#' Evaluate strength of correlation between HAB measure and other factor
#'
#' @param hab.data A data frame with columns "Date" (must be of class Date) and a measure of HAB (proxy measure of HAB, for example: cell count, chl-a concentration, phycocyanin concentration, etc.)
#' @param hab.name Name of the column for the measure of HAB
#' @param var.data A data frame with columns "Date" (must be of class Date) and some other factor to evaluate
#' @param var.name Name of the column for the parameter or factor
#' @return The correlation (using non-parametric Spearman method) between the HAB measure and another variable
#' @examples
#' @import dplyr
#' @export
habcorr <- function(hab.data,hab.name=NULL,var.data,var.name=NULL){
#Format data frames given the user inputs
if(is.null(hab.name)){
if("Value" %in% colnames(hab.data)){
hab.data <- hab.data[,c("Date","Value")]
hab.name <- "Measure of HAB"
}
print("Please provide name of HAB measure")
}else{
hab.data <- hab.data[,c("Date",hab.name)]
}
if(is.null(var.name)){
if("Value" %in% colnames(var.data)){
var.data <- var.data[,c("Date","Value")]
var.name <- "Other Parameter"
}
print("Please provide name of a parameter to evaluate")
}else{
var.data <- var.data[,c("Date",var.name)]
}
names(hab.data) <- c("Date","HABvalue")
names(var.data) <- c("Date","Varvalue")
#Join HABs data frame and other variable data frame
all.data <- dplyr::left_join(hab.data,var.data,by='Date')
#Remove any incomplete cases
all.data <- na.omit(all.data)
#Calculate correlation between the two parameters
result <- cor(all.data$HABvalue,all.data$Varvalue,method="spearman")
#Output correlation
if(!is.null(result)){
return(result)
}
#Plot scatterplot
plot(data=all.data,x=HABvalue,y=Varvalue,type = "p",xlab=hab.name,ylab=var.name)
}
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