# **************************************************************************** #
# Copyright (C) 2017 Jillian Anderson #
# This file is part of the cydr package developed by Jillian Anderson during #
# her 4th year Knowledge Integration Honours thesis at the University of #
# Waterloo. #
# #
# cydr is free software: you can redistribute it and/or modify it under the #
# terms of a GNU General Public License as published by the Free Software #
# Foundation. cydr is distributed in the hope that it will be useful, but #
# WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY #
# or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for #
# more details. #
# #
# You should have received a copy of the GNU General Public License along with #
# cydr. If not, see <http://www.gnu.org/licenses/>. #
# **************************************************************************** #
#' Identify outlying yields
#'
#' @description Adds a column called \code{cydr_ResidualError} to a
#' dataframe to identify observations associated with outlying yields.
#'
#' Will identify all observations with yields greater than \code{sd} standard
#' deviations away from the mean. If \code{remove} is \code{TRUE} all
#' observations associated with a residual error will be removed from the
#' dataframe.
#'
#' @usage residual_outliers(data, remove=FALSE, type="both", sd=2)
#'
#' @param data a dataframe, standardized and outputted from AgLeader.
#' @param remove a boolean. Defaults to \code{FALSE}. Indicates whether to remove
#' identified errors.
#' @param type one of \code{"high"}, \code{"low"}, or \code{"both"}. Indicates
#' which types of data to identify as erroneous \code{"high"} will identify,
#' high yields, \code{"low"} will identify low speeds, and \code{"both"} will
#' identify high and low speeds.
#' @param sd a number >= 0. Defaults to 2. Used as the standard deviation
#' threshold for error identification.
#' @return A dataframe with an added column called \code{cydr_ResidualError}.
#' This column will be set to \code{TRUE} if an observation is deemed erroneous.
#'
#' If \code{remove = TRUE} all observations cydr identifies as erroneous are
#' removed from the returned dataframe.
#' @examples
#' residual_outliers(data)
#' residual_outliers(data, TRUE)
#' residual_outliers(data, TRUE, type="low", sd=3)
#'
#' @family core functions
#' @export
residual_outliers <- function(data, remove=FALSE, type="both", sd=2){
# Compute the standard deviation of yield
std_dev <- sd(data$Yld_Vol_Dr, na.rm=TRUE)
# Compute the mean yield
meann <- mean(data$Yld_Vol_Dr, na.rm=TRUE)
if (type=="both"){
# Identify high and low yielding observations
data_errors <- data %>%
mutate(cydr_ResidualError = Yld_Vol_Dr < (meann - sd*std_dev) |
Yld_Vol_Dr > (meann + sd*std_dev))
} else if (type=="low"){
# Identify low yielding observations
data_errors <- data %>%
mutate(cydr_ResidualError = Yld_Vol_Dr < (meann - sd*std_dev))
} else if (type == "high"){
# Identify high yielding observations
data_errors <- data %>%
mutate(cydr_ResidualError = Yld_Vol_Dr > (meann + sd*std_dev))
}
if (remove) {
# Filter out observations identified as residual errors
data_errors <- data_errors %>%
filter(is.na(cydr_ResidualError) | !cydr_ResidualError)
}
return(data_errors)
}
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