Map showing the asymmetric distribution of the ratio of excess deaths to COVID-19 deaths (2020-2021)

plot-map.R

# Data source: http://ghdx.healthdata.org/record/ihme-data/covid_19_excess_mortality

library(tidyverse)
library(sf)
library(rnaturalearth)
library(ggridges)
library(patchwork)

world_map <- ne_countries(type = "countries", returnclass = "sf")

data <- read_csv(
  "IHME_EM_COVID_19_2020_2021_DATA_Y2022M03D10.csv",
  locale = locale(encoding = "iso-8859-1")
) %>%
  filter(level == 3) %>%  # country data
  filter(measure_name == "ratio_excess_over_covid") %>%
  select(
    location_name,
    measure_name,
    mean_value,
    lower_ci,
    upper_ci
  ) %>%
  mutate(
    iso3c = countrycode::countrycode(
      location_name,
      origin = "country.name.en",
      destination = "iso3c"),
    ratio_class = cut(
      mean_value,
      breaks = c(-20, 0, 1, 2, 5, 10, 20, 200),
      labels = c(
        "< 0",
        "(0, 1]",
        "(1, 2]",
        "(2, 5]",
        "(5, 10]",
        "(10, 20]",
        "> 20"
      ),
      right = TRUE,
      ordered_result = TRUE
    )
  ) %>%
  left_join(
    data.frame(
      iso3c = rworldmap::countriesLow$ISO_A3,
      x = rworldmap::countriesLow$LON,
      y = rworldmap::countriesLow$LAT
    ),
    by = "iso3c"
  )

world_df <- world_map %>%
  filter(sov_a3 != "ATA") %>%  # remove Anctartica
  left_join(
    data,
    by = c("iso_a3" = "iso3c")
  ) %>%
  filter(!is.na(ratio_class))

map1 <- ggplot(
  world_df
) +
  geom_sf(aes(fill = ratio_class)) +
  scale_fill_brewer(
    name = "Ratio: ",
    type = "seq", palette = "YlGnBu"
  ) +
  coord_sf(crs = "+proj=moll") +
  guides(
    fill = guide_legend(nrow = 1)
  ) +
  labs(
    title = "Worldwide asymmetric distribution of excess deaths to COVID-19 deaths",
    subtitle = "Data source: \"COVID-19 Excess Mortality Estimates 2020-2021\" (http://ghdx.healthdata.org/record/ihme-data/covid_19_excess_mortality)",
    caption = "@jmcastagnetto, Jesus M. Castagnetto (2022-03-11)"
  ) +
  theme_minimal() +
  theme(
    axis.text = element_blank(),
    axis.ticks = element_blank(),
    legend.position = "top",
    plot.title = element_text(size = 28, face = "bold"),
    plot.subtitle = element_text(size = 14, colour = "grey50"),
    plot.caption = element_text(family = "Inconsolata", size = 14)
  )

inset <- ggplot(
  world_df,
  aes(y = str_wrap(income_grp, 15), x = mean_value, fill = income_grp)
) +
  geom_density_ridges(show.legend = FALSE, alpha = .7) +
  geom_vline(xintercept = 3.07, color = "blue", linetype = "dashed") +
  annotate(
    geom = "text",
    x = 3.5,
    y = 6,
    hjust = 0,
    size = 3,
    label = "Global average\n(3.07)",
    fontface = "bold",
    color = "blue"
  ) +
  scale_x_log10() +
  annotation_logticks(sides = "b") +
  scale_y_discrete(limits = rev) +
  scale_fill_brewer(palette = "Dark2") +
  labs(
    y = "",
    x = "Ratio of excess deaths\nto COVID-19 deaths"
  ) +
  theme_ridges() +
  theme(
    axis.text.y = element_text(size = 12),
    axis.title.x = element_text(size = 10),
    panel.grid = element_blank()
  )

text_comment <- ggplot() +
  annotate(
    geom = "text",
    label = str_wrap("The difference is more noticable for lower income countries", 20),
    hjust = 0,
    size = 5,
    fontface = "italic",
    x = 0,
    y = 0
  ) +
  theme_void()


# composed chart

map1 +
  inset_element(
    inset,
    left = 0,
    bottom = 0,
    right = 0.3,
    top = 0.6
  ) +
  inset_element(
    text_comment,
    left = 0,
    bottom = .65,
    right = .2,
    top = .9
  )

ggsave(
  filename = "worldwide-distribution-ratio-excess-deaths-to-covid19-deaths.png",
  width = 18,
  height = 9.5
)

Tried to make the colors distinctive enough to be easy to differentiate for colorblind people. Hope this works.
Perhaps color palettes for the inset ridge plot and the map could be make more compatible.