Regional Share in Global Population

An animated donut chart for the share in World Population of different regions using World Bank Data

Share of different regions in the total world population, from 1960 to 2023

Global population distribution, visualized through an animated donut chart. Using midyear estimates from the World Bank DataBank and inspired by the compelling visual style of James Eagle’s analysis on internet browser popularity, this chart illustrates the percentage share of the total world population across seven distinct regions: East Asia & Pacific, Middle East & North Africa, Europe & Central Asia, South Asia, Latin America & Caribbean, Sub-Saharan Africa, and North America. This engaging animation provides a dynamic perspective on demographic trends, reflecting data sourced from the United Nations Population Division, national statistical offices, and other reputable agencies. It shows how the world’s population is distributed and how these regions contribute to the global demographic landscape.

Animated Donut (Pie) Chart of the population-share of 7 different regions in the World’s Total Population. Notice the rising share of South Asia and Africa, and the falling share of the western world.

How I made this graphic?

Loading required libraries and data import

# Data Import and Wrangling Tools
library(tidyverse)            # All things tidy
library(janitor)              # Cleaning names etc.
library(wbstats)              # Fetching World Bank Data

# Final plot tools
library(scales)               # Nice Scales for ggplot2
library(fontawesome)          # Icons display in ggplot2
library(ggtext)               # Markdown text support for ggplot2
library(showtext)             # Display fonts in ggplot2
library(gganimate)            # For animation


rawdf <- wb_data(
  indicator = "SP.POP.TOTL",
  country = "regions_only",
  start_date = 1960,
  end_date = 2023,
  return_wide = FALSE,
  gapfill = TRUE,
  mrv = 65
) |> 
  select(
    region = country,
    year = date,
    population = value
  ) 

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

Visualization Parameters

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

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

# Font for plot text
font_add_google("Saira Semi Condensed",
  family = "body_font"
) 

showtext_auto()

# Colour Palette
mypal <- paletteer::paletteer_d("feathers::bee_eater")
mypal <- paletteer::paletteer_d("futurevisions::grand_tour")
mypal_fill <- paletteer::paletteer_d("ghibli::PonyoLight")
mypal_medium <- paletteer::paletteer_d("ghibli::PonyoMedium")
mypal_col <- paletteer::paletteer_d("ghibli::PonyoDark")

# Background Colour
bg_col <- "white"

# Colour for the text
text_col <- "grey20"

# Colour for highlighted text
text_hil <- "grey30" 

Annotation Text for the Plot

plot_title <- "Global Population Distribution"

plot_subtitle <- "(1960 to 2023)"

plot_caption <- "Data: World Bank.  |   Graphics: @adityadahiyaias on X"

Data Wrangling

df <- rawdf |> 
  group_by(year) |> 
  mutate(percentage = population / sum(population)) |> 
  mutate(region = fct(region, levels = region_levels)) |> 
  ungroup() |> 
  mutate(print_pop = number(population, 
                            accuracy = 0.01,
                            scale_cut = cut_short_scale())) |> 
  group_by(year) |> 
  mutate(totalpop = sum(population)) |> 
  ungroup()

The static plot & animating it

g <- df |> 
  ggplot(
    aes(
      x = 1,
      y = percentage,
      fill = region,
      group = region
    )
  ) + 
  geom_col(
    colour = "white",
    position = position_stack()
  ) +
  
  # Text in the plot / animation
  geom_text(
    mapping = aes(
      label = paste0(
        round(100*percentage, 0),
        "%"
        ),
      colour = region),
    position = position_stack(vjust = 0.5),
    family = "body_font",
    fontface = "bold",
    size = 6
  ) +
  geom_text(
    mapping = aes(
      label = paste0(
        str_wrap(region, 15),
        "\n",
        print_pop
        ),
      x = 1.6,
      colour = region
    ),
    position = position_stack(vjust = 0.5),
    hjust = "outward",
    lineheight = 1,
    family = "body_font",
    size = 3
  ) +
  
  # Central Year Annotation
  geom_text(
    aes(
      label = as.factor(year),
      x = -0.4,
      y = 0
    ),
    size = 10,
    family = "title_font",
    colour = text_hil
  ) +
  
  geom_text(
    aes(
      label = paste0("Total: ", round(totalpop / 1e9, 1), " Billion"),
      x = -0.8,
      y = 0
    ),
    size = 5,
    family = "title_font",
    colour = text_hil
  ) +
  
  # Scales
  scale_y_continuous(expand = expansion(0)) +
  scale_x_continuous(
    limits = c(-0.8, 2)
  ) +
  scale_fill_manual(values = mypal_fill) +
  scale_colour_manual(values = mypal_col) +
  labs(
    title = plot_title,
    caption = plot_caption,
    subtitle = plot_subtitle
  ) +
  coord_polar(theta = "y") +
  theme_minimal(
    base_family = "body_font"
  ) +
  theme(
    legend.position = "none",
    panel.grid = element_blank(),
    axis.title = element_blank(),
    axis.text = element_blank(),
    axis.ticks = element_blank(),
    plot.title = element_text(
      hjust = 0.5, 
      family = "title_font",
      colour = text_hil,
      margin = margin(0,0,0,0),
      size = 30,
      face = "bold"
    ),
    plot.subtitle = element_text(
      margin = margin(0,0,0,0),
      colour = text_hil,
      family = "title_font",
      hjust = 0.5,
      size = 15
    ),
    plot.caption = element_text(
      hjust = 0.5,
      family = "caption_font",
      colour = text_hil,
      margin = margin(0,0,0,0)
    )
  )


g_anim <- g +
  transition_states(year) +
  ease_aes("linear")

Savings the animation

anim_save(
  filename = here::here(
    "projects", 
    "world_population_animated_donut.gif"
    ),
  animation = g_anim,
  fps = 5,
  duration = 20,
  start_pause = 3,
  end_pause = 10,
  height = 500,
  width = 480
)