fishi: Package Fisheries Indicators

Documented in set_per_searching_day

#' @name set_per_searching_day
#' @title Annual number of sets per searching day
#' @description Annual number of sets per searching day on FOB-associated and free-swimming schools.
#' @param dataframe {\link[base]{data.frame}} expected. 'Csv' or 'output' of the function {\link[furdeb]{data_extraction}}, which must be done before using the set_per_searching_day() function.
#' @param fishing_type {\link[base]{character}} expected. 'FOB' and 'FSC'.
#' @param graph_type {\link[base]{character}} expected. 'plot', 'plotly' or 'table.' Plot by default.
#' @param title TRUE or FALSE expected. Title for plotly graph_type. False by default.
#' @details
#' The input dataframe must contain all these columns for the function to work [\href{https://ob7-ird.github.io/fishi/articles/Db_and_csv.html}{see referentials}]:
#' \preformatted{
#'    activity_date | school_type | set_duration | positive_set | total_set | total_hour_fished
#'    -----------------------------------------------------------------------------------------
#'    2010-03-06    | FOB         | 0            | 0            | 0         |  1.00
#'    2010-12-04    | FOB         | 0            | 0            | 0         | 11.8
#'    2010-05-19    | FOB         | 0            | 0            | 0         |  2.05
#' }
#' Add these columns for an automatic title (optional):
#' \itemize{
#'  \item{\code{  country_code}}
#'  \item{\code{  ocean_code}}
#'  \item{\code{  vessel_type_code}}
#' }
#' @return The function return ggplot R plot.
#' @export
set_per_searching_day <- function(dataframe,
                                  fishing_type,
                                  graph_type = "plot",
                                  title = FALSE) {
  # 0 - Global variables assignement ----
  activity_date <- NULL
  total_hour_fished <- NULL
  set_duration <- NULL
  school_type <- NULL
  positive_set <- NULL
  total_set <- NULL
  sets_per_day_all <- NULL
  sets_per_day_fad <- NULL
  sets_per_day_fsc <- NULL
  time_period <- NULL
  year <- NULL
  # 1 - Arguments verification ----
  if (codama::r_type_checking(r_object = fishing_type,
                              type = "character",
                              output = "logical") != TRUE) {
    return(codama::r_type_checking(r_object = fishing_type,
                                   type = "character",
                                   output = "message"))
  }
  if (codama::r_type_checking(r_object = graph_type,
                              type = "character",
                              output = "logical") != TRUE) {
    return(codama::r_type_checking(r_object = graph_type,
                                   type = "character",
                                   output = "message"))
  }
  if (codama::r_type_checking(r_object = title,
                              type = "logical",
                              output = "logical") != TRUE) {
    return(codama::r_type_checking(r_object = title,
                                   type = "logical",
                                   output = "message"))
  }
  # 2 - Data design ----
  dataframe <-  dataframe %>%
    dplyr::mutate(year = lubridate::year(x = activity_date))
  ocean_code <- dataframe$ocean_code[1]
  if (ocean_code == 1) {
    set_time <- as.integer(x = 12)
  } else if (ocean_code == 2) {
    set_time <- as.integer(x = 13)
  }
  # db t1 - Add columns : nb_sets_pos and nb_sets
  t1 <- dataframe %>%
    dplyr::group_by(year,
                    school_type) %>%
    dplyr::summarise(nb_sets_pos = sum(positive_set,
                                       na.rm = TRUE),
                     nb_sets = sum(total_set,
                                   na.rm = TRUE),
                     .groups = "drop")
  # db t2 - Add columns t_peche and t_recherche
  t2 <- dataframe %>%
    dplyr::group_by(year) %>%
    dplyr::summarise(t_peche = sum(total_hour_fished,
                                   na.rm = TRUE),
                     t_recherche = sum(total_hour_fished - set_duration,
                                       na.rm = TRUE),
                     .groups = "drop")
  # merge t1 and t2 by year
  table_cpue_set_per_day <- merge(t1, t2, by = "year")
  # Create columns sets_per_day for ALL, FOB and FSC
  table_cpue_set_per_day <- table_cpue_set_per_day %>%
    dplyr::group_by(year) %>%
    dplyr::reframe(sets_per_day_all = dplyr::case_when(school_type %in% "FOB" | school_type %in% "FSC" | school_type %in% "UND" ~ nb_sets / (t_recherche / set_time),
                                                       TRUE ~ 0),
                   sets_per_day_fad = dplyr::case_when(school_type %in% "FOB" ~ nb_sets / (t_recherche / set_time),
                                                       TRUE ~ 0),
                   sets_per_day_fsc = dplyr::case_when(school_type %in% c("FSC", "UND") ~ nb_sets / (t_recherche / set_time)))
  # Sum columns sets_per_day for ALL, FOB and FSC
  table_cpue_set_per_day <- table_cpue_set_per_day %>%
    dplyr::group_by(year) %>%
    dplyr::summarise(sets_per_day_all = sum(sets_per_day_all,
                                            na.rm = TRUE),
                     sets_per_day_fad = sum(sets_per_day_fad,
                                            na.rm = TRUE),
                     sets_per_day_fsc = sum(sets_per_day_fsc,
                                            na.rm = TRUE),
                     .groups = "drop")
  # 3 - Legend design ----
  if (title == TRUE) {
    #Ocean
    ocean_legend <- code_manipulation(data         = dataframe$ocean_code,
                                      referential  = "ocean",
                                      manipulation = "legend")
    #country
    country_legend <- code_manipulation(data         = dataframe$country_code,
                                        referential  = "country",
                                        manipulation = "legend")
    #vessel
    vessel_type_legend <- code_manipulation(data         = dataframe$vessel_type_code,
                                            referential  = "vessel_simple_type",
                                            manipulation = "legend")
    # time_period
    time_period <- c(unique(min(dataframe$year):max(dataframe$year)))
  }
  # 4 - Graphic design ----
  if (fishing_type == "FOB") {
    table_cpue_set_per_day$sets_per_day_fad <- round(table_cpue_set_per_day$sets_per_day_fad, 3)
    ggplot_table_cpue <- ggplot2::ggplot(data = table_cpue_set_per_day) +
      ggplot2::geom_hline(yintercept = c(0.25, 0.5, 0.75, 1, 1.25),
                          color = "grey",
                          linetype = "longdash",
                          alpha = 0.5) +
      ggplot2::geom_line(ggplot2::aes(x = year,
                                      y = sets_per_day_fad),
                         color = "black") +
      ggplot2::geom_point(ggplot2::aes(x = year,
                                       y = sets_per_day_fad),
                          shape = 16,
                          size = 2) +
      ggplot2::labs(x = "",
                    y = "Number of sets per searching day") +
      ggplot2::ylim(0,
                    max(table_cpue_set_per_day$sets_per_day_fad)) +
      ggplot2::theme(panel.background = ggplot2::element_rect(fill = "white",
                                                              color = "black"),
                     axis.text.x = ggplot2::element_text(angle = 45,
                                                         hjust = 1,
                                                         size = 13),
                     axis.text.y = ggplot2::element_text(size = 13),
                     axis.title.y = ggplot2::element_text(size = 14)) +
      ggplot2::labs(colour = "") +
      ggplot2::scale_x_continuous(breaks = unique(table_cpue_set_per_day$year)) +
      ggplot2::theme(legend.position = c(0.84, 0.97),
                     legend.justification = c(0, 1)) +
      ggplot2::annotate("text", x = max(table_cpue_set_per_day$year),
                        y = 0.05,
                        label = "(FOB)",
                        color = "black",
                        hjust = 1,
                        vjust = 1,
                        size = 7)
  } else if (fishing_type == "FSC") {
    table_cpue_set_per_day$sets_per_day_fsc <- round(table_cpue_set_per_day$sets_per_day_fsc, 3)
    ggplot_table_cpue <- ggplot2::ggplot(data = table_cpue_set_per_day) +
      ggplot2::geom_hline(yintercept = c(0.25, 0.5, 0.75, 1),
                          color = "grey",
                          linetype = "longdash",
                          alpha = 0.5) +
      ggplot2::geom_line(ggplot2::aes(x = year,
                                      y = sets_per_day_fsc),
                         color = "black") +
      ggplot2::geom_point(ggplot2::aes(x = year,
                                       y = sets_per_day_fsc),
                          shape = 16,
                          size = 2) +

      ggplot2::labs(x = "",
                    y = "Number of sets per searching day") +
      ggplot2::ylim(0, 1) +
      ggplot2::theme(panel.background = ggplot2::element_rect(fill = "white",
                                                              color = "black"),
                     axis.text.x = ggplot2::element_text(angle = 45,
                                                         hjust = 1,
                                                         size = 13),
                     axis.text.y = ggplot2::element_text(size = 13),
                     axis.title.y = ggplot2::element_text(size = 14)) +
      ggplot2::labs(colour = "") +
      ggplot2::scale_x_continuous(breaks = unique(table_cpue_set_per_day$year)) +
      ggplot2::theme(legend.position = c(0.84, 0.97),
                     legend.justification = c(0, 1)) +
      ggplot2::annotate("text", x = max(table_cpue_set_per_day$year),
                        y = 0.05,
                        label = "(FSC)",
                        color = "black",
                        hjust = 1,
                        vjust = 1,
                        size = 7)
  }
  if (graph_type == "plot") {
    return(ggplot_table_cpue)
  } else if (graph_type == "plotly") {
    plotly_graph <- plotly::ggplotly(ggplot_table_cpue)
    if (title == TRUE) {
      plotly_graph <- plotly_graph %>%
        plotly::layout(title = list(text = paste0("Annual number of sets per searching day on ",
                                                  fishing_type,
                                                  " fishing mode schoolsfor the ",
                                                  country_legend,
                                                  "\n",
                                                  vessel_type_legend,
                                                  " fishing fleet in the Atlantic Ocean during ",
                                                  min(time_period),
                                                  "-",
                                                  max(time_period),
                                                  ", in the ",
                                                  ocean_legend,
                                                  " ocean."),
                                    font = list(size = 15)),
                       margin = list(t = 120))

    }
    return(plotly_graph)
  } else if (graph_type == "table") {
    table_cpue_set_per_day <- table_cpue_set_per_day %>%
      dplyr::rename("Year" = year,
                    "ALL" = sets_per_day_all,
                    "FOB" = sets_per_day_fad,
                    "FSC" = sets_per_day_fsc)
    table_cpue_set_per_day <- round(table_cpue_set_per_day, 2)
    as.data.frame(table_cpue_set_per_day)
  }
}