TidyTuesday Week 42/2021

TidyTuesday week 42 Global Seafood, data from OurWorldinData.org.

Datasets:
* aquaculture-farmed-fish-production.csv
* capture-fisheries-vs-aquaculture.csv
* capture-fishery-production.csv
* fish-and-seafood-consumption-per-capita.csv
* fish-stocks-within-sustainable-levels.csv
* global-fishery-catch-by-sector.csv
* seafood-and-fish-production-thousand-tonnes.csv

library(tidyverse)
library(ggtext)
library(countrycode)
library(janitor)
library(ggrepel)

Fish Stocks

• shared on Twitter

stock <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-10-12/fish-stocks-within-sustainable-levels.csv') %>% clean_names()

theme1 = theme_minimal(base_size = 9, base_family = "Roboto") +
  theme(legend.position = "top",
        panel.grid.minor=element_blank(),
        plot.title=element_text(face="bold", size=14),
        plot.title.position = "plot",
        axis.text=element_text(size=6.5),
        panel.grid=element_line(size=.2),
        plot.margin = unit(c(.75, 1, .3, .75), "cm"),
        axis.title=element_text(size=7.5),
        plot.subtitle=element_text(lineheight = 1.4),
        plot.caption.position = "plot",
        plot.caption=element_text(size=6.5, hjust=0, margin=margin(t=8), color="grey10")
        ) 

stock %>% filter(code=="OWID_WRL") %>%
  pivot_longer(share_of_fish_stocks_within_biologically_sustainable_levels_fao_2020:share_of_fish_stocks_that_are_overexploited) %>%
  mutate(value2 = ifelse(name=="share_of_fish_stocks_within_biologically_sustainable_levels_fao_2020",-1*value, value)) %>%
  ggplot(aes(x=year, y=value2/100, fill=name)) +
  scale_y_continuous(limits=c(-1,.55), expand=c(0.02,0.02), breaks=seq(-1,.5,0.25), labels=c("100%", "75%", "50%","25%", "0%","25%","50%"),
                     position="right")+
  scale_x_continuous(breaks=c(1974,1980,1990, 2000,2010,2017),expand=c(0.03,0.03)) +
  geom_col(aes(alpha=ifelse(year==1978|year==2017,1,0.55)), show.legend=F) +
  scale_alpha_identity() +
  scale_fill_manual(values=c("#c1121f","#3d5a80")) +
  geom_hline(yintercept = 0) +
  annotate(geom="text",x=1990, y=-.95, label=("Sustainable share"), 
           color="#3d5a80", size=6, alpha=.8, family="Roboto",hjust=0) +
  annotate(geom="text",x=1990, y=.45, label=("Overexploited share"), 
           color="#c1121f", size=6, alpha=.8, family="Roboto",hjust=0) +
  annotate(geom="richtext", label.color=NA, fill=NA,size=2.8, x=2015, y=.53, 
           label="2017: <span style = 'color:#c1121f;'>**34.2%**</span>", family="Roboto") +
  annotate(geom="richtext", label.color=NA, fill=NA,size=2.8, x=1976.5, y=.35, 
           label="1978: <span style = 'color:#c1121f;'>**8.5%**</span>",, family="Roboto") +
  annotate(geom="curve", xend=1978, yend=0.1, x=1976.5, y=.32,color="grey70",arrow=arrow(length = unit(0.1, "cm")),curvature = -0.2) +
  annotate(geom="curve", xend=2017, yend=0.36, x=2015, y=.49,color="grey70",arrow=arrow(length = unit(0.1, "cm")),curvature = -0.2) +
  theme1 +
   labs(y="Share of Fish Stocks", x="Year", 
        title=str_to_upper("Increasing pressures on fish populations"), 
        caption="#TidyTuesday Week 42 | Data from OurWorldinData.org",
        subtitle="More than one-third of global fish stocks are overexploited in 2017.") 

ALT TEXT: Diverging bar plot of global fish stocks displaying sustainable and overexploited share from 1974 to 2017. The plot shows increasing pressures on the global fish population, where 34.2% of the global fish stocks are overexploited in 2017, which is a significant increase from the 8.5% in 1978.

Wild fish catch vs aquaculture production

captured_vs_farmed <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-10-12/capture-fisheries-vs-aquaculture.csv') %>% clean_names()

reg = c("World","East Asia & Pacific","Europe & Central Asia","Latin America & Caribbean","Middle East & North Africa",
  "North America","South Asia","Sub-Saharan Africa")

captured_vs_farmed %>% filter(entity %in% reg) %>% 
  pivot_longer(4:5) %>%
  mutate(value = value/1000000) %>%
  ggplot(aes(x=year, y=value, color=name)) +
  geom_line(size=.55) +
  facet_wrap(~factor(entity, levels=reg), scales="free", ncol=4) +
  scale_x_continuous(breaks=c(1960,1980,2000,2018)) +
  scale_color_manual(values=c("#289f9d","#fae588")) +
  theme_minimal(base_size = 8, base_family = "Roboto Condensed") +
  theme(legend.position = "none",
        axis.line=element_line(size=.2,color="white"),
        axis.text=element_text(color="white"),
        axis.title=element_text(color="white"),
        panel.grid=element_blank(),
        axis.ticks=element_line(size=.2, color="white"),
        strip.text=element_text(size=8,color="white"),
        plot.margin = unit(c(.75, 1.5, .75, .75), "cm"),
        plot.subtitle = element_text(color="white", family="Roboto", size=7),
        panel.spacing = unit(1.5, "lines"),
        plot.background=element_rect(color=NA, fill="#1A2E40"),
        plot.title=element_markdown(size=13, color="white", family="Roboto"),
        plot.caption = element_text(color="white")
        ) +
  labs(title="Seafood production: <span style = 'color:#fae588;'>capture fisheries</span> vs <span style = 'color:#289f9d;'>aquaculture production</span>",
       subtitle="From 1960 to 2018. Aquaculture is the farming of aquatic organisms including fish, molluscs, crustaceans and aquatic plants. Capture fishery\nproduction is the volume of wild fish catches landed for all commercial, industrial, recreational and subsistence purposes.\n",
       caption="Data source: OurWorldinData.org",
       y="Volume (million metric tons)", x="Year")

Global fishery catch by sector

fishery <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-10-12/global-fishery-catch-by-sector.csv',show_col_types = FALSE)

f1 = fishery %>% summarise(across(4:8, sum)) %>%
  pivot_longer(everything()) %>% 
  arrange(value) %>%
  mutate(name=fct_inorder(name))

themef =
  theme_minimal(base_size = 8, base_family = "Roboto") +
  theme(panel.grid.major.x=element_blank(),
        panel.grid.minor=element_blank(),
        panel.grid=element_line(linetype="dotted",color="grey50", size=.2),
        axis.ticks.x=element_line(color="grey50", size=.2),
        axis.title=element_text(size=6.5),
        plot.subtitle = element_text(size=7),
        plot.margin = unit(c(.5, .5, .5, .5), "cm"),
        legend.position="top",
        legend.title=element_text(size=7.5),
        plot.title.position="plot",
        plot.title=element_text(size=12),
        plot.caption.position = "plot",
        plot.caption=element_text(hjust=0),
        axis.text.x=element_text(size=6.2,margin=margin(t=3)),
        axis.text.y=element_text(size=6.2),
        legend.key.width = unit(.3,"cm"),
        legend.key.height = unit(.3,"cm")
        ) 

fc1 = fishery %>% pivot_longer(4:8) %>%
  ggplot(aes(x=Year, y=value/1000000, fill=factor(name, levels=f1$name))) +
  geom_area() +
  scale_y_continuous("Volume (million tonnes)",limits=c(0,140), breaks=seq(0,140,20), expand=c(.01,.01)) +
  scale_x_continuous("Year",expand=c(.01,.01)) +
  scale_fill_manual("Purposes",values=c("#fd151b","#437f97","#dcccbb","#ffb30f","#01295f"),labels = function(x) str_wrap(x, width = 20)) +
  guides(fill = guide_legend(override.aes = list(size = 4))) +
  themef +
  labs(title="Global fishery catch by sector", caption="Data source: OurWorldinData.org",
       subtitle="Breakdown of global wild fishery catch by sector. This relates only to wild fishery catch, and does not include aquaculture (fish farming) production.")

fc2 = fishery %>% pivot_longer(4:8) %>%
  ggplot(aes(x=Year, y=value/1000000, color=factor(name, levels=f1$name))) +
  geom_line(show.legend = F) +
  geom_point(size=.3,show.legend = F) +
  scale_y_continuous("Volume (million tonnes)", expand=c(.01,.01), breaks=seq(0,100,20), limits=c(0,100)) +
  scale_x_continuous("Year", expand=c(.01,.01)) +
  scale_color_manual("Purposes",values=c("#fd151b","#437f97","#dcccbb","#ffb30f","#01295f"),labels = function(x) str_wrap(x, width = 20)) 

(fc1 + fc2) +  plot_layout(guides = "collect") & themef

Seafood and fish production

production <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-10-12/seafood-and-fish-production-thousand-tonnes.csv',show_col_types = FALSE) 

cont = c("World","Africa","Americas","Asia","Europe","Oceania")

prod = production %>% #rename_with(~str_remove(., 'commodity_balances_livestock_and_fish_primary_equivalent_')) %>%
  rename("Pelagic Fish "= 4, "Crustaceans"=5,"Cephalopods"=6, "Demersal Fish"=7, "Freshwater Fish"=8, "Molluscs (other)" =9,
  "Marine Fish (other)"=10) 

p1 = prod %>% 
  filter(Entity=="World") %>%
  summarise(across(4:10, sum)) %>%
  pivot_longer(everything()) %>%
  arrange(value) %>% 
  mutate(name=fct_inorder(name))

prod %>% 
  filter(Entity %in% cont) %>%
  pivot_longer(4:10) %>%
  ggplot(aes(x=Year, y=value/1000000, fill=factor(name, levels=p1$name))) +
  geom_area() +
  facet_wrap(~factor(Entity, levels=cont), scales="free", ncol=3) +
  ggsci::scale_fill_npg() +
  scale_y_continuous("Volume (million tonnes)",expand = expansion(mult = c(0, .1))) +
  scale_x_continuous(breaks=c(1961,1980,2000,2013), expand=c(.01,.01)) +
  theme_minimal(base_size = 8, base_family="Roboto") +
  theme(legend.position = "top",
        legend.justification = "left",
        legend.margin=margin(l=-20),
        legend.text=element_text(size=7.5, color="white"),
        legend.key.width = unit(.3,"cm"),
        legend.key.height = unit(.5,"cm"),
        legend.title=element_blank(),
        axis.ticks=element_line(size=.2, color="#ced4da"),
        axis.text.x=element_text(margin=margin(t=3), color="#f8f9fa"),
        axis.text.y=element_text(color="#f8f9fa"),
        axis.title=element_text(color="white"),
        strip.text=element_text(size=9.5, color="white"),
        panel.grid=element_blank(),
        panel.spacing.x = unit(1.3, "lines"),
        panel.spacing.y = unit(.7, "lines"),
        plot.margin = unit(c(.75, 1.25, .75, .75), "cm"),
        plot.title.position = "plot",
        plot.title=element_text(size=13, margin=margin(b=8),color="white"),
        plot.background = element_rect(fill="#14213d", color=NA),
        plot.caption=element_text(color="white")
        ) +
  guides(fill = guide_legend(nrow = 1,reverse=T)) +
  labs(title="Seafood and fish production", caption="Data source: OurWorldinData.org")

Global fish and seafood production

prod2 = prod %>% 
  filter(Entity=="World") %>%
  pivot_longer(4:10) %>%
  group_by(name) %>%
  mutate(total=round(sum(value)/1000000))
  
prod2 %>% ggplot(aes(x=Year, y=reorder(name, value), fill=value/1000000)) +
  annotate(geom= "segment", x=c(1970,1990,2010), xend=c(1970,1990,2010), y=0.3, yend=7.5, color="grey70", size=.2) +
  geom_tile(height=.5, color="white") +
  geom_text(data=prod2 %>% filter(Year==min(Year)), aes(label=name, x=1953), size=3.1,family="sans", color="grey10") +
  geom_text(data=prod2 %>% filter(Year==min(Year)), aes(label=glue::glue("{total}M"), x=1944.5),color="grey10",family="sans", size=3.1, hjust=1) +
  annotate(geom="text", x=c(1953,1944.5,1960.5), y=7.8, label=c("Category","Total","Production by year"), 
           size=3, color="grey45", family="Roboto", hjust=c(.5, 1,0)) +
  #scale_fill_gradientn(colours = wesanderson::wes_palette("Zissou1", 100, type = "continuous"), labels=scales::unit_format(unit = "M")) +
  rcartocolor::scale_fill_carto_c(palette="Fall", labels=scales::unit_format(unit = "M"))+
  scale_x_continuous(limits=c(1943, 2014), breaks=c(1970,1990,2010)) +
  scale_y_discrete(expand = expansion(mult = c(.1, .2))) +
  theme_void(base_family = "sans") +
  theme(plot.margin = unit(c(1, 1.25, 1, 1), "cm"),
        legend.title=element_blank(),
        legend.text=element_text(color="grey45",size=7.5),
        plot.title=element_text(margin=margin(b=7), size=12),
        axis.text.x=element_text(size=7.5, color="grey45"),
        plot.background = element_rect(fill="#f8f9fa", color=NA),
        plot.subtitle=element_text(size=8, color="grey20", margin=margin(b=10))) +
  guides(fill = guide_colorbar(barwidth = unit(.5, "lines"), barheight = unit(10, "lines"))) +
  labs(title= "Global seafood and fish production, in tonnes, 1961 to 2013",subtitle="Data source: OurWorldinData.org")

Fish stocks that are overexploited

stock %>% 
  select(-share_of_fish_stocks_within_biologically_sustainable_levels_fao_2020) %>%
  filter(entity!="World") %>%
  ggplot(aes(x=factor(year), fill=factor(year), y=share_of_fish_stocks_that_are_overexploited)) +
  geom_col(width=.5) +
  geom_text(aes(label=scales::percent(share_of_fish_stocks_that_are_overexploited/100,accuracy=.1)), 
            family="Roboto Condensed", size=2.8, vjust=1.5) +
  facet_wrap(~entity, ncol=5, strip.position = "bottom") +
  scale_fill_manual(values=c("#eca400","#eaf8bf")) +
  scale_y_continuous(limits=c(0,70)) +
  theme_void(base_size = 9, base_family = "Roboto") +
  theme(strip.text=element_text(family="Roboto Condensed", color="white", size=8, margin=margin(t=5)),
        panel.spacing.y = unit(1, "lines"),
        legend.position = "none",
        strip.placement = "outside",
        plot.margin = unit(c(.75, .75, .75, .75), "cm"),
        plot.title=element_markdown(size=12, color="white"),
        plot.background = element_rect(fill="#212529", color=NA),
        plot.subtitle=element_text(color="#f8f9fa", size=7),
        ) +
  labs(title="Share of fish stocks that are overexploited, in <span style = 'color:#eca400;'>2015</span> and <span style = 'color:#eaf8bf;'>2017</span>", 
       subtitle="A key concern for the sustainability of global patterns of seafood consumption has been the overexploitation of wild fish stocks. If the amount of wild fish\nwe catch exceeds the rate at which fish can reproduce and replenish, populations will decline over time. Such populations we would call ‘overexploited’.\n\nData source: OurWorldinData.org")

Capture fishery production by region

cf1 = captured_vs_farmed %>% filter(entity %in% reg) %>% 
  filter(entity!="World") %>%
  mutate(value = capture_fisheries_production_metric_tons/1000000)

cf1 %>%
  ggplot(aes(x=year, y=value, color=entity)) +
  annotate(geom="segment", y=0, yend=0, x=1960, xend=2018, size=.3, color="#343a40") +
  annotate(geom="segment", y=seq(0,40,5), yend=seq(0,40,5),x=1960, xend=2018,size=.2, linetype="dotted", color="grey50") +
  geom_line(show.legend=F) +
  geom_point(size=.5,show.legend=F) +
  geom_text_repel(data = cf1 %>% filter(year==2018),aes(x=2020, y=value, label=entity, color=entity), 
                  hjust=0, size=2.5, show.legend=F, direction="y", family="Roboto") +
  scale_x_continuous("Year",breaks=c(1960,1970,1980,1990,2000,2010,2018), limits=c(1959,2035.5)) +
  scale_y_continuous("Volume (million tonnes)",limits=c(0,45), breaks=seq(0,40,5)) +
  coord_cartesian(expand=F, clip="off") +
  ggsci::scale_color_lancet() +
  theme_minimal(base_size = 8, base_family = "Roboto") +
   theme(panel.grid=element_blank(),
        axis.ticks.x=element_line(color="grey30", size=.3),
        axis.title.x=element_text(size=7.5, hjust=0.403),
        axis.title.y=element_text(size=7.5, margin=margin(r=3)),
        axis.text.x=element_text(margin=margin(t=3)),
        plot.margin = unit(c(.7, 1, .5, 1), "cm"),
        plot.title=element_text(size=13),
        plot.caption=element_text(hjust=0),
        plot.background = element_rect(fill="#f8f9fa", color=NA),
        plot.title.position="plot",
        plot.caption.position = "plot",
        ) +
  labs(title="Capture fishery production",
       subtitle="Capture (wild) fishery production does not include seafood produced from fish farming (aquaculture).",
       caption="Data source: OurWorldinData.org")

Aquaculture proportion

west = c("Austria","Belgium","France","Germany","Netherlands","Switzerland","Liechtenstein","Luxembourg","Monaco") 

cf2 = captured_vs_farmed %>% 
  filter(entity %in% west) %>%
  #filter(year>=1980) %>%
  mutate(prop=aquaculture_production_metric_tons/(aquaculture_production_metric_tons+capture_fisheries_production_metric_tons)) %>%
  drop_na() 
  
cf2 %>%
  ggplot(aes(x=year, y=prop, color=entity)) +
  annotate(geom="segment", y=0, yend=0, x=1960, xend=2018, size=.3, color="#343a40") +
  annotate(geom="segment", y=seq(.25,1,.25), yend=seq(.25,1,.25),x=1960, xend=2018,size=.2, linetype="dotted", color="grey50") +
  geom_line(show.legend = F) +
  geom_point(size=.5,show.legend=F) +
  geom_text_repel(data=cf2 %>% filter(year==2018), aes(label=entity, x=2019.5),
                  size=3, hjust=0, show.legend = F, direction="y", box.padding = 0, nudge_y = .007,family="Roboto") +
  scale_y_continuous("Aquaculture Proportion",limits=c(0,1), expand=c(0,0)) +
  scale_x_continuous("Year",limits=c(1960,2028), breaks=c(1960,1980,2000,2018), expand=c(0.01,0.01)) +
  scale_color_manual(values=c("#01295f","#29715d","#849324","#f95738","#07a0c3","#eca400")) +
  theme_minimal(base_size = 9, base_family = "Roboto") +
   theme(panel.grid=element_blank(),
        axis.ticks.x=element_line(color="grey30", size=.3),
        axis.ticks.length=unit(.15, "cm"),
        axis.title.x=element_text(size=7.5,hjust=0.403),
        axis.title.y=element_text(size=7.5, margin=margin(r=3)),
        axis.text.x=element_text(margin=margin(t=5)),
        plot.margin = unit(c(.7, 1, .5, 1), "cm"),
        plot.subtitle=element_text(margin=margin(b=15), size=8.5),
        plot.caption=element_text(hjust=0),
        plot.caption.position = "plot",
        plot.title=element_text(size=12),
        plot.title.position = "plot",
        #plot.background = element_rect(fill="#f5f3f4", color=NA)
        ) +
  labs(title="Aquaculture Production in Western Europe from 1960 to 2018",
       subtitle="Expressed as Aquaculture production divided by the sum of acquaculture and captured fisheries production",
       caption="Data source: OurWorldinData.org")