R/06-setupTemperatureData.R

In danStich/shadia: American shad dam passage performance standard model for R

#' @title Set up data for simulations
#'
#' @description Internal functions for manipulations to
#' empirical data used as inputs, and for fitting of
#' empirical relationships (e.g. discharge regressions).
#'
#' Not intended to be called directly, but visible
#' for transparency.
#'
#' @return A list of generic parameters for code benchmarking
#' and progress monitoring.
#'
#' @details Have retained data manipulation to retain
#' integrity of raw data files in built-in data sets and
#' provide transparency in methods.
#'
#' @export
#'
setUpTemperatureData <- function(river) {
  if (river == "penobscot") {

    # Load the SSR temperature data from
    # years 2012-2014 in the built-in data set
    # and summarize as mean by day and year for
    # mvnorm
    mu <- shadia::tempData %>%
      group_by(day, year) %>%
      summarize(val = mean(val, na.rm = TRUE), .groups = "keep")
    mu <- data.frame(mu)
    mu <- mu[, c(3, 2, 1)]
    mu <- na.omit(mu)
  }

  if (river == "merrimack") {

    # Load the SSR temperature data from
    # years 2012-2014 in the built-in data set
    # and summarize as mean by day and year for
    # mvnorm
    mu <- shadia::tempData_merrimack %>%
      group_by(day, year) %>%
      summarize(val = mean(val, na.rm = TRUE), .groups = "keep")
    mu <- data.frame(mu)
    mu <- mu[, c(3, 2, 1)]
    mu <- na.omit(mu)
  }

  if (river == "connecticut") {

    # Load the SSR temperature data from
    # years 2012-2014 in the built-in data set
    # and summarize as mean by day and year for
    # mvnorm
    mu <- shadia::tempData_connecticut %>%
      # filter(year >= 2012) %>%
      group_by(day, year) %>%
      summarize(val = mean(val, na.rm = TRUE), .groups = "keep")
    mu <- data.frame(mu)
    mu <- mu[, c(3, 2, 1)]
    mu <- na.omit(mu)
  }

  if (river == "saco") {

    # Load the SSR temperature data from
    # years 2012-2014 in the built-in data set
    # and summarize as mean by day and year for
    # mvnorm
    mu <- shadia::tempData_saco %>%
      filter(day >= 32) %>%
      group_by(day, year) %>%
      summarize(val = mean(val, na.rm = TRUE), .groups = "keep")
    mu <- data.frame(mu)
    mu <- mu[, c(3, 2, 1)]
    mu <- na.omit(mu)
  }

  if (river == "susquehanna") {

    # Load the SSR temperature data from
    # years 2012-2014 in the built-in data set
    # and summarize as mean by day and year for
    # mvnorm
    mu <- shadia::tempData_susquehanna %>%
      filter(year >= 2012) %>%
      group_by(day, year) %>%
      summarize(val = mean(val, na.rm = TRUE), .groups = "keep")
    mu <- data.frame(mu)
    mu <- mu[, c(3, 2, 1)]
    mu <- na.omit(mu)
  }

  if (river == "kennebec") {

    # Load the SSR temperature data from
    # years 2012-2014 in the built-in data set
    # and summarize as mean by day and year for
    # mvnorm
    mu <- shadia::tempData_kennebec %>%
      filter(day >= 50) %>%
      group_by(day, year) %>%
      summarize(val = mean(val, na.rm = TRUE))
    mu <- data.frame(mu)
    mu <- mu[, c(3, 2, 1)]
    mu <- na.omit(mu)
  }

  if (river == "hudson") {

    # Load the SSR temperature data from
    # years 2012-2014 in the built-in data set
    # and summarize as mean by day and year for
    # mvnorm
    mu <- shadia::tempData_hudson %>%
      # filter(day >= 50) %>%
      group_by(day, year) %>%
      summarize(val = mean(val, na.rm = TRUE))
    mu <- data.frame(mu)
    mu <- mu[, c(3, 2, 1)]
    mu <- na.omit(mu)
  }

  if (river == "androscoggin") {

    # Load the andro temperature data 
    # from built-in object
    mu <- shadia::tempData_androscoggin %>%
      filter(staid > 2000 & staid !=2064) %>%
      group_by(day, year) %>%
      summarize(val = mean(val, na.rm = TRUE))
    mu <- data.frame(mu)
    mu <- mu[, c(3, 2, 1)]
    mu <- na.omit(mu)
  }  
  
  return(mu)
}