R/choppers.R

In waspasR: Tool Kit to Implement a W.A.S.P.A.S. Based Multi-Criteria Decision Analysis Solution

#' Data about helicopters
#'
#' A small database with real helicopter names and criteria that make sense.
#' The values and weights are generated at random.
#' The only requirement for the methods is that their sum be 1.
#' The set of Cost - Benefit flags make no sense in terms of the criteria they
#' refer to and are just a set of strings starting with the letter "C" (cost) or
#' "B" (benefit). That is, a criterion that must be monotonic of benefit
#' want to maximize it) can have a "c" flag, while a criterion that the (we user
#' would rightly judge to be cost (we want to minimize it) can have a "b" flag.
#'
#' @docType data
#'
#' @usage data(choppers)
#'
#' @format An object of class \code{"data.frame"}
#' \describe{
#'  \item{alternatives}{A set of helicopters names that can be bought in the
#'  marketplace.These data are in the first column, from row 4 to the last.}
#'  \item{criteria}{set of criteria that a helicopter buyer may deem relevant
#'  for making apurchase decision. These data are in the 3rd row, from col 2
#'  to the last.}
#'  \item{weights}{Arbitrated by the decision maker, they attribute different
#'     relative importance to the values of the criteria in percentage terms,
#'     thus making a weighting of these criteria.}
#'  \item{flags}{They determine whether the specific criterion is cost, that is,
#'  the smaller, the better, or benefit, the greater the better.
#'  These data are in the 1st row, from col 2 to the last.}
#'  \item{values}{Randomly generated value, within a range that makes sense
#'  These data are in the fourth row, from col 2 to the last.}
#'  }
#' @references This data set was created with the help of Gustavo, Marcos &
#' Marcio, in the work cited below:
#' Soares de Assis, Gustavo & Santos, Marcos & Basilio, Marcio. (2023). Use of
#' the WASPAS Method to Select Suitable Helicopters for Aerial Activity Carried
#' Out by the Military Police of the State of Rio de Janeiro.
#' Axioms. 12. 77. 10.3390.
#'
#' @keywords datasets
#'
#' @examples
#' data(choppers)
#' head(choppers)
#'
"choppers"