#' Get the prepared species dataset used for JAGS
#'
#' \code{get_prepared_data} returns a data frame of the data that
#' was used for JAGS. This is the subsetted data based on the
#' selected species to model, with zero counts filled in
#' and any other route/strata filter applied.
#'
#' @param jags_data List of JAGS input data produced by
#' \code{prepare_jags_data}
#'
#' @return Data frame of 9 variables:
#' \item{count}{Number of species observed for this route run}
#' \item{strat}{Numerical factors of the stratum}
#' \item{obser}{Numerical factor of the observer}
#' \item{year}{Numerical factor of the year}
#' \item{firstyr}{1 if this was the observer's first year, 0 otherwise}
#' \item{strat_name}{Name of the stratum}
#' \item{route}{Route that this count was taken on}
#' \item{rYear}{Year this count was conducted}
#' \item{yearscale}{Scaled year}
#'
#' @export
#'
#' @examples
#' # Toy example with Pacific Wren sample data
#' # First, stratify the sample data
#'
#' strat_data <- stratify(by = "bbs_cws", sample_data = TRUE)
#'
#' # Prepare the stratified data for use in a JAGS model. In this
#' # toy example, we will set the minimum year as 2009 and
#' # maximum year as 2018, effectively only setting up to
#' # model 10 years of data. We will use the "first difference
#' # model.
#' jags_data <- prepare_jags_data(strat_data = strat_data,
#' species_to_run = "Pacific Wren",
#' model = "firstdiff",
#' min_year = 2009,
#' max_year = 2018)
#'
#' # Obtain the reassembled data frame for the data sent to JAGS
#' prepped_data <- get_prepared_data(jags_data = jags_data)
#'
#'
get_prepared_data <- function(jags_data = NULL)
{
to_return <- data.frame(Year = jags_data$r_year,
Year_Factored = jags_data$year,
Count = jags_data$count,
Stratum = jags_data$strat_name,
Stratum_Factored = jags_data$strat,
Observer_Factored = jags_data$obser,
Route = jags_data$route,
First_Year = jags_data$firstyr)
return(to_return)
}
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