R/catch_per_unit_effort.R
In OB7-IRD/fishi: Package Fisheries Indicators
Defines functions
catch_per_unit_effort
Documented in
catch_per_unit_effort
#' @name catch_per_unit_effort
#' @title Annual catch rates (in t per searching day)
#' @description Annual catch rates (in t per searching day) on FOB- associated and free-swimming tuna schools (FSC).
#' @param dataframe1 {\link[base]{data.frame}} expected. 'Csv' or 'output' of the function {\link[furdeb]{data_extraction}}, which must be done before using the catch_per_searching_day() function.
#' @param dataframe2 {\link[base]{data.frame}} expected. 'Csv' or 'output' of the function {\link[furdeb]{data_extraction}}, which must be done before using the catch_per_searching_day() function.
#' @param fishing_type {\link[base]{character}} expected. 'FOB' or '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}]:
#' Dataframe 1:
#' \preformatted{
#' activity_date | species_code | school_type | set_duration | positive_set | total_set | total_catch_weight | total_hour_fished
#' -------------------------------------------------------------------------------------------------------------------------------
#' 1999-07-09 | 2 | FOB | 3.54 | 1 | 1 | 119.0 | 12.1
#' 1999-07-09 | 1 | FOB | 3.54 | 1 | 1 | 20.6 | 12.1
#' 1999-07-09 | 1 | FOB | 3.54 | 1 | 1 | 24.4 | 12.1
#' }
#'
#' Dataframe 2:
#' \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
catch_per_unit_effort <- function(dataframe1,
dataframe2,
fishing_type,
graph_type = "plot",
title = FALSE) {
# 0 - Global variables assignement ----
activity_date <- NULL
total_hour_fished <- NULL
set_duration <- NULL
yft <- NULL
t_recherche <- NULL
skj <- NULL
bet <- NULL
alb <- NULL
total <- 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 extraction ----
# 3.a - Data design for FOB----
#Creation of t0
dataframe1 <- dataframe1 %>%
dplyr::mutate(year = lubridate::year(x = activity_date))
dataframe2 <- dataframe2 %>%
dplyr::mutate(year = lubridate::year(x = activity_date))
ocean_code <- dataframe1$ocean_code[1]
if (ocean_code == 1) {
set_time <- as.integer(x = 12)
} else if (ocean_code == 2) {
set_time <- as.integer(x = 13)
}
t0 <- dataframe2 %>%
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")
#Creation of t1 database from dataframe2
t1 <- dataframe1 %>%
dplyr::group_by(year) %>%
dplyr::summarise(yft = sum(dplyr::case_when(school_type %in% "FOB" & species_code == 1 ~ total_catch_weight,
TRUE ~ 0), na.rm = TRUE),
skj = sum(dplyr::case_when(school_type %in% "FOB" & species_code == 2 ~ total_catch_weight,
TRUE ~ 0), na.rm = TRUE),
bet = sum(dplyr::case_when(school_type %in% "FOB" & species_code == 3 ~ total_catch_weight,
TRUE ~ 0), na.rm = TRUE),
alb = sum(dplyr::case_when(school_type %in% "FOB" & species_code == 4 ~ total_catch_weight,
TRUE ~ 0), na.rm = TRUE),
total = sum(dplyr::case_when(school_type %in% "FOB" ~ total_catch_weight,
TRUE ~ 0), na.rm = TRUE),
.groups = "drop")
#merge t0 and t1
table_cpue_fad <- merge(t0, t1, by = "year")
#final table
table_cpue_fad <- table_cpue_fad %>%
dplyr::reframe(year = year,
yft = (yft / (t_recherche / set_time)),
skj = (skj / (t_recherche / set_time)),
bet = (bet / (t_recherche / set_time)),
ALB = (alb / (t_recherche / set_time)),
total = (total / (t_recherche / set_time)))
# 3.b - Data design for FSC----
#Creation of t2 database from dataframe1
t2 <- dataframe2 %>%
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")
#Creation of t3 database from dataframe2
t3 <- dataframe1 %>%
dplyr::group_by(year) %>%
dplyr::summarise(yft = sum(dplyr::case_when(school_type %in% c("FSC", "UND") & species_code == 1 ~ total_catch_weight,
TRUE ~ 0), na.rm = TRUE),
skj = sum(dplyr::case_when(school_type %in% c("FSC", "UND") & species_code == 2 ~ total_catch_weight,
TRUE ~ 0), na.rm = TRUE),
bet = sum(dplyr::case_when(school_type %in% c("FSC", "UND") & species_code == 3 ~ total_catch_weight,
TRUE ~ 0), na.rm = TRUE),
alb = sum(dplyr::case_when(school_type %in% c("FSC", "UND") & species_code == 4 ~ total_catch_weight,
TRUE ~ 0), na.rm = TRUE),
total = sum(dplyr::case_when(school_type %in% c("FSC", "UND") ~ total_catch_weight,
TRUE ~ 0), na.rm = TRUE),
.groups = "drop")
#merge t2 and t3
table_cpue_fsc <- merge(t2, t3, by = "year")
#final table
table_cpue_fsc <- table_cpue_fsc %>%
dplyr::reframe(year = year,
yft = (yft / (t_recherche / set_time)),
skj = (skj / (t_recherche / set_time)),
bet = (bet / (t_recherche / set_time)),
ALB = (alb / (t_recherche / set_time)),
total = (total / (t_recherche / set_time)))
# 4 - Legend design ----
if (title == TRUE) {
#Ocean
ocean_legend <- code_manipulation(data = dataframe1$ocean_code,
referential = "ocean",
manipulation = "legend")
#country
country_legend <- code_manipulation(data = dataframe1$country_code,
referential = "country",
manipulation = "legend")
#vessel
vessel_type_legend <- code_manipulation(data = dataframe1$vessel_type_code,
referential = "vessel_simple_type",
manipulation = "legend")
time_period <- c(unique(min(dataframe1$year):max(dataframe1$year)))
}
# 5 - Graphic design ----
# round values
table_cpue_fad$yft <- round(table_cpue_fad$yft, 3)
table_cpue_fad$skj <- round(table_cpue_fad$skj, 3)
table_cpue_fad$bet <- round(table_cpue_fad$bet, 3)
table_cpue_fad$ALB <- round(table_cpue_fad$ALB, 3)
table_cpue_fad$total <- round(table_cpue_fad$total, 3)
# round values
table_cpue_fsc$yft <- round(table_cpue_fsc$yft, 3)
table_cpue_fsc$skj <- round(table_cpue_fsc$skj, 3)
table_cpue_fsc$bet <- round(table_cpue_fsc$bet, 3)
table_cpue_fsc$ALB <- round(table_cpue_fsc$ALB, 3)
table_cpue_fsc$total <- round(table_cpue_fsc$total, 3)
# Fishing type
if (fishing_type == "FOB") {
label_ft <- " (FOB) "
dataframe <- table_cpue_fad
} else if (fishing_type == "FSC") {
label_ft <- " (FSC) "
dataframe <- table_cpue_fsc
}
# plot
ggplot_graph <- ggplot2::ggplot(data = dataframe) +
# Theme and background
ggplot2::geom_hline(yintercept = c(40, 30, 20, 15, 10, 5),
color = "grey",
linetype = "longdash",
alpha = 0.5) +
ggplot2::scale_x_continuous(expand = c(0, 0),
breaks = dataframe$year) +
ggplot2::theme_bw() +
ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45,
hjust = 1,
size = 13),
axis.text.y = ggplot2::element_text(size = 13),
axis.title.y = ggplot2::element_text(size = 14),
legend.position = "top",
legend.justification = "right",
legend.text = ggplot2::element_text(size = 10),
panel.background = ggplot2::element_rect(fill = "white",
color = "black"),
panel.grid.major = ggplot2::element_blank(),
panel.grid.minor.x = ggplot2::element_blank(),
panel.grid.major.y = ggplot2::element_line(size = 0.2,
color = "gray90")) +
# Lines and points
ggplot2::geom_line(ggplot2::aes(x = year,
y = yft),
size = 0.15) +
ggplot2::geom_line(ggplot2::aes(x = year,
y = skj),
size = 0.15) +
ggplot2::geom_line(ggplot2::aes(x = year,
y = bet),
size = 0.15) +
ggplot2::geom_line(ggplot2::aes(x = year,
y = total),
size = 0.15) +
ggplot2::geom_point(ggplot2::aes(x = year,
y = yft,
shape = "Yellowfin"),
size = 2) +
ggplot2::geom_point(ggplot2::aes(x = year,
y = skj,
shape = "Skipjack"),
size = 2) +
ggplot2::geom_point(ggplot2::aes(x = year,
y = bet,
shape = "Bigeye"),
size = 2) +
ggplot2::geom_point(ggplot2::aes(x = year,
y = total,
shape = "Total"),
size = 2) +
ggplot2::scale_shape_manual(values = c("Yellowfin" = 15,
"Skipjack" = 5,
"Bigeye" = 2,
"Total" = 16)) +
ggplot2::labs(x = "",
y = "Catch per unit effort (t/d)") +
ggplot2::ylim(0, max(dataframe$total)) +
ggplot2::guides(shape = ggplot2::guide_legend(title = NULL)) +
ggplot2::annotate("text",
x = 1994,
y = max(dataframe$total) - 1,
label = label_ft,
hjust = 1.2,
vjust = 0.002,
size = 5,
color = "black") +
ggplot2::scale_x_continuous(breaks = unique(dataframe$year))
# Add title conditionally
if (title == TRUE) {
title <- paste0("Annual catch rates (in t per searching day) of the ",
country_legend,
" ",
vessel_type_legend,
" fishing fleet on ",
"\n",
fishing_type,
" fishing",
"mode schools in the ",
ocean_legend,
" ocean during ",
min(time_period),
"-",
max(time_period),
".")
ggplot_graph <- ggplot_graph +
ggplot2::ggtitle(title)
}
if (graph_type == "plot") {
return(ggplot_graph)
} else if (graph_type == "plotly") {
# 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 = 0.85,
y = 0.97))
} else if (graph_type == "table") {
dataframe <- round(dataframe, 2)
dataframe <- dataframe %>%
dplyr::summarise(Year = year,
YFT = yft,
SKJ = skj,
BET = bet,
TOTAL = total)
as.data.frame(dataframe)
}
}
OB7-IRD/fishi documentation built on Feb. 4, 2025, 2:03 a.m.
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Defines functions catch_per_unit_effort
Documented in catch_per_unit_effort
#' @name catch_per_unit_effort
#' @title Annual catch rates (in t per searching day)
#' @description Annual catch rates (in t per searching day) on FOB- associated and free-swimming tuna schools (FSC).
#' @param dataframe1 {\link[base]{data.frame}} expected. 'Csv' or 'output' of the function {\link[furdeb]{data_extraction}}, which must be done before using the catch_per_searching_day() function.
#' @param dataframe2 {\link[base]{data.frame}} expected. 'Csv' or 'output' of the function {\link[furdeb]{data_extraction}}, which must be done before using the catch_per_searching_day() function.
#' @param fishing_type {\link[base]{character}} expected. 'FOB' or '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}]:
#' Dataframe 1:
#' \preformatted{
#' activity_date | species_code | school_type | set_duration | positive_set | total_set | total_catch_weight | total_hour_fished
#' -------------------------------------------------------------------------------------------------------------------------------
#' 1999-07-09 | 2 | FOB | 3.54 | 1 | 1 | 119.0 | 12.1
#' 1999-07-09 | 1 | FOB | 3.54 | 1 | 1 | 20.6 | 12.1
#' 1999-07-09 | 1 | FOB | 3.54 | 1 | 1 | 24.4 | 12.1
#' }
#'
#' Dataframe 2:
#' \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
catch_per_unit_effort <- function(dataframe1,
dataframe2,
fishing_type,
graph_type = "plot",
title = FALSE) {
# 0 - Global variables assignement ----
activity_date <- NULL
total_hour_fished <- NULL
set_duration <- NULL
yft <- NULL
t_recherche <- NULL
skj <- NULL
bet <- NULL
alb <- NULL
total <- 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 extraction ----
# 3.a - Data design for FOB----
#Creation of t0
dataframe1 <- dataframe1 %>%
dplyr::mutate(year = lubridate::year(x = activity_date))
dataframe2 <- dataframe2 %>%
dplyr::mutate(year = lubridate::year(x = activity_date))
ocean_code <- dataframe1$ocean_code[1]
if (ocean_code == 1) {
set_time <- as.integer(x = 12)
} else if (ocean_code == 2) {
set_time <- as.integer(x = 13)
}
t0 <- dataframe2 %>%
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")
#Creation of t1 database from dataframe2
t1 <- dataframe1 %>%
dplyr::group_by(year) %>%
dplyr::summarise(yft = sum(dplyr::case_when(school_type %in% "FOB" & species_code == 1 ~ total_catch_weight,
TRUE ~ 0), na.rm = TRUE),
skj = sum(dplyr::case_when(school_type %in% "FOB" & species_code == 2 ~ total_catch_weight,
TRUE ~ 0), na.rm = TRUE),
bet = sum(dplyr::case_when(school_type %in% "FOB" & species_code == 3 ~ total_catch_weight,
TRUE ~ 0), na.rm = TRUE),
alb = sum(dplyr::case_when(school_type %in% "FOB" & species_code == 4 ~ total_catch_weight,
TRUE ~ 0), na.rm = TRUE),
total = sum(dplyr::case_when(school_type %in% "FOB" ~ total_catch_weight,
TRUE ~ 0), na.rm = TRUE),
.groups = "drop")
#merge t0 and t1
table_cpue_fad <- merge(t0, t1, by = "year")
#final table
table_cpue_fad <- table_cpue_fad %>%
dplyr::reframe(year = year,
yft = (yft / (t_recherche / set_time)),
skj = (skj / (t_recherche / set_time)),
bet = (bet / (t_recherche / set_time)),
ALB = (alb / (t_recherche / set_time)),
total = (total / (t_recherche / set_time)))
# 3.b - Data design for FSC----
#Creation of t2 database from dataframe1
t2 <- dataframe2 %>%
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")
#Creation of t3 database from dataframe2
t3 <- dataframe1 %>%
dplyr::group_by(year) %>%
dplyr::summarise(yft = sum(dplyr::case_when(school_type %in% c("FSC", "UND") & species_code == 1 ~ total_catch_weight,
TRUE ~ 0), na.rm = TRUE),
skj = sum(dplyr::case_when(school_type %in% c("FSC", "UND") & species_code == 2 ~ total_catch_weight,
TRUE ~ 0), na.rm = TRUE),
bet = sum(dplyr::case_when(school_type %in% c("FSC", "UND") & species_code == 3 ~ total_catch_weight,
TRUE ~ 0), na.rm = TRUE),
alb = sum(dplyr::case_when(school_type %in% c("FSC", "UND") & species_code == 4 ~ total_catch_weight,
TRUE ~ 0), na.rm = TRUE),
total = sum(dplyr::case_when(school_type %in% c("FSC", "UND") ~ total_catch_weight,
TRUE ~ 0), na.rm = TRUE),
.groups = "drop")
#merge t2 and t3
table_cpue_fsc <- merge(t2, t3, by = "year")
#final table
table_cpue_fsc <- table_cpue_fsc %>%
dplyr::reframe(year = year,
yft = (yft / (t_recherche / set_time)),
skj = (skj / (t_recherche / set_time)),
bet = (bet / (t_recherche / set_time)),
ALB = (alb / (t_recherche / set_time)),
total = (total / (t_recherche / set_time)))
# 4 - Legend design ----
if (title == TRUE) {
#Ocean
ocean_legend <- code_manipulation(data = dataframe1$ocean_code,
referential = "ocean",
manipulation = "legend")
#country
country_legend <- code_manipulation(data = dataframe1$country_code,
referential = "country",
manipulation = "legend")
#vessel
vessel_type_legend <- code_manipulation(data = dataframe1$vessel_type_code,
referential = "vessel_simple_type",
manipulation = "legend")
time_period <- c(unique(min(dataframe1$year):max(dataframe1$year)))
}
# 5 - Graphic design ----
# round values
table_cpue_fad$yft <- round(table_cpue_fad$yft, 3)
table_cpue_fad$skj <- round(table_cpue_fad$skj, 3)
table_cpue_fad$bet <- round(table_cpue_fad$bet, 3)
table_cpue_fad$ALB <- round(table_cpue_fad$ALB, 3)
table_cpue_fad$total <- round(table_cpue_fad$total, 3)
# round values
table_cpue_fsc$yft <- round(table_cpue_fsc$yft, 3)
table_cpue_fsc$skj <- round(table_cpue_fsc$skj, 3)
table_cpue_fsc$bet <- round(table_cpue_fsc$bet, 3)
table_cpue_fsc$ALB <- round(table_cpue_fsc$ALB, 3)
table_cpue_fsc$total <- round(table_cpue_fsc$total, 3)
# Fishing type
if (fishing_type == "FOB") {
label_ft <- " (FOB) "
dataframe <- table_cpue_fad
} else if (fishing_type == "FSC") {
label_ft <- " (FSC) "
dataframe <- table_cpue_fsc
}
# plot
ggplot_graph <- ggplot2::ggplot(data = dataframe) +
# Theme and background
ggplot2::geom_hline(yintercept = c(40, 30, 20, 15, 10, 5),
color = "grey",
linetype = "longdash",
alpha = 0.5) +
ggplot2::scale_x_continuous(expand = c(0, 0),
breaks = dataframe$year) +
ggplot2::theme_bw() +
ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45,
hjust = 1,
size = 13),
axis.text.y = ggplot2::element_text(size = 13),
axis.title.y = ggplot2::element_text(size = 14),
legend.position = "top",
legend.justification = "right",
legend.text = ggplot2::element_text(size = 10),
panel.background = ggplot2::element_rect(fill = "white",
color = "black"),
panel.grid.major = ggplot2::element_blank(),
panel.grid.minor.x = ggplot2::element_blank(),
panel.grid.major.y = ggplot2::element_line(size = 0.2,
color = "gray90")) +
# Lines and points
ggplot2::geom_line(ggplot2::aes(x = year,
y = yft),
size = 0.15) +
ggplot2::geom_line(ggplot2::aes(x = year,
y = skj),
size = 0.15) +
ggplot2::geom_line(ggplot2::aes(x = year,
y = bet),
size = 0.15) +
ggplot2::geom_line(ggplot2::aes(x = year,
y = total),
size = 0.15) +
ggplot2::geom_point(ggplot2::aes(x = year,
y = yft,
shape = "Yellowfin"),
size = 2) +
ggplot2::geom_point(ggplot2::aes(x = year,
y = skj,
shape = "Skipjack"),
size = 2) +
ggplot2::geom_point(ggplot2::aes(x = year,
y = bet,
shape = "Bigeye"),
size = 2) +
ggplot2::geom_point(ggplot2::aes(x = year,
y = total,
shape = "Total"),
size = 2) +
ggplot2::scale_shape_manual(values = c("Yellowfin" = 15,
"Skipjack" = 5,
"Bigeye" = 2,
"Total" = 16)) +
ggplot2::labs(x = "",
y = "Catch per unit effort (t/d)") +
ggplot2::ylim(0, max(dataframe$total)) +
ggplot2::guides(shape = ggplot2::guide_legend(title = NULL)) +
ggplot2::annotate("text",
x = 1994,
y = max(dataframe$total) - 1,
label = label_ft,
hjust = 1.2,
vjust = 0.002,
size = 5,
color = "black") +
ggplot2::scale_x_continuous(breaks = unique(dataframe$year))
# Add title conditionally
if (title == TRUE) {
title <- paste0("Annual catch rates (in t per searching day) of the ",
country_legend,
" ",
vessel_type_legend,
" fishing fleet on ",
"\n",
fishing_type,
" fishing",
"mode schools in the ",
ocean_legend,
" ocean during ",
min(time_period),
"-",
max(time_period),
".")
ggplot_graph <- ggplot_graph +
ggplot2::ggtitle(title)
}
if (graph_type == "plot") {
return(ggplot_graph)
} else if (graph_type == "plotly") {
# 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 = 0.85,
y = 0.97))
} else if (graph_type == "table") {
dataframe <- round(dataframe, 2)
dataframe <- dataframe %>%
dplyr::summarise(Year = year,
YFT = yft,
SKJ = skj,
BET = bet,
TOTAL = total)
as.data.frame(dataframe)
}
}
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