Salmonid Mortality

animals
Published

March 17, 2026

Salmon deaths in Norway.

Load Packages

require(pacman)
p_load(tidytuesdayR, tidyverse, janitor, here, trashpanda, magick)

Load Data

tuesdata <- tidytuesdayR::tt_load('2026-03-17')
monthly_losses_data <- tuesdata$monthly_losses_data
monthly_mortality_data <- tuesdata$monthly_mortality_data

Plot

heatmap_data <- monthly_mortality_data |> 
  mutate(year = year(date),
         month = month(date, label = TRUE, abbr = TRUE)) |> 
   mutate(mortality_anomaly = median - mean(median),
          .by = species) |> 
  mutate(species = case_when(species == "rainbowtrout" ~ "Rainbow Trout",
                             species == "salmon" ~ "Salmon"))

plot <- heatmap_data |> 
  ggplot() +
  geom_tile(aes(y = fct_rev(month), x = factor(year), fill = mortality_anomaly),
            colour = "black", linewidth = 0.1) +
  facet_wrap(~species) +
  scale_fill_gradient2(
    low = "blue",
    mid = "white",
    high = "red",
    midpoint = 0,
    name = "Deviation from\nmean (%)") +
  labs(x = "Year", y = "Month",
       title = "Salmon Confidential: Norwegian Salmon Mortality",
       subtitle = str_wrap("Based on data from the Norwegian Veterinary Institute, the majority of anomolous excess death occurs from January to March for Salmon, but later in the year for Rainbow Trout with 2022 being a particularly bad year for Rainbow Trout.")) +
  theme_cole(remove_grid = TRUE, base_size = 16) +
  add_caption_cwb() +
  theme(plot.title = element_text(hjust = 0, colour = "salmon"),
        plot.subtitle = element_text(hjust = 0, colour = "salmon"),
        legend.position = "right",
        legend.key.height = unit(1.2, "cm"),
        legend.key.width = unit(0.8, "cm"))
  

# Save and display images
current_dir <- dirname(knitr::current_input())
plot_name <- "salmon_mortality.png"

ggsave(plot = plot, 
       dpi = "screen",
       width = 16,
       height = 14,
       device = ragg::agg_png,
       filename = file.path(current_dir, plot_name))


# Read the big plot
img <- image_read(file.path(current_dir, plot_name))

# Force 16:9 aspect ratio with minimal padding
# Target size: 1200x675 px (16:9)
img_card <- image_scale(img, "1200x675")           # scale to fit inside 16:9
img_card <- image_extent(
  img_card,
  geometry = "1200x675",
  gravity = "center"
)

# Save as card preview
image_write(img_card, path = file.path(current_dir, "preview.png"))

knitr::include_graphics(
  file.path(current_dir, plot_name)
)

References

cite_packages(format = "rmd")

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