#' Recipes Time Series Acceleration Generator
#'
#' @family Recipes
#'
#' @description
#' `step_ts_acceleration` creates a a *specification* of a recipe
#' step that will convert numeric data into from a time series into its
#' acceleration.
#'
#' @param recipe A recipe object. The step will be added to the
#' sequence of operations for this recipe.
#' @param ... One or more selector functions to choose which
#' variables that will be used to create the new variables. The
#' selected variables should have class `numeric`
#' @param trained A logical to indicate if the quantities for
#' preprocessing have been estimated.
#' @param role For model terms created by this step, what analysis
#' role should they be assigned?. By default, the function assumes
#' that the new variable columns created by the original variables
#' will be used as predictors in a model.
#' @param columns A character string of variables that will be
#' used as inputs. This field is a placeholder and will be
#' populated once `recipes::prep()` is used.
#' @param 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 = TRUE as it may affect the computations for subsequent operations.
#' @param id A character string that is unique to this step to identify it.
#'
#' @return For `step_ts_acceleration`, an updated version of recipe with
#' the new step added to the sequence of existing steps (if any).
#'
#' Main Recipe Functions:
#' - `recipes::recipe()`
#' - `recipes::prep()`
#' - `recipes::bake()`
#'
#'
#' @details
#'
#' __Numeric Variables__
#' Unlike other steps, `step_ts_acceleration` does *not*
#' remove the original numeric variables. [recipes::step_rm()] can be
#' used for this purpose.
#'
#' @examples
#' suppressPackageStartupMessages(library(dplyr))
#' suppressPackageStartupMessages(library(recipes))
#'
#' len_out = 10
#' by_unit = "month"
#' start_date = as.Date("2021-01-01")
#'
#' data_tbl <- tibble(
#' date_col = seq.Date(from = start_date, length.out = len_out, by = by_unit),
#' a = rnorm(len_out),
#' b = runif(len_out)
#' )
#'
#' # Create a recipe object
#' rec_obj <- recipe(a ~ ., data = data_tbl) %>%
#' step_ts_acceleration(b)
#'
#' # View the recipe object
#' rec_obj
#'
#' # Prepare the recipe object
#' prep(rec_obj)
#'
#' # Bake the recipe object - Adds the Time Series Signature
#' bake(prep(rec_obj), data_tbl)
#'
#' rec_obj %>% prep() %>% juice()
#'
#' @name step_ts_acceleration
NULL
#' @export
#' @rdname step_ts_acceleration
#' @importFrom recipes prep bake rand_id
step_ts_acceleration <- function(recipe,
...,
role = "predictor",
trained = FALSE,
columns = NULL,
skip = FALSE,
id = rand_id("ts_acceleration")
){
terms <- recipes::ellipse_check(...)
recipes::add_step(
recipe,
step_ts_acceleration_new(
terms = terms,
role = role,
trained = trained,
columns = columns,
skip = skip,
id = id
)
)
}
step_ts_acceleration_new <-
function(terms, role, trained, columns, skip, id){
recipes::step(
subclass = "ts_acceleration",
terms = terms,
role = role,
trained = trained,
columns = columns,
skip = skip,
id = id
)
}
#' @export
prep.step_ts_acceleration <- function(x, training, info = NULL, ...) {
col_names <- recipes::recipes_eval_select(x$terms, training, info)
recipes::check_type(training[, col_names])
step_ts_acceleration_new(
terms = x$terms,
role = x$role,
trained = TRUE,
columns = col_names,
skip = x$skip,
id = x$id
)
}
#' @export
bake.step_ts_acceleration <- function(object, new_data, ...){
make_call <- function(col, scale_type){
rlang::call2(
"ts_acceleration_vec",
.x = rlang::sym(col)
, .ns = "healthyR.ts"
)
}
grid <- expand.grid(
col = object$columns
, stringsAsFactors = FALSE
)
calls <- purrr::pmap(.l = list(grid$col), make_call)
# Column Names
newname <- paste0("acceleration_", grid$col)
calls <- recipes::check_name(calls, new_data, object, newname, TRUE)
tibble::as_tibble(dplyr::mutate(new_data, !!!calls))
}
#' @export
print.step_ts_acceleration <-
function(x, width = max(20, options()$width - 35), ...) {
title <- "Time Series Acceleration transformation on "
recipes::print_step(x$terms, x$columns, x$trained, width = width,
title = title)
invisible(x)
}
#' Requited Packages
#' @rdname required_pkgs.healthyR.ts
#' @keywords internal
#' @return A character vector
#' @param x A recipe step
# @noRd
#' @export
required_pkgs.step_ts_acceleration <- function(x, ...) {
c("healthyR.ts")
}
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.