R/isKey.R

In dataReporter: Reproducible Data Screening Checks and Report of Possible Errors

#' @title Check if a variable qualifies as a key
#' 
#' @description A \code{\link{checkFunction}} that checks if \code{v} 
#' is a key, that is, if every observation has a unique value in \code{v} and 
#' \code{v} is not a numeric/integer nor a Date variable. This
#' function is intended for use as a precheck in \code{\link{makeDataReport}}.
#' 
#' @param v A variable (vector) to check. All variable types are allowed.
#' 
#' @return A \code{\link{checkResult}} with three entires: 
#' \code{$problem} (a logical indicating whether \code{v} is a key),
#' \code{$message} (if a problem was found, the following message: 
#' "The variable is a key (distinct values for each observation).", 
#' otherwise "") and \code{$problemValues} (always \code{NULL}).  
#'
#' @details Note that numeric or integer variables are not considered candidates 
#' for keys, as truly continuous measurements will most likely result in unique 
#' values for each observation.
#'  
#' @examples 
#' keyVar <- c("a", "b", "c", "d", "e", "f")
#' notKeyVar <- c("a", "a", "b", "c", "d", "e", "f")
#' 
#' isKey(keyVar)
#' isKey(notKeyVar)
#' 
#' @seealso \code{\link{check}}, \code{\link{allCheckFunctions}}, 
#' \code{\link{checkFunction}}, \code{\link{checkResult}}
#' 
#' @export
isKey <- function(v) {
  out <- list(problem = FALSE, message = "", problemValues = NULL)
  if (length(unique(v)) == length(v) & !any(class(v) %in% c("numeric", "integer", "Date"))) {
    out$problem <- TRUE
    out$message <- "The variable is a key (distinct values for each observation)." 
  }
  checkResult(out)
}


#make it a checkFunction
#' @include allClasses.R checkFunction.R
isKey <- checkFunction(isKey, "Check if the variable is a key", allClasses())