R/clean_catch.R

In commfish/scallopr: Analyze ADF&G weathervane scallop survey data

#' Title
#'
#' @param catch_data
#' @param tows
#' @param YEAR
#'
#' @return scal_catch data.frame
#' @export clean_catch
#'
#' @examples
#'
#' # year of analysis (from "global" head matter in analysis script)
#' # YEAR <- 2019
#'
#' scal_catch <- clean_catch(catch_data, tows, YEAR)
clean_catch <- function(catch_data, tows, YEAR){

  if(YEAR < 2019){
    catch_data %>%
      dplyr::filter(YEAR == YEAR,
                    RACE_CODE == 74120,
                    CONDITION_CODE_RII %in% c("01", "1", "52"),
                    EVENT_ID %in% tows$tow_id) %>%  # filter out uneccessary data
      dplyr::transmute(tow_id = EVENT_ID,
                       size = dplyr::case_when(SCAL_SIZE_CLASS==2 ~ "small",
                                               SCAL_SIZE_CLASS==1 ~ "large",
                                               SCAL_SIZE_CLASS==3 ~ "clapper"),
                       count = COUNT,
                       wt_lb = SAMPLE_WT_KG * 2.20462) %>%
      dplyr::group_by(tow_id, size) %>%
      dplyr::summarize(count = sum(count), wt_lb = sum(wt_lb)) %>%
      dplyr::right_join(expand.grid(size = c("clapper", "small", "large", "all"),
                                    tow_id = tows$tow_id)) %>%
      dplyr::mutate_if(is.numeric, funs(replace(., is.na(.), 0))) %>%
      within(count[size == "all"] <- count[size == "small"] +
               count[size == "large"]) %>% # add "all" to counts
      within(wt_lb[size == "all"] <- wt_lb[size == "small"] +
               wt_lb[size == "large"]) %>% # add "all" to wts
      dplyr::mutate(Size = factor(size, levels = Size_levels)) %>%
      dplyr::select(-size) %>%
      dplyr::left_join(tows, by = "tow_id") %>%
      dplyr::ungroup() -> x

  } else {
    catch_data %>%
      dplyr::filter(rcode == 74120,
                    samp_grp %in% c(1, 2),
                    tow %in% tows$tow_id) %>%  # filter out uneccessary data
      dplyr::transmute(tow_id = tow,
                       size = dplyr::case_when(samp_grp == 1 ~ "large",
                                               samp_grp == 2 ~ "small"),
                       count = samp_cnt,
                       wt_lb = samp_wt * 2.20462) %>%
      dplyr::group_by(tow_id, size) %>%
      dplyr::summarize(count = sum(count), wt_lb = sum(wt_lb)) %>%
      dplyr::right_join(expand.grid(size = c("small", "large"),
                                    tow_id = tows$tow_id)) %>%
      dplyr::mutate_if(is.numeric, funs(replace(., is.na(.), 0))) %>%
      # within(count[size == "all"] <- count[size == "small"] + count[size == "large"]) %>% # add "all" to counts
      # within(wt_lb[size == "all"] <- wt_lb[size == "small"] + wt_lb[size == "large"]) %>% # add "all" to wts
      dplyr::mutate(Size = factor(size, levels = Size_levels)) %>%
      dplyr::select(-size) %>%
      dplyr::left_join(tows, by = "tow_id") %>%
      dplyr::ungroup() -> x

  }

  write_csv(x, here::here(paste0("output/",YEAR,"/scal_catch.csv")))
  x

}