R/ts_age_comp.R

In BenWilliams-NOAA/groundfishr: What the Package Does (One Line, Title Case)

#' trawl survey age comp analysis
#'
#' @param year assessment year
#' @param area default is "goa"
#' @param rec_age recruitment age
#' @param plus_age plus group age
#' @param rmv_yrs any survey years to exclude
#'
#' @return
#' @export ts_age_comp
#'
#' @examples ts_age_comp(year = 2020, rec_age = 2, plus_age = 45)
ts_age_comp <- function(year, area = "goa", rec_age, plus_age, rmv_yrs = NULL){

  read.csv(here::here(year, "data", "raw", paste0(area, "_ts_age_specimen_data.csv"))) %>%
    dplyr::rename_all(tolower) %>%
    dplyr::filter(!is.na(age)) %>%
    dplyr::group_by(year) %>%
    dplyr::summarise(n_s = dplyr::n(),
                     n_h = length(unique(hauljoin))) -> dat1


  read.csv(here::here(year, "data", "raw", paste0(area, "_ts_age_data.csv"))) %>%
    dplyr::rename_all(tolower) %>%
    dplyr::rename(year = survey_year) %>%
    dplyr::filter(age >= rec_age) %>%
    dplyr::group_by(year) %>%
    dplyr::mutate(tot = sum(agepop),
                  age = ifelse(age < plus_age, age, plus_age)) %>%
    dplyr::group_by(age, year) %>%
    dplyr::summarise(prop = sum(agepop) / mean(tot)) %>%
    dplyr::left_join(dat1) %>%
    dplyr::left_join(expand.grid(year = unique(.$year),
                                 age = rec_age:plus_age), .) %>%
    tidyr::replace_na(list(prop = 0)) %>%
    dplyr::group_by(year) %>%
    dplyr::mutate(AA_Index = 1,
                  n_s = mean(n_s, na.rm = T),
                  n_h = mean(n_h, na.rm = T)) %>%
    tidyr::pivot_wider(names_from = age, values_from = prop) %>%
    dplyr::arrange(year) -> age_comp


  if(!is.null(rmv_yrs)){
    age_comp |>
      dplyr::filter(!(year %in% rmv_yrs)) -> age_comp
  }


  readr::write_csv(age_comp, here::here(year, "data", "output", paste0(area, "_ts_age_comp.csv")))

  age_comp

}