rec, 1)

Perform frequency encoding

Description

step_encoding_frequency() creates a specification of a recipe step that will perform frequency encoding.

Usage

step_encoding_frequency(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  res = NULL,
  columns = NULL,
  skip = FALSE,
  id = rand_id("encoding_frequency")
)
step_encoding_frequency(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  res = NULL,
  columns = NULL,
  skip = FALSE,
  id = rand_id("encoding_frequency")
)

Arguments

`recipe`	A recipe object. The step will be added to the sequence of operations for this recipe.
`...`	One or more selector functions to choose which variables are affected by the step. See `recipes::selections()` for more details. For the `tidy` method, these are not currently used.
`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.
`res`	A list frequencies of the levels of the training variables is stored here once this preprocessing step has be trained by `recipes::prep()`.
`columns`	A character string of variable names that will be populated (eventually) by the `terms` argument.
`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.

Value

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 (the columns that will be affected) and base.

Examples

library(recipes)
library(modeldata)

data(ames)

rec <- recipe(~ Land_Contour + Neighborhood, data = ames) %>%
  step_encoding_frequency(all_nominal_predictors()) %>%
  prep()

rec %>%
  bake(new_data = NULL)

tidy(rec, 1)
library(recipes)
library(modeldata)

data(ames)

rec <- recipe(~ Land_Contour + Neighborhood, data = ames) %>%
  step_encoding_frequency(all_nominal_predictors()) %>%
  prep()

rec %>%
  bake(new_data = NULL)

tidy(rec, 1)

Perform Max Abs Scaling

Description

step_maxabs() creates a specification of a recipe step that will perform Max Abs scaling.

Usage

step_maxabs(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  res = NULL,
  columns = NULL,
  skip = FALSE,
  id = rand_id("maxabs")
)
step_maxabs(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  res = NULL,
  columns = NULL,
  skip = FALSE,
  id = rand_id("maxabs")
)

Arguments

`recipe`	A recipe object. The step will be added to the sequence of operations for this recipe.
`...`	One or more selector functions to choose which variables are affected by the step. See `recipes::selections()` for more details. For the `tidy` method, these are not currently used.
`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.
`res`	A list containing absolute max of training variables is stored here once this preprocessing step has be trained by `recipes::prep()`.
`columns`	A character string of variable names that will be populated (eventually) by the `terms` argument.
`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.

Value

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 (the columns that will be affected) and base.

Examples

library(recipes)

rec <- recipe(~., data = mtcars) %>%
  step_maxabs(all_predictors()) %>%
  prep()

rec %>%
  bake(new_data = NULL)

tidy(rec, 1)
library(recipes)

rec <- recipe(~., data = mtcars) %>%
  step_maxabs(all_predictors()) %>%
  prep()

rec %>%
  bake(new_data = NULL)

tidy(rec, 1)

Perform Min Max Scaling

Description

step_minmax() creates a specification of a recipe step that will perform Min Max scaling.

Usage

step_minmax(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  res = NULL,
  columns = NULL,
  skip = FALSE,
  id = rand_id("minmax")
)
step_minmax(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  res = NULL,
  columns = NULL,
  skip = FALSE,
  id = rand_id("minmax")
)

Arguments

`recipe`	A recipe object. The step will be added to the sequence of operations for this recipe.
`...`	One or more selector functions to choose which variables are affected by the step. See `recipes::selections()` for more details. For the `tidy` method, these are not currently used.
`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.
`res`	A list containing min and max of training variables is stored here once this preprocessing step has be trained by `recipes::prep()`.
`columns`	A character string of variable names that will be populated (eventually) by the `terms` argument.
`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.

Value

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 (the columns that will be affected) and base.

Examples

library(recipes)

rec <- recipe(~., data = mtcars) %>%
  step_minmax(all_predictors()) %>%
  prep()

rec %>%
  bake(new_data = NULL)

tidy(rec, 1)
library(recipes)

rec <- recipe(~., data = mtcars) %>%
  step_minmax(all_predictors()) %>%
  prep()

rec %>%
  bake(new_data = NULL)

tidy(rec, 1)

Perform Robust Scaling

Description

step_robust() creates a specification of a recipe step that will perform Robust scaling.

Usage

step_robust(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  range = c(0.25, 0.75),
  res = NULL,
  columns = NULL,
  skip = FALSE,
  id = rand_id("robust")
)
step_robust(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  range = c(0.25, 0.75),
  res = NULL,
  columns = NULL,
  skip = FALSE,
  id = rand_id("robust")
)

Arguments

`recipe`	A recipe object. The step will be added to the sequence of operations for this recipe.
`...`	One or more selector functions to choose which variables are affected by the step. See `recipes::selections()` for more details. For the `tidy` method, these are not currently used.
`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.
`range`	A numeric vector with 2 values denoting the lower and upper quantile that is used for scaling. Defaults to `c(0.25, 0.75)`.
`res`	A list containing the 3 quantiles of training variables is stored here once this preprocessing step has be trained by `recipes::prep()`.
`columns`	A character string of variable names that will be populated (eventually) by the `terms` argument.
`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

The scaling performed by this step is done using the following transformation

$x_new = (x - Q2(x)) / (Q3(x) - Q1(x))$

where Q2(x) is the median, Q3(x) is the upper quantile (defaults to 0.75) and Q1(x) is the lower quantile (defaults to 0.25). The upper and lower quantiles can be changed with the range argument.

Value

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 (the columns that will be affected) and base.

Examples

library(recipes)

rec <- recipe(~., data = mtcars) %>%
  step_robust(all_predictors()) %>%
  prep()

rec %>%
  bake(new_data = NULL)

tidy(rec, 1)

rec <- recipe(~., data = mtcars) %>%
  step_robust(all_predictors(), range = c(0.1, 0.9)) %>%
  prep()

rec %>%
  bake(new_data = NULL)

tidy(rec, 1)
library(recipes)

rec <- recipe(~., data = mtcars) %>%
  step_robust(all_predictors()) %>%
  prep()

rec %>%
  bake(new_data = NULL)

tidy(rec, 1)

rec <- recipe(~., data = mtcars) %>%
  step_robust(all_predictors(), range = c(0.1, 0.9)) %>%
  prep()

rec %>%
  bake(new_data = NULL)

tidy(rec, 1)

Indicate Recurrent Date Time Event

Description

step_time_event() creates a specification of a recipe step that will create new columns indicating if the date fall on recurrent event.

Usage

step_time_event(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  rules = list(),
  columns = NULL,
  keep_original_cols = FALSE,
  skip = FALSE,
  id = rand_id("time_event")
)
step_time_event(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  rules = list(),
  columns = NULL,
  keep_original_cols = FALSE,
  skip = FALSE,
  id = rand_id("time_event")
)

Arguments

`recipe`	A recipe object. The step will be added to the sequence of operations for this recipe.
`...`	One or more selector functions to choose variables for this step. See `selections()` 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.
`rules`	Named list of `almanac` rules.
`columns`	A character string of variables that will be used as inputs. This field is a placeholder and will be populated once `recipes::prep.recipe()` is used.
`keep_original_cols`	A logical to keep the original variables in the output. Defaults to `TRUE`.
`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

Unlike some other steps step_time_event does not remove the original date variables by default. Set keep_original_cols to FALSE to remove them.

Value

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

Examples

library(recipes)
library(extrasteps)
library(almanac)
library(modeldata)

data(Chicago)

on_easter <- yearly() %>% recur_on_easter()
on_weekend <- weekly() %>% recur_on_weekends()

rules <- list(easter = on_easter, weekend = on_weekend)

rec_spec <- recipe(ridership ~ date, data = Chicago) %>%
  step_time_event(date, rules = rules)

rec_spec_preped <- prep(rec_spec)

bake(rec_spec_preped, new_data = NULL)
library(recipes)
library(extrasteps)
library(almanac)
library(modeldata)

data(Chicago)

on_easter <- yearly() %>% recur_on_easter()
on_weekend <- weekly() %>% recur_on_weekends()

rules <- list(easter = on_easter, weekend = on_weekend)

rec_spec <- recipe(ridership ~ date, data = Chicago) %>%
  step_time_event(date, rules = rules)

rec_spec_preped <- prep(rec_spec)

bake(rec_spec_preped, new_data = NULL)

Perform Unit Normalization

Description

step_unit_normalize() creates a specification of a recipe step that will perform unit normalization by scaling individual samples to have unit norm.

Usage

step_unit_normalize(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  norm = c("l2", "l1", "max"),
  columns = NULL,
  skip = FALSE,
  id = rand_id("unit_normalize")
)
step_unit_normalize(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  norm = c("l2", "l1", "max"),
  columns = NULL,
  skip = FALSE,
  id = rand_id("unit_normalize")
)

Arguments

`recipe`	A recipe object. The step will be added to the sequence of operations for this recipe.
`...`	One or more selector functions to choose which variables are affected by the step. See `recipes::selections()` for more details. For the `tidy` method, these are not currently used.
`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.
`norm`	Character denoting which type of normalization to perform. Must be one of `"l1"`, `"l2"`, or "`"max"`.
`columns`	A character string of variable names that will be populated (eventually) by the `terms` argument.
`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.

Value

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 (the columns that will be affected) and base.

Examples

library(recipes)

rec <- recipe(~., data = mtcars) %>%
  step_unit_normalize(all_predictors()) %>%
  prep()

rec %>%
  bake(new_data = NULL)

tidy(rec, 1)
library(recipes)

rec <- recipe(~., data = mtcars) %>%
  step_unit_normalize(all_predictors()) %>%
  prep()

rec %>%
  bake(new_data = NULL)

tidy(rec, 1)

Package 'extrasteps'

Help Index

Time after Recurrent Date Time Event

Description

Usage

Arguments

Details

Value

Examples

Time before Recurrent Date Time Event

Description

Usage

Arguments

Details

Value

Examples

Time to Nearest Recurrent Date Time Event

Description

Usage

Arguments

Details

Value

Examples

difftimearithmic Transformation

Description

Usage

Arguments

Value

Examples

Perform binary encoding of factor variables

Description

Usage

Arguments

Value

Examples

Perform frequency encoding

Description

Usage

Arguments

Value

Examples

Perform Max Abs Scaling

Description

Usage

Arguments

Value

Examples

Perform Min Max Scaling

Description

Usage

Arguments

Value

Examples

Perform Robust Scaling

Description

Usage

Arguments

Details

Value

Examples

Indicate Recurrent Date Time Event

Description

Usage

Arguments

Details

Value

Examples

Perform Unit Normalization

Description

Usage

Arguments

Value

Examples