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#' Cultural Importance index (CI)
#'
#' Calculates the Cultural Importance Index (CI) per species.
#' @usage CIs(data)
#'
#' @references
#' Tardio, Javier, and Manuel Pardo-de-Santayana. 2008. “Cultural Importance Indices: A Comparative Analysis Based on the Useful Wild Plants of Southern Cantabria (Northern Spain) 1.” Economic Botany 62 (1): 24–39.
#' @references
#' Whitney, C. W., Bahati, J., and Gebauer, J. (2018), Ethnobotany and agrobiodiversity; valuation of plants in the homegardens of southwestern Uganda. Ethnobiology Letters, 9(2), 90-100. \doi{10.14237/ebl.9.2.2018.503}
#'
#' @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 informant (should be 0 or 1 values).
#'
#' @keywords arith math logic methods misc survey
#'
#' @return Data frame of species and Cultural Importance Index (CI) 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 filter summarize select left_join group_by arrange mutate
#' @importFrom magrittr %>%
#'
#'
#' @examples
#'
#' #Use built-in ethnobotany data example
#' CIs(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)
#'
#' CIs(eb_data)
#'
#'@export CIs
#'
CIs <- function(data) {
#Add error stops ####
#Check that packages are loaded
{
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 loading 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
CI <- CIs <- URdata <- data_Ci <- data_URs <- URps <- sp_name <- informant <- NULL # Setting the variables to NULL first, appeasing R CMD check
URdata <- data #create complete subset-able data
data_URs <- URs(URdata) #calculate URs()
#create new subset-able data
data_Ci <- data_URs
#calcualte CI (UR/N)
data_Ci$CI <- data_URs$URs/(length(unique(URdata$informant)))
#change sort order, arrange and round
CIs <- data_Ci %>% dplyr::select(-URs) %>%
dplyr::arrange(-CI) %>%
dplyr::mutate(CI = round(CI, 3))
as.data.frame(CIs)
}
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