#' A wrapper for define_covars
#'
#' @description Identifies for each sampling point (before-after-control-reserve) which covariates to use using define_covars. It then identifies the common covariates available for each sampling point and returns a string that bio_model can use to implement the correct model
#'
#' @param data dataset
#' @param year.imp year of implementation
#'
#' @return a vector stating which covariates to be used
#' @export
#'
#' @importFrom magrittr %>%
#' @importFrom magrittr %$%
covars_wrapper <- function(data, year.imp){
data$Ano = as.numeric(as.character(data$Ano))
before_reserve <- data %>%
filter(Ano <= year.imp, Zona == "Reserva") %>%
define_covars()
after_reserve <- data %>%
filter(Ano > year.imp, Zona == "Reserva") %>%
define_covars()
before_control <- data %>%
filter(Ano <= year.imp, !Zona == "Reserva") %>%
define_covars()
after_control <- data %>%
filter(Ano > year.imp, !Zona == "Reserva") %>%
define_covars()
covars <- c("Temperatura", "Visibilidad", "Profundidad")
length_covars <- data.frame(set = c("BR", "AR", "BC", "AC"),
Temperatura = NA,
Vibilididad = NA,
Profundidad = NA)
if("Temperatura" %in% before_reserve){
length_covars$Temperatura[1] <- 1
}
if("Temperatura" %in% after_reserve){
length_covars$Temperatura[2] <- 1
}
if("Temperatura" %in% before_control){
length_covars$Temperatura[3] <- 1
}
if("Temperatura" %in% after_control){
length_covars$Temperatura[4] <- 1
}
#
if("Profundidad" %in% before_reserve){
length_covars$Profundidad[1] <- 1
}
if("Profundidad" %in% after_reserve){
length_covars$Profundidad[2] <- 1
}
if("Profundidad" %in% before_control){
length_covars$Profundidad[3] <- 1
}
if("Profundidad" %in% after_control){
length_covars$Profundidad[4] <- 1
}
#
if("Visibilidad" %in% before_reserve){
length_covars$Visibilidad[1] <- 1
}
if("Visibilidad" %in% after_reserve){
length_covars$Visibilidad[2] <- 1
}
if("Visibilidad" %in% before_control){
length_covars$Visibilidad[3] <- 1
}
if("Visibilidad" %in% after_control){
length_covars$Visibilidad[4] <- 1
}
covars <- length_covars %>%
gather(var, value, -set) %>%
group_by(var) %>%
summarize(value = sum(value, na.rm = T)) %>%
filter(value == max(.$value), value > 0) %$%
unique(var)
if(length(covars)<1){
return("None")
}
if(length(covars)>=1){
return(covars)
}
}
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