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#' Calculate features of a `tsibble` object in conjunction with [features()]
#'
#' You can calculate a series of summary statistics (features) of a given
#' variable for a dataset. For example, a three number summary, the minimum,
#' median, and maximum, can be calculated for a given variable. This is
#' designed to work with the [features()] function shown in the examples.
#' Other available features in `loadflux` include:
#'
#'
#' @param x A vector to extract features from.
#' @name loadflux-features
#' @examples
#'
#' # You can use any of the features `feat_*` in conjunction with `features`
#' # like so:
#' library(dplyr)
#' library(fabletools)
#' library(tsibble)
#'
#' djants <- djan %>%
#' hydro_events(
#' q = discharge,
#' datetime = time,
#' window = 21
#' ) %>%
#' as_tsibble(
#' key = he,
#' index = time
#' )
#'
#' djants %>%
#' features(
#' time, # variable you want to explore
#' feat_event
#' ) # the feature summarisation you want to perform
#' @rdname loadflux-features
#' @export
#'
#' @importFrom dplyr "%>%" first last tibble
#' @importFrom rlang abort
#' @importFrom lubridate is.POSIXct
#' @importFrom tsibble tsibble as_tsibble
feat_event <- function(x) {
if (!lubridate::is.POSIXct(x)) {
rlang::abort(paste0(
"`datetime` must be a vector of date-time objects; not ",
typeof(x), "."
))
}
start <- dplyr::first(x)
end <- dplyr::last(x)
length <- difftime(end, start, units = "h")
output <- tibble(
start = unname(start),
end = unname(end),
length = unname(length)
)
output
}
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