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#' Number of Uses (NU)
#'
#' Calculates the number of uses (NU) per species.
#' @usage NUs(data)
#'
#' @references
#' Prance, G. T., W. Balee, B. M. Boom, and R. L. Carneiro. 1987. “Quantitative Ethnobotany and the Case for Conservation in Amazonia.” Conservation Biology 1 (4): 296–310.
#'
#' @param data is an ethnobotany data set with column 1 'informant' and 2 'sp_name' as row identifiers of informants and of species names respectively.
#' The rest of the columns are the identified ethnobotany use categories. The data should be populated with counts of uses per person (should be 0 or 1 values).
#'
#' @keywords arith math logic methods misc survey
#'
#' @return Data frame of species and number of uses (NU) values.
#'
#' @section Warning:
#'
#' Identification for informants and species must be listed by the names 'informant' and 'sp_name' respectively in the data set.
#' The rest of the columns should all represent separate identified ethnobotany use categories. These data should be populated with counts of uses per informant (should be 0 or 1 values).
#'
#' @importFrom dplyr select
#' @importFrom stats aggregate
#'
#' @examples
#'
#' #Use built-in ethnobotany data example
#'
#' NUs(ethnobotanydata)
#'
#' #Generate random dataset of three informants uses for four species
#'
#' eb_data <- data.frame(replicate(10,sample(0:1,20,rep=TRUE)))
#' names(eb_data) <- gsub(x = names(eb_data), pattern = "X", replacement = "Use_")
#' eb_data$informant<-sample(c('User_1', 'User_2', 'User_3'), 20, replace=TRUE)
#' eb_data$sp_name<-sample(c('sp_1', 'sp_2', 'sp_3', 'sp_4'), 20, replace=TRUE)
#'
#' NUs(eb_data)
#'
#'@export NUs
NUs <- function(data) {
#Add error stops ####
#Check that packages are loaded
{
if (!requireNamespace("stats", quietly = TRUE)) {
stop("Package \"stats\" needed for this function to work. Please install it.",
call. = FALSE)
}
if (!requireNamespace("dplyr", quietly = TRUE)) {
stop("Package \"dplyr\" needed for this function to work. Please install it.",
call. = FALSE)
}
if (!requireNamespace("magrittr", quietly = TRUE)) {
stop("Package \"magrittr\" needed for this function to work. Please install it.",
call. = FALSE)
}
}# end package check
## Check that use categories are greater than zero
if (!any(sum(dplyr::select(data, -informant, -sp_name)>0))){
warning("The sum of all UR is not greater than zero. Perhaps not all uses have values or are not numeric.")
data<-data[stats::complete.cases(data), ]
}
## Use 'complete.cases' from stats to get to the collection of obs without NA
if (any(is.na(data))) {
warning("Some of your observations included \"NA\" and were removed. Consider using \"0\" instead.")
data<-data[stats::complete.cases(data), ]
}#end error stops
# Set the variables to NULL first, appeasing R CMD check
NUdata <- NUdataaggr <- NUs <- informant <- sp_name <- NULL # Setting the variables to NULL first, appeasing R CMD check
NUdata <- data #create complete subset-able data
#Calculate NUs
NUdataaggr <- stats::aggregate(dplyr::select(NUdata, -informant, -sp_name),
by = list(sp_name = data$sp_name),FUN = sum)
NUdataaggr <- NUdataaggr %>% dplyr::mutate_if(is.numeric, ~1 * (. != 0))
NUdataaggr$NUs <- NUdataaggr %>% dplyr::select(-sp_name) %>% rowSums()
#change sort order
NUs <- dplyr::select(NUdataaggr, sp_name, NUs) %>%
dplyr::arrange(-NUs)
as.data.frame(NUs)
}
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