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#' @export
#' @importFrom rlang .data
#'
#' @title Order \emph{mts} time series by metadata values
#'
#' @param mts \emph{mts} object.
#' @param ... variables in \code{mts$meta}.
#'
#' @description The variable(s) in \code{...} are used to specify columns of
#' \code{mts$meta} to use for ordering. Under the hood, this
#' function uses \code{\link[dplyr]{arrange}} on \code{mts$meta} and then
#' reorders \code{mts$data} to match.
#'
#' @return A reorderd version of the incoming \emph{mts} time series object.
#' (A list with \code{meta} and \code{data} dataframes.)
#'
#' @examples
#' library(MazamaTimeSeries)
#'
#' example_mts$meta$latitude[1:10]
#'
#' # Filter for all labels with "SCSH"
#' byElevation <-
#' example_mts %>%
#' mts_arrange(latitude)
#'
#' byElevation$meta$latitude[1:10]
#'
mts_arrange <- function(
mts,
...
) {
# ----- Validate parameters --------------------------------------------------
# A little involved to catch the case where the user forgets to pass in 'mts'
result <- try({
if ( !mts_isValid(mts) )
stop("First argument is not a valid 'mts' object.")
}, silent = TRUE)
if ( class(result) %in% "try-error" ) {
err_msg <- geterrmessage()
if ( stringr::str_detect(err_msg, "object .* not found") ) {
stop(paste0(err_msg, "\n(Did you forget to pass in the 'mts' object?)"))
}
}
# Return the mts if it is empty so pipelines don't break
if ( mts_isEmpty(mts) )
return(mts)
# Remove any duplicate data records
mts <- mts_distinct(mts)
# ----- Order meta -----------------------------------------------------------
mts$meta <-
dplyr::arrange(mts$meta, ...)
# ----- Order data -----------------------------------------------------------
# NOTE: The columns in 'data' must always match the rows in 'meta'
colNames <- c('datetime', mts$meta$deviceDeploymentID)
mts$data <-
dplyr::select(mts$data, dplyr::all_of(colNames))
# ----- Return ---------------------------------------------------------------
return(mts)
}
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