Chord Diagram for wealth-driven migration

The circular Chord Diagram visualizes the migration flows in 2020, highlighting the near universal migration to wealthier countries, and almost none in the reverse direction.

Migration: Internal and Inter-Continental Flows (2020)

Created using {circlize} package (Gu et al. 2014) and Inkscape Image Processing software.

The data for this circular Chord Diagram diagram, depicting migration flows in 2020, is sourced from the United Nations Department of Economic and Social Affairs (UN DESA) and processed by Our World in Data. It includes comprehensive international migrant statistics, standardized and refined through several processing steps to ensure accuracy and clarity. The graphic highlights key findings, showing the near universal migration to High_income countries, especially from Middle-Income countries. For more details, visit the UN DESA International Migrant Stock page.

This graphic visualizes global migration flows for the year 2020 using data sourced from the United Nations Department of Economic and Social Affairs (UN DESA) and processed by Our World in Data. It highlights migration patterns in countries of different income groups through a Circular Chord Diagram, with arrows representing the flow of migration.

How I made this graphic?

Getting the data

# Data Import and Wrangling Tools
library(tidyverse)            # All things tidy
library(owidR)                # Get data from Our World in R

# Final plot tools
library(scales)               # Nice Scales for ggplot2
library(fontawesome)          # Icons display in ggplot2
library(ggtext)               # Markdown text support
library(showtext)             # Display fonts in ggplot2
library(colorspace)           # To lighten and darken colours
library(patchwork)            # Combining plots

library(ggsankeyfier)         # Sankey Diagrams with ggplot2

# Getting the data
# search1 <- owid_search("migrant")
# rawdf <- owid("migrant-stock-total")

url1 <- "https://www.un.org/development/desa/pd/sites/www.un.org.development.desa.pd/files/undesa_pd_2020_ims_stock_by_sex_destination_and_origin.xlsx"

rawdf2 <- openxlsx::read.xlsx(
  xlsxFile = url1,
  sheet = "Table 2",
  startRow = 10,
  colNames = TRUE
)

rm(url1)

Visualization Parameters

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

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

# Font for plot text
font_add_google("Changa",
  family = "body_font"
) 

showtext_auto()

# Base Text Size
bts <- 80

# Colour Palette
mypal <- paletteer::paletteer_d("MoMAColors::Alkalay1")
text_hil <- mypal[2]
text_col <- mypal[1]
bg_col <- lighten(mypal[5], 0.8)
seecolor::print_color(bg_col)

plot_title <- "Migration Towards Wealth: 2020"

plot_subtitle <- str_wrap(
  glue::glue("Nearly half of global migration in 2020 was directed towards high-income countries. Interestingly, middle-income countries, rather than low-income ones, were the primary contributors to this migration towards wealthier nations."
      ),
  90
  )
str_view(plot_subtitle)

# 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:** United Nations & Our World in Data  |  ",
  "**Code:** ", 
  social_caption_1, 
  " |  **Graphics:** ", 
  social_caption_2
  )

rm(github, github_username, xtwitter, 
   xtwitter_username, social_caption_1, social_caption_2)

Data Wrangling

income_levels <- c(
  "Low Income Countries",
  "Lower Middle Income Countries",
# "Middle Income Countries",
  "Upper Middle Income Countries",
  "High Income Countries"
)

income1 <- rawdf2 |> 
  
  # Remove minor duplication of "Australia.And.New.Zealand"
  select(-12) |> 
  janitor::clean_names() |> 
  as_tibble() |>
  rename(origin = region_development_group_of_destination) |> 
  mutate(origin = str_squish(origin)) |> 
  select(-c(x1, x3, x4)) |> 
  filter(str_detect(origin, "income")) |> 
  select(-world) |> 
  pivot_longer(
    cols = -origin,
    names_to = "destination",
    values_to = "value"
  ) |> 
  filter(str_detect(destination, "income")) |> 
  mutate(
    origin = snakecase::to_title_case(origin),
    destination = snakecase::to_title_case(destination)
  ) |> 
  rename(from = destination, to = origin) |> 
  filter(
    (to != "Middle Income Countries")
    &
    (from != "Middle Income Countries")
  ) |> 
  mutate(
    from = fct(from, levels = income_levels),
    to = fct(to, levels = income_levels)
  ) |> 
  mutate(value = value / 1e6) 

income1

Visualization

library(circlize)
library(cowplot)
library(grid)
library(ggplotify)

# Credits: https://github.com/tashapiro/TidyTuesday/blob/master/2022/W10/erasmus-mobility.R
# Credits: tutorial from https://jokergoo.github.io/circlize_book/book/the-chorddiagram-function.html

mypal <- paletteer::paletteer_d("fishualize::Epinephelus_striatus")

income1_arrows <- income1 |> 
  select(-value) |> 
  mutate(
    colour = if_else(
      to == "High Income Countries",
      as.character(mypal[1]),
      "transparent"
    )
  )

income1_border <- income1 |> 
  select(-value) |> 
  mutate(
    border = if_else(
      to == "High Income Countries",
      as.character(mypal[1]),
      "transparent"
    )
  ) 
  
circos.clear()
circos.par(
  gap.after = c(15, 5, 5, 15),
  start.degree = 22,
  clock.wise = FALSE
    )

chordDiagram(
    x = income1,
    grid.col = mypal[2:5],
    directional = -1,
    transparency = 0.5,
    direction.type = c("diffHeight", "arrows"),
  # link.arr.type = "big.arrow",
    link.target.prop = T,
  # link.arr.col = ,
  # link.arr.length = 0.2,
    link.arr.col = income1_arrows,
    diffHeight = mm_h(2),
    link.border = income1_border
  )

svg(
  filename = here::here(
    "data_vizs", "owid_income_circlize_migrants.svg"
    ),
  width = 7, height = 7, 
  pointsize = 12,
  onefile = FALSE, family = "sans", 
  bg = bg_col)

dev.off()

Remaining Work done in Inkscape Image Processing software. The image work file is here.

Save the graphic and a thumbnail

library(magick)
# Saving a thumbnail for the webpage
image_read(here::here("data_vizs", 
                      "owid_income_circlize_migrants.png")) |> 
  image_resize(geometry = "400") |> 
  image_write(here::here("data_vizs", "thumbnails", 
                         "owid_income_circlize_migrants.png"))

References

Gu, Zuguang, Lei Gu, Roland Eils, Matthias Schlesner, and Benedikt Brors. 2014. “Circlize Implements and Enhances Circular Visualization in r” 30: 2811–12.