A map of Australia’s Frog Families

Five subfamilies, two patterns: mapping Australia’s climate-constrained frog diversity through citizen science.

#TidyTuesday
Maps
{ggforce}
Convex Mark Hull
Author

Aditya Dahiya

Published

August 31, 2025

About the Data

This week’s dataset explores Australian frog biodiversity through the 2023 release of FrogID data, representing the sixth annual publication from this groundbreaking citizen science initiative. FrogID is an innovative mobile app that enables citizen scientists across Australia to record and submit frog calls, which are then expertly identified by museum professionals. Since its launch in 2017, this collaborative effort has generated data that has contributed to over 30 scientific papers examining frog ecology, taxonomy, and conservation. The dataset is particularly significant given that Australia hosts 257 unique native frog species—most found nowhere else on Earth—with nearly one in five species currently threatened with extinction due to climate change, urbanization, disease, and invasive species. The data includes occurrence records with precise geographic coordinates, temporal information, and species identifications validated by experts, offering researchers and data enthusiasts a comprehensive view of Australian frog distributions and calling patterns. This rich dataset, formally documented in ZooKeys by Rowley & Callaghan (2020), provides an invaluable resource for understanding the current state of Australia’s imperiled amphibian fauna and supports ongoing conservation efforts through community-driven scientific discovery.

Figure 1: Distribution map based on citizen-contributed frog call recordings shows marked geographic segregation among Australia’s five major amphibian subfamilies. Each dot represents a validated frog call recording from the FrogID citizen science database, with colors indicating the five major Australian frog subfamilies. Shaded regions highlight core distribution areas where each subfamily’s populations are most concentrated. The map demonstrates the stark biogeographic divide between widespread generalist subfamilies (Hylidae and Myobatrachidae) that occur across Australia’s diverse environments, and tropical specialists (Ranidae, Bufonidae, and Microhylidae) restricted to the humid northeastern coastal regions.

How I Made This Graphic

This visualization was created using R with several specialized packages for spatial analysis and advanced plotting techniques. The core data manipulation relied on the tidyverse ecosystem, particularly dplyr for data wrangling and ggplot2 for the base mapping framework. Spatial operations were handled using sf for coordinate transformations and map clipping, while the Australian map boundaries came from rnaturalearth. The key analytical challenge—identifying the 70% most concentrated points for each subfamily—was solved using kernel density estimation through MASS::kde2d() and fields::interp.surface() to calculate density at each point location, then ranking and selecting the highest-density observations. The striking convex hulls that define each subfamily’s core distribution were created using ggforce::geom_mark_hull(), which automatically generates smooth, labeled boundary polygons around point clusters. Additional aesthetic enhancements included the fishualize color palette for subfamily differentiation, ggtext for HTML-formatted text elements with coloured subfamily names, and showtext for custom Google Fonts integration, creating a publication-ready visualization that clearly communicates Australia’s distinct amphibian bio-geographic patterns.

Loading required libraries

Code 
pacman::p_load(ggforce)
# To plot geom_convex_hull()
pacman::p_load(MASS, fields)

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

  patchwork,            # Composing Plots
  sf                    # Spatial Operations
)

# Using R
frogID_data <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-09-02/frogID_data.csv')

frog_names <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-09-02/frog_names.csv')

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

# 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:**  FrogID App; Rowley & Callaghan (2020)", 
  " |  **Code:** ", 
  social_caption_1, 
  " |  **Graphics:** ", 
  social_caption_2
  )
rm(github, github_username, xtwitter, 
   xtwitter_username, social_caption_1, 
   social_caption_2)

# Add text to plot-------------------------------------------------
plot_title <- "Endemic Boundaries:\nAustralia's Frog Geography"

# Adding Subtitle:
# First, get the color palette to match your plot
mypal <- paletteer::paletteer_d("fishualize::Balistapus_undulatus")

# Create color mapping for subfamilies
subfamily_colors <- setNames(
  mypal, c("Hylid", "Myobatrachid", "Microhylidae", "Toad", "Ranid"))

# Formatted subtitle with ggtext color coding and line breaks
plot_subtitle <- paste0(
  "Australia's five frog subfamilies reveal a striking biogeographic pattern. While <span style='color:",
  subfamily_colors["Hylid"], "'>**Hylid**</span> and <span style='color:",
  subfamily_colors["Myobatrachid"], "'>**Myobatrachid**</span><br>",
  "frogs colonize diverse habitats across the continent, <span style='color:",
  subfamily_colors["Ranid"], "'>**Ranid**</span>, <span style='color:",
  subfamily_colors["Toad"], "'>**Toad**</span>, and <span style='color:",
  subfamily_colors["Microhylidae"], "'>**Microhylidae**</span> species are confined<br>",
  "to Australia's tropical northeastern coast, highlighting how climate zones have shaped evolution."
)

str_view(plot_subtitle)

# Get a map of Australia to plot

# Create bounding box for latitude restriction
clip_polygon <- st_as_sfc(
  st_bbox(
    c(xmin = 110, ymin = -44, xmax = 155, ymax = -8), 
    crs = st_crs(4326)
  )
)

# Get Australia map and apply latitude restriction
aus_map <- rnaturalearth::ne_countries(
  country = "Australia",
  scale = "large",
  returnclass = "sf"
) |> 
  dplyr::select(geometry) |> 
  # Clip to latitude bounds before transforming
  st_intersection(clip_polygon) |> 
  # Transform to Australian coordinate system
  st_transform(crs = 7845)

Exploratory Data Analysis and Wrangling

Code 
# Exploring the data to understand it
# pacman::p_load(summarytools)
# frogID_data |> 
#   dfSummary() |> 
#   view()
# 
# frog_names |> 
#   dfSummary() |> 
#   view()
# 
# pacman::p_unload(summarytools)

# Get the data to plot as an SF object
plotdf_points <- frogID_data |>
  
  # drop uncertain measurements
  filter(coordinateUncertaintyInMeters < 100) |> 
  
  # drop out of mainland measurements
  filter(
    decimalLatitude > -44 &
      decimalLatitude < -8 &
      decimalLongitude > 112 &
      decimalLongitude < 154
  ) |> 
  left_join(
    frog_names |> 
      dplyr::select(scientificName, subfamily, tribe),
    relationship = "many-to-many"
  ) |> 
  janitor::clean_names() |> 
  dplyr::select(
    event_id, decimal_latitude, decimal_longitude,
    subfamily, tribe
  ) |> 
  filter(!is.na(subfamily) & !is.na(tribe)) |> 
  mutate(
    subfamily = fct(
      subfamily,
      levels = c(
        "Hylid",        
        "Myobatrachid", 
        "Microhylidae", 
        "Toad",
        "Ranid"
      )
    )
  )

# plotdf_points |> 
#   count(subfamily) |> 
#   pull(subfamily)

# Function to calculate density-based core points for each subfamily
get_core_points <- function(data, prop = 0.5) {
  data |>
    group_by(subfamily) |>
    group_modify(~ {
      if (nrow(.x) < 3) return(.x)  # Keep all points if too few for hull
      
      # Calculate 2D kernel density
      coords <- cbind(.x$decimal_longitude, .x$decimal_latitude)
      kde <- MASS::kde2d(coords[,1], coords[,2], n = 50)
      
      # Get density at each point location
      density_at_points <- fields::interp.surface(
        obj = list(x = kde$x, y = kde$y, z = kde$z),
        loc = coords
      )
      
      # Select top proportion of points by density
      n_keep <- ceiling(nrow(.x) * prop)
      density_rank <- rank(-density_at_points, ties.method = "random")
      core_mask <- density_rank <= n_keep
      
      .x[core_mask, ]
    }) |>
    ungroup()
}

# plotdf_points |> 
#   distinct(subfamily)

filter_fams <- c("Hylid", "Myobatrachid")

# Create core points dataset (most concentrated 50%)
plotdf_core1 <- get_core_points(
  plotdf_points |> 
    filter(decimal_longitude > 140) |> 
    filter(
      !(subfamily %in% filter_fams & decimal_latitude > -25)
    ), 
  prop = 0.7
)

plotdf_core2 <- get_core_points(
  plotdf_points |> 
    filter(
      decimal_longitude < 140 &
        decimal_longitude > 120 &
        decimal_latitude > -25 &
        !(subfamily %in% filter_fams)
    ), 
  prop = 0.7
)

plotdf_core3 <- get_core_points(
  plotdf_points |> 
    filter(
      decimal_longitude < 140 &
        decimal_longitude > 130 &
        decimal_latitude < -25
    ), 
  prop = 0.7
)

plotdf_core4 <- get_core_points(
  plotdf_points |> 
    filter(
      decimal_longitude < 120
    ), 
  prop = 0.7
)

The Plot

Code 
mypal <- paletteer::paletteer_d("fishualize::Balistapus_undulatus")

# Define a custom geom_mark_hull function
geom_frog_hull <- function(dataframe = plotdf_core1, ...) {
  ggforce::geom_mark_hull(
    data = dataframe,
    mapping = aes(
      x = decimal_longitude,
      y = decimal_latitude,
      colour = subfamily,
      fill = subfamily,
      label = subfamily
    ),
    expand = unit(10, "mm"),
    radius = unit(10, "mm"),
    concavity = 5,
    label.margin = margin(3,1,-3,1, "mm"),
    label.family = "body_font",
    label.fontsize = bts,
    label.fontface = "plain",
    label.fill = alpha("white", 0.3),
    label.colour = text_hil,
    label.buffer = unit(2, "mm"),
    con.size = 0.4,
    con.cap = unit(1, "mm"),
    ...
  )
}

g <- ggplot() +
  
  # Map of Australia
  geom_sf(
    data = aus_map,
    linewidth = 0.5,
    fill = "grey90",
    colour = text_col,
    alpha = 0.8
  ) +  
  
  geom_point(
    data = plotdf_points,
    mapping = aes(
      x = decimal_longitude,
      y = decimal_latitude,
      fill = subfamily
    ),
    alpha = 0.4,
    size = 1,
    position = ggforce::position_jitternormal(
      sd_x = 0.2,
      sd_y = 0.2
    ),
    pch = 21,
    colour = "transparent"
  ) +
  
  # Drawing the Convex Hulls
  geom_frog_hull(plotdf_core1) +
  geom_frog_hull(plotdf_core2) +
  geom_frog_hull(plotdf_core3) +
  geom_frog_hull(plotdf_core4) +
  
  
  # Scales and Coordinates
  scale_colour_manual(
    values = mypal
  ) +
  scale_fill_manual(
    values = mypal
  ) +
  labs(
    title = plot_title,
    subtitle = plot_subtitle,
    caption = plot_caption,
    x = NULL,
    y = NULL,
    colour = NULL, 
    fill = NULL
  ) +
  coord_sf(
    crs = 7845,
    default_crs = "EPSG:4326",
    clip = "off",
    expand = TRUE
  ) +
  guides(
    fill = guide_legend(
      override.aes = list(
        size = 15,
        pch = 1
      )
    )
  ) +
  ggthemes::theme_map(
    base_family = "body_font",
    base_size = bts
  ) +
  theme(
    
    legend.position = "inside",
    legend.position.inside = c(0.05, 0.05),
    legend.justification = c(0, 0),
    legend.margin = margin(0,0,0,0, "mm"),
    legend.box.margin = margin(0,0,0,0, "mm"),
    legend.direction = "horizontal",
    legend.text = element_text(
      margin = margin(0,0,0,2, "mm"),
      face = "bold"
    ),
    legend.title = element_text(
      margin = margin(0,0,3,0, "mm"),
      hjust = 0.5
    ),
    legend.title.position = "top",
    legend.key.spacing.x = unit(7, "mm"),
    
    # Overall
    text = element_text(
      margin = margin(0,0,0,0, "mm"),
      colour = text_col,
      lineheight = 0.3
    ),
    
    
    # Labels and Strip Text
    plot.title = element_text(
      margin = margin(5,0,5,0, "mm"),
      hjust = 0.5,
      vjust = 0.5,
      colour = text_hil,
      size = 4 * bts,
      family = "body_font",
      face = "bold"
      ),
    plot.subtitle = element_textbox(
      margin = margin(5,0,-5,0, "mm"),
      vjust = 0.5,
      colour = text_hil,
      size =  bts * 1.3,
      hjust = 0.5,
      halign = 0.5,
      valign = 0.5,
      family = "caption_font",
      lineheight = 0.4
      ),
    plot.caption = element_textbox(
      margin = margin(5,0,2,0, "mm"),
      hjust = 0.5,
      halign = 0.5,
      colour = text_hil,
      size = bts * 0.8,
      family = "caption_font"
    ),
    plot.caption.position = "plot",
    plot.title.position = "plot",
    plot.margin = margin(5,5,5,5, "mm")
  )

ggsave(
  filename = here::here(
    "data_vizs",
    "tidy_australian_frogs.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_australian_frogs.png")) |> 
  image_resize(geometry = "x400") |> 
  image_write(
    here::here(
      "data_vizs", 
      "thumbnails", 
      "tidy_australian_frogs.png"
    )
  )

Session Info

Code 
sessioninfo::session_info()$packages |> 
  as_tibble() |> 
  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