R/fishing_effort.R
In OB7-IRD/fishi: Package Fisheries Indicators
Defines functions
fishing_effort
Documented in
fishing_effort
#' @name fishing_effort
#' @title Annual total number of fishing and searching days
#' @description Changes in nominal effort over time. Annual total number of fishing and searching days.
#' @param dataframe {\link[base]{data.frame}} expected. 'Csv' or 'output' of the function {\link[furdeb]{data_extraction}}, which must be done before using the fishing_effort() function.
#' @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 | vessel_code | flag | fleet | vessel_label | landing_date | ocean_code | harbour_label | set_duration | vessel_type_code | total_hour_at_sea | total_hour_fished
#' --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#' 2010-03-06 | 679 | France | France | MEN GOE | 2010-03-06 | 1 | ABIDJAN | 0.0 | 1 | 7.0 | 1.005
#' 2010-12-04 | 492 | France | France | GUERIDEN | 2010-12-05 | 1 | TEMA | 0.0 | 1 | 24.0 | 11.838
#' 2010-05-19 | 401 | France | France | VIA HARMATTAN | 2010-05-19 | 1 | ABIDJAN | 0.0 | 1 | 8.0 | 2.048
#' }
#' Add these columns for an automatic title (optional):
#' \itemize{
#' \item{\code{ country_code}}
#' }
#' @return The function return ggplot R plot.
#' @export
fishing_effort <- function(dataframe,
graph_type = "plot",
title = FALSE) {
# 0 - Global variables assignement ----
activity_date <- NULL
landing_date <- NULL
ocean_code <- NULL
fleet <- NULL
vessel_type_code <- NULL
flag <- NULL
vessel_code <- NULL
vessel_label <- NULL
harbour_label <- NULL
year <- NULL
total_hour_at_sea <- NULL
total_hour_fished <- NULL
set_duration <- NULL
fishing_days <- NULL
searching_days <- NULL
fishing_days_1000 <- NULL
searching_days_1000 <- NULL
landing_in_activity_year <- NULL
nb_landings_in_activity_year <- NULL
nb_days <- NULL
vessel_type <- NULL
time_period <- NULL
# 1 - Arguments verification ----
if (codama::r_type_checking(r_object = graph_type,
type = "character",
output = "logical") != TRUE) {
return(codama::r_type_checking(r_object = vessel_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 ----
#Adding columns years
fishing_effort_t1 <- dataframe %>%
dplyr::mutate(year = lubridate::year(x = activity_date),
landing_in_activity_year = dplyr::case_when(landing_date == activity_date ~ 1,
TRUE ~ 0))
#Adding columns by condition (vtmer, vtpec, ndurcal, nbdays)
fishing_effort_t2 <- fishing_effort_t1 %>%
dplyr::group_by(ocean_code,
fleet,
vessel_type_code,
flag,
vessel_code,
vessel_label,
harbour_label,
landing_date,
year) %>%
dplyr::summarise("total_hour_at_sea" = sum(total_hour_at_sea,
na.rm = TRUE),
"total_hour_fished" = sum(total_hour_fished,
na.rm = TRUE),
"set_duration" = sum(set_duration,
na.rm = TRUE),
"nb_days" = max(activity_date) - min(activity_date),
"nb_landings_in_activity_year" = sum(landing_in_activity_year,
na.rm = TRUE),
.groups = "drop")
#Group rows by conditions
fishing_effort_t2b <- fishing_effort_t2 %>%
dplyr::group_by(ocean_code,
fleet,
flag,
vessel_type_code,
year,
total_hour_at_sea,
total_hour_fished,
set_duration) %>%
dplyr::reframe(nb_landings_in_activity_year = nb_landings_in_activity_year,
nb_days = nb_days)
#Adding columns by years (daysatsea, fishingdays, ...)
fishing_effort_t3 <- fishing_effort_t2b %>%
dplyr::group_by(year) %>%
dplyr::reframe("days_at_sea" = round(sum(total_hour_at_sea / 24,
na.rm = TRUE)),
"nb_landings_in_activity_year" = sum(nb_landings_in_activity_year, na.rm = TRUE),
"average_nb_days_by_trip" = mean(nb_days, 0),
"fishing_days_1000" = ifelse(test = ocean_code == 1,
yes = round(sum(total_hour_fished / 12,
na.rm = TRUE)),
no = round(sum(total_hour_fished / 13,
na.rm = TRUE))),
"set_duration_in_days" = ifelse(test = ocean_code == 1,
yes = round(sum(set_duration / 12,
na.rm = TRUE)),
no = round(sum(set_duration / 13,
na.rm = TRUE))),
"searching_days_1000" = ifelse(test = ocean_code == 1,
yes = round(sum((total_hour_fished - set_duration) / 12,
na.rm = TRUE)),
no = round(sum((total_hour_fished - set_duration) / 13, na.rm = TRUE))))
#remove duplicates
fishing_effort_t4 <- unique(fishing_effort_t3[, c("year",
"nb_landings_in_activity_year",
"average_nb_days_by_trip",
"days_at_sea",
"fishing_days_1000",
"set_duration_in_days",
"searching_days_1000")])
table_effort <- fishing_effort_t4 %>%
dplyr::mutate("fishing_days" = fishing_days_1000 / 1000,
"searching_days" = searching_days_1000 / 1000)
# 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$vessel_type_code,
referential = "vessel_simple_type",
manipulation = "legend")
# time_period
time_period <- c(unique(min(fishing_effort_t1$year):max(fishing_effort_t1$year)))
}
# 4 - Graphic design ----
(ggplot_table_effort <- ggplot2::ggplot(data = table_effort) +
ggplot2::theme(panel.background = ggplot2::element_rect(fill = "white",
color = "black"),
axis.text.x = ggplot2::element_text(angle = 45,
hjust = 1,
size = 13),
axis.text.y = ggplot2::element_text(size = 13),
axis.title.y = ggplot2::element_text(size = 14),
legend.position = "top",
legend.justification = "right",
legend.text = ggplot2::element_text(size = 10)) +
ggplot2::geom_hline(yintercept = c(1:5),
color = "grey",
linetype = "longdash",
alpha = 0.5) +
ggplot2::geom_line(ggplot2::aes(x = year,
y = fishing_days,
color = "Fishing")) +
ggplot2::geom_line(ggplot2::aes(x = year,
y = searching_days,
color = "Searching"),
linetype = "dashed") +
ggplot2::scale_color_manual(values = c("black",
"grey")) +
ggplot2::geom_point(ggplot2::aes(x = year,
y = fishing_days)) +
ggplot2::geom_point(ggplot2::aes(x = year,
y = searching_days),
shape = 4) +
ggplot2::labs(colour = "",
x = "",
y = "Activity duration (x1000 days)") +
ggplot2::ylim(0.5,
5) +
ggplot2::scale_x_continuous(breaks = unique(table_effort$year)))
# Add title conditionally
if (title == TRUE) {
title <- paste0("Changes in nominal effort over time. Annual total number of fishing and searching ", "\n",
"days for the ",
country_legend, " ",
vessel_type_legend,
" in the ",
ocean_legend,
" ocean during ",
min(time_period),
"-",
max(time_period), ".")
ggplot_table_effort <- ggplot_table_effort +
ggplot2::ggtitle(title)
}
if (graph_type == "plot") {
return(ggplot_table_effort)
} else if (graph_type == "plotly") {
plotly_graph <- plotly::ggplotly(ggplot_table_effort)
# Add a title
if (title == TRUE) {
plotly_graph <- plotly_graph %>%
plotly::layout(title = list(text = title,
font = list(size = 17)),
margin = list(t = 120))
}
# Plot the plotly
plotly_graph %>%
plotly::layout(legend = list(orientation = "v",
x = 0.85,
y = 0.95))
} else if (graph_type == "table") {
table_effort$average_nb_days_by_trip <- round(table_effort$average_nb_days_by_trip, 0)
table_effort$average_nb_days_by_trip <- as.integer(table_effort$average_nb_days_by_trip)
table_effort <- table_effort[, c(-8:-9)]
# rename columns
table_effort <- table_effort %>%
dplyr::rename("Days at sea" = "days_at_sea",
"Fishing days" = "fishing_days_1000",
"Set duration in days" = "set_duration_in_days",
"Searching days" = "searching_days_1000",
"Number of trips" = "nb_landings_in_activity_year",
"Mean duration in days" = "average_nb_days_by_trip")
as.data.frame(table_effort)
}
}
OB7-IRD/fishi documentation built on Feb. 4, 2025, 2:03 a.m.
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Defines functions fishing_effort
Documented in fishing_effort
#' @name fishing_effort
#' @title Annual total number of fishing and searching days
#' @description Changes in nominal effort over time. Annual total number of fishing and searching days.
#' @param dataframe {\link[base]{data.frame}} expected. 'Csv' or 'output' of the function {\link[furdeb]{data_extraction}}, which must be done before using the fishing_effort() function.
#' @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 | vessel_code | flag | fleet | vessel_label | landing_date | ocean_code | harbour_label | set_duration | vessel_type_code | total_hour_at_sea | total_hour_fished
#' --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#' 2010-03-06 | 679 | France | France | MEN GOE | 2010-03-06 | 1 | ABIDJAN | 0.0 | 1 | 7.0 | 1.005
#' 2010-12-04 | 492 | France | France | GUERIDEN | 2010-12-05 | 1 | TEMA | 0.0 | 1 | 24.0 | 11.838
#' 2010-05-19 | 401 | France | France | VIA HARMATTAN | 2010-05-19 | 1 | ABIDJAN | 0.0 | 1 | 8.0 | 2.048
#' }
#' Add these columns for an automatic title (optional):
#' \itemize{
#' \item{\code{ country_code}}
#' }
#' @return The function return ggplot R plot.
#' @export
fishing_effort <- function(dataframe,
graph_type = "plot",
title = FALSE) {
# 0 - Global variables assignement ----
activity_date <- NULL
landing_date <- NULL
ocean_code <- NULL
fleet <- NULL
vessel_type_code <- NULL
flag <- NULL
vessel_code <- NULL
vessel_label <- NULL
harbour_label <- NULL
year <- NULL
total_hour_at_sea <- NULL
total_hour_fished <- NULL
set_duration <- NULL
fishing_days <- NULL
searching_days <- NULL
fishing_days_1000 <- NULL
searching_days_1000 <- NULL
landing_in_activity_year <- NULL
nb_landings_in_activity_year <- NULL
nb_days <- NULL
vessel_type <- NULL
time_period <- NULL
# 1 - Arguments verification ----
if (codama::r_type_checking(r_object = graph_type,
type = "character",
output = "logical") != TRUE) {
return(codama::r_type_checking(r_object = vessel_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 ----
#Adding columns years
fishing_effort_t1 <- dataframe %>%
dplyr::mutate(year = lubridate::year(x = activity_date),
landing_in_activity_year = dplyr::case_when(landing_date == activity_date ~ 1,
TRUE ~ 0))
#Adding columns by condition (vtmer, vtpec, ndurcal, nbdays)
fishing_effort_t2 <- fishing_effort_t1 %>%
dplyr::group_by(ocean_code,
fleet,
vessel_type_code,
flag,
vessel_code,
vessel_label,
harbour_label,
landing_date,
year) %>%
dplyr::summarise("total_hour_at_sea" = sum(total_hour_at_sea,
na.rm = TRUE),
"total_hour_fished" = sum(total_hour_fished,
na.rm = TRUE),
"set_duration" = sum(set_duration,
na.rm = TRUE),
"nb_days" = max(activity_date) - min(activity_date),
"nb_landings_in_activity_year" = sum(landing_in_activity_year,
na.rm = TRUE),
.groups = "drop")
#Group rows by conditions
fishing_effort_t2b <- fishing_effort_t2 %>%
dplyr::group_by(ocean_code,
fleet,
flag,
vessel_type_code,
year,
total_hour_at_sea,
total_hour_fished,
set_duration) %>%
dplyr::reframe(nb_landings_in_activity_year = nb_landings_in_activity_year,
nb_days = nb_days)
#Adding columns by years (daysatsea, fishingdays, ...)
fishing_effort_t3 <- fishing_effort_t2b %>%
dplyr::group_by(year) %>%
dplyr::reframe("days_at_sea" = round(sum(total_hour_at_sea / 24,
na.rm = TRUE)),
"nb_landings_in_activity_year" = sum(nb_landings_in_activity_year, na.rm = TRUE),
"average_nb_days_by_trip" = mean(nb_days, 0),
"fishing_days_1000" = ifelse(test = ocean_code == 1,
yes = round(sum(total_hour_fished / 12,
na.rm = TRUE)),
no = round(sum(total_hour_fished / 13,
na.rm = TRUE))),
"set_duration_in_days" = ifelse(test = ocean_code == 1,
yes = round(sum(set_duration / 12,
na.rm = TRUE)),
no = round(sum(set_duration / 13,
na.rm = TRUE))),
"searching_days_1000" = ifelse(test = ocean_code == 1,
yes = round(sum((total_hour_fished - set_duration) / 12,
na.rm = TRUE)),
no = round(sum((total_hour_fished - set_duration) / 13, na.rm = TRUE))))
#remove duplicates
fishing_effort_t4 <- unique(fishing_effort_t3[, c("year",
"nb_landings_in_activity_year",
"average_nb_days_by_trip",
"days_at_sea",
"fishing_days_1000",
"set_duration_in_days",
"searching_days_1000")])
table_effort <- fishing_effort_t4 %>%
dplyr::mutate("fishing_days" = fishing_days_1000 / 1000,
"searching_days" = searching_days_1000 / 1000)
# 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$vessel_type_code,
referential = "vessel_simple_type",
manipulation = "legend")
# time_period
time_period <- c(unique(min(fishing_effort_t1$year):max(fishing_effort_t1$year)))
}
# 4 - Graphic design ----
(ggplot_table_effort <- ggplot2::ggplot(data = table_effort) +
ggplot2::theme(panel.background = ggplot2::element_rect(fill = "white",
color = "black"),
axis.text.x = ggplot2::element_text(angle = 45,
hjust = 1,
size = 13),
axis.text.y = ggplot2::element_text(size = 13),
axis.title.y = ggplot2::element_text(size = 14),
legend.position = "top",
legend.justification = "right",
legend.text = ggplot2::element_text(size = 10)) +
ggplot2::geom_hline(yintercept = c(1:5),
color = "grey",
linetype = "longdash",
alpha = 0.5) +
ggplot2::geom_line(ggplot2::aes(x = year,
y = fishing_days,
color = "Fishing")) +
ggplot2::geom_line(ggplot2::aes(x = year,
y = searching_days,
color = "Searching"),
linetype = "dashed") +
ggplot2::scale_color_manual(values = c("black",
"grey")) +
ggplot2::geom_point(ggplot2::aes(x = year,
y = fishing_days)) +
ggplot2::geom_point(ggplot2::aes(x = year,
y = searching_days),
shape = 4) +
ggplot2::labs(colour = "",
x = "",
y = "Activity duration (x1000 days)") +
ggplot2::ylim(0.5,
5) +
ggplot2::scale_x_continuous(breaks = unique(table_effort$year)))
# Add title conditionally
if (title == TRUE) {
title <- paste0("Changes in nominal effort over time. Annual total number of fishing and searching ", "\n",
"days for the ",
country_legend, " ",
vessel_type_legend,
" in the ",
ocean_legend,
" ocean during ",
min(time_period),
"-",
max(time_period), ".")
ggplot_table_effort <- ggplot_table_effort +
ggplot2::ggtitle(title)
}
if (graph_type == "plot") {
return(ggplot_table_effort)
} else if (graph_type == "plotly") {
plotly_graph <- plotly::ggplotly(ggplot_table_effort)
# Add a title
if (title == TRUE) {
plotly_graph <- plotly_graph %>%
plotly::layout(title = list(text = title,
font = list(size = 17)),
margin = list(t = 120))
}
# Plot the plotly
plotly_graph %>%
plotly::layout(legend = list(orientation = "v",
x = 0.85,
y = 0.95))
} else if (graph_type == "table") {
table_effort$average_nb_days_by_trip <- round(table_effort$average_nb_days_by_trip, 0)
table_effort$average_nb_days_by_trip <- as.integer(table_effort$average_nb_days_by_trip)
table_effort <- table_effort[, c(-8:-9)]
# rename columns
table_effort <- table_effort %>%
dplyr::rename("Days at sea" = "days_at_sea",
"Fishing days" = "fishing_days_1000",
"Set duration in days" = "set_duration_in_days",
"Searching days" = "searching_days_1000",
"Number of trips" = "nb_landings_in_activity_year",
"Mean duration in days" = "average_nb_days_by_trip")
as.data.frame(table_effort)
}
}
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