Mapping Endangered Languages Worldwide

A circle-packed visualization of linguistic diversity at risk across six macroareas

#TidyTuesday
{packcircles}
Circle Visualization
Author

Aditya Dahiya

Published

December 27, 2025

About the Data

This dataset explores the world’s languages using data from Glottolog 5.2.1, the most comprehensive language database in linguistics. Maintained by the Max Planck Institute for Evolutionary Anthropology, Glottolog contains detailed information on over 8,000 languages worldwide, including names, genealogy, geographical distributions, and endangerment status. The dataset comprises three interconnected tables: languages.csv provides core information about each language including geographic coordinates, ISO 639-3 codes, macroarea classifications, and whether the language is an isolate with no known relatives; families.csv catalogs the language families to which non-isolate languages belong; and endangered_status.csv documents the endangerment status of languages using a six-category classification system. This rich dataset enables exploration of fascinating questions about linguistic diversity, such as which regions have the highest concentrations of endangered languages, whether language isolates face greater endangerment risks, and how language families are distributed geographically across the globe. The data was curated by Darakhshan Nehal for #TidyTuesday, a weekly data project in the R for Data Science online learning community.

Figure 1: This visualization maps the distribution of endangered languages across countries and macroareas worldwide. Each circle represents a country, with size proportional to the number of endangered languages it contains. Countries are colored by their macroarea—the broad geographic region they belong to—revealing distinct patterns of linguistic endangerment. Africa (red) and Papunesia (teal) show particularly high concentrations, with Nigeria and Indonesia harboring the most endangered languages. The varying transparency of circles adds visual depth while highlighting the complex, overlapping nature of global linguistic diversity under threat.

How I Made This Graphic

.

Loading required libraries

Code 
pacman::p_load(
  tidyverse, # All things tidy

  scales, # Nice Scales for ggplot2
  fontawesome, # Icons display in ggplot2
  ggtext, # Markdown text support for ggplot2
  showtext, # Display fonts in ggplot2
  colorspace, # Lighten and Darken colours
  sf, # Spatial Features

  patchwork,  # Composing Plots
  packcircles # for hierarchichal packing circles
)

endangered_status <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-12-23/endangered_status.csv')

families <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-12-23/families.csv')

languages <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-12-23/languages.csv')

world <- rnaturalearth::ne_countries(returnclass = "sf")

Visualization Parameters

Code 
# Font for titles
font_add_google("Saira",
  family = "title_font"
)

# Font for the caption
font_add_google("Saira Condensed",
  family = "body_font"
)

# Font for plot text
font_add_google("Saira Extra Condensed",
  family = "caption_font"
)

showtext_auto()

# A base Colour
bg_col <- "white"
seecolor::print_color(bg_col)

# Colour for highlighted text
text_hil <- "grey40"
seecolor::print_color(text_hil)

# Colour for the text
text_col <- "grey30"
seecolor::print_color(text_col)

line_col <- "grey30"

# Define Base Text Size
bts <- 80

mypal <- paletteer::paletteer_d("ltc::trio3")[c(2, 3, 1)]

# Caption stuff for the plot
sysfonts::font_add(
  family = "Font Awesome 6 Brands",
  regular = here::here("docs", "Font Awesome 6 Brands-Regular-400.otf")
)
github <- "&#xf09b"
github_username <- "aditya-dahiya"
xtwitter <- "&#xe61b"
xtwitter_username <- "@adityadahiyaias"
social_caption_1 <- glue::glue("<span style='font-family:\"Font Awesome 6 Brands\";'>{github};</span> <span style='color: {text_hil}'>{github_username}  </span>")
social_caption_2 <- glue::glue("<span style='font-family:\"Font Awesome 6 Brands\";'>{xtwitter};</span> <span style='color: {text_hil}'>{xtwitter_username}</span>")
plot_caption <- paste0(
  "**Data:**  Max Planck Institute for Evolutionary Anthropology",
  " |  **Code:** ",
  social_caption_1,
  " |  **Graphics:** ",
  social_caption_2
)
rm(
  github, github_username, xtwitter,
  xtwitter_username, social_caption_1,
  social_caption_2
)

plot_title <- "Global Linguistic Diversity"

plot_subtitle <- "<span style='color:#D4526E'>Africa</span> (3,266 languages) and <span style='color:#C8B591'>Eurasia</span> (3,225) lead in endangered linguistic diversity,<br>followed by <span style='color:#42B7BD'>Papunesia</span> (2,242), <span style='color:#2F6A6C'>South America</span> (871), and <span style='color:#729B79'>North America</span> (819).<br><span style='color:#F4D35E'>Australia</span> (375) has fewer languages but faces significant endangerment risks."

Exploratory Data Analysis and Wrangling

Code 
bts = 80

plotdf <- languages |> 
  select(id, name, countries, macroarea) |> 
  left_join(endangered_status) |> 
  select(-status_code) |> 
  drop_na() |> 
  separate_longer_delim(cols = countries, ";") |> 
  separate_longer_delim(cols = macroarea, ";") |> 
  count(macroarea, countries) |> 
  mutate(
    countries = str_to_lower(countries),
    country = countrycode::countrycode(
      sourcevar = countries,
      origin = "iso2c",
      destination = "country.name.en"
    ),
    country = str_wrap(country, 15)
  )

plotdf <- plotdf |> 
  mutate(alpha_var = runif(nrow(plotdf)))

# endangered_status |> 
#   count(status_label)
# 
# languages |> 
#   drop_na(longitude, latitude) |> 
#   st_as_sf(
#     coords = c("longitude", "latitude"),
#     crs = "EPSG:4326"
#   ) |> 
#   ggplot() +
#   geom_sf(data = world) +
#   geom_sf()


# Prepare data for circle packing
# Need to create a proper hierarchy with groups and subgroups
packing_data <- plotdf |>
  select(macroarea, country, n, alpha_var) |>
  mutate(
    group = macroarea,
    subgroup = country,
    value = n
  )

# Create the packing layout
# First, we need to format data for circleProgressiveLayout
packing <- circleProgressiveLayout(packing_data$value, sizetype = 'area')
packing$radius <- 0.95 * packing$radius  # Add some spacing

# Add packing coordinates back to data
packing_data <- bind_cols(packing_data, packing)

# Generate circle coordinates for plotting
circle_data <- circleLayoutVertices(packing, npoints = 50)

# Join back the grouping information
circle_data <- circle_data |>
  left_join(
    packing_data |> 
      mutate(id = row_number()) |> 
      select(id, group, subgroup, value, alpha_var),
    by = "id"
  )

# plotdf |> 
#   group_by(macroarea) |> 
#   summarise(n = sum(n))

The Plot

Code 
bts = 90

mypal <- paletteer::paletteer_d("calecopal::kelp1")

# Create the plot
g <- ggplot() +
  # Draw the circles
  geom_polygon(
    data = circle_data,
    aes(x = x, y = y, group = id, fill = group, alpha = alpha_var),
    colour = "white",
    linewidth = 0.5
  ) +
  # Add country labels for larger circles
  geom_text(
    data = packing_data |> filter(value > 50),
    aes(x = x, y = y, label = subgroup, size = value),
    colour = "white",
    fontface = "bold",
    alpha = 0.8,
    lineheight = 0.3
  ) +
  
  # Styling
  scale_fill_manual(values = mypal) +
  scale_alpha_continuous(range = c(0.3, 0.9), guide = "none") +
  scale_size_continuous(range = c(bts / 10, bts / 3), guide = "none") +
  coord_equal() +
  guides(
    size = "none",
    alpha = "none",
    fill = guide_legend(
      nrow = 1
    )
  ) +
  theme_void(
    base_family = "body_font",
    base_size = bts
  ) +
  labs(
    x = NULL,
    y = NULL,
    caption = plot_caption,
    title = plot_title,
    subtitle = plot_subtitle
  ) +
  theme(
    legend.position = "bottom",
    legend.margin = margin(0,0,0,0, "mm"),
    legend.title = element_blank(),
    legend.text = element_text(
      family = "caption_font",
      size = bts * 1.2,
      margin = margin(0,0,0,3, "mm")
    ),
    legend.text.position = "right",
    
    # Overall
    text = element_text(
      margin = margin(0, 0, 0, 0, "mm"),
      colour = text_hil,
      lineheight = 0.3
    ),
    
    # Axes
    plot.title = element_text(
      margin = margin(5,0,5,0, "mm"),
      hjust = 0.5,
      size = bts * 3.5,
      face = "bold"
    ),
    plot.subtitle = element_textbox(
      margin = margin(5,0,-5,0, "mm"),
      hjust = 0.5,
      lineheight = 0.35,
      size = bts * 1.2
    ),
    plot.caption = element_markdown(
      family = "caption_font",
      hjust = 0.5,
      margin = margin(5,0,0,0, "mm"),
      colour = text_hil
    ),
    plot.caption.position = "plot",
    plot.title.position = "plot",
    plot.margin = margin(5, -15, 5, -15, "mm"),
    legend.key.width = unit(10, "mm")
  )

ggsave(
  filename = here::here(
    "data_vizs",
    "tidy_languages_world.png"
  ),
  plot = g,
  width = 400,
  height = 500,
  units = "mm",
  bg = bg_col
)

Savings the thumbnail for the webpage

Code 
# Saving a thumbnail

library(magick)

# Saving a thumbnail for the webpage
image_read(here::here(
  "data_vizs",
  "tidy_languages_world.png"
)) |>
  image_resize(geometry = "x400") |>
  image_write(
    here::here(
      "data_vizs",
      "thumbnails",
      "tidy_languages_world.png"
    )
  )

Session Info

Code 
pacman::p_load(
  tidyverse, # All things tidy

  scales, # Nice Scales for ggplot2
  fontawesome, # Icons display in ggplot2
  ggtext, # Markdown text support for ggplot2
  showtext, # Display fonts in ggplot2
  colorspace, # Lighten and Darken colours
  sf, # Spatial Features

  patchwork,  # Composing Plots
  packcircles # for hierarchichal packing circles
)

sessioninfo::session_info()$packages |>
  as_tibble() |>
  
  # The attached column is TRUE for packages that were 
  # explicitly loaded with library()
  dplyr::filter(attached == TRUE) |>
  dplyr::select(package,
    version = loadedversion,
    date, source
  ) |>
  dplyr::arrange(package) |>
  janitor::clean_names(
    case = "title"
  ) |>
  gt::gt() |>
  gt::opt_interactive(
    use_search = TRUE
  ) |>
  gtExtras::gt_theme_espn()
Table 1: R Packages and their versions used in the creation of this page and graphics

Links