R/fishery_production.R
In OB7-IRD/fishi: Package Fisheries Indicators
Defines functions
fishery_production
Documented in
fishery_production
#' @name fishery_production
#' @title Total fishery production
#' @description Total fishery production (catch by species).
#' Generates a figure for catches (x 1000 t) of the three main tunas: BET, SKJ and YFT.
#' @param dataframe {\link[base]{data.frame}} expected. 'Csv' or 'output' of the function {\link[furdeb]{data_extraction}}, which must be done before using the fishery_production() function.
#' @param fishing_type {\link[base]{character}} expected. 'FSC', 'FOB' or 'ALL'. ALL by default.
#' @param graph_type {\link[base]{character}} expected. 'plot', 'plotly', 'table' or 'percentage'. 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 | species_code | flag | fleet | gear | school_type | ocean_label | total_catch_weight
#' -----------------------------------------------------------------------------------------
#' 1999-07-09 | 2 | France | France | PS | FOB | 1 | 119.
#' 1999-07-09 | 1 | France | France | PS | FOB | 1 | 20.6
#' 1999-07-09 | 1 | France | France | PS | FOB | 1 | 24.4
#' }
#' 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
fishery_production <- function(dataframe,
fishing_type = "ALL",
graph_type = "plot",
title = FALSE) {
# 0 - Global variables assignement ----
activity_date <- NULL
total_catch_weight <- NULL
ocean_label <- NULL
gear <- NULL
fleet <- NULL
flag <- NULL
school_type <- NULL
YFT <- NULL
SKJ <- NULL
BET <- NULL
ALB <- NULL
OTH <- NULL
DSC <- NULL
total_with_DSC <- NULL
total <- NULL
count <- NULL
specie <- NULL
time_period <- NULL
Catch <- NULL
Species <- 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 design ----
# Add columns year, school type and species
fishery_production_t1 <- dataframe %>%
dplyr::mutate(year = lubridate::year(x = activity_date),
school_type = dplyr::case_when(school_type == "UND" ~ "free",
school_type == "FSC" ~ "free",
school_type == "FOB" ~ "log",
TRUE ~ "und"),
YFT = dplyr::case_when(species_code == 1 ~ total_catch_weight,
TRUE ~ 0),
SKJ = dplyr::case_when(species_code == 2 ~ total_catch_weight,
TRUE ~ 0),
BET = dplyr::case_when(species_code == 3 ~ total_catch_weight,
TRUE ~ 0),
ALB = dplyr::case_when(species_code == 4 ~ total_catch_weight,
TRUE ~ 0),
DSC = dplyr::case_when(species_code == 8 | (species_code >= 800 & species_code <= 899) ~ total_catch_weight,
TRUE ~ 0),
OTH = dplyr::case_when(species_code == 1 | species_code == 2 | species_code == 3 | species_code == 4 | species_code == 8 | (species_code >= 800 & species_code <= 899) ~ 0,
TRUE ~ total_catch_weight),
total_with_DSC = total_catch_weight)
# Sum columns species
fishery_production_t2 <- fishery_production_t1 %>%
dplyr::group_by(ocean_label,
year,
gear,
fleet,
flag,
school_type) %>%
dplyr::summarise(YFT = sum(YFT, na.rm = TRUE),
SKJ = sum(SKJ, na.rm = TRUE),
BET = sum(BET, na.rm = TRUE),
ALB = sum(ALB, na.rm = TRUE),
DSC = sum(DSC, na.rm = TRUE),
OTH = sum(OTH, na.rm = TRUE),
total = sum(total_with_DSC, na.rm = TRUE) - sum(DSC, na.rm = TRUE),
.groups = "drop")
if (fishing_type == "ALL") {
### Percentage of catches by species on all fishing mode
# Group results by year
table_catch_all <- fishery_production_t2 %>%
dplyr::group_by(year) %>%
dplyr::summarise(YFT = sum(YFT, na.rm = TRUE),
SKJ = sum(SKJ, na.rm = TRUE),
BET = sum(BET, na.rm = TRUE),
ALB = sum(ALB, na.rm = TRUE),
OTH = sum(OTH, na.rm = TRUE),
total = sum(total, na.rm = TRUE),
.groups = "drop")
} else if (fishing_type == "FSC") {
### Percentage of catches by species on FSC
# Group results by year
table_catch_all <- fishery_production_t2 %>%
dplyr::filter(school_type == "free") %>%
dplyr::group_by(year) %>%
dplyr::summarise(YFT = sum(YFT, na.rm = TRUE),
SKJ = sum(SKJ, na.rm = TRUE),
BET = sum(BET, na.rm = TRUE),
ALB = sum(ALB, na.rm = TRUE),
OTH = sum(OTH, na.rm = TRUE),
total = sum(total, na.rm = TRUE),
.groups = "drop")
} else if (fishing_type == "FOB") {
### Percentage of catches by species on FOB
# Group results by year
table_catch_all <- fishery_production_t2 %>%
dplyr::filter(school_type == "log") %>%
dplyr::group_by(year) %>%
dplyr::summarise(YFT = sum(YFT, na.rm = TRUE),
SKJ = sum(SKJ, na.rm = TRUE),
BET = sum(BET, na.rm = TRUE),
ALB = sum(ALB, na.rm = TRUE),
OTH = sum(OTH, na.rm = TRUE),
total = sum(total, na.rm = TRUE),
.groups = "drop")
}
# 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$vessel_type_code,
referential = "vessel_simple_type",
manipulation = "legend")
# time_period
time_period <- c(unique(min(fishery_production_t1$year):max(fishery_production_t1$year)))
# fishing_type
fishing_type <- fishing_type
}
# 4 - Graphic design ----
if (graph_type == "plot") {
table_long <- tidyr::pivot_longer(table_catch_all,
cols = c("YFT",
"SKJ",
"BET"),
names_to = "Species",
values_to = "Catch")
fishery_ggplot <- ggplot2::ggplot(table_long,
ggplot2::aes(x = year,
y = Catch / 1000,
fill = Species)) +
ggplot2::geom_area(position = "stack") +
ggplot2::scale_fill_manual(values = c("YFT" = "khaki1",
"SKJ" = "firebrick2",
"BET" = "cornflowerblue")) +
ggplot2::labs(x = "",
y = "Catch (x1000 t)",
title = "") +
ggplot2::ylim(0, max((table_catch_all$total * 1.02) / 1000,
na.rm = TRUE)) +
ggplot2::scale_x_continuous(expand = c(0, 0),
breaks = table_long$year) +
ggplot2::scale_y_continuous(expand = c(0, 0)) +
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.grid.major = ggplot2::element_blank(),
panel.grid.minor.x = ggplot2::element_blank(),
panel.grid.major.y = ggplot2::element_line(size = 0.2,
color = "gray90")) +
ggplot2::labs(fill = NULL)
if (title == TRUE) {
fishery_ggplot <- fishery_ggplot + ggplot2::ggtitle(paste0("Fishery production by ",
fishing_type,
" fishing mode. \nCatch by species of the ",
country_legend,
" ",
vessel_type_legend,
" fishing",
"\n",
"fleet during ",
min(time_period),
"-",
max(time_period),
", in the ",
ocean_legend,
" ocean."))
}
return(fishery_ggplot)
} else if (graph_type == "plotly") {
# pivot wider
table_catch_3 <- table_catch_all %>%
dplyr::select(1:4)
data_pivot <- tidyr::pivot_longer(table_catch_3,
cols = c(2:4),
names_to = "specie",
values_to = "count")
# ggplot
ggplot_table_catch <- ggplot2::ggplot(data_pivot, ggplot2::aes(x = year,
y = count,
fill = specie)) +
ggplot2::geom_area() +
ggplot2::scale_fill_manual(values = c("cornflowerblue",
"firebrick2",
"khaki1")) +
ggplot2::scale_y_continuous(name = "Catch (x1000 t)") +
ggplot2::theme_bw() +
ggplot2::labs(fill = "")
# plotly
plotly_graph <- plotly::ggplotly(ggplot_table_catch)
# Add a title
if (title == TRUE) {
plotly_graph <- plotly_graph %>%
plotly::layout(title = list(text = paste0("Fishery production by ",
fishing_type,
" fishing mode. Catch by species of the ",
country_legend,
" ",
vessel_type_legend,
" fishing",
"\n",
"fleet during ",
min(time_period),
"-",
max(time_period),
", in the ",
ocean_legend,
" ocean."),
font = list(size = 17)),
margin = list(t = 120))
}
# Plot the plotly
plotly_graph %>%
plotly::layout(legend = list(orientation = "v",
x = 0.9,
y = 0.95))
} else if (graph_type == "table") {
table_catch_all <- round(table_catch_all, 0)
table_catch_all <- table_catch_all %>%
dplyr::summarise(Year = year,
YFT = YFT,
SKJ = SKJ,
BET = BET,
ALB = ALB,
OTH = OTH,
TOTAL = total)
as.data.frame(table_catch_all)
} else if (graph_type == "percentage") {
table_catch_all <- table_catch_all %>%
dplyr::summarise(Year = year,
YFT = YFT / total * 100,
SKJ = SKJ / total * 100,
BET = BET / total * 100,
ALB = ALB / total * 100,
OTH = OTH / total * 100,
TOTAL = total)
table_catch_all <- round(table_catch_all, 1)
table_catch_all$TOTAL <- round(table_catch_all$TOTAL, 0)
as.data.frame(table_catch_all)
}
}
OB7-IRD/fishi documentation built on Feb. 4, 2025, 2:03 a.m.
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Defines functions fishery_production
Documented in fishery_production
#' @name fishery_production
#' @title Total fishery production
#' @description Total fishery production (catch by species).
#' Generates a figure for catches (x 1000 t) of the three main tunas: BET, SKJ and YFT.
#' @param dataframe {\link[base]{data.frame}} expected. 'Csv' or 'output' of the function {\link[furdeb]{data_extraction}}, which must be done before using the fishery_production() function.
#' @param fishing_type {\link[base]{character}} expected. 'FSC', 'FOB' or 'ALL'. ALL by default.
#' @param graph_type {\link[base]{character}} expected. 'plot', 'plotly', 'table' or 'percentage'. 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 | species_code | flag | fleet | gear | school_type | ocean_label | total_catch_weight
#' -----------------------------------------------------------------------------------------
#' 1999-07-09 | 2 | France | France | PS | FOB | 1 | 119.
#' 1999-07-09 | 1 | France | France | PS | FOB | 1 | 20.6
#' 1999-07-09 | 1 | France | France | PS | FOB | 1 | 24.4
#' }
#' 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
fishery_production <- function(dataframe,
fishing_type = "ALL",
graph_type = "plot",
title = FALSE) {
# 0 - Global variables assignement ----
activity_date <- NULL
total_catch_weight <- NULL
ocean_label <- NULL
gear <- NULL
fleet <- NULL
flag <- NULL
school_type <- NULL
YFT <- NULL
SKJ <- NULL
BET <- NULL
ALB <- NULL
OTH <- NULL
DSC <- NULL
total_with_DSC <- NULL
total <- NULL
count <- NULL
specie <- NULL
time_period <- NULL
Catch <- NULL
Species <- 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 design ----
# Add columns year, school type and species
fishery_production_t1 <- dataframe %>%
dplyr::mutate(year = lubridate::year(x = activity_date),
school_type = dplyr::case_when(school_type == "UND" ~ "free",
school_type == "FSC" ~ "free",
school_type == "FOB" ~ "log",
TRUE ~ "und"),
YFT = dplyr::case_when(species_code == 1 ~ total_catch_weight,
TRUE ~ 0),
SKJ = dplyr::case_when(species_code == 2 ~ total_catch_weight,
TRUE ~ 0),
BET = dplyr::case_when(species_code == 3 ~ total_catch_weight,
TRUE ~ 0),
ALB = dplyr::case_when(species_code == 4 ~ total_catch_weight,
TRUE ~ 0),
DSC = dplyr::case_when(species_code == 8 | (species_code >= 800 & species_code <= 899) ~ total_catch_weight,
TRUE ~ 0),
OTH = dplyr::case_when(species_code == 1 | species_code == 2 | species_code == 3 | species_code == 4 | species_code == 8 | (species_code >= 800 & species_code <= 899) ~ 0,
TRUE ~ total_catch_weight),
total_with_DSC = total_catch_weight)
# Sum columns species
fishery_production_t2 <- fishery_production_t1 %>%
dplyr::group_by(ocean_label,
year,
gear,
fleet,
flag,
school_type) %>%
dplyr::summarise(YFT = sum(YFT, na.rm = TRUE),
SKJ = sum(SKJ, na.rm = TRUE),
BET = sum(BET, na.rm = TRUE),
ALB = sum(ALB, na.rm = TRUE),
DSC = sum(DSC, na.rm = TRUE),
OTH = sum(OTH, na.rm = TRUE),
total = sum(total_with_DSC, na.rm = TRUE) - sum(DSC, na.rm = TRUE),
.groups = "drop")
if (fishing_type == "ALL") {
### Percentage of catches by species on all fishing mode
# Group results by year
table_catch_all <- fishery_production_t2 %>%
dplyr::group_by(year) %>%
dplyr::summarise(YFT = sum(YFT, na.rm = TRUE),
SKJ = sum(SKJ, na.rm = TRUE),
BET = sum(BET, na.rm = TRUE),
ALB = sum(ALB, na.rm = TRUE),
OTH = sum(OTH, na.rm = TRUE),
total = sum(total, na.rm = TRUE),
.groups = "drop")
} else if (fishing_type == "FSC") {
### Percentage of catches by species on FSC
# Group results by year
table_catch_all <- fishery_production_t2 %>%
dplyr::filter(school_type == "free") %>%
dplyr::group_by(year) %>%
dplyr::summarise(YFT = sum(YFT, na.rm = TRUE),
SKJ = sum(SKJ, na.rm = TRUE),
BET = sum(BET, na.rm = TRUE),
ALB = sum(ALB, na.rm = TRUE),
OTH = sum(OTH, na.rm = TRUE),
total = sum(total, na.rm = TRUE),
.groups = "drop")
} else if (fishing_type == "FOB") {
### Percentage of catches by species on FOB
# Group results by year
table_catch_all <- fishery_production_t2 %>%
dplyr::filter(school_type == "log") %>%
dplyr::group_by(year) %>%
dplyr::summarise(YFT = sum(YFT, na.rm = TRUE),
SKJ = sum(SKJ, na.rm = TRUE),
BET = sum(BET, na.rm = TRUE),
ALB = sum(ALB, na.rm = TRUE),
OTH = sum(OTH, na.rm = TRUE),
total = sum(total, na.rm = TRUE),
.groups = "drop")
}
# 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(fishery_production_t1$year):max(fishery_production_t1$year)))
# fishing_type
fishing_type <- fishing_type
}
# 4 - Graphic design ----
if (graph_type == "plot") {
table_long <- tidyr::pivot_longer(table_catch_all,
cols = c("YFT",
"SKJ",
"BET"),
names_to = "Species",
values_to = "Catch")
fishery_ggplot <- ggplot2::ggplot(table_long,
ggplot2::aes(x = year,
y = Catch / 1000,
fill = Species)) +
ggplot2::geom_area(position = "stack") +
ggplot2::scale_fill_manual(values = c("YFT" = "khaki1",
"SKJ" = "firebrick2",
"BET" = "cornflowerblue")) +
ggplot2::labs(x = "",
y = "Catch (x1000 t)",
title = "") +
ggplot2::ylim(0, max((table_catch_all$total * 1.02) / 1000,
na.rm = TRUE)) +
ggplot2::scale_x_continuous(expand = c(0, 0),
breaks = table_long$year) +
ggplot2::scale_y_continuous(expand = c(0, 0)) +
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.grid.major = ggplot2::element_blank(),
panel.grid.minor.x = ggplot2::element_blank(),
panel.grid.major.y = ggplot2::element_line(size = 0.2,
color = "gray90")) +
ggplot2::labs(fill = NULL)
if (title == TRUE) {
fishery_ggplot <- fishery_ggplot + ggplot2::ggtitle(paste0("Fishery production by ",
fishing_type,
" fishing mode. \nCatch by species of the ",
country_legend,
" ",
vessel_type_legend,
" fishing",
"\n",
"fleet during ",
min(time_period),
"-",
max(time_period),
", in the ",
ocean_legend,
" ocean."))
}
return(fishery_ggplot)
} else if (graph_type == "plotly") {
# pivot wider
table_catch_3 <- table_catch_all %>%
dplyr::select(1:4)
data_pivot <- tidyr::pivot_longer(table_catch_3,
cols = c(2:4),
names_to = "specie",
values_to = "count")
# ggplot
ggplot_table_catch <- ggplot2::ggplot(data_pivot, ggplot2::aes(x = year,
y = count,
fill = specie)) +
ggplot2::geom_area() +
ggplot2::scale_fill_manual(values = c("cornflowerblue",
"firebrick2",
"khaki1")) +
ggplot2::scale_y_continuous(name = "Catch (x1000 t)") +
ggplot2::theme_bw() +
ggplot2::labs(fill = "")
# plotly
plotly_graph <- plotly::ggplotly(ggplot_table_catch)
# Add a title
if (title == TRUE) {
plotly_graph <- plotly_graph %>%
plotly::layout(title = list(text = paste0("Fishery production by ",
fishing_type,
" fishing mode. Catch by species of the ",
country_legend,
" ",
vessel_type_legend,
" fishing",
"\n",
"fleet during ",
min(time_period),
"-",
max(time_period),
", in the ",
ocean_legend,
" ocean."),
font = list(size = 17)),
margin = list(t = 120))
}
# Plot the plotly
plotly_graph %>%
plotly::layout(legend = list(orientation = "v",
x = 0.9,
y = 0.95))
} else if (graph_type == "table") {
table_catch_all <- round(table_catch_all, 0)
table_catch_all <- table_catch_all %>%
dplyr::summarise(Year = year,
YFT = YFT,
SKJ = SKJ,
BET = BET,
ALB = ALB,
OTH = OTH,
TOTAL = total)
as.data.frame(table_catch_all)
} else if (graph_type == "percentage") {
table_catch_all <- table_catch_all %>%
dplyr::summarise(Year = year,
YFT = YFT / total * 100,
SKJ = SKJ / total * 100,
BET = BET / total * 100,
ALB = ALB / total * 100,
OTH = OTH / total * 100,
TOTAL = total)
table_catch_all <- round(table_catch_all, 1)
table_catch_all$TOTAL <- round(table_catch_all$TOTAL, 0)
as.data.frame(table_catch_all)
}
}
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