R/fishing_activity.R
In OB7-IRD/fishi: Package Fisheries Indicators
Defines functions
fishing_activity
Documented in
fishing_activity
#' @name fishing_activity
#' @title Annual number of fishing sets
#' @description Fishing operations. Annual number of fishing sets on FOB-associated and free-swimming tuna 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 fishing_activity() function.
#' @param with_catch {\link[base]{character}} expected. 'with' or 'without' depending on whether we want the number of sets with or without catches. With by default.
#' @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 | positive_set | total_set
#' ------------------------------------------------------
#' 2010-03-06 | FOB | 0 | 0
#' 2010-12-04 | FOB | 0 | 0
#' 2010-05-19 | FOB | 0 | 0
#' }
#' 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
fishing_activity <- function(dataframe,
graph_type = "plot",
with_catch = "with",
title = FALSE) {
# 0 - Global variables assignement ----
activity_date <- NULL
total_set <- NULL
positive_set <- NULL
school_type <- NULL
l_total <- NULL
a_total <- NULL
f_total <- NULL
nb_sets <- NULL
type <- NULL
`%_log` <- NULL
time_period <- NULL
year <- NULL
l_null <- NULL
a_null <- NULL
f_null <- 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 = 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 ----
fishing_activity_t1 <- dataframe %>%
dplyr::mutate(year = lubridate::year(x = activity_date))
fishing_activity_t1$positive_set <- as.integer(fishing_activity_t1$positive_set)
# db a1 - Add : Number of total, positive, and null sets by ALL
a1 <- fishing_activity_t1 %>%
dplyr::group_by(year) %>%
dplyr::summarise(a_total = sum(total_set,
na.rm = TRUE),
a_positive = sum(positive_set,
na.rm = TRUE),
a_null = sum(total_set - positive_set,
na.rm = TRUE),
.groups = "drop")
# db a2 - Add : Number of total, positive, and null sets by FOB
a2 <- fishing_activity_t1 %>%
dplyr::filter(school_type %in% "FOB") %>%
dplyr::group_by(year) %>%
dplyr::summarise(l_total = sum(total_set,
na.rm = TRUE),
l_positive = sum(positive_set,
na.rm = TRUE),
l_null = sum(total_set - positive_set,
na.rm = TRUE),
.groups = "drop")
# db a3 - Add : Number of total, positive, and null sets by FSC
a3 <- fishing_activity_t1 %>%
dplyr::filter(school_type %in% "FSC" | school_type %in% "UND") %>%
dplyr::group_by(year) %>%
dplyr::summarise(f_total = sum(total_set,
na.rm = TRUE),
f_positive = sum(positive_set,
na.rm = TRUE),
f_null = sum(total_set - positive_set,
na.rm = TRUE),
.groups = "drop")
# Merge db by Year
table_sets <- merge(a1, a2, by = "year")
table_sets <- merge(table_sets, a3, by = "year")
# For ggplot graph
if (with_catch == "with") {
table_sets <- table_sets %>%
dplyr::mutate("%_log" = l_total / a_total * 100)
set <- as.matrix(table_sets[, c(1, 5, 8, 11)])
t_set <- as.data.frame(set)
t_set <- t_set %>%
dplyr::rename(`Free swimming schools` = l_total,
`FOB-associated schools` = f_total)
t_set_pivot <- tidyr::pivot_longer(t_set,
cols = c(2:3),
names_to = "type",
values_to = "nb_sets")
perc <- 35
name_set <- "Number of sets"
} else if (with_catch == "without") {
table_sets <- table_sets %>%
dplyr::mutate("%_log" = l_null / a_null * 100)
set <- as.matrix(table_sets[, c(1, 7, 10, 11)])
t_set <- as.data.frame(set)
t_set <- t_set %>%
dplyr::rename(`Free swimming schools` = l_null,
`FOB-associated schools` = f_null)
t_set_pivot <- tidyr::pivot_longer(t_set,
cols = c(2:3),
names_to = "type",
values_to = "nb_sets")
perc <- 8.01
name_set <- "Number of sets without catches"
}
# 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$vessel_type_code,
referential = "vessel_simple_type",
manipulation = "legend")
# time_period
time_period <- c(unique(min(fishing_activity_t1$year):max(fishing_activity_t1$year)))
}
# 4 - Graphic design ----
t_set$`%_log` <- round(t_set$`%_log`, 3)
ggplot_graph <- ggplot2::ggplot() +
# Theme and background
ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45,
hjust = 1,
size = 13),
axis.text.y = ggplot2::element_text(size = 13),
axis.title.y = ggplot2::element_text(size = 14),
legend.position = "top",
legend.justification = "right",
legend.text = ggplot2::element_text(size = 10),
panel.background = ggplot2::element_rect(fill = "white",
color = "black"),
panel.grid.major = ggplot2::element_blank(),
panel.grid.minor.x = ggplot2::element_blank(),
panel.grid.major.y = ggplot2::element_line(linewidth = 0.2,
color = "gray90")) +
ggplot2::scale_x_continuous(breaks = t_set$year) +
# Lines and point
ggplot2::geom_bar(data = t_set_pivot,
mapping = ggplot2::aes(x = year,
y = nb_sets,
fill = type),
stat = "identity",
colour = "black") +
ggplot2::scale_fill_manual(values = c("grey95", "grey26")) +
ggplot2::geom_line(data = t_set,
ggplot2::aes(x = year,
y = `%_log` * perc),
size = 0.5,
linetype = "longdash",
color = "black") +
ggplot2::geom_point(data = t_set,
ggplot2::aes(x = year,
y = `%_log` * perc)) +
ggplot2::labs(fill = "",
x = "") +
ggplot2::scale_y_continuous(name = name_set,
sec.axis = ggplot2::sec_axis(~ . / perc,
name = "% FOB-associated sets"))
# Add title conditionally
if (title == TRUE) {
title <- paste0("Fishing operations. Annual number of fishing sets in the ",
country_legend, " ",
vessel_type_legend,
" fishery \n",
"on FOB-associated and free-swimming tuna schools during ",
min(time_period),
"-",
max(time_period),
" in the ",
ocean_legend,
" ocean.")
ggplot_graph <- ggplot_graph +
ggplot2::ggtitle(title)
}
if (graph_type == "plot") {
return(ggplot_graph)
} else if (graph_type == "plotly") {
plotly_graph <- plotly::ggplotly(ggplot_graph)
# Add a title
if (title == TRUE) {
plotly_graph <- plotly_graph %>%
plotly::layout(title = list(text = title,
font = list(size = 15)),
margin = list(t = 120))
}
# Plot the plotly
plotly_graph %>%
plotly::layout(legend = list(orientation = "v",
x = 0.7,
y = 0.95))
} else if (graph_type == "table") {
table_sets <- table_sets %>%
dplyr::rename("% on FOB" = "%_log")
table_sets <- round(table_sets, 0)
as.data.frame(table_sets)
}
}
OB7-IRD/fishi documentation built on Feb. 4, 2025, 2:03 a.m.
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Defines functions fishing_activity
Documented in fishing_activity
#' @name fishing_activity
#' @title Annual number of fishing sets
#' @description Fishing operations. Annual number of fishing sets on FOB-associated and free-swimming tuna 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 fishing_activity() function.
#' @param with_catch {\link[base]{character}} expected. 'with' or 'without' depending on whether we want the number of sets with or without catches. With by default.
#' @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 | positive_set | total_set
#' ------------------------------------------------------
#' 2010-03-06 | FOB | 0 | 0
#' 2010-12-04 | FOB | 0 | 0
#' 2010-05-19 | FOB | 0 | 0
#' }
#' 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
fishing_activity <- function(dataframe,
graph_type = "plot",
with_catch = "with",
title = FALSE) {
# 0 - Global variables assignement ----
activity_date <- NULL
total_set <- NULL
positive_set <- NULL
school_type <- NULL
l_total <- NULL
a_total <- NULL
f_total <- NULL
nb_sets <- NULL
type <- NULL
`%_log` <- NULL
time_period <- NULL
year <- NULL
l_null <- NULL
a_null <- NULL
f_null <- 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 = 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 ----
fishing_activity_t1 <- dataframe %>%
dplyr::mutate(year = lubridate::year(x = activity_date))
fishing_activity_t1$positive_set <- as.integer(fishing_activity_t1$positive_set)
# db a1 - Add : Number of total, positive, and null sets by ALL
a1 <- fishing_activity_t1 %>%
dplyr::group_by(year) %>%
dplyr::summarise(a_total = sum(total_set,
na.rm = TRUE),
a_positive = sum(positive_set,
na.rm = TRUE),
a_null = sum(total_set - positive_set,
na.rm = TRUE),
.groups = "drop")
# db a2 - Add : Number of total, positive, and null sets by FOB
a2 <- fishing_activity_t1 %>%
dplyr::filter(school_type %in% "FOB") %>%
dplyr::group_by(year) %>%
dplyr::summarise(l_total = sum(total_set,
na.rm = TRUE),
l_positive = sum(positive_set,
na.rm = TRUE),
l_null = sum(total_set - positive_set,
na.rm = TRUE),
.groups = "drop")
# db a3 - Add : Number of total, positive, and null sets by FSC
a3 <- fishing_activity_t1 %>%
dplyr::filter(school_type %in% "FSC" | school_type %in% "UND") %>%
dplyr::group_by(year) %>%
dplyr::summarise(f_total = sum(total_set,
na.rm = TRUE),
f_positive = sum(positive_set,
na.rm = TRUE),
f_null = sum(total_set - positive_set,
na.rm = TRUE),
.groups = "drop")
# Merge db by Year
table_sets <- merge(a1, a2, by = "year")
table_sets <- merge(table_sets, a3, by = "year")
# For ggplot graph
if (with_catch == "with") {
table_sets <- table_sets %>%
dplyr::mutate("%_log" = l_total / a_total * 100)
set <- as.matrix(table_sets[, c(1, 5, 8, 11)])
t_set <- as.data.frame(set)
t_set <- t_set %>%
dplyr::rename(`Free swimming schools` = l_total,
`FOB-associated schools` = f_total)
t_set_pivot <- tidyr::pivot_longer(t_set,
cols = c(2:3),
names_to = "type",
values_to = "nb_sets")
perc <- 35
name_set <- "Number of sets"
} else if (with_catch == "without") {
table_sets <- table_sets %>%
dplyr::mutate("%_log" = l_null / a_null * 100)
set <- as.matrix(table_sets[, c(1, 7, 10, 11)])
t_set <- as.data.frame(set)
t_set <- t_set %>%
dplyr::rename(`Free swimming schools` = l_null,
`FOB-associated schools` = f_null)
t_set_pivot <- tidyr::pivot_longer(t_set,
cols = c(2:3),
names_to = "type",
values_to = "nb_sets")
perc <- 8.01
name_set <- "Number of sets without catches"
}
# 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$vessel_type_code,
referential = "vessel_simple_type",
manipulation = "legend")
# time_period
time_period <- c(unique(min(fishing_activity_t1$year):max(fishing_activity_t1$year)))
}
# 4 - Graphic design ----
t_set$`%_log` <- round(t_set$`%_log`, 3)
ggplot_graph <- ggplot2::ggplot() +
# Theme and background
ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45,
hjust = 1,
size = 13),
axis.text.y = ggplot2::element_text(size = 13),
axis.title.y = ggplot2::element_text(size = 14),
legend.position = "top",
legend.justification = "right",
legend.text = ggplot2::element_text(size = 10),
panel.background = ggplot2::element_rect(fill = "white",
color = "black"),
panel.grid.major = ggplot2::element_blank(),
panel.grid.minor.x = ggplot2::element_blank(),
panel.grid.major.y = ggplot2::element_line(linewidth = 0.2,
color = "gray90")) +
ggplot2::scale_x_continuous(breaks = t_set$year) +
# Lines and point
ggplot2::geom_bar(data = t_set_pivot,
mapping = ggplot2::aes(x = year,
y = nb_sets,
fill = type),
stat = "identity",
colour = "black") +
ggplot2::scale_fill_manual(values = c("grey95", "grey26")) +
ggplot2::geom_line(data = t_set,
ggplot2::aes(x = year,
y = `%_log` * perc),
size = 0.5,
linetype = "longdash",
color = "black") +
ggplot2::geom_point(data = t_set,
ggplot2::aes(x = year,
y = `%_log` * perc)) +
ggplot2::labs(fill = "",
x = "") +
ggplot2::scale_y_continuous(name = name_set,
sec.axis = ggplot2::sec_axis(~ . / perc,
name = "% FOB-associated sets"))
# Add title conditionally
if (title == TRUE) {
title <- paste0("Fishing operations. Annual number of fishing sets in the ",
country_legend, " ",
vessel_type_legend,
" fishery \n",
"on FOB-associated and free-swimming tuna schools during ",
min(time_period),
"-",
max(time_period),
" in the ",
ocean_legend,
" ocean.")
ggplot_graph <- ggplot_graph +
ggplot2::ggtitle(title)
}
if (graph_type == "plot") {
return(ggplot_graph)
} else if (graph_type == "plotly") {
plotly_graph <- plotly::ggplotly(ggplot_graph)
# Add a title
if (title == TRUE) {
plotly_graph <- plotly_graph %>%
plotly::layout(title = list(text = title,
font = list(size = 15)),
margin = list(t = 120))
}
# Plot the plotly
plotly_graph %>%
plotly::layout(legend = list(orientation = "v",
x = 0.7,
y = 0.95))
} else if (graph_type == "table") {
table_sets <- table_sets %>%
dplyr::rename("% on FOB" = "%_log")
table_sets <- round(table_sets, 0)
as.data.frame(table_sets)
}
}
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