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#' Create a differenced predictor
#'
#' `step_diff` creates a *specification* of a recipe step that
#' will add new columns of differenced data. Differenced data will
#' include NA values where a difference was induced.
#' These can be removed with [step_naomit()].
#'
#' @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 are
#' affected by the step. See [selections()] for more details.
#' @param role Defaults to "predictor"
#' @param trained A logical to indicate if the quantities for preprocessing
#' have been estimated.
#' @param lag A vector of positive integers identifying which lags (how far back)
#' to be included in the differencing calculation.
#' @param difference The number of differences to perform.
#' @param log Calculates log differences instead of differences.
#' @param prefix A prefix for generated column names, default to "diff_".
#' @param columns A character string of variable names that will
#' be populated (eventually) by the `terms` argument.
#' @param id A character string that is unique to this step to identify it.
#' @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
#'
#' @return An updated version of `recipe` with the
#' new step added to the sequence of existing steps (if any).
#'
#' @details The step assumes that the data are already _in the proper sequential
#' order_ for lagging.
#'
#' @seealso
#' Time Series Analysis:
#' - Engineered Features: [step_timeseries_signature()], [step_holiday_signature()], [step_fourier()]
#' - Diffs & Lags [step_diff()], [recipes::step_lag()]
#' - Smoothing: [step_slidify()], [step_smooth()]
#' - Variance Reduction: [step_box_cox()]
#' - Imputation: [step_ts_impute()], [step_ts_clean()]
#' - Padding: [step_ts_pad()]
#'
#' Remove NA Values:
#' - [recipes::step_naomit()]
#'
#' Main Recipe Functions:
#' - `recipes::recipe()`
#' - `recipes::prep()`
#' - `recipes::bake()`
#'
#' @export
#' @rdname step_diff
#'
#' @examples
#' library(recipes)
#'
#'
#' FANG_wide <- FANG %>%
#' dplyr::select(symbol, date, adjusted) %>%
#' tidyr::pivot_wider(names_from = symbol, values_from = adjusted)
#'
#'
#' # Make and apply recipe ----
#'
#' recipe_diff <- recipe(~ ., data = FANG_wide) %>%
#' step_diff(FB, AMZN, NFLX, GOOG, lag = 1:3, difference = 1) %>%
#' prep()
#'
#' recipe_diff %>% bake(FANG_wide)
#'
#'
#' # Get information with tidy ----
#'
#' recipe_diff %>% tidy()
#'
#' recipe_diff %>% tidy(1)
#'
step_diff <-
function(recipe,
...,
role = "predictor",
trained = FALSE,
lag = 1,
difference = 1,
log = FALSE,
prefix = "diff_",
columns = NULL,
skip = FALSE,
id = rand_id("diff")) {
recipes::add_step(
recipe,
step_diff_new(
terms = recipes::ellipse_check(...),
role = role,
trained = trained,
lag = lag,
difference = difference,
log = log,
prefix = prefix,
columns = columns,
skip = skip,
id = id
)
)
}
step_diff_new <-
function(terms, role, trained, lag, difference, log, prefix, columns, skip, id) {
step(
subclass = "diff",
terms = terms,
role = role,
trained = trained,
lag = lag,
difference = difference,
log = log,
prefix = prefix,
columns = columns,
skip = skip,
id = id
)
}
#' @export
prep.step_diff <- function(x, training, info = NULL, ...) {
# TODO - PRESERVE INITIAL VALUES x[1:(lag * difference)]
step_diff_new(
terms = x$terms,
role = x$role,
trained = TRUE,
lag = x$lag,
difference = x$difference,
log = x$log,
prefix = x$prefix,
columns = recipes_eval_select(x$terms, data = training, info = info),
skip = x$skip,
id = x$id
)
}
#' @export
bake.step_diff <- function(object, new_data, ...) {
if (!all(object$lag == as.integer(object$lag)))
rlang::abort("step_diff() requires 'lag' argument to be integer valued.")
if (!all(object$difference == as.integer(object$difference)))
rlang::abort("step_diff() requires 'difference' argument to be integer valued.")
make_call <- function(col, lag_val, diff_val) {
rlang::call2(
"diff_vec",
x = rlang::sym(col),
lag = lag_val,
difference = diff_val,
log = object$log,
silent = TRUE,
.ns = "timetk"
)
}
grid <- expand.grid(
col = object$columns,
lag_val = object$lag,
diff_val = object$difference,
stringsAsFactors = FALSE)
calls <- purrr::pmap(.l = list(grid$col, grid$lag_val, grid$diff_val), make_call)
newname <- paste0(object$prefix, grid$lag_val, "_", grid$diff_val, "_", grid$col)
calls <- recipes::check_name(calls, new_data, object, newname, TRUE)
tibble::as_tibble(dplyr::mutate(new_data, !!!calls))
}
#' @export
print.step_diff <-
function(x, width = max(20, options()$width - 30), ...) {
title <- "Differencing "
recipes::print_step(x$columns, x$terms, x$trained, width = width, title = title)
invisible(x)
}
#' @rdname step_diff
#' @param x A `step_diff` object.
#' @export
tidy.step_diff <- function(x, ...) {
res <- expand.grid(
terms = x$columns,
lag = x$lag,
diff = x$difference,
log = x$log,
stringsAsFactors = FALSE)
res$id <- x$id
res$terms <- paste0(x$prefix, res$terms, "_", res$lag, "_", res$diff)
tibble::as_tibble(res)
}
#' @rdname required_pkgs.timetk
#' @export
required_pkgs.step_diff <- function(x, ...) {
c("timetk")
}
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