R/bio_size_tuna.R
In OB7-IRD/fishi: Package Fisheries Indicators
Defines functions
bio_size_tuna
Documented in
bio_size_tuna
#' @name bio_size_tuna
#' @title Size distribution of major tuna catches
#' @description Size distribution of major tuna catches (in percentage of the total number of fishes).
#' @param dataframe {\link[base]{data.frame}} expected. 'Csv' or 'output' of the function {\link[furdeb]{data_extraction}}, which must be done before using the bio_size_tuna() function.
#' @param report_year {\link[base]{integer}} expected. Year of the statistical report.
#' @param graph_type {\link[base]{character}} expected. 'plot' or 'plotly'. 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 | species_code | size_class | estimated_individual
#' -------------------------------------------------------------------------------
#' 2018 | FOB | 6 | 45 | 1.97
#' 2018 | FSC | 6 | 44 | 1.25
#' 2018 | FOB | 6 | 43 | 28.7
#' }
#'
#' Add these columns for an automatic title (optional):
#' \itemize{
#' \item{\code{ country_code}}
#' \item{\code{ ocean_code}}
#' }
#' @return The function return ggplot R plot.
#' @export
bio_size_tuna <- function(dataframe,
report_year,
graph_type = "plot",
title = FALSE) {
# 0 - Global variables assignement ----
species_code <- NULL
school_type <- NULL
activity_date <- NULL
estimated_individual <- NULL
size_class <- NULL
numbers_total <- NULL
numbers <- NULL
log_avg_5_years <- NULL
log_current_year <- NULL
free_current_year <- NULL
free_avg_5_years <- NULL
all_avg_5_years <- NULL
all_current_year <- NULL
# 1 - Arguments verification ----
if (codama::r_type_checking(r_object = report_year,
type = "integer",
output = "logical") != TRUE) {
return(codama::r_type_checking(r_object = report_year,
type = "integer",
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 extraction ----
# Report_year
five_previous <- c((report_year - 1):(report_year - 5))
year <- as.character(report_year)
years <- as.character(paste0(report_year - 5,
"-",
report_year - 1))
# 3.a - Data design for SKJ ----
# Dataframe - Mode : LOG, Year : Report year
t0 <- dataframe %>%
dplyr::filter(species_code %in% 2,
school_type %in% "FOB",
activity_date %in% report_year) %>%
dplyr::mutate(numbers_total = sum(estimated_individual, na.rm = TRUE)) %>%
dplyr::group_by(size_class,
numbers_total) %>%
dplyr::summarise(numbers = sum(estimated_individual, na.rm = TRUE),
.groups = "drop") %>%
dplyr::group_by(size_class) %>%
dplyr::summarise(log_current_year = numbers / numbers_total * 100,
.groups = "drop")
# Dataframe - Mode : LOG, Year : Five previous
t1 <- dataframe %>%
dplyr::filter(species_code %in% 2,
school_type %in% "FOB",
activity_date %in% five_previous) %>%
dplyr::mutate(numbers_total = sum(estimated_individual / 5, na.rm = TRUE)) %>%
dplyr::group_by(size_class,
numbers_total) %>%
dplyr::summarise(numbers = sum(estimated_individual / 5, na.rm = TRUE),
.groups = "drop") %>%
dplyr::group_by(size_class) %>%
dplyr::summarise(log_avg_5_years = numbers / numbers_total * 100,
.groups = "drop")
# Dataframe - Mode : FREE, Year : Report year
t2 <- dataframe %>%
dplyr::filter(species_code %in% 2,
school_type %in% "FSC",
activity_date %in% report_year) %>%
dplyr::mutate(numbers_total = sum(estimated_individual, na.rm = TRUE)) %>%
dplyr::group_by(size_class,
numbers_total) %>%
dplyr::summarise(numbers = sum(estimated_individual, na.rm = TRUE),
.groups = "drop") %>%
dplyr::group_by(size_class) %>%
dplyr::summarise(free_current_year = numbers / numbers_total * 100,
.groups = "drop")
# Dataframe - Mode : FREE, Year : Five previous
t3 <- dataframe %>%
dplyr::filter(species_code %in% 2,
school_type %in% "FSC",
activity_date %in% five_previous) %>%
dplyr::mutate(numbers_total = sum(estimated_individual / 5, na.rm = TRUE)) %>%
dplyr::group_by(size_class,
numbers_total) %>%
dplyr::summarise(numbers = sum(estimated_individual / 5, na.rm = TRUE),
.groups = "drop") %>%
dplyr::group_by(size_class) %>%
dplyr::summarise(free_avg_5_years = numbers / numbers_total * 100,
.groups = "drop")
# Dataframe - Mode : ALL, Year : Report year
t4 <- dataframe %>%
dplyr::filter(species_code %in% 2,
activity_date %in% report_year) %>%
dplyr::mutate(numbers_total = sum(estimated_individual, na.rm = TRUE)) %>%
dplyr::group_by(size_class,
numbers_total) %>%
dplyr::summarise(numbers = sum(estimated_individual, na.rm = TRUE),
.groups = "drop") %>%
dplyr::group_by(size_class) %>%
dplyr::summarise(all_current_year = numbers / numbers_total * 100,
.groups = "drop")
# Dataframe - Mode : ALL, Year : Five previous
t5 <- dataframe %>%
dplyr::filter(species_code %in% 2,
activity_date %in% five_previous) %>%
dplyr::mutate(numbers_total = sum(estimated_individual / 5, na.rm = TRUE)) %>%
dplyr::group_by(size_class,
numbers_total) %>%
dplyr::summarise(numbers = sum(estimated_individual / 5, na.rm = TRUE),
.groups = "drop") %>%
dplyr::group_by(size_class) %>%
dplyr::summarise(all_avg_5_years = numbers / numbers_total * 100,
.groups = "drop")
# Merge
table_size_skj_n <- merge(t0, t1, by = "size_class")
table_size_skj_n <- merge(table_size_skj_n, t2, by = "size_class")
table_size_skj_n <- merge(table_size_skj_n, t3, by = "size_class")
table_size_skj_n <- merge(table_size_skj_n, t4, by = "size_class")
table_size_skj_n <- merge(table_size_skj_n, t5, by = "size_class")
# 3.b - Data design for BET ----
# Dataframe - Mode : LOG, Year : Report year
t0 <- dataframe %>%
dplyr::filter(species_code %in% 3,
school_type %in% "FOB",
activity_date %in% report_year) %>%
dplyr::mutate(numbers_total = sum(estimated_individual,
na.rm = TRUE)) %>%
dplyr::group_by(size_class,
numbers_total) %>%
dplyr::summarise(numbers = sum(estimated_individual,
na.rm = TRUE),
.groups = "drop") %>%
dplyr::group_by(size_class) %>%
dplyr::summarise(log_current_year = numbers / numbers_total * 100,
.groups = "drop")
# Dataframe - Mode : LOG, Year : Previous years
t1 <- dataframe %>%
dplyr::filter(species_code %in% 3,
school_type == "FOB",
activity_date %in% five_previous) %>%
dplyr::mutate(numbers_total = sum(estimated_individual / 5,
na.rm = TRUE)) %>%
dplyr::group_by(size_class,
numbers_total) %>%
dplyr::summarise(numbers = sum(estimated_individual / 5,
na.rm = TRUE),
.groups = "drop") %>%
dplyr::group_by(size_class) %>%
dplyr::summarise(log_avg_5_years = numbers / numbers_total * 100,
.groups = "drop")
# Dataframe - Mode : FREE, Year : Report year
t2 <- dataframe %>%
dplyr::filter(species_code %in% 3,
school_type %in% "FSC",
activity_date %in% report_year) %>%
dplyr::mutate(numbers_total = sum(estimated_individual,
na.rm = TRUE)) %>%
dplyr::group_by(size_class,
numbers_total) %>%
dplyr::summarise(numbers = sum(estimated_individual,
na.rm = TRUE),
.groups = "drop") %>%
dplyr::group_by(size_class) %>%
dplyr::summarise(free_current_year = numbers / numbers_total * 100,
.groups = "drop")
# Dataframe - Mode : FREE, Year : Previous years
t3 <- dataframe %>%
dplyr::filter(species_code %in% 3,
school_type %in% "FSC",
activity_date %in% five_previous) %>%
dplyr::mutate(numbers_total = sum(estimated_individual / 5,
na.rm = TRUE)) %>%
dplyr::group_by(size_class,
numbers_total) %>%
dplyr::summarise(numbers = sum(estimated_individual / 5,
na.rm = TRUE),
.groups = "drop") %>%
dplyr::group_by(size_class) %>%
dplyr::summarise(free_avg_5_years = numbers / numbers_total * 100,
.groups = "drop")
# Dataframe - Mode : ALL, Year : Report year
t4 <- dataframe %>%
dplyr::filter(species_code %in% 3,
activity_date %in% report_year) %>%
dplyr::mutate(numbers_total = sum(estimated_individual,
na.rm = TRUE)) %>%
dplyr::group_by(size_class,
numbers_total) %>%
dplyr::summarise(numbers = sum(estimated_individual,
na.rm = TRUE),
.groups = "drop") %>%
dplyr::group_by(size_class) %>%
dplyr::summarise(all_current_year = numbers / numbers_total * 100,
.groups = "drop")
# Dataframe - Mode : ALL, Year : Previous years
t5 <- dataframe %>%
dplyr::filter(species_code %in% 3,
activity_date %in% five_previous) %>%
dplyr::mutate(numbers_total = sum(estimated_individual / 5,
na.rm = TRUE)) %>%
dplyr::group_by(size_class,
numbers_total) %>%
dplyr::summarise(numbers = sum(estimated_individual / 5,
na.rm = TRUE),
.groups = "drop") %>%
dplyr::group_by(size_class) %>%
dplyr::summarise(all_avg_5_years = numbers / numbers_total * 100,
.groups = "drop")
# Merge
table_size_bet_n <- merge(t0, t1, by = "size_class")
table_size_bet_n <- merge(table_size_bet_n, t2, by = "size_class")
table_size_bet_n <- merge(table_size_bet_n, t3, by = "size_class")
table_size_bet_n <- merge(table_size_bet_n, t4, by = "size_class")
table_size_bet_n <- merge(table_size_bet_n, t5, by = "size_class")
# 3.c - Data design for YFT ----
# Dataframe - Mode : LOG, Year : Report year
t0 <- dataframe %>%
dplyr::filter(species_code %in% 1,
school_type %in% "FOB",
activity_date %in% report_year) %>%
dplyr::mutate(numbers_total = sum(estimated_individual,
na.rm = TRUE)) %>%
dplyr::group_by(size_class,
numbers_total) %>%
dplyr::summarise(numbers = sum(estimated_individual,
na.rm = TRUE),
.groups = "drop") %>%
dplyr::group_by(size_class) %>%
dplyr::summarise(log_current_year = numbers / numbers_total * 100,
.groups = "drop")
# Dataframe - Mode : LOG, Year : Previous years
t1 <- dataframe %>%
dplyr::filter(species_code %in% 1,
school_type %in% "FOB",
activity_date %in% five_previous) %>%
dplyr::mutate(numbers_total = sum(estimated_individual / 5,
na.rm = TRUE)) %>%
dplyr::group_by(size_class,
numbers_total) %>%
dplyr::summarise(numbers = sum(estimated_individual / 5,
na.rm = TRUE),
.groups = "drop") %>%
dplyr::group_by(size_class) %>%
dplyr::summarise(log_avg_5_years = numbers / numbers_total * 100,
.groups = "drop")
# Dataframe - Mode : FREE, Year : Report year
t2 <- dataframe %>%
dplyr::filter(species_code %in% 1,
school_type %in% "FSC",
activity_date %in% report_year) %>%
dplyr::mutate(numbers_total = sum(estimated_individual, na.rm = TRUE)) %>%
dplyr::group_by(size_class,
numbers_total) %>%
dplyr::summarise(numbers = sum(estimated_individual, na.rm = TRUE),
.groups = "drop") %>%
dplyr::group_by(size_class) %>%
dplyr::summarise(free_current_year = numbers / numbers_total * 100,
.groups = "drop")
# Dataframe - Mode : FREE, Year : Previous years
t3 <- dataframe %>%
dplyr::filter(species_code %in% 1,
school_type %in% "FSC",
activity_date %in% five_previous) %>%
dplyr::mutate(numbers_total = sum(estimated_individual / 5,
na.rm = TRUE)) %>%
dplyr::group_by(size_class,
numbers_total) %>%
dplyr::summarise(numbers = sum(estimated_individual / 5,
na.rm = TRUE),
.groups = "drop") %>%
dplyr::group_by(size_class) %>%
dplyr::summarise(free_avg_5_years = numbers / numbers_total * 100,
.groups = "drop")
# Dataframe - Mode : ALL, Year : Report year
t4 <- dataframe %>%
dplyr::filter(species_code %in% 1,
activity_date %in% report_year) %>%
dplyr::mutate(numbers_total = sum(estimated_individual, na.rm = TRUE)) %>%
dplyr::group_by(size_class,
numbers_total) %>%
dplyr::summarise(numbers = sum(estimated_individual, na.rm = TRUE),
.groups = "drop") %>%
dplyr::group_by(size_class) %>%
dplyr::summarise(all_current_year = numbers /
numbers_total * 100,
.groups = "drop")
# Dataframe - Mode : ALL, Year : Previous years
t5 <- dataframe %>%
dplyr::filter(species_code %in% 1,
activity_date %in% five_previous) %>%
dplyr::mutate(numbers_total = sum(estimated_individual / 5,
na.rm = TRUE)) %>%
dplyr::group_by(size_class,
numbers_total) %>%
dplyr::summarise(numbers = sum(estimated_individual / 5,
na.rm = TRUE),
.groups = "drop") %>%
dplyr::group_by(size_class) %>%
dplyr::summarise(all_avg_5_years = numbers / numbers_total * 100,
.groups = "drop")
# Merge
table_size_yft_n <- merge(t0, t1, by = "size_class")
table_size_yft_n <- merge(table_size_yft_n, t2, by = "size_class")
table_size_yft_n <- merge(table_size_yft_n, t3, by = "size_class")
table_size_yft_n <- merge(table_size_yft_n, t4, by = "size_class")
table_size_yft_n <- merge(table_size_yft_n, t5, by = "size_class")
# 4 - 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")
}
# 5 - Graphic design ----
## YFT LOG ----
(yft_fob <- ggplot2::ggplot(data = table_size_yft_n) +
ggplot2::theme(panel.background = ggplot2::element_rect(fill = "white",
color = "black")) +
ggplot2::geom_line(ggplot2::aes(x = size_class,
y = log_avg_5_years),
color = "black",
linetype = "dashed") +
ggplot2::geom_line(ggplot2::aes(x = size_class,
y = log_current_year,
color = year),
color = "red") +
ggplot2::labs(x = " ",
y = " ") +
ggplot2::ylim(0, max(table_size_yft_n$log_current_year,
table_size_yft_n$log_avg_5_years) * 1.1) +
ggplot2::xlim(20, 200) +
ggplot2::theme(legend.title = ggplot2::element_blank()) +
ggplot2::annotate("text", x = 160, y = max(table_size_yft_n$log_current_year, table_size_yft_n$log_avg_5_years) * 1.1,
label = "YFT - FOB", color = "black",
hjust = 1,
vjust = 1,
size = 3))
## YFT FSC ----
(yft_free <- ggplot2::ggplot(data = table_size_yft_n) +
ggplot2::theme(panel.background = ggplot2::element_rect(fill = "white",
color = "black")) +
ggplot2::geom_line(ggplot2::aes(x = size_class,
y = free_avg_5_years),
color = "black",
linetype = "dashed") +
ggplot2::geom_line(ggplot2::aes(x = size_class,
y = free_current_year,
color = year),
color = "red") +
ggplot2::labs(x = " ",
y = "Percentage") +
ggplot2::ylim(0, max(table_size_yft_n$free_current_year,
table_size_yft_n$free_avg_5_years) * 1.1) +
ggplot2::xlim(20, 200) +
ggplot2::theme(legend.title = ggplot2::element_blank()) +
ggplot2::annotate("text", x = 160, y = max(table_size_yft_n$free_current_year, table_size_yft_n$free_avg_5_years) * 1.1,
label = "YFT - FSC", color = "black",
hjust = 1,
vjust = 1,
size = 3))
## YFT ALL ----
(yft_all <- ggplot2::ggplot(data = table_size_yft_n) +
ggplot2::theme(panel.background = ggplot2::element_rect(fill = "white",
color = "black")) +
ggplot2::geom_line(ggplot2::aes(x = size_class,
y = all_avg_5_years),
color = "black",
linetype = "dashed") +
ggplot2::geom_line(ggplot2::aes(x = size_class,
y = all_current_year,
color = year),
color = "red") +
ggplot2::labs(x = " ",
y = " ") +
ggplot2::ylim(0, max(table_size_yft_n$all_current_year,
table_size_yft_n$all_avg_5_years) * 1.1) +
ggplot2::xlim(20, 200) +
ggplot2::theme(legend.position = c(0.85, 0.85),
legend.title = ggplot2::element_blank()) +
ggplot2::annotate("text", x = 160, y = max(table_size_yft_n$all_current_year, table_size_yft_n$all_avg_5_years) * 1.1,
label = "YFT - ALL", color = "black",
hjust = 1,
vjust = 1,
size = 3))
## BET LOG ----
(bet_fob <- ggplot2::ggplot(data = table_size_bet_n) +
ggplot2::theme(panel.background = ggplot2::element_rect(fill = "white",
color = "black")) +
ggplot2::geom_line(ggplot2::aes(x = size_class,
y = log_avg_5_years),
color = "black",
linetype = "dashed") +
ggplot2::geom_line(ggplot2::aes(x = size_class,
y = log_current_year,
color = year),
color = "red") +
ggplot2::labs(x = " ",
y = " ") +
ggplot2::ylim(0, max(table_size_bet_n$log_current_year,
table_size_bet_n$log_avg_5_years) * 1.1) +
ggplot2::xlim(20, 200) +
ggplot2::theme(legend.title = ggplot2::element_blank()) +
ggplot2::annotate("text", x = 160, y = max(table_size_bet_n$log_current_year, table_size_bet_n$log_avg_5_years) * 1.1,
label = "BET - LOG", color = "black",
hjust = 1,
vjust = 1,
size = 3))
## BET FSC ----
(bet_free <- ggplot2::ggplot(data = table_size_bet_n) +
ggplot2::theme(panel.background = ggplot2::element_rect(fill = "white",
color = "black")) +
ggplot2::geom_line(ggplot2::aes(x = size_class,
y = free_avg_5_years),
color = "black",
linetype = "dashed") +
ggplot2::geom_line(ggplot2::aes(x = size_class,
y = free_current_year,
color = year),
color = "red") +
ggplot2::labs(x = " ",
y = " ") +
ggplot2::ylim(0, max(table_size_bet_n$free_current_year,
table_size_bet_n$free_avg_5_years) * 1.1) +
ggplot2::xlim(20, 200) +
ggplot2::theme(legend.title = ggplot2::element_blank()) +
ggplot2::annotate("text",
x = 160,
y = max(table_size_bet_n$free_current_year, table_size_bet_n$free_avg_5_years) * 1.1,
label = "BET - FSC", color = "black",
hjust = 1,
vjust = 1,
size = 3))
## BET ALL ----
(bet_all <- ggplot2::ggplot(data = table_size_bet_n) +
ggplot2::theme(panel.background = ggplot2::element_rect(fill = "white",
color = "black")) +
ggplot2::geom_line(ggplot2::aes(x = size_class,
y = all_avg_5_years),
color = "black",
linetype = "dashed") +
ggplot2::geom_line(ggplot2::aes(x = size_class,
y = all_current_year,
color = year),
color = "red") +
ggplot2::labs(x = "size class (cm)",
y = " ") +
ggplot2::ylim(0, max(table_size_bet_n$all_current_year,
table_size_bet_n$all_avg_5_years) * 1.1) +
ggplot2::xlim(20, 200) +
ggplot2::theme(legend.title = ggplot2::element_blank()) +
ggplot2::annotate("text", x = 160, y = max(table_size_bet_n$all_current_year, table_size_bet_n$all_avg_5_years) * 1.1,
label = "BET - ALL", color = "black",
hjust = 1,
vjust = 1,
size = 3))
## SKJ LOG ----
(skj_fob <- ggplot2::ggplot(data = table_size_skj_n) +
ggplot2::theme(panel.background = ggplot2::element_rect(fill = "white",
color = "black")) +
ggplot2::geom_line(ggplot2::aes(x = size_class,
y = log_avg_5_years),
color = "black",
linetype = "dashed") +
ggplot2::geom_line(ggplot2::aes(x = size_class,
y = log_current_year,
color = year),
color = "red") +
ggplot2::labs(x = " ",
y = " ") +
ggplot2::ylim(0, max(table_size_skj_n$log_current_year,
table_size_skj_n$log_avg_5_years) * 1.1) +
ggplot2::xlim(20, 80) +
ggplot2::theme(legend.title = ggplot2::element_blank()) +
ggplot2::annotate("text",
x = 70,
y = max(table_size_skj_n$log_current_year, table_size_skj_n$log_avg_5_years) * 1.1,
label = "SKJ - LOG",
color = "black",
hjust = 1,
vjust = 1,
size = 3))
## SKJ FREE ----
(skj_free <- ggplot2::ggplot(data = table_size_skj_n) +
ggplot2::theme(panel.background = ggplot2::element_rect(fill = "white",
color = "black")) +
ggplot2::geom_line(ggplot2::aes(x = size_class,
y = free_avg_5_years),
color = "black",
linetype = "dashed") +
ggplot2::geom_line(ggplot2::aes(x = size_class,
y = free_current_year,
color = year),
color = "red") +
ggplot2::labs(x = " ",
y = " ") +
ggplot2::ylim(0, max(table_size_skj_n$free_current_year,
table_size_skj_n$free_avg_5_years) * 1.1) +
ggplot2::xlim(20, 80) +
ggplot2::theme(legend.position = c(0.85, 0.85),
legend.title = ggplot2::element_blank()) +
ggplot2::annotate("text",
x = 70,
y = max(table_size_skj_n$free_current_year, table_size_skj_n$free_avg_5_years) * 1.1,
label = "SKJ - FSC",
color = "black",
hjust = 1,
vjust = 1,
size = 3))
## SKJ ALL ----
(skj_all <- ggplot2::ggplot(data = table_size_skj_n) +
ggplot2::theme(panel.background = ggplot2::element_rect(fill = "white",
color = "black")) +
ggplot2::geom_line(ggplot2::aes(x = size_class,
y = all_avg_5_years),
color = "black",
linetype = "dashed") +
ggplot2::geom_line(ggplot2::aes(x = size_class,
y = all_current_year,
color = year),
color = "red") +
ggplot2::labs(x = " ",
y = " ") +
ggplot2::ylim(0, max(table_size_skj_n$all_current_year,
table_size_skj_n$all_avg_5_years) * 1.1) +
ggplot2::xlim(20, 80) +
ggplot2::theme(legend.title = ggplot2::element_blank()) +
ggplot2::annotate("text",
x = 70,
y = max(table_size_skj_n$all_current_year, table_size_skj_n$all_avg_5_years) * 1.1,
label = "SKJ - ALL", color = "black",
hjust = 1,
vjust = 1,
size = 3))
if (graph_type == "plot") {
# legend
colors <- c(stats::setNames(rep("black",
length(years)),
years),
stats::setNames("red", year))
yft_fob_leg <- ggplot2::ggplot(data = table_size_yft_n) +
ggplot2::geom_line(ggplot2::aes(x = size_class,
y = log_avg_5_years,
color = years),
linetype = "dashed") +
ggplot2::geom_line(ggplot2::aes(x = size_class,
y = log_current_year,
color = year)) +
ggplot2::scale_color_manual(values = colors) +
ggplot2::labs(x = " ", y = " ") +
ggplot2::ylim(0, max(table_size_yft_n$log_current_year,
table_size_yft_n$log_avg_5_years) * 1.1) +
ggplot2::xlim(20, 200) +
ggplot2::theme_bw() +
ggplot2::theme(legend.position = "bottom", legend.title = ggplot2::element_blank())
legend <- ggpubr::get_legend(yft_fob_leg)
# grid extra
(ggplot_final <- gridExtra::grid.arrange(yft_fob, bet_fob, skj_fob,
yft_free, bet_free, skj_free,
yft_all, bet_all, skj_all,
bottom = legend))
return(grid::grid.draw(ggplot_final))
} else if (graph_type == "plotly") {
yft_fob <- plotly::ggplotly(yft_fob) %>%
plotly::layout(showlegend = FALSE)
yft_free <- plotly::ggplotly(yft_free) %>%
plotly::layout(showlegend = FALSE)
yft_all <- plotly::ggplotly(yft_all) %>%
plotly::layout(showlegend = FALSE)
# BET
bet_fob <- plotly::ggplotly(bet_fob) %>%
plotly::layout(showlegend = FALSE)
bet_free <- plotly::ggplotly(bet_free) %>%
plotly::layout(showlegend = FALSE)
bet_all <- plotly::ggplotly(bet_all) %>%
plotly::layout(showlegend = FALSE)
# SKJ
skj_fob <- plotly::ggplotly(skj_fob) %>%
plotly::layout(showlegend = FALSE)
skj_free <- plotly::ggplotly(skj_free) %>%
plotly::layout(showlegend = FALSE)
skj_all <- plotly::ggplotly(skj_all) %>%
plotly::layout(showlegend = FALSE)
# Plot
(plotly_size <- plotly::subplot(yft_fob, bet_fob, skj_fob,
yft_free, bet_free, skj_free,
yft_all, bet_all, skj_all, nrows = 3,
titleX = TRUE, titleY = TRUE,
shareX = FALSE, shareY = FALSE,
margin = 0.03))
if (title == TRUE) {
(plotly_size <- plotly_size %>%
plotly::layout(title = list(text = paste0("Size distribution of major tuna catches for the ",
country_legend,
" purse seine fleet in ",
report_year,
" and for an average year ",
"\n",
"representing the period ",
min(five_previous),
"-",
max(five_previous),
" in the ",
ocean_legend,
" ocean."),
font = list(size = 12)),
margin = list(t = 120)))
}
return(plotly_size)
}
}
OB7-IRD/fishi documentation built on Feb. 4, 2025, 2:03 a.m.
R Package Documentation
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Defines functions bio_size_tuna
Documented in bio_size_tuna
#' @name bio_size_tuna
#' @title Size distribution of major tuna catches
#' @description Size distribution of major tuna catches (in percentage of the total number of fishes).
#' @param dataframe {\link[base]{data.frame}} expected. 'Csv' or 'output' of the function {\link[furdeb]{data_extraction}}, which must be done before using the bio_size_tuna() function.
#' @param report_year {\link[base]{integer}} expected. Year of the statistical report.
#' @param graph_type {\link[base]{character}} expected. 'plot' or 'plotly'. 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 | species_code | size_class | estimated_individual
#' -------------------------------------------------------------------------------
#' 2018 | FOB | 6 | 45 | 1.97
#' 2018 | FSC | 6 | 44 | 1.25
#' 2018 | FOB | 6 | 43 | 28.7
#' }
#'
#' Add these columns for an automatic title (optional):
#' \itemize{
#' \item{\code{ country_code}}
#' \item{\code{ ocean_code}}
#' }
#' @return The function return ggplot R plot.
#' @export
bio_size_tuna <- function(dataframe,
report_year,
graph_type = "plot",
title = FALSE) {
# 0 - Global variables assignement ----
species_code <- NULL
school_type <- NULL
activity_date <- NULL
estimated_individual <- NULL
size_class <- NULL
numbers_total <- NULL
numbers <- NULL
log_avg_5_years <- NULL
log_current_year <- NULL
free_current_year <- NULL
free_avg_5_years <- NULL
all_avg_5_years <- NULL
all_current_year <- NULL
# 1 - Arguments verification ----
if (codama::r_type_checking(r_object = report_year,
type = "integer",
output = "logical") != TRUE) {
return(codama::r_type_checking(r_object = report_year,
type = "integer",
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 extraction ----
# Report_year
five_previous <- c((report_year - 1):(report_year - 5))
year <- as.character(report_year)
years <- as.character(paste0(report_year - 5,
"-",
report_year - 1))
# 3.a - Data design for SKJ ----
# Dataframe - Mode : LOG, Year : Report year
t0 <- dataframe %>%
dplyr::filter(species_code %in% 2,
school_type %in% "FOB",
activity_date %in% report_year) %>%
dplyr::mutate(numbers_total = sum(estimated_individual, na.rm = TRUE)) %>%
dplyr::group_by(size_class,
numbers_total) %>%
dplyr::summarise(numbers = sum(estimated_individual, na.rm = TRUE),
.groups = "drop") %>%
dplyr::group_by(size_class) %>%
dplyr::summarise(log_current_year = numbers / numbers_total * 100,
.groups = "drop")
# Dataframe - Mode : LOG, Year : Five previous
t1 <- dataframe %>%
dplyr::filter(species_code %in% 2,
school_type %in% "FOB",
activity_date %in% five_previous) %>%
dplyr::mutate(numbers_total = sum(estimated_individual / 5, na.rm = TRUE)) %>%
dplyr::group_by(size_class,
numbers_total) %>%
dplyr::summarise(numbers = sum(estimated_individual / 5, na.rm = TRUE),
.groups = "drop") %>%
dplyr::group_by(size_class) %>%
dplyr::summarise(log_avg_5_years = numbers / numbers_total * 100,
.groups = "drop")
# Dataframe - Mode : FREE, Year : Report year
t2 <- dataframe %>%
dplyr::filter(species_code %in% 2,
school_type %in% "FSC",
activity_date %in% report_year) %>%
dplyr::mutate(numbers_total = sum(estimated_individual, na.rm = TRUE)) %>%
dplyr::group_by(size_class,
numbers_total) %>%
dplyr::summarise(numbers = sum(estimated_individual, na.rm = TRUE),
.groups = "drop") %>%
dplyr::group_by(size_class) %>%
dplyr::summarise(free_current_year = numbers / numbers_total * 100,
.groups = "drop")
# Dataframe - Mode : FREE, Year : Five previous
t3 <- dataframe %>%
dplyr::filter(species_code %in% 2,
school_type %in% "FSC",
activity_date %in% five_previous) %>%
dplyr::mutate(numbers_total = sum(estimated_individual / 5, na.rm = TRUE)) %>%
dplyr::group_by(size_class,
numbers_total) %>%
dplyr::summarise(numbers = sum(estimated_individual / 5, na.rm = TRUE),
.groups = "drop") %>%
dplyr::group_by(size_class) %>%
dplyr::summarise(free_avg_5_years = numbers / numbers_total * 100,
.groups = "drop")
# Dataframe - Mode : ALL, Year : Report year
t4 <- dataframe %>%
dplyr::filter(species_code %in% 2,
activity_date %in% report_year) %>%
dplyr::mutate(numbers_total = sum(estimated_individual, na.rm = TRUE)) %>%
dplyr::group_by(size_class,
numbers_total) %>%
dplyr::summarise(numbers = sum(estimated_individual, na.rm = TRUE),
.groups = "drop") %>%
dplyr::group_by(size_class) %>%
dplyr::summarise(all_current_year = numbers / numbers_total * 100,
.groups = "drop")
# Dataframe - Mode : ALL, Year : Five previous
t5 <- dataframe %>%
dplyr::filter(species_code %in% 2,
activity_date %in% five_previous) %>%
dplyr::mutate(numbers_total = sum(estimated_individual / 5, na.rm = TRUE)) %>%
dplyr::group_by(size_class,
numbers_total) %>%
dplyr::summarise(numbers = sum(estimated_individual / 5, na.rm = TRUE),
.groups = "drop") %>%
dplyr::group_by(size_class) %>%
dplyr::summarise(all_avg_5_years = numbers / numbers_total * 100,
.groups = "drop")
# Merge
table_size_skj_n <- merge(t0, t1, by = "size_class")
table_size_skj_n <- merge(table_size_skj_n, t2, by = "size_class")
table_size_skj_n <- merge(table_size_skj_n, t3, by = "size_class")
table_size_skj_n <- merge(table_size_skj_n, t4, by = "size_class")
table_size_skj_n <- merge(table_size_skj_n, t5, by = "size_class")
# 3.b - Data design for BET ----
# Dataframe - Mode : LOG, Year : Report year
t0 <- dataframe %>%
dplyr::filter(species_code %in% 3,
school_type %in% "FOB",
activity_date %in% report_year) %>%
dplyr::mutate(numbers_total = sum(estimated_individual,
na.rm = TRUE)) %>%
dplyr::group_by(size_class,
numbers_total) %>%
dplyr::summarise(numbers = sum(estimated_individual,
na.rm = TRUE),
.groups = "drop") %>%
dplyr::group_by(size_class) %>%
dplyr::summarise(log_current_year = numbers / numbers_total * 100,
.groups = "drop")
# Dataframe - Mode : LOG, Year : Previous years
t1 <- dataframe %>%
dplyr::filter(species_code %in% 3,
school_type == "FOB",
activity_date %in% five_previous) %>%
dplyr::mutate(numbers_total = sum(estimated_individual / 5,
na.rm = TRUE)) %>%
dplyr::group_by(size_class,
numbers_total) %>%
dplyr::summarise(numbers = sum(estimated_individual / 5,
na.rm = TRUE),
.groups = "drop") %>%
dplyr::group_by(size_class) %>%
dplyr::summarise(log_avg_5_years = numbers / numbers_total * 100,
.groups = "drop")
# Dataframe - Mode : FREE, Year : Report year
t2 <- dataframe %>%
dplyr::filter(species_code %in% 3,
school_type %in% "FSC",
activity_date %in% report_year) %>%
dplyr::mutate(numbers_total = sum(estimated_individual,
na.rm = TRUE)) %>%
dplyr::group_by(size_class,
numbers_total) %>%
dplyr::summarise(numbers = sum(estimated_individual,
na.rm = TRUE),
.groups = "drop") %>%
dplyr::group_by(size_class) %>%
dplyr::summarise(free_current_year = numbers / numbers_total * 100,
.groups = "drop")
# Dataframe - Mode : FREE, Year : Previous years
t3 <- dataframe %>%
dplyr::filter(species_code %in% 3,
school_type %in% "FSC",
activity_date %in% five_previous) %>%
dplyr::mutate(numbers_total = sum(estimated_individual / 5,
na.rm = TRUE)) %>%
dplyr::group_by(size_class,
numbers_total) %>%
dplyr::summarise(numbers = sum(estimated_individual / 5,
na.rm = TRUE),
.groups = "drop") %>%
dplyr::group_by(size_class) %>%
dplyr::summarise(free_avg_5_years = numbers / numbers_total * 100,
.groups = "drop")
# Dataframe - Mode : ALL, Year : Report year
t4 <- dataframe %>%
dplyr::filter(species_code %in% 3,
activity_date %in% report_year) %>%
dplyr::mutate(numbers_total = sum(estimated_individual,
na.rm = TRUE)) %>%
dplyr::group_by(size_class,
numbers_total) %>%
dplyr::summarise(numbers = sum(estimated_individual,
na.rm = TRUE),
.groups = "drop") %>%
dplyr::group_by(size_class) %>%
dplyr::summarise(all_current_year = numbers / numbers_total * 100,
.groups = "drop")
# Dataframe - Mode : ALL, Year : Previous years
t5 <- dataframe %>%
dplyr::filter(species_code %in% 3,
activity_date %in% five_previous) %>%
dplyr::mutate(numbers_total = sum(estimated_individual / 5,
na.rm = TRUE)) %>%
dplyr::group_by(size_class,
numbers_total) %>%
dplyr::summarise(numbers = sum(estimated_individual / 5,
na.rm = TRUE),
.groups = "drop") %>%
dplyr::group_by(size_class) %>%
dplyr::summarise(all_avg_5_years = numbers / numbers_total * 100,
.groups = "drop")
# Merge
table_size_bet_n <- merge(t0, t1, by = "size_class")
table_size_bet_n <- merge(table_size_bet_n, t2, by = "size_class")
table_size_bet_n <- merge(table_size_bet_n, t3, by = "size_class")
table_size_bet_n <- merge(table_size_bet_n, t4, by = "size_class")
table_size_bet_n <- merge(table_size_bet_n, t5, by = "size_class")
# 3.c - Data design for YFT ----
# Dataframe - Mode : LOG, Year : Report year
t0 <- dataframe %>%
dplyr::filter(species_code %in% 1,
school_type %in% "FOB",
activity_date %in% report_year) %>%
dplyr::mutate(numbers_total = sum(estimated_individual,
na.rm = TRUE)) %>%
dplyr::group_by(size_class,
numbers_total) %>%
dplyr::summarise(numbers = sum(estimated_individual,
na.rm = TRUE),
.groups = "drop") %>%
dplyr::group_by(size_class) %>%
dplyr::summarise(log_current_year = numbers / numbers_total * 100,
.groups = "drop")
# Dataframe - Mode : LOG, Year : Previous years
t1 <- dataframe %>%
dplyr::filter(species_code %in% 1,
school_type %in% "FOB",
activity_date %in% five_previous) %>%
dplyr::mutate(numbers_total = sum(estimated_individual / 5,
na.rm = TRUE)) %>%
dplyr::group_by(size_class,
numbers_total) %>%
dplyr::summarise(numbers = sum(estimated_individual / 5,
na.rm = TRUE),
.groups = "drop") %>%
dplyr::group_by(size_class) %>%
dplyr::summarise(log_avg_5_years = numbers / numbers_total * 100,
.groups = "drop")
# Dataframe - Mode : FREE, Year : Report year
t2 <- dataframe %>%
dplyr::filter(species_code %in% 1,
school_type %in% "FSC",
activity_date %in% report_year) %>%
dplyr::mutate(numbers_total = sum(estimated_individual, na.rm = TRUE)) %>%
dplyr::group_by(size_class,
numbers_total) %>%
dplyr::summarise(numbers = sum(estimated_individual, na.rm = TRUE),
.groups = "drop") %>%
dplyr::group_by(size_class) %>%
dplyr::summarise(free_current_year = numbers / numbers_total * 100,
.groups = "drop")
# Dataframe - Mode : FREE, Year : Previous years
t3 <- dataframe %>%
dplyr::filter(species_code %in% 1,
school_type %in% "FSC",
activity_date %in% five_previous) %>%
dplyr::mutate(numbers_total = sum(estimated_individual / 5,
na.rm = TRUE)) %>%
dplyr::group_by(size_class,
numbers_total) %>%
dplyr::summarise(numbers = sum(estimated_individual / 5,
na.rm = TRUE),
.groups = "drop") %>%
dplyr::group_by(size_class) %>%
dplyr::summarise(free_avg_5_years = numbers / numbers_total * 100,
.groups = "drop")
# Dataframe - Mode : ALL, Year : Report year
t4 <- dataframe %>%
dplyr::filter(species_code %in% 1,
activity_date %in% report_year) %>%
dplyr::mutate(numbers_total = sum(estimated_individual, na.rm = TRUE)) %>%
dplyr::group_by(size_class,
numbers_total) %>%
dplyr::summarise(numbers = sum(estimated_individual, na.rm = TRUE),
.groups = "drop") %>%
dplyr::group_by(size_class) %>%
dplyr::summarise(all_current_year = numbers /
numbers_total * 100,
.groups = "drop")
# Dataframe - Mode : ALL, Year : Previous years
t5 <- dataframe %>%
dplyr::filter(species_code %in% 1,
activity_date %in% five_previous) %>%
dplyr::mutate(numbers_total = sum(estimated_individual / 5,
na.rm = TRUE)) %>%
dplyr::group_by(size_class,
numbers_total) %>%
dplyr::summarise(numbers = sum(estimated_individual / 5,
na.rm = TRUE),
.groups = "drop") %>%
dplyr::group_by(size_class) %>%
dplyr::summarise(all_avg_5_years = numbers / numbers_total * 100,
.groups = "drop")
# Merge
table_size_yft_n <- merge(t0, t1, by = "size_class")
table_size_yft_n <- merge(table_size_yft_n, t2, by = "size_class")
table_size_yft_n <- merge(table_size_yft_n, t3, by = "size_class")
table_size_yft_n <- merge(table_size_yft_n, t4, by = "size_class")
table_size_yft_n <- merge(table_size_yft_n, t5, by = "size_class")
# 4 - 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")
}
# 5 - Graphic design ----
## YFT LOG ----
(yft_fob <- ggplot2::ggplot(data = table_size_yft_n) +
ggplot2::theme(panel.background = ggplot2::element_rect(fill = "white",
color = "black")) +
ggplot2::geom_line(ggplot2::aes(x = size_class,
y = log_avg_5_years),
color = "black",
linetype = "dashed") +
ggplot2::geom_line(ggplot2::aes(x = size_class,
y = log_current_year,
color = year),
color = "red") +
ggplot2::labs(x = " ",
y = " ") +
ggplot2::ylim(0, max(table_size_yft_n$log_current_year,
table_size_yft_n$log_avg_5_years) * 1.1) +
ggplot2::xlim(20, 200) +
ggplot2::theme(legend.title = ggplot2::element_blank()) +
ggplot2::annotate("text", x = 160, y = max(table_size_yft_n$log_current_year, table_size_yft_n$log_avg_5_years) * 1.1,
label = "YFT - FOB", color = "black",
hjust = 1,
vjust = 1,
size = 3))
## YFT FSC ----
(yft_free <- ggplot2::ggplot(data = table_size_yft_n) +
ggplot2::theme(panel.background = ggplot2::element_rect(fill = "white",
color = "black")) +
ggplot2::geom_line(ggplot2::aes(x = size_class,
y = free_avg_5_years),
color = "black",
linetype = "dashed") +
ggplot2::geom_line(ggplot2::aes(x = size_class,
y = free_current_year,
color = year),
color = "red") +
ggplot2::labs(x = " ",
y = "Percentage") +
ggplot2::ylim(0, max(table_size_yft_n$free_current_year,
table_size_yft_n$free_avg_5_years) * 1.1) +
ggplot2::xlim(20, 200) +
ggplot2::theme(legend.title = ggplot2::element_blank()) +
ggplot2::annotate("text", x = 160, y = max(table_size_yft_n$free_current_year, table_size_yft_n$free_avg_5_years) * 1.1,
label = "YFT - FSC", color = "black",
hjust = 1,
vjust = 1,
size = 3))
## YFT ALL ----
(yft_all <- ggplot2::ggplot(data = table_size_yft_n) +
ggplot2::theme(panel.background = ggplot2::element_rect(fill = "white",
color = "black")) +
ggplot2::geom_line(ggplot2::aes(x = size_class,
y = all_avg_5_years),
color = "black",
linetype = "dashed") +
ggplot2::geom_line(ggplot2::aes(x = size_class,
y = all_current_year,
color = year),
color = "red") +
ggplot2::labs(x = " ",
y = " ") +
ggplot2::ylim(0, max(table_size_yft_n$all_current_year,
table_size_yft_n$all_avg_5_years) * 1.1) +
ggplot2::xlim(20, 200) +
ggplot2::theme(legend.position = c(0.85, 0.85),
legend.title = ggplot2::element_blank()) +
ggplot2::annotate("text", x = 160, y = max(table_size_yft_n$all_current_year, table_size_yft_n$all_avg_5_years) * 1.1,
label = "YFT - ALL", color = "black",
hjust = 1,
vjust = 1,
size = 3))
## BET LOG ----
(bet_fob <- ggplot2::ggplot(data = table_size_bet_n) +
ggplot2::theme(panel.background = ggplot2::element_rect(fill = "white",
color = "black")) +
ggplot2::geom_line(ggplot2::aes(x = size_class,
y = log_avg_5_years),
color = "black",
linetype = "dashed") +
ggplot2::geom_line(ggplot2::aes(x = size_class,
y = log_current_year,
color = year),
color = "red") +
ggplot2::labs(x = " ",
y = " ") +
ggplot2::ylim(0, max(table_size_bet_n$log_current_year,
table_size_bet_n$log_avg_5_years) * 1.1) +
ggplot2::xlim(20, 200) +
ggplot2::theme(legend.title = ggplot2::element_blank()) +
ggplot2::annotate("text", x = 160, y = max(table_size_bet_n$log_current_year, table_size_bet_n$log_avg_5_years) * 1.1,
label = "BET - LOG", color = "black",
hjust = 1,
vjust = 1,
size = 3))
## BET FSC ----
(bet_free <- ggplot2::ggplot(data = table_size_bet_n) +
ggplot2::theme(panel.background = ggplot2::element_rect(fill = "white",
color = "black")) +
ggplot2::geom_line(ggplot2::aes(x = size_class,
y = free_avg_5_years),
color = "black",
linetype = "dashed") +
ggplot2::geom_line(ggplot2::aes(x = size_class,
y = free_current_year,
color = year),
color = "red") +
ggplot2::labs(x = " ",
y = " ") +
ggplot2::ylim(0, max(table_size_bet_n$free_current_year,
table_size_bet_n$free_avg_5_years) * 1.1) +
ggplot2::xlim(20, 200) +
ggplot2::theme(legend.title = ggplot2::element_blank()) +
ggplot2::annotate("text",
x = 160,
y = max(table_size_bet_n$free_current_year, table_size_bet_n$free_avg_5_years) * 1.1,
label = "BET - FSC", color = "black",
hjust = 1,
vjust = 1,
size = 3))
## BET ALL ----
(bet_all <- ggplot2::ggplot(data = table_size_bet_n) +
ggplot2::theme(panel.background = ggplot2::element_rect(fill = "white",
color = "black")) +
ggplot2::geom_line(ggplot2::aes(x = size_class,
y = all_avg_5_years),
color = "black",
linetype = "dashed") +
ggplot2::geom_line(ggplot2::aes(x = size_class,
y = all_current_year,
color = year),
color = "red") +
ggplot2::labs(x = "size class (cm)",
y = " ") +
ggplot2::ylim(0, max(table_size_bet_n$all_current_year,
table_size_bet_n$all_avg_5_years) * 1.1) +
ggplot2::xlim(20, 200) +
ggplot2::theme(legend.title = ggplot2::element_blank()) +
ggplot2::annotate("text", x = 160, y = max(table_size_bet_n$all_current_year, table_size_bet_n$all_avg_5_years) * 1.1,
label = "BET - ALL", color = "black",
hjust = 1,
vjust = 1,
size = 3))
## SKJ LOG ----
(skj_fob <- ggplot2::ggplot(data = table_size_skj_n) +
ggplot2::theme(panel.background = ggplot2::element_rect(fill = "white",
color = "black")) +
ggplot2::geom_line(ggplot2::aes(x = size_class,
y = log_avg_5_years),
color = "black",
linetype = "dashed") +
ggplot2::geom_line(ggplot2::aes(x = size_class,
y = log_current_year,
color = year),
color = "red") +
ggplot2::labs(x = " ",
y = " ") +
ggplot2::ylim(0, max(table_size_skj_n$log_current_year,
table_size_skj_n$log_avg_5_years) * 1.1) +
ggplot2::xlim(20, 80) +
ggplot2::theme(legend.title = ggplot2::element_blank()) +
ggplot2::annotate("text",
x = 70,
y = max(table_size_skj_n$log_current_year, table_size_skj_n$log_avg_5_years) * 1.1,
label = "SKJ - LOG",
color = "black",
hjust = 1,
vjust = 1,
size = 3))
## SKJ FREE ----
(skj_free <- ggplot2::ggplot(data = table_size_skj_n) +
ggplot2::theme(panel.background = ggplot2::element_rect(fill = "white",
color = "black")) +
ggplot2::geom_line(ggplot2::aes(x = size_class,
y = free_avg_5_years),
color = "black",
linetype = "dashed") +
ggplot2::geom_line(ggplot2::aes(x = size_class,
y = free_current_year,
color = year),
color = "red") +
ggplot2::labs(x = " ",
y = " ") +
ggplot2::ylim(0, max(table_size_skj_n$free_current_year,
table_size_skj_n$free_avg_5_years) * 1.1) +
ggplot2::xlim(20, 80) +
ggplot2::theme(legend.position = c(0.85, 0.85),
legend.title = ggplot2::element_blank()) +
ggplot2::annotate("text",
x = 70,
y = max(table_size_skj_n$free_current_year, table_size_skj_n$free_avg_5_years) * 1.1,
label = "SKJ - FSC",
color = "black",
hjust = 1,
vjust = 1,
size = 3))
## SKJ ALL ----
(skj_all <- ggplot2::ggplot(data = table_size_skj_n) +
ggplot2::theme(panel.background = ggplot2::element_rect(fill = "white",
color = "black")) +
ggplot2::geom_line(ggplot2::aes(x = size_class,
y = all_avg_5_years),
color = "black",
linetype = "dashed") +
ggplot2::geom_line(ggplot2::aes(x = size_class,
y = all_current_year,
color = year),
color = "red") +
ggplot2::labs(x = " ",
y = " ") +
ggplot2::ylim(0, max(table_size_skj_n$all_current_year,
table_size_skj_n$all_avg_5_years) * 1.1) +
ggplot2::xlim(20, 80) +
ggplot2::theme(legend.title = ggplot2::element_blank()) +
ggplot2::annotate("text",
x = 70,
y = max(table_size_skj_n$all_current_year, table_size_skj_n$all_avg_5_years) * 1.1,
label = "SKJ - ALL", color = "black",
hjust = 1,
vjust = 1,
size = 3))
if (graph_type == "plot") {
# legend
colors <- c(stats::setNames(rep("black",
length(years)),
years),
stats::setNames("red", year))
yft_fob_leg <- ggplot2::ggplot(data = table_size_yft_n) +
ggplot2::geom_line(ggplot2::aes(x = size_class,
y = log_avg_5_years,
color = years),
linetype = "dashed") +
ggplot2::geom_line(ggplot2::aes(x = size_class,
y = log_current_year,
color = year)) +
ggplot2::scale_color_manual(values = colors) +
ggplot2::labs(x = " ", y = " ") +
ggplot2::ylim(0, max(table_size_yft_n$log_current_year,
table_size_yft_n$log_avg_5_years) * 1.1) +
ggplot2::xlim(20, 200) +
ggplot2::theme_bw() +
ggplot2::theme(legend.position = "bottom", legend.title = ggplot2::element_blank())
legend <- ggpubr::get_legend(yft_fob_leg)
# grid extra
(ggplot_final <- gridExtra::grid.arrange(yft_fob, bet_fob, skj_fob,
yft_free, bet_free, skj_free,
yft_all, bet_all, skj_all,
bottom = legend))
return(grid::grid.draw(ggplot_final))
} else if (graph_type == "plotly") {
yft_fob <- plotly::ggplotly(yft_fob) %>%
plotly::layout(showlegend = FALSE)
yft_free <- plotly::ggplotly(yft_free) %>%
plotly::layout(showlegend = FALSE)
yft_all <- plotly::ggplotly(yft_all) %>%
plotly::layout(showlegend = FALSE)
# BET
bet_fob <- plotly::ggplotly(bet_fob) %>%
plotly::layout(showlegend = FALSE)
bet_free <- plotly::ggplotly(bet_free) %>%
plotly::layout(showlegend = FALSE)
bet_all <- plotly::ggplotly(bet_all) %>%
plotly::layout(showlegend = FALSE)
# SKJ
skj_fob <- plotly::ggplotly(skj_fob) %>%
plotly::layout(showlegend = FALSE)
skj_free <- plotly::ggplotly(skj_free) %>%
plotly::layout(showlegend = FALSE)
skj_all <- plotly::ggplotly(skj_all) %>%
plotly::layout(showlegend = FALSE)
# Plot
(plotly_size <- plotly::subplot(yft_fob, bet_fob, skj_fob,
yft_free, bet_free, skj_free,
yft_all, bet_all, skj_all, nrows = 3,
titleX = TRUE, titleY = TRUE,
shareX = FALSE, shareY = FALSE,
margin = 0.03))
if (title == TRUE) {
(plotly_size <- plotly_size %>%
plotly::layout(title = list(text = paste0("Size distribution of major tuna catches for the ",
country_legend,
" purse seine fleet in ",
report_year,
" and for an average year ",
"\n",
"representing the period ",
min(five_previous),
"-",
max(five_previous),
" in the ",
ocean_legend,
" ocean."),
font = list(size = 12)),
margin = list(t = 120)))
}
return(plotly_size)
}
}
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