#' Prepare the individual line data for slope health plot
#'
#' This function starts with the health data generated by the model
#' takes the entanglement event window, and using these to create
#' a data object summarising the change from start to finish of
#' an entanglement window. The input values of health are:
#' \code{healthmeanSP} and \code{anomSP}, which are the estimated
#' health of each animal, and the anomaly of each animal's health
#' compared to the population median
#'
#' @return \code{dfout} A data frame containing the health and health anomaly
#' for each entanglement event at each of the three locations
#' @export
#' @examples
#' \dontrun{
#' prepSlopeHealthData()
#' }
prepSlopeHealthData <- function(){
# pvec <- tangleOut[, 'EndDate'] - tangleOut[, 'StartDate'] <= 365
# tangleOut <- tangleOut[pvec, ]
dfout <- numeric(0)
for(i in 1:nrow(tangleOut)){
ind <- tangleOut$EGNo[i]
tsub <- tangleOut[i, ]
htest <- healthmeanSP[which(ID == ind),]
atest <- anomSP[which(ID == ind),]
s <- match(tsub[, 'smonyr'], myName)
e <- match(tsub[, 'ewindmonyr'], myName)
r <- match(tsub[, 'rec12monyr'], myName)
gstat <- tsub[, 'gearInj']
sVal <- htest[s]
eVal <- htest[e]
rVal <- htest[r]
asVal <- atest[s]
aeVal <- atest[e]
arVal <- atest[r]
dfi <- data.frame(egno = ind, startHealth = sVal, endHealth = eVal, recHealth = rVal,
startAnom = asVal, endAnom = aeVal, recAnom = arVal, gearInjury = gstat)
dfout <- rbind(dfout, dfi)
}
labels <- data.frame(gearInjury = 1:6,
fullLab = c('Severe Gear', 'Moderate Gear', 'Severe No Gear',
'Minor Gear', 'Moderate No Gear', 'Minor No Gear'),
sevLab = c('Severe', 'Moderate', 'Severe',
'Minor', 'Moderate', 'Minor'),
gearLab = c('Gear', 'Gear', 'No Gear',
'Gear', 'No Gear', 'No Gear'))
labels$gearLab <- factor(labels$gearLab, levels = c('No Gear', 'Gear'))
dfout <- tbl_df(merge(dfout, labels, by.x = 'gearInj', by.y = 'gearInjury'))
dfout
}
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