R/map_catch_distribution.R
In OB7-IRD/fishi: Package Fisheries Indicators
Defines functions
map_catch_distribution
Documented in
map_catch_distribution
#' @name map_catch_distribution
#' @title Spatial distribution of tuna catches
#' @description Spatial distribution of tuna catches.
#' @param dataframe {\link[base]{data.frame}} expected. 'Csv' or 'output' of the function {\link[furdeb]{data_extraction}}, which must be done before using the map_catch_distribution() function.
#' @param fishing_type {\link[base]{character}} expected. 'FOB', 'FSC' or 'ALL'. ALL by default.
#' @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 | vessel_code | species_code | ocean_code | school_type | cwp11_act | activity_id | positive_set | total_catch_weight
#' -----------------------------------------------------------------------------------------------------------------------------------
#' 2022-01-02 | 703 | 3 | 1 | FSC | 301000 | 1 | 1 | 2.60
#' 2022-01-02 | 703 | 1 | 1 | FSC | 301000 | 1 | 1 | 17.8
#' 2022-01-02 | 703 | 3 | 1 | FSC | 301000 | 2 | 1 | 1.11
#' }
#' Add these columns for an automatic title (optional):
#' \itemize{
#' \item{\code{ country_code}}
#' \item{\code{ vessel_type_code}}
#' }
#' @return The function return ggplot R plot.
#' @export
map_catch_distribution <- function(dataframe,
fishing_type = "ALL",
graph_type = "plot",
title = FALSE) {
# 0 - Global variables assignement ----
activity_id <- NULL
activity_date <- NULL
vessel_code <- NULL
species_code <- NULL
positive_set <- NULL
school_type <- NULL
cwp11_act <- NULL
total_catch_weight <- NULL
total <- NULL
bet <- NULL
yft <- NULL
skj <- NULL
LONG <- NULL
LAT <- NULL
TOTAL <- NULL
CWP11_ACT <- NULL
lat <- NULL
long <- 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 = "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 = "integer",
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 ----
# time period and ocean
dataframe <- dataframe %>%
dplyr::mutate(year = lubridate::year(x = activity_date))
time_period <- c(unique(min(dataframe$year):max(dataframe$year)))
ocean <- dataframe$ocean_code[1]
# dataframe
t1 <- dataframe %>%
dplyr::group_by(activity_id,
activity_date,
vessel_code,
species_code,
positive_set,
school_type,
cwp11_act) %>%
dplyr::summarise(poids = sum(total_catch_weight,
na.rm = TRUE),
.groups = "drop")
if (fishing_type == "ALL") {
datafile <- t1 %>%
dplyr::filter(positive_set > 0) %>%
dplyr::group_by(cwp11_act) %>%
dplyr::summarise(yft = sum(dplyr::case_when(species_code == 1 ~ poids,
TRUE ~ 0.00000001), na.rm = TRUE),
skj = sum(dplyr::case_when(species_code == 2 ~ poids,
TRUE ~ 0.00000001), na.rm = TRUE),
bet = sum(dplyr::case_when(species_code == 3 ~ poids,
TRUE ~ 0.00000001), na.rm = TRUE),
total = sum(dplyr::case_when(species_code %in% c(1:3) ~ poids,
TRUE ~ 0.00000001), na.rm = TRUE))
} else if (fishing_type == "FSC") {
datafile <- t1 %>%
dplyr::filter(positive_set > 0,
school_type %in% c("FSC", "UND")) %>%
dplyr::group_by(cwp11_act) %>%
dplyr::summarise(yft = sum(dplyr::case_when(species_code == 1 ~ poids,
TRUE ~ 0.00000001), na.rm = TRUE),
skj = sum(dplyr::case_when(species_code == 2 ~ poids,
TRUE ~ 0.00000001), na.rm = TRUE),
bet = sum(dplyr::case_when(species_code == 3 ~ poids,
TRUE ~ 0.00000001), na.rm = TRUE),
total = sum(dplyr::case_when(species_code %in% c(1:3) ~ poids,
TRUE ~ 0.00000001), na.rm = TRUE))
} else if (fishing_type == "FOB") {
datafile <- t1 %>%
dplyr::filter(positive_set > 0,
school_type %in% "FOB") %>%
dplyr::group_by(cwp11_act) %>%
dplyr::summarise(yft = sum(dplyr::case_when(species_code == 1 ~ poids,
TRUE ~ 0.00000001), na.rm = TRUE),
skj = sum(dplyr::case_when(species_code == 2 ~ poids,
TRUE ~ 0.00000001), na.rm = TRUE),
bet = sum(dplyr::case_when(species_code == 3 ~ poids,
TRUE ~ 0.00000001), na.rm = TRUE),
total = sum(dplyr::case_when(species_code %in% c(1:3) ~ poids,
TRUE ~ 0.00000001), na.rm = TRUE))
} else {
stop(format(x = Sys.time(),
format = "%Y-%m-%d %H:%M:%S"),
" - Indicator not developed yet for this \"fishing_type\" argument.\n",
sep = "")
}
# 3 - Legend design ----
# Define fuction quad2pos
quad2pos <- function(id) {
latsiz <- c(5, 10, 10, 20, 1, 5)
lonsiz <- c(10, 20, 10, 20, 1, 5)
siz <- trunc(id / 1000000)
id <- id - siz * 1000000
quad <- trunc(id / 100000)
id <- id - quad * 100000
lat <- trunc(id / 1000)
id <- id - lat * 1000
lon <- id
quadlat <- c(1, -1, -1, 1)
quadlon <- c(1, 1, -1, -1)
latm <- quadlat[quad]
lonm <- quadlon[quad]
lat <- (latm * (lat + latsiz[siz] / 2))
lon <- (lonm * (lon + lonsiz[siz] / 2))
invisible(list(x = lon,
y = lat,
sizelat = latsiz[siz],
sizelon = lonsiz[siz]))
}
if (title == TRUE) {
#Ocean
ocean_legend <- code_manipulation(data = dataframe$ocean_code,
referential = "ocean",
manipulation = "legend")
#vessel
vessel_type_legend <- code_manipulation(data = dataframe$vessel_type_code,
referential = "vessel_simple_type",
manipulation = "legend")
#country
country_legend <- code_manipulation(data = dataframe$country_code,
referential = "country",
manipulation = "legend")
}
# 4 - Graphic design ----
if (graph_type == "plot") {
if (ocean == 1) {
ocean_xlim <- c(-40, 15)
ocean_ylim <- c(-25, 25)
ocean_xintercept <- c(-30, -20, -10, 0, 10)
ocean_yintercept <- c(-20, -10, 0, 10, 20)
} else if (ocean == 2) {
ocean_xlim <- c(30, 90)
ocean_ylim <- c(-30, 20)
ocean_xintercept <- c(40, 50, 60, 70, 80)
ocean_yintercept <- c(10, 0, -10, -20)
}
datafile$cwp11_act <- as.integer(datafile$cwp11_act)
datafile$lat <- quad2pos(as.numeric(datafile$cwp11_act + 5 * 1e6))$y
datafile$long <- quad2pos(as.numeric(datafile$cwp11_act + 5 * 1e6))$x
world_boundaries <- rnaturalearth::ne_countries(returnclass = "sf",
scale = "medium")
datafile$total <- round(datafile$total, 3)
datafile$yft <- round(datafile$yft, 3)
datafile$skj <- round(datafile$skj, 3)
datafile$bet <- round(datafile$bet, 3)
datafile <- datafile %>%
dplyr::rename_all(toupper)
data_pivot <- tidyr::pivot_longer(datafile,
cols = c(2:4),
names_to = "specie",
values_to = "catch (t)")
data_pivot$`catch (t)` <- round(data_pivot$`catch (t)`, 3)
map <- ggplot2::ggplot() +
ggplot2::theme(legend.position = "top",
legend.justification = "right",
legend.text = ggplot2::element_text(size = 10),
panel.background = ggplot2::element_rect(fill = "white"),
panel.border = ggplot2::element_rect(color = "black",
fill = NA,
size = 0.3)) +
ggplot2::geom_sf(data = world_boundaries) +
ggspatial::coord_sf(xlim = ocean_xlim,
ylim = ocean_ylim) +
scatterpie::geom_scatterpie(data = datafile,
ggplot2::aes(x = LONG,
y = LAT,
r = (sqrt(TOTAL) / sqrt(2000)),
group = CWP11_ACT),
cols = c("YFT", "SKJ", "BET")) +
ggplot2::scale_fill_manual(values = c("YFT" = "khaki1",
"SKJ" = "firebrick2",
"BET" = "cornflowerblue")) +
ggplot2::geom_hline(yintercept = ocean_yintercept,
linetype = "dashed",
color = "darkgrey",
linewidth = 0.2) +
ggplot2::labs(fill = "Catch in t") +
ggplot2::geom_vline(xintercept = ocean_xintercept,
linetype = "dashed",
color = "darkgrey",
linewidth = 0.2)
# Add title conditionally
if (title == TRUE) {
title <- paste0("Spatial distribution of tuna catches of the ",
country_legend, " ",
vessel_type_legend, " fishing fleet made on ", "\n",
fishing_type,
" fishing mode in ",
ifelse(test = length(x = time_period) != 1,
yes = paste0(min(time_period), "-", max(time_period)),
no = time_period),
", in the ",
ocean_legend,
" ocean.")
map <- map +
ggplot2::ggtitle(title)
}
return(map)
} else if (graph_type == "plotly") {
if (ocean == 1) {
ocean_xlim <- c(-40, 15)
ocean_ylim <- c(-25, 25)
ocean_xintercept <- c(-30, -20, -10, 0, 10)
ocean_yintercept <- c(-20, -10, 0, 10, 20)
} else if (ocean == 2) {
ocean_xlim <- c(30, 90)
ocean_ylim <- c(-30, 20)
ocean_xintercept <- c(40, 50, 60, 70, 80)
ocean_yintercept <- c(10, 0, -10, -20)
}
datafile$lat <- quad2pos(as.numeric(datafile$cwp11_act + 5 * 1e6))$y
datafile$long <- quad2pos(as.numeric(datafile$cwp11_act + 5 * 1e6))$x
world_boundaries <- rnaturalearth::ne_countries(returnclass = "sf",
scale = "medium")
datafile$total <- round(datafile$total, 3)
datafile$yft <- round(datafile$yft, 3)
datafile$skj <- round(datafile$skj, 3)
datafile$bet <- round(datafile$bet, 3)
data_pivot <- tidyr::pivot_longer(datafile,
cols = c(2:4),
names_to = "specie",
values_to = "catch (t)")
data_pivot$`catch (t)` <- round(data_pivot$`catch (t)`, 3)
map <- ggplot2::ggplot() +
ggplot2::theme(legend.position = "top",
legend.justification = "right",
panel.background = ggplot2::element_rect(fill = "white"),
panel.border = ggplot2::element_rect(color = "black",
fill = NA,
linewidth = 0.3),
axis.title.x = ggplot2::element_blank(),
axis.title.y = ggplot2::element_blank()) +
ggplot2::geom_sf(data = world_boundaries) +
ggspatial::coord_sf(xlim = ocean_xlim,
ylim = ocean_ylim) +
ggplot2::geom_point(data = datafile,
ggplot2::aes(x = long,
y = lat,
color = total,
size = total,
text = paste("bet: ", bet,
"<br>yft: ", yft,
"<br>skj: ", skj)),
alpha = 0.65) +
ggplot2::scale_color_viridis_c(option = "plasma") +
ggplot2::guides(size = "none") +
ggplot2::labs(color = "Catch in t") +
ggplot2::theme(panel.background = ggplot2::element_rect(fill = "white"),
panel.border = ggplot2::element_rect(color = "black", fill = NA, size = 0.3)) +
ggplot2::geom_hline(yintercept = ocean_yintercept,
linetype = "dashed",
color = "darkgrey",
linewidth = 0.2) +
ggplot2::geom_vline(xintercept = ocean_xintercept,
linetype = "dashed",
color = "darkgrey",
linewidth = 0.2)
plotly_map <- plotly::ggplotly(map) %>%
plotly::style(hoverinfo = "text")
# Add a title
if (title == TRUE) {
plotly_map <- plotly_map %>%
plotly::layout(title = list(text = title,
font = list(size = 15)),
margin = list(t = 120))
}
# Plot the plotly
return(plotly_map)
}
}
OB7-IRD/fishi documentation built on Feb. 4, 2025, 2:03 a.m.
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Defines functions map_catch_distribution
Documented in map_catch_distribution
#' @name map_catch_distribution
#' @title Spatial distribution of tuna catches
#' @description Spatial distribution of tuna catches.
#' @param dataframe {\link[base]{data.frame}} expected. 'Csv' or 'output' of the function {\link[furdeb]{data_extraction}}, which must be done before using the map_catch_distribution() function.
#' @param fishing_type {\link[base]{character}} expected. 'FOB', 'FSC' or 'ALL'. ALL by default.
#' @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 | vessel_code | species_code | ocean_code | school_type | cwp11_act | activity_id | positive_set | total_catch_weight
#' -----------------------------------------------------------------------------------------------------------------------------------
#' 2022-01-02 | 703 | 3 | 1 | FSC | 301000 | 1 | 1 | 2.60
#' 2022-01-02 | 703 | 1 | 1 | FSC | 301000 | 1 | 1 | 17.8
#' 2022-01-02 | 703 | 3 | 1 | FSC | 301000 | 2 | 1 | 1.11
#' }
#' Add these columns for an automatic title (optional):
#' \itemize{
#' \item{\code{ country_code}}
#' \item{\code{ vessel_type_code}}
#' }
#' @return The function return ggplot R plot.
#' @export
map_catch_distribution <- function(dataframe,
fishing_type = "ALL",
graph_type = "plot",
title = FALSE) {
# 0 - Global variables assignement ----
activity_id <- NULL
activity_date <- NULL
vessel_code <- NULL
species_code <- NULL
positive_set <- NULL
school_type <- NULL
cwp11_act <- NULL
total_catch_weight <- NULL
total <- NULL
bet <- NULL
yft <- NULL
skj <- NULL
LONG <- NULL
LAT <- NULL
TOTAL <- NULL
CWP11_ACT <- NULL
lat <- NULL
long <- 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 = "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 = "integer",
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 ----
# time period and ocean
dataframe <- dataframe %>%
dplyr::mutate(year = lubridate::year(x = activity_date))
time_period <- c(unique(min(dataframe$year):max(dataframe$year)))
ocean <- dataframe$ocean_code[1]
# dataframe
t1 <- dataframe %>%
dplyr::group_by(activity_id,
activity_date,
vessel_code,
species_code,
positive_set,
school_type,
cwp11_act) %>%
dplyr::summarise(poids = sum(total_catch_weight,
na.rm = TRUE),
.groups = "drop")
if (fishing_type == "ALL") {
datafile <- t1 %>%
dplyr::filter(positive_set > 0) %>%
dplyr::group_by(cwp11_act) %>%
dplyr::summarise(yft = sum(dplyr::case_when(species_code == 1 ~ poids,
TRUE ~ 0.00000001), na.rm = TRUE),
skj = sum(dplyr::case_when(species_code == 2 ~ poids,
TRUE ~ 0.00000001), na.rm = TRUE),
bet = sum(dplyr::case_when(species_code == 3 ~ poids,
TRUE ~ 0.00000001), na.rm = TRUE),
total = sum(dplyr::case_when(species_code %in% c(1:3) ~ poids,
TRUE ~ 0.00000001), na.rm = TRUE))
} else if (fishing_type == "FSC") {
datafile <- t1 %>%
dplyr::filter(positive_set > 0,
school_type %in% c("FSC", "UND")) %>%
dplyr::group_by(cwp11_act) %>%
dplyr::summarise(yft = sum(dplyr::case_when(species_code == 1 ~ poids,
TRUE ~ 0.00000001), na.rm = TRUE),
skj = sum(dplyr::case_when(species_code == 2 ~ poids,
TRUE ~ 0.00000001), na.rm = TRUE),
bet = sum(dplyr::case_when(species_code == 3 ~ poids,
TRUE ~ 0.00000001), na.rm = TRUE),
total = sum(dplyr::case_when(species_code %in% c(1:3) ~ poids,
TRUE ~ 0.00000001), na.rm = TRUE))
} else if (fishing_type == "FOB") {
datafile <- t1 %>%
dplyr::filter(positive_set > 0,
school_type %in% "FOB") %>%
dplyr::group_by(cwp11_act) %>%
dplyr::summarise(yft = sum(dplyr::case_when(species_code == 1 ~ poids,
TRUE ~ 0.00000001), na.rm = TRUE),
skj = sum(dplyr::case_when(species_code == 2 ~ poids,
TRUE ~ 0.00000001), na.rm = TRUE),
bet = sum(dplyr::case_when(species_code == 3 ~ poids,
TRUE ~ 0.00000001), na.rm = TRUE),
total = sum(dplyr::case_when(species_code %in% c(1:3) ~ poids,
TRUE ~ 0.00000001), na.rm = TRUE))
} else {
stop(format(x = Sys.time(),
format = "%Y-%m-%d %H:%M:%S"),
" - Indicator not developed yet for this \"fishing_type\" argument.\n",
sep = "")
}
# 3 - Legend design ----
# Define fuction quad2pos
quad2pos <- function(id) {
latsiz <- c(5, 10, 10, 20, 1, 5)
lonsiz <- c(10, 20, 10, 20, 1, 5)
siz <- trunc(id / 1000000)
id <- id - siz * 1000000
quad <- trunc(id / 100000)
id <- id - quad * 100000
lat <- trunc(id / 1000)
id <- id - lat * 1000
lon <- id
quadlat <- c(1, -1, -1, 1)
quadlon <- c(1, 1, -1, -1)
latm <- quadlat[quad]
lonm <- quadlon[quad]
lat <- (latm * (lat + latsiz[siz] / 2))
lon <- (lonm * (lon + lonsiz[siz] / 2))
invisible(list(x = lon,
y = lat,
sizelat = latsiz[siz],
sizelon = lonsiz[siz]))
}
if (title == TRUE) {
#Ocean
ocean_legend <- code_manipulation(data = dataframe$ocean_code,
referential = "ocean",
manipulation = "legend")
#vessel
vessel_type_legend <- code_manipulation(data = dataframe$vessel_type_code,
referential = "vessel_simple_type",
manipulation = "legend")
#country
country_legend <- code_manipulation(data = dataframe$country_code,
referential = "country",
manipulation = "legend")
}
# 4 - Graphic design ----
if (graph_type == "plot") {
if (ocean == 1) {
ocean_xlim <- c(-40, 15)
ocean_ylim <- c(-25, 25)
ocean_xintercept <- c(-30, -20, -10, 0, 10)
ocean_yintercept <- c(-20, -10, 0, 10, 20)
} else if (ocean == 2) {
ocean_xlim <- c(30, 90)
ocean_ylim <- c(-30, 20)
ocean_xintercept <- c(40, 50, 60, 70, 80)
ocean_yintercept <- c(10, 0, -10, -20)
}
datafile$cwp11_act <- as.integer(datafile$cwp11_act)
datafile$lat <- quad2pos(as.numeric(datafile$cwp11_act + 5 * 1e6))$y
datafile$long <- quad2pos(as.numeric(datafile$cwp11_act + 5 * 1e6))$x
world_boundaries <- rnaturalearth::ne_countries(returnclass = "sf",
scale = "medium")
datafile$total <- round(datafile$total, 3)
datafile$yft <- round(datafile$yft, 3)
datafile$skj <- round(datafile$skj, 3)
datafile$bet <- round(datafile$bet, 3)
datafile <- datafile %>%
dplyr::rename_all(toupper)
data_pivot <- tidyr::pivot_longer(datafile,
cols = c(2:4),
names_to = "specie",
values_to = "catch (t)")
data_pivot$`catch (t)` <- round(data_pivot$`catch (t)`, 3)
map <- ggplot2::ggplot() +
ggplot2::theme(legend.position = "top",
legend.justification = "right",
legend.text = ggplot2::element_text(size = 10),
panel.background = ggplot2::element_rect(fill = "white"),
panel.border = ggplot2::element_rect(color = "black",
fill = NA,
size = 0.3)) +
ggplot2::geom_sf(data = world_boundaries) +
ggspatial::coord_sf(xlim = ocean_xlim,
ylim = ocean_ylim) +
scatterpie::geom_scatterpie(data = datafile,
ggplot2::aes(x = LONG,
y = LAT,
r = (sqrt(TOTAL) / sqrt(2000)),
group = CWP11_ACT),
cols = c("YFT", "SKJ", "BET")) +
ggplot2::scale_fill_manual(values = c("YFT" = "khaki1",
"SKJ" = "firebrick2",
"BET" = "cornflowerblue")) +
ggplot2::geom_hline(yintercept = ocean_yintercept,
linetype = "dashed",
color = "darkgrey",
linewidth = 0.2) +
ggplot2::labs(fill = "Catch in t") +
ggplot2::geom_vline(xintercept = ocean_xintercept,
linetype = "dashed",
color = "darkgrey",
linewidth = 0.2)
# Add title conditionally
if (title == TRUE) {
title <- paste0("Spatial distribution of tuna catches of the ",
country_legend, " ",
vessel_type_legend, " fishing fleet made on ", "\n",
fishing_type,
" fishing mode in ",
ifelse(test = length(x = time_period) != 1,
yes = paste0(min(time_period), "-", max(time_period)),
no = time_period),
", in the ",
ocean_legend,
" ocean.")
map <- map +
ggplot2::ggtitle(title)
}
return(map)
} else if (graph_type == "plotly") {
if (ocean == 1) {
ocean_xlim <- c(-40, 15)
ocean_ylim <- c(-25, 25)
ocean_xintercept <- c(-30, -20, -10, 0, 10)
ocean_yintercept <- c(-20, -10, 0, 10, 20)
} else if (ocean == 2) {
ocean_xlim <- c(30, 90)
ocean_ylim <- c(-30, 20)
ocean_xintercept <- c(40, 50, 60, 70, 80)
ocean_yintercept <- c(10, 0, -10, -20)
}
datafile$lat <- quad2pos(as.numeric(datafile$cwp11_act + 5 * 1e6))$y
datafile$long <- quad2pos(as.numeric(datafile$cwp11_act + 5 * 1e6))$x
world_boundaries <- rnaturalearth::ne_countries(returnclass = "sf",
scale = "medium")
datafile$total <- round(datafile$total, 3)
datafile$yft <- round(datafile$yft, 3)
datafile$skj <- round(datafile$skj, 3)
datafile$bet <- round(datafile$bet, 3)
data_pivot <- tidyr::pivot_longer(datafile,
cols = c(2:4),
names_to = "specie",
values_to = "catch (t)")
data_pivot$`catch (t)` <- round(data_pivot$`catch (t)`, 3)
map <- ggplot2::ggplot() +
ggplot2::theme(legend.position = "top",
legend.justification = "right",
panel.background = ggplot2::element_rect(fill = "white"),
panel.border = ggplot2::element_rect(color = "black",
fill = NA,
linewidth = 0.3),
axis.title.x = ggplot2::element_blank(),
axis.title.y = ggplot2::element_blank()) +
ggplot2::geom_sf(data = world_boundaries) +
ggspatial::coord_sf(xlim = ocean_xlim,
ylim = ocean_ylim) +
ggplot2::geom_point(data = datafile,
ggplot2::aes(x = long,
y = lat,
color = total,
size = total,
text = paste("bet: ", bet,
"<br>yft: ", yft,
"<br>skj: ", skj)),
alpha = 0.65) +
ggplot2::scale_color_viridis_c(option = "plasma") +
ggplot2::guides(size = "none") +
ggplot2::labs(color = "Catch in t") +
ggplot2::theme(panel.background = ggplot2::element_rect(fill = "white"),
panel.border = ggplot2::element_rect(color = "black", fill = NA, size = 0.3)) +
ggplot2::geom_hline(yintercept = ocean_yintercept,
linetype = "dashed",
color = "darkgrey",
linewidth = 0.2) +
ggplot2::geom_vline(xintercept = ocean_xintercept,
linetype = "dashed",
color = "darkgrey",
linewidth = 0.2)
plotly_map <- plotly::ggplotly(map) %>%
plotly::style(hoverinfo = "text")
# Add a title
if (title == TRUE) {
plotly_map <- plotly_map %>%
plotly::layout(title = list(text = title,
font = list(size = 15)),
margin = list(t = 120))
}
# Plot the plotly
return(plotly_map)
}
}
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