R/data_availability.R

In OB7-IRD/fishi: Package Fisheries Indicators

#' @name data_availability
#' @title Assessing data availability
#' @description Control to verify the availability of logbook, observer and vms data.
#' @param dataframe_observe {\link[base]{data.frame}} expected. Dataframe from the Observe database. Csv or output of the function {\link[furdeb]{data_extraction}}, which must be done before using the data_availability() function.
#' @param dataframe_logbook {\link[base]{data.frame}} expected. Dataframe from the logbook database. Csv or output of the function {\link[furdeb]{data_extraction}}, which must be done before using the data_availability() function.
#' @param dataframe_vms {\link[base]{data.frame}} expected. Dataframe from the Vms database. Csv or output of the function {\link[furdeb]{data_extraction}}, which must be done before using the data_availability() function.
#' @param reported_year  {\link[base]{integer}} expected. Year of the report.
#' @param graph_type {\link[base]{character}} expected. plot or plotly. Plot by default.
#' @param ocean {\link[base]{character}} expected. Atlantic or Indian Atlantic by default.
#' @param path_to_png {\link[base]{character}} expected. Add a link to the path to save the figure as a png.
#' @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}]:
#' Dataframe observe:
#' \preformatted{
#'    vessel_label  |  observation_date |
#'    ----------------------------------
#'    AVEL VAD      |  2022-04-13       |
#'    BELLE ISLE    |  2022-04-13       |
#'    BELLE RIVE    |  2022-04-13       |
#' }
#' Dataframe logbook:
#' \preformatted{
#'    vessel_label |  date        |
#'    -----------------------------
#'    AVEL VAD     |  2022-04-13  |
#'    BELLE ISLE   |  2022-04-13  |
#'    BELLE RIVE   |  2022-04-13  |
#' }
#' Dataframe vms:
#' \preformatted{
#'    vessel_label |  date        | longitude |
#'    -----------------------------------------
#'    AVEL VAD     |  2022-04-13  | 59.197    |
#'    BELLE ISLE   |  2022-04-13  | 59.654    |
#'    BELLE RIVE   |  2022-04-13  | 59.951    |
#' }
#' @return The function return ggplot R plot.
#' @export
data_availability <- function(dataframe_observe,
                              dataframe_logbook,
                              dataframe_vms,
                              reported_year,
                              ocean = "Atlantic",
                              graph_type = "plot",
                              path_to_png = NULL) {
  # 0 - Global variables assignement ----
  vessel_label <- NULL
  activity_date <- NULL
  observation_date <- NULL
  # 1 - Arguments verification ----
  if (codama::r_type_checking(r_object = reported_year,
                              type = "integer",
                              output = "logical") != TRUE) {
    return(codama::r_type_checking(r_object = reported_year,
                                   type = "integer",
                                   output = "message"))
  }
  if (codama::r_type_checking(r_object = ocean,
                              type = "character",
                              output = "logical") != TRUE) {
    return(codama::r_type_checking(r_object = ocean,
                                   type = "character",
                                   output = "message"))
  }
  # 2 - Data design ----
  # vessel_label
  vessel_label <- as.character(sort(unique(dataframe_logbook$vessel_label)))
  # day
  day <- seq(as.Date(paste(min(reported_year),
                           "01-01",
                           sep = "-")),
             as.Date(paste(max(reported_year) + 1,
                           "01-01",
                           sep = "-")),
             by = 1)
  # vms
  if (ocean == "Atlantic") {
    dataframe_vms <- dataframe_vms[dataframe_vms$longitude < 25, ]
  }
  if (ocean == "Indian") {
    dataframe_vms <- dataframe_vms[dataframe_vms$longitude > 25, ]
  }
  # 3 - Graphic design ----
  # Creating a data frame for boat names
  vessel_data <- data.frame(vessel_label = as.character(sort(unique(dataframe_logbook$vessel_label))))
  # Creating a data frame for dates
  day_data <- data.frame(day = seq(as.Date(paste(min(reported_year), "01-01", sep = "-")),
                                   as.Date(paste(max(reported_year) + 1, "01-01", sep = "-")),
                                   by = 1))
  # Merge the data to create the ggplot graph
  data <- merge(vessel_data, day_data, all = TRUE)
  dataframe_vms$activity_date <- as.Date(dataframe_vms$activity_date)
  dataframe_vms$vessel_label <- trimws(dataframe_vms$vessel_label)
  dataframe_vms <- dataframe_vms %>%
    dplyr::filter(vessel_label %in% vessel_label)
  # Ggplot
  graph <- ggplot2::ggplot(data,
                           ggplot2::aes(x = day,
                                        y = vessel_label)) +
    ggplot2::geom_blank() +
    ggplot2::geom_hline(yintercept = seq_along(vessel_label),
                        linetype = "dotted",
                        color = "grey") +
    ggplot2::geom_vline(xintercept = data$day[format(data$day, "%d") == "01"],
                        linetype = "dotted",
                        color = "grey") +
    ggplot2::scale_x_date(date_labels = "%Y-%m-%d",
                          date_breaks = "1 month") +
    ggplot2::scale_y_discrete(labels = vessel_label) +
    ggplot2::labs(x = NULL,
                  y = NULL) +
    ggplot2::theme_minimal() +
    ggplot2::ggtitle(paste("Data availability for purse seiners in the",
                           ocean,
                           "Ocean in",
                           reported_year)) +
    ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5),
                   plot.margin = ggplot2::margin(6.1, 10.1, 4.1, 2.1)) +
    ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90,
                                                       hjust = 1),
                   panel.background = ggplot2::element_rect(fill = NA)) +
    ggplot2::geom_point(data = dataframe_vms,
                        ggplot2::aes(x = activity_date,
                                     y = vessel_label,
                                     color = "vms"),
                        shape = 3,
                        size = 1.5,
                        position = ggplot2::position_nudge(y = - 0.17),
                        na.rm = TRUE) +
    ggplot2::geom_point(data = dataframe_logbook,
                        ggplot2::aes(x = activity_date,
                                     y = vessel_label,
                                     color = "logbook"),
                        shape = 3,
                        size = 1.5,
                        na.rm = TRUE) +
    ggplot2::geom_point(data = dataframe_observe,
                        ggplot2::aes(x = observation_date,
                                     y = vessel_label,
                                     color = "observe"),
                        shape = 3,
                        position = ggplot2::position_nudge(y = 0.17),
                        size = 1.5,
                        na.rm = TRUE) +
    ggplot2::scale_color_manual(values = c("vms" = "grey",
                                           "logbook" = "red",
                                           "observe" = "blue")) +
    ggplot2::theme(legend.position = "bottom") +
    ggplot2::labs(color = "")
  if (graph_type == "plot") {
    if (!is.null(path_to_png)) {
      ggplot2::ggsave(filename = paste0(path_to_png,
                                        "availability_observe_vms_ps_fr_",
                                        tolower(substr(ocean, 1, 3)),
                                        "_",
                                        reported_year,
                                        ".png"),
                      plot = graph,
                      width = 15,
                      height = 8,
                      bg = "white",
                      device = "png")
    }
    return(graph)
  } else if (graph_type == "plotly") {
    graph_plotly <- plotly::ggplotly(graph)
    return(graph_plotly)
  }
}