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report3_STECF.R
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report3_STECF.R
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library(icesTAF)
taf.library(icesFO)
library(sf)
library(ggplot2)
library(dplyr)
##########
#Load data
##########
frmt_effort <- read.taf("data/frmt_effort.csv")
frmt_landings <- read.taf("data/frmt_landings.csv")
################################
## 2: STECF effort and landings#
################################
#This should go to utilities I guess
STECF_landings_BtS <- dplyr::filter(STECF_landings, grepl("27.3.B|27.3.C.22|27.3.D.24|27.3.D.25|27.3.D.28.2|
27.3.D.29|27.3.D.26|27.3.D.27|27.3.D.23|27.3.D.28.1|27.3.D.30|27.3.D.31|27.3.D.32", Sub.region))
STECF_landings_NrS <- dplyr::filter(STECF_landings, grepl("27.4.A|27.4.B|27.4.C|27.4.D|27.4.E", Sub.region))
STECF_landings_BI <- dplyr::filter(STECF_landings, grepl("27.8.A|27.8.B|27.8.C|27.8.D|27.8.E|
27.9.A|27.9.B", Sub.region))
effort_BtS$total.kW.days.at.Sea <- effort_BtS$Total.kW.days.at.sea
df <- STECF_effort_BtS
#~~~~~~~~~~~~~~~#
# Effort by country
#~~~~~~~~~~~~~~~#
plot_stecf <- function(df, type, variable = NULL, cap_year, cap_month, line_count, stecf_report, return_data = FALSE) {
if(type == "effort"){
if(variable=="COUNTRY"){
dat <- dplyr::rename_(df, "type_var" = "Country",
"VALUE" = "Total.kW.days.at.Sea")
}
if(variable=="GEAR"){
dat <- dplyr::rename_(df, "type_var" ="gear_class",
"VALUE" = "Total.kW.days.at.Sea")
}
Label <- "Nominal effort (1000 kW days at sea)"
}
if(type == "landings"){
dat <- dplyr::rename(df, "type_var" ="gear_class",
"VALUE" = "total.live.weight.landed")
Label <- "Landings (thousand tonnes)"
}
dat$type_var <- as.factor(dat$type_var)
dat$VALUE <- as.numeric(dat$VALUE)
Plot <- dplyr::group_by(dat,type_var)
Plot <- dplyr::summarise(Plot,typeTotal = sum(VALUE, na.rm = TRUE))
Plot <- dplyr::arrange(Plot,-typeTotal)
Plot <- dplyr::filter(Plot, typeTotal >= 1)
Plot <- dplyr::mutate(Plot,RANK = dplyr::min_rank(dplyr::desc(typeTotal)))
Plot <- subset(Plot,select = -typeTotal)
dat <- dplyr::left_join(dat, Plot)
# dat <- dat[complete.cases(dat), ]
# dat <- dat %>% filter(typeTotal>0)
# unique(dat[,c('type_var','confidential')])
# confidential data here, from Denmark, Spain, Lithuania and Finland
if(type == "effort"){
dat <- dat %>% filter(Confidential == "N")
}
# dat <- dplyr::mutate(dat, type_var = replace(type_var, RANK > 7, "other"))
dat <- dplyr::group_by(dat,type_var, Year)
dat <- dplyr::summarise(dat, typeTotal = sum(VALUE, na.rm = TRUE))
dat <- dplyr::filter(dat,!is.na(Year))
dat <- rbind(dat[!dat$type_var == "other",],
dat[dat$type_var == "other",])
my_caption = sprintf("STECF EWG 23-10. Accessed %s/%s.",
"October",
"2024")
cap_lab <- ggplot2::labs(title = "", x = "", y = Label,
caption = my_caption)
colList <- ggthemes::tableau_color_pal('Tableau 20')(7 + 1)
order <- dplyr::group_by(dat, type_var)
order <- dplyr::summarise(order, total = sum(typeTotal, na.rm = TRUE))
order <- dplyr::arrange(order, -total)
order <- dplyr::ungroup(order)
order <- dplyr::mutate(order,type_var = factor(type_var, type_var))
dat$type_var <- factor(dat$type_var,
levels = order$type_var[order(order$total)])
myColors <- colList[1:length(unique(dat$type_var))]
names(myColors) <- levels(dat$type_var)
myColors["other"] <- "#7F7F7F"
if(type == "effort"){
dat$typeTotal <- dat$typeTotal/1000
}
pl <- ggplot2::ggplot(dplyr::ungroup(dat), ggplot2::aes(x = Year, y = typeTotal)) +
ggplot2::scale_fill_manual(values = myColors) +
ggplot2::scale_color_manual(values = myColors) +
ggplot2::scale_x_continuous(breaks = seq(min(dat$Year, na.rm = TRUE),
max(dat$Year, na.rm = TRUE), by = 2)) +
ggplot2::geom_segment(ggplot2::aes(x = -Inf, xend = 2020.5, y = -Inf, yend = -Inf), color = "grey50")+
ggplot2::geom_segment(ggplot2::aes(y = -Inf, yend = Inf, x = -Inf, xend = -Inf), color = "grey50")+
ggplot2::expand_limits(x = c(min(dat$Year, na.rm = TRUE), 2022)) + # So that we have enough room along x-axis for labels.
cap_lab +
ggplot2::theme_bw(base_size = 9) +
ggplot2::theme(legend.position = 'none',
panel.grid.minor = ggplot2::element_blank(),
panel.grid.major = ggplot2::element_blank(),
panel.border = ggplot2::element_blank(),
strip.background = ggplot2::element_blank(),
plot.caption = ggplot2::element_text(size = 6),
axis.line = ggplot2::element_blank())
pl <- pl + ggplot2::geom_line(ggplot2::aes(color = type_var),
alpha = .9, position = "identity")
dat2 <- dplyr::filter(dat,Year == max(Year, na.rm = TRUE))
pl <- pl + ggrepel::geom_label_repel(data = dat2 ,
ggplot2::aes(label = type_var,
fill = type_var),
nudge_x = 3,
label.size = 0.2,
segment.size = 0.25,
size = 2,
color = 'white',
force = 3,
segment.color = 'grey60')
if(return_data == T){
dat
}else{
pl
}
}
#Plot
plot_stecf(STECF_effort_BtS,type = "effort", variable= "COUNTRY", "2024","October", 9, "23-10", return_data = FALSE)
# frmt_effort <- dplyr::filter(frmt_effort, COUNTRY %in% c("Sweden", "Poland", "Germany", "Denmark", "Lithuania","Latvia"))
# plot_stecf(frmt_effort,type = "effort", variable= "COUNTRY", "2019","August", 9, "15-23", return_data = FALSE)
ggplot2::ggsave(paste0(cap_year, "_", ecoreg,"_FO_STECF_effortCountry.png"), path = "report/", width = 178, height = 130, units = "mm", dpi = 300)
#data
dat <- plot_stecf(effort_BtS,type = "effort", variable= "COUNTRY", "2019","August", 9, "15-23", return_data = TRUE)
write.taf(dat, file= paste0(cap_year, "_", ecoreg,"_FO_STECF_effortCountry.csv"), dir = "report")
#~~~~~~~~~~~~~~~#
#Effort by gear
#~~~~~~~~~~~~~~~#
# plot_stecf <- function(x, type, variable = NULL, cap_Year, cap_month, line_count, stecf_report, return_data = FALSE) {
#
# if(type == "effort"){
# if(variable=="COUNTRY"){
# dat <- dplyr::rename_(effort_BtS, "type_var" ="country.name",
# "VALUE" = "total.kW.days.at.sea")}
# if(variable=="GEAR"){
# dat <- dplyr::rename_(effort_BtS, "type_var" ="gear_class",
# "VALUE" = "total.kW.days.at.sea")
# }
# Label <- "Nominal effort (1000 kW days at sea)"
# }
# if(type == "landings"){
# dat <- dplyr::rename(landings_BtS, "type_var" ="gear_class",
# "VALUE" = "total.live.weight.landed..tonnes.")
# Label <- "Landings (thousand tonnes)"
# }
#
# dat$type_var <- as.factor(dat$type_var)
#
# dat$VALUE <- as.numeric(dat$VALUE)
# Plot <- dplyr::group_by(dat,type_var)
# Plot <- dplyr::summarise(Plot,typeTotal = sum(VALUE, na.rm = TRUE))
# Plot <- dplyr::arrange(Plot,-typeTotal)
# Plot <- dplyr::filter(Plot, typeTotal >= 1)
# Plot <- dplyr::mutate(Plot,RANK = dplyr::min_rank(dplyr::desc(typeTotal)))
#
# Plot <- subset(Plot,select = -typeTotal)
# dat <- dplyr::left_join(dat, Plot)
# # dat <- dat[complete.cases(dat), ]
# dat <- dplyr::mutate(dat, type_var = replace(type_var, RANK > 7, "other"))
# dat <- dplyr::group_by(dat,type_var, Year)
# dat <- dplyr::summarise(dat, typeTotal = sum(VALUE, na.rm = TRUE))
# dat <- dplyr::filter(dat,!is.na(Year))
#
# dat <- rbind(dat[!dat$type_var == "other",],
# dat[dat$type_var == "other",])
#
# my_caption = sprintf("STECF %s. Accessed %s/%s.",
# "19-11",
# "August",
# "2020")
#
# cap_lab <- ggplot2::labs(title = "", x = "", y = Label,
# caption = my_caption)
#
# colList <- ggthemes::tableau_color_pal('Tableau 20')(7 + 1)
#
# order <- dplyr::group_by(dat, type_var)
# order <- dplyr::summarise(order, total = sum(typeTotal, na.rm = TRUE))
# order <- dplyr::arrange(order, -total)
# order <- dplyr::ungroup(order)
# order <- dplyr::mutate(order,type_var = factor(type_var, type_var))
#
# dat$type_var <- factor(dat$type_var,
# levels = order$type_var[order(order$total)])
#
# myColors <- colList[1:length(unique(dat$type_var))]
# names(myColors) <- levels(dat$type_var)
# myColors["other"] <- "#7F7F7F"
# if(type == "effort"){
# dat$typeTotal <- dat$typeTotal/1000
# }
#
# pl <- ggplot2::ggplot(dplyr::ungroup(dat), ggplot2::aes(x = Year, y = typeTotal)) +
# ggplot2::scale_fill_manual(values = myColors) +
# ggplot2::scale_color_manual(values = myColors) +
# ggplot2::scale_x_continuous(breaks = seq(min(dat$Year, na.rm = TRUE),
# max(dat$Year, na.rm = TRUE), by = 2)) +
# ggplot2::geom_segment(ggplot2::aes(x = -Inf, xend = 2018, y = -Inf, yend = -Inf), color = "grey50")+
# ggplot2::geom_segment(ggplot2::aes(y = -Inf, yend = Inf, x = -Inf, xend = -Inf), color = "grey50")+
# ggplot2::expand_limits(x = c(min(dat$Year, na.rm = TRUE), 2022)) + # So that we have enough room along x-axis for labels.
# cap_lab +
# ggplot2::theme_bw(base_size = 9) +
# ggplot2::theme(legend.position = 'none',
# panel.grid.minor = ggplot2::element_blank(),
# panel.grid.major = ggplot2::element_blank(),
# panel.border = ggplot2::element_blank(),
# strip.background = ggplot2::element_blank(),
# plot.caption = ggplot2::element_text(size = 6),
# axis.line = ggplot2::element_blank())
#
#
# pl <- pl + ggplot2::geom_line(ggplot2::aes(color = type_var),
# alpha = .9, position = "identity")
# dat2 <- dplyr::filter(dat,Year == max(Year, na.rm = TRUE))
# pl <- pl + ggrepel::geom_label_repel(data = dat2 ,
# ggplot2::aes(label = type_var,
# fill = type_var),
# nudge_x = 3,
# label.size = 0.2,
# segment.size = 0.25,
# size = 2,
# color = 'white',
# force = 3,
# segment.color = 'grey60')
#
#
# if(return_data == T){
# dat
# }else{
# pl
# }
# }
#Plot
plot_stecf(effort_BtS,type = "effort", variable= "GEAR", "2019","August", 9, "15-23")
ggplot2::ggsave(paste0(cap_year, "_", ecoreg,"_FO_STECF_effortGear.png"), path = "report/", width = 178, height = 130, units = "mm", dpi = 300)
#data
dat<-plot_stecf(effort_BtS,type = "effort", variable= "GEAR", "2019","August", 9, "15-23", return_data = TRUE)
write.taf(dat, file= paste0(cap_year, "_", ecoreg,"_FO_STECF_effortGear.csv"), dir = "report")
#~~~~~~~~~~~~~~~#
#Landings by country
#~~~~~~~~~~~~~~~#
plot_stecf <- function(df, type, variable = NULL, cap_year, cap_month, line_count, stecf_report, return_data = FALSE) {
if(type == "effort"){
if(variable=="COUNTRY"){
dat <- dplyr::rename_(df, "type_var" = "Country",
"VALUE" = "Total.kW.days.at.Sea")
}
if(variable=="GEAR"){
dat <- dplyr::rename_(df, "type_var" ="gear_class",
"VALUE" = "Total.kW.days.at.Sea")
}
Label <- "Nominal effort (1000 kW days at sea)"
}
if(type == "landings"){
dat <- dplyr::rename(df, "type_var" ="gear_class",
"VALUE" = "tot_live_weight_landed..tonnes.")
Label <- "Landings (thousand tonnes)"
}
dat$type_var <- as.factor(dat$type_var)
dat$VALUE <- as.numeric(dat$VALUE)
Plot <- dplyr::group_by(dat,type_var)
Plot <- dplyr::summarise(Plot,typeTotal = sum(VALUE, na.rm = TRUE))
Plot <- dplyr::arrange(Plot,-typeTotal)
Plot <- dplyr::filter(Plot, typeTotal >= 1)
Plot <- dplyr::mutate(Plot,RANK = dplyr::min_rank(dplyr::desc(typeTotal)))
Plot <- subset(Plot,select = -typeTotal)
dat <- dplyr::left_join(dat, Plot)
# dat <- dat[complete.cases(dat), ]
# dat <- dat %>% filter(typeTotal>0)
# unique(dat[,c('type_var','confidential')])
# confidential data here, from Denmark, Spain, Lithuania and Finland
if(type == "effort"){
dat <- dat %>% filter(Confidential == "N")
}
# dat <- dplyr::mutate(dat, type_var = replace(type_var, RANK > 7, "other"))
dat <- dplyr::group_by(dat,type_var, Year)
dat <- dplyr::summarise(dat, typeTotal = sum(VALUE, na.rm = TRUE))
dat <- dplyr::filter(dat,!is.na(Year))
dat <- rbind(dat[!dat$type_var == "other",],
dat[dat$type_var == "other",])
my_caption = sprintf("STECF EWG 23-10. Accessed %s/%s.",
"October",
"2024")
cap_lab <- ggplot2::labs(title = "", x = "", y = Label,
caption = my_caption)
colList <- ggthemes::tableau_color_pal('Tableau 20')(7 + 1)
order <- dplyr::group_by(dat, type_var)
order <- dplyr::summarise(order, total = sum(typeTotal, na.rm = TRUE))
order <- dplyr::arrange(order, -total)
order <- dplyr::ungroup(order)
order <- dplyr::mutate(order,type_var = factor(type_var, type_var))
dat$type_var <- factor(dat$type_var,
levels = order$type_var[order(order$total)])
myColors <- colList[1:length(unique(dat$type_var))]
names(myColors) <- levels(dat$type_var)
myColors["other"] <- "#7F7F7F"
dat$typeTotal <- dat$typeTotal/1000
pl <- ggplot2::ggplot(dplyr::ungroup(dat), ggplot2::aes(x = Year, y = typeTotal)) +
ggplot2::scale_fill_manual(values = myColors) +
ggplot2::scale_color_manual(values = myColors) +
ggplot2::scale_x_continuous(breaks = seq(min(dat$Year, na.rm = TRUE)+1,
max(dat$Year, na.rm = TRUE), by = 2)) +
ggplot2::geom_segment(ggplot2::aes(x = -Inf, xend = 2022.5, y = -Inf, yend = -Inf), color = "grey50")+
ggplot2::geom_segment(ggplot2::aes(y = -Inf, yend = Inf, x = -Inf, xend = -Inf), color = "grey50")+
ggplot2::expand_limits(x = c(min(dat$Year, na.rm = TRUE), 2023)) + # So that we have enough room along x-axis for labels.
cap_lab +
ggplot2::theme_bw(base_size = 9) +
ggplot2::theme(legend.position = 'none',
panel.grid.minor = ggplot2::element_blank(),
panel.grid.major = ggplot2::element_blank(),
panel.border = ggplot2::element_blank(),
strip.background = ggplot2::element_blank(),
plot.caption = ggplot2::element_text(size = 6),
axis.line = ggplot2::element_blank())
pl <- pl + ggplot2::geom_line(ggplot2::aes(color = type_var),
alpha = .9, position = "identity")
dat2 <- dplyr::filter(dat,Year == max(Year, na.rm = TRUE))
pl <- pl + ggrepel::geom_label_repel(data = dat2 ,
ggplot2::aes(label = type_var,
fill = type_var),
nudge_x = 3,
label.size = 0.2,
segment.size = 0.25,
size = 2,
color = 'white',
force = 3,
segment.color = 'grey60')
if(return_data == T){
dat
}else{
pl
}
}
#Plot
plot_stecf(STECF_landings_BI,type = "landings", variable= "GEAR", "2024","October", 9, "23-10")
ggplot2::ggsave(paste0(cap_year, "_", ecoregion,"_FO_STECF_landings.png"), path = "report/", width = 178, height = 130, units = "mm", dpi = 300)
#dat
dat <- plot_stecf(STECF_landings_BtS, type = "landings", variable="landings", "2019","August", 9, "15-23", return_data = TRUE)
write.taf(dat, file= paste0(cap_year, "_", ecoregion,"_FO_STECF_landings.csv"), dir = "report")