AFS_database_code/app/R/functions.R at 201268f0e11c16d03494820ca4be61ae8b357d33 · cct-datascience/AFS_database_code · GitHub

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time_methods <- c("boat_electrofishing", "raft_electrofishing")
number_methods <- c("gill_net_fall", "gill_net_spring", "drifting_trammel_net",
                    "large_catfish_hoopnet", "bag_seine", "stream_seine",
                    "backpack_electrofishing", "tow_barge_electrofishing")

calculate_cpue <- function(df){
  stopifnot(c("type", "area", "common_name", "method", "waterbody_type",
              "year", "effort") %in% colnames(df)) #todo check these
  cpue <- df %>%
    group_by(type, area, waterbody_name, year, method, waterbody_type, common_name) %>%
    summarize(n_fish = n(),
              effort_sampleID = mean(effort)) %>%
    mutate(effort = case_when(method %in% time_methods ~ effort_sampleID / 3600,
                              method %in% number_methods ~ effort_sampleID)) %>%
    mutate(cpue = n_fish / effort) %>% # n is number of fish
    group_by(type, area, common_name, method, waterbody_type) %>%
    summarize_at(vars(cpue), list(mean = mean, se = se,
                                  `5%` = ~quantile(x = ., probs = 0.05),
                                  `25%` = ~quantile(x = ., probs = 0.25),
                                  `50%` = ~quantile(x = ., probs = 0.50),
                                  `75%` = ~quantile(x = ., probs = 0.75),
                                  `95%` = ~quantile(x = ., probs = 0.95)),
                 na.rm = TRUE) %>%
    mutate(metric = "CPUE")
  return(cpue)
}

calculate_lf <- function(df){
  stopifnot(c("type", "area", "common_name", "method", "waterbody_type",
              "year", "total_length") %in% colnames(df))
  lf <- df %>%
    mutate(gcat = FSA::psdAdd(total_length, common_name, what = "incremental")) %>%
    group_by(type, area, common_name, method, waterbody_type, year) %>%
    count(gcat) %>%
    mutate(lenfreq = (n / sum(n)) * 100) %>%
    group_by(type, area, common_name, method, waterbody_type, gcat) %>%
    summarize_at(vars(lenfreq), list(mean = mean, se = FSA::se), na.rm = TRUE) %>%
    mutate(metric = "Length Frequency",
           gcat = factor(gcat, levels = c("stock", "quality", "preferred",
                                          "memorable", "trophy"))) %>%
    filter(!is.na(gcat))
  return(lf)
}

calculate_rw <- function(df){
  stopifnot(c("type", "area", "common_name", "method", "waterbody_type", "year",
              "total_length", "weight") %in% colnames(df))
  rw <- df %>%
    mutate(gcat = FSA::psdAdd(total_length, common_name, what = "incremental"),
           relweight = FSA::wrAdd(wt = weight, len = total_length, spec = common_name)) %>%
    filter(!is.na(relweight)) %>%
    group_by(type, area, common_name, method, waterbody_type, gcat) %>%
    summarize_at(vars(relweight), list(mean = mean, se = FSA::se,
                                       `5%` = ~quantile(x = ., probs = 0.05),
                                       `25%` = ~quantile(x = ., probs = 0.25),
                                       `50%` = ~quantile(x = ., probs = 0.50),
                                       `75%` = ~quantile(x = ., probs = 0.75),
                                       `95%` = ~quantile(x = ., probs = 0.95)), na.rm = TRUE) %>%
    mutate(metric = "Relative Weight",
           gcat = factor(gcat, levels = c("stock", "quality", "preferred",
                                          "memorable", "trophy"))) %>%
    filter(!is.na(gcat))
  return(rw)
}

read_wrangle_metrics <- function(path = 'standardized_fish_data.csv') {
  read_csv(path) %>%
    relocate(area) %>%
    relocate(N, .after = last_col()) %>%
    mutate(gcat = case_when(gcat == "Stock-Quality" ~ "S-Q",
                            gcat == "Quality-Preferred" ~ "Q-P",
                            gcat == "Preferred-Memorable" ~ "P-M",
                            gcat == "Memorable-Trophy" ~ "M-T",
                            gcat == "Trophy" ~ "T") %>%
             factor(levels = c("S-Q", "Q-P", "P-M", "M-T", "T"))) %>%
    mutate(method = str_replace_all(method, " ", "_"),
           waterbody_type = str_replace_all(waterbody_type, " ", "_"),
           metric = str_replace_all(metric, "CPUE distance", "CPUE")) %>%
    filter(method %in% c("boat_electrofishing", "raft_electrofishing", "gill_net_fall", "gill_net_spring", "drifting_trammel_net", "large_catfish_hoopnet", "bag_seine", "stream_seine", "backpack_electrofishing", "tow_barge_electrofishing"))
}


read_transform_ecoregions <- function(path = "ecoregions1/NA_CEC_Eco_Level1.shp") {
  ecoregions <- read_sf(path)
  ecoregions_crs <- "+proj=laea +lat_0=45 +lon_0=-100 +x_0=0 +y_0=0 +a=6370997 +b=6370997 +units=m +no_defs"
  ecoregions_trans <- ecoregions %>%
    rmapshaper::ms_simplify() %>%
    st_set_crs(ecoregions_crs) %>%
    st_transform("+proj=longlat +datum=WGS84")
  ecoregions_trans
}

# Create a "not in" function
`%nin%` <- purrr::negate(`%in%`)