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#' Add many rolling window calculations to the data
#'
#' Quickly use any function as a rolling function and apply to multiple `.periods`.
#' Works with `dplyr` groups too.
#'
#' @param .data A tibble.
#' @param .value One or more column(s) to have a transformation applied. Usage
#' of `tidyselect` functions (e.g. `contains()`) can be used to select multiple columns.
#' @param .period One or more periods for the rolling window(s)
#' @param .f A summary `[function / formula]`,
#' @param ... Optional arguments for the summary function
#' @param .align Rolling functions generate `.period - 1` fewer values than the incoming vector.
#' Thus, the vector needs to be aligned. Select one of "center", "left", or "right".
#' @param .partial .partial Should the moving window be allowed to return partial (incomplete) windows instead of `NA` values.
#' Set to FALSE by default, but can be switched to TRUE to remove `NA`'s.
#' @param .names A vector of names for the new columns. Must be of same length as `.period`. Default is "auto".
#'
#'
#' @return Returns a `tibble` object describing the timeseries.
#'
#' @details
#' `tk_augment_slidify()` scales the [`slidify_vec()`] function to multiple
#' time series `.periods`. See [`slidify_vec()`] for examples and usage of the core function
#' arguments.
#'
#'
#'
#' @seealso
#'
#' Augment Operations:
#'
#' - [tk_augment_timeseries_signature()] - Group-wise augmentation of timestamp features
#' - [tk_augment_holiday_signature()] - Group-wise augmentation of holiday features
#' - [tk_augment_slidify()] - Group-wise augmentation of rolling functions
#' - [tk_augment_lags()] - Group-wise augmentation of lagged data
#' - [tk_augment_differences()] - Group-wise augmentation of differenced data
#' - [tk_augment_fourier()] - Group-wise augmentation of fourier series
#'
#' Underlying Function:
#'
#' - [`slidify_vec()`] - The underlying function that powers `tk_augment_slidify()`
#'
#' @examples
#' library(dplyr)
#'
#' # Single Column | Multiple Rolling Windows
#' FANG %>%
#' select(symbol, date, adjusted) %>%
#' group_by(symbol) %>%
#' tk_augment_slidify(
#' .value = contains("adjust"),
#' # Multiple rolling windows
#' .period = c(10, 30, 60, 90),
#' .f = mean,
#' .partial = TRUE,
#' .names = stringr::str_c("MA_", c(10, 30, 60, 90))
#' ) %>%
#' ungroup()
#'
#' # Multiple Columns | Multiple Rolling Windows
#' FANG %>%
#' select(symbol, date, adjusted, volume) %>%
#' group_by(symbol) %>%
#' tk_augment_slidify(
#' .value = c(adjusted, volume),
#' .period = c(10, 30, 60, 90),
#' .f = mean,
#' .partial = TRUE
#' ) %>%
#' ungroup()
#'
#' @name tk_augment_slidify
NULL
#' @export
#' @rdname tk_augment_slidify
tk_augment_slidify <- function(.data,
.value,
.period,
.f,
...,
.align = c("center", "left", "right"),
.partial = FALSE,
.names = "auto") {
# Tidy Eval Setup
column_expr <- enquo(.value)
# Checks
if (rlang::quo_is_missing(column_expr)) stop(call. = FALSE, "tk_augment_slidify(.value) is missing.")
if (rlang::is_missing(.period)) stop(call. = FALSE, "tk_augment_slidify(.period) is missing.")
if (rlang::is_missing(.f)) stop(call. = FALSE, "tk_augment_slidify(.f) is missing.")
UseMethod("tk_augment_slidify", .data)
}
#' @export
tk_augment_slidify.data.frame <- function(.data,
.value,
.period,
.f,
...,
.align = c("center", "left", "right"),
.partial = FALSE,
.names = "auto") {
# column_expr <- enquo(.value)
col_nms <- names(tidyselect::eval_select(rlang::enquo(.value), .data))
col_exprs <- rlang::syms(col_nms)
# print(col_nms)
# print(.period)
grid <- purrr::cross_df(
.l = list(
col = col_nms,
per = .period
)
)
# print(grid)
ret_1 <- .data
ret_2 <- purrr::map2(.x = grid$col, .y = grid$per, .f = function(col, per) {
.data %>%
dplyr::pull(!! rlang::sym(col)) %>%
slidify_vec(
.period = per,
.f = .f,
...,
.align = .align[1],
.partial = .partial[1]
)
})
# Adjust Names
if (any(.names == "auto")) {
newname <- paste0(grid$col, "_roll_", grid$per)
} else {
newname <- .names
}
ret_2 <- ret_2 %>%
purrr::set_names(newname) %>%
dplyr::bind_cols()
# print(ret_2)
# Perform Overwrite
ret <- bind_cols_overwrite(ret_1, ret_2)
return(ret)
}
#' @export
tk_augment_slidify.grouped_df <- function(.data,
.value,
.period,
.f,
...,
.align = c("center", "left", "right"),
.partial = FALSE,
.names = "auto") {
# col_nms <- names(tidyselect::eval_select(rlang::enquo(.value), .data))
# col_exprs <- rlang::syms(col_nms)
group_names <- dplyr::group_vars(.data)
.data %>%
tidyr::nest() %>%
dplyr::mutate(nested.col = purrr::map(
.x = data,
.f = function(df) tk_augment_slidify(
.data = df,
.value = !! rlang::enquo(.value),
.period = .period,
.f = .f,
...,
.align = .align[1],
.partial = .partial[1],
.names = .names
)
)) %>%
dplyr::select(-"data") %>%
tidyr::unnest(cols = nested.col) %>%
dplyr::group_by_at(.vars = group_names)
}
#' @export
tk_augment_slidify.default <- function(.data,
.value,
.period,
.f,
...,
.align = c("center", "left", "right"),
.partial = FALSE,
.names = "auto") {
stop(paste0("`tk_augment_slidify` has no method for class ", class(data)[[1]]))
}
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