Worldwide Bureaucracy Indicators

Comparison of the salaries of the Government Doctors in different countries

For these visualization(s), we are examining the Worldwide Bureaucracy Indicators (WWBI) dataset provided by the World Bank. The WWBI database offers a comprehensive cross-national dataset on public sector employment and wages. Its goal is to bridge an information gap, aiding researchers, development practitioners, and policymakers in gaining a deeper insight into various aspects such as the personnel dimensions of state capability, the public sector’s role within the labor market, and the fiscal implications of the public sector wage bill. The dataset is compiled from administrative data and household surveys, complementing existing expert perception-based approaches.

How I made this graphic?

Loading required libraries, data import & creating custom functions

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

# 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(colorspace)           # Lighten and Darken colours
library(patchwork)            # Combining plots
library(ggbeeswarm)           # For beeswarm plots

# library(ggfx)                 # Outer glow in the map
# library(magick)               # Adding images to plot
# library(ggthemes)             # Themes for ggplot2
# 
# # Mapping tools
# library(rnaturalearth)        # Maps of the World   
# library(sf)                   # All spatial objects in R
# library(geojsonio)            # To read geojson files into R

# Load Data
wwbi_data <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2024/2024-04-30/wwbi_data.csv')
wwbi_series <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2024/2024-04-30/wwbi_series.csv')
wwbi_country <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2024/2024-04-30/wwbi_country.csv')

Exploratory Data Analysis & Data Wrangling

# The indicator we want to display is Public Doctors salary
selected_indicator <- wwbi_series |> 
  filter(str_detect(indicator_name, "Pay compression ratio in public sector, by occupation: Hospital doctor")) |> 
  select(indicator_code) |> 
  pull(indicator_code)

# Names of Countries to display in the graphic
display_countries <- c("India", "United States", "China", 
                       "United Kingdom", "United States", 
                       "Nigeria", "Qatar", "Costa Rica", "Thailand",
                       "Nicaragua", "The Gambia")

df <- wwbi_data |>
  filter(indicator_code == selected_indicator) |> 
  left_join(
    wwbi_country |> 
      select(country_code, 
             short_name, 
             code2 = x2_alpha_code,
             region,
             income_group) |> 
      mutate(code2 = str_to_lower(code2))
  ) |> 
  left_join(wwbi_series) |> 
  select(-indicator_code) |> 
  mutate(
    display_name = if_else(
      short_name %in% display_countries,
      short_name,
      NA
    )
  )

# List of countries with flags in ggflags
flag_cons <- ggflags::lflags |> names()

# Creating a custom y-axis variable and an interval x-axis variable to plot flags

levels_income <- c(
  "Low income",
  "Lower middle income",
  "Upper middle income",
  "High income"
  )

# An intermediate step
df1 <- df |>
  select(-indicator_name) |> 
  filter(code2 %in% flag_cons) |> 
  
  # Roudning to make it easier to plot flags nicely
  mutate(value = round_to_fraction(value, 4)) |> 
  mutate(income_group = fct(income_group, levels = levels_income))
  
df2 <- df1 |> 
  group_by(income_group, value) |> 
  slice_head(n = 6) |> 
  # Geting number of countries in each interval
  count() |> 
  rename(gp_nos = n)

Manually positioning in a sort-of bee-swarm:

I have a vector c(1,2,3,4,5,6). I want to convert it into c(0.08, -0.08, 0.24, 0.24, 0.4, -0.4). You can achieve this transformation manually for now.

# A position multiplication Factor (to manually create a beeswarm)
position_vector <- seq(-0.4, +0.4, length.out = 6)
position_vector

plotdf <- df1 |> 
  left_join(df2) |> 
  group_by(income_group, value) |>
  arrange(income_group, value) |> 
  mutate(group_num = row_number()) |> 
  mutate(y_jizz = case_when(
    group_num == 1 ~ +0.08,
    group_num == 2 ~ -0.08,
    group_num == 3 ~ +0.24,
    group_num == 4 ~ -0.24,
    group_num == 5 ~ +0.40,
    group_num == 6 ~ -0.40,
  )) |> 
  mutate(y_var = as.numeric(income_group) + y_jizz)

# Labelling income groups - a tibble
df_labels <- tibble(
  x_var = 8.5,
  y_var = (1:4 + 0.25),
  label = paste0(levels_income, " countries"),
  income_group = levels_income
)

Visualization Parameters

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

# Font for the caption
font_add_google("Stint Ultra Condensed",
  family = "caption_font"
) 

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

showtext_auto()

# Background Colour
bg_col <- "grey90"

# Colour for the text
text_col <- "grey20" 

# Colour for highlighted text
text_hil <- "grey30" 

# Annotation colour
ann_col <- "red"

# 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>")

Annotation Text for the Plot

plot_title <- "Public Doctors' Salaries"

plot_caption <- paste0(
  "Data: **World Bank:** Worldwide Bureaucracy Indicators", 
  " |  **Code:** ", 
  social_caption_1, 
  " |  **Graphics:** ", 
  social_caption_2
  )

plot_subtitle <- glue::glue("Salaries of Government doctors in different countries, compared to a clerk.<br>
Compared to clerks, <b style='color:{ann_col}'>Qatar</b> pays its doctors the least, while <b style='color:{ann_col}'>The Gambia</b> pays the most.
")

Previous Plot / Graphic attempt - using geom_beeswarm() - it doesn’t work for geom_flag()

ts = 80

g_base <- ggplot(
  data = df |> filter(!is.na(income_group)),
  mapping = aes(
    x = value,
    y = income_group,
    colour = income_group,
    fill = income_group)
  ) +
  
  # Actual points for the Countries
  geom_point(
    size = 8,
    pch = 1,
    stroke = 1,
    position = position_beeswarm(
      cex = 2.5,
      method = "hex",
      priority = "density",
      corral = "omit",
      corral.width = 1.2
    ),
    alpha = 0.4
  ) +
  
  scale_x_continuous(
    limits = c(0, 8),
    breaks = c(2,3,4,6,7,8)
  ) +
  
  # Labels for the countries and their values
  geom_text(
    aes(
      label = display_name
      ),
    position = position_beeswarm(
      cex = 2.5,
      method = "hex",
      priority = "density",
      corral = "omit",
      corral.width = 1.2
    ),
    lineheight = 0.35,
    size = ts / 5
  ) +
  
  # Basic annotations for the background
  annotate(
    geom = "text",
    label = "Public Doctor's salary is equal to a clerk",
    x = 0.95, 
    y = 4.5,
    angle = 90,
    hjust = 1,
    vjust = 0,
    colour = ann_col,
    alpha = 0.5,
    family = "caption_font",
    size = ts / 3
  ) +
  geom_vline(
    xintercept = 1,
    colour = ann_col,
    linetype = 1,
    linewidth = 0.5,
    alpha = 0.5
  ) +
  annotate(
    geom = "text",
    label = "Public Doctor's salary is 4 times that of a clerk",
    x = 5.05, 
    y = 4.5,
    angle = -90,
    hjust = 0,
    vjust = 0,
    colour = ann_col,
    alpha = 0.5,
    family = "caption_font",
    size = ts / 3
  ) +
  geom_vline(
    xintercept = 5,
    colour = ann_col,
    linetype = 1,
    linewidth = 0.5,
    alpha = 0.5
  ) +
  
  geom_text(
    aes(
      label = paste0(income_group, " countries"),
      x = 8.5
    ),
    hjust ="inward",
    nudge_y = +0.2,
    family = "caption_font",
    size = ts/1.5
  ) +
  
  # Labels
  labs(
    x = "Ratio of Salary of a public sector (Government Doctor) to a clerk in the country",
    y = NULL,
    title = plot_title,
    subtitle = plot_subtitle,
    caption = plot_caption
  ) +
  
  # Themeing customization
  theme_minimal(
    base_family = "body_font",
    base_size = ts
  ) +
  theme(
    panel.grid.major.y = element_blank(),
    panel.grid.minor.y = element_blank(),
    legend.position = "none",
    panel.grid.minor.x = element_blank(),
    plot.caption = element_textbox(
      family = "caption_font",
      colour = text_hil,
      hjust = 0.5
    ),
    plot.title = element_markdown(
      family = "title_font",
      colour = text_hil,
      hjust = 0.5,
      size = 3*ts
    ),
    plot.subtitle = element_markdown(
      family = "body_font",
      colour = text_col,
      hjust = 0.5,
      lineheight = 0.35
    ),
    panel.grid.major.x = element_line(
      linetype = 3,
      linewidth = 0.5,
      colour = "grey75"
    ),
    axis.text.y = element_blank(),
    axis.ticks = element_blank(),
    axis.text.x = element_text(
      colour = text_col,
      hjust = 1, 
      margin = margin(0,0,0,0,"mm")
    ),
    plot.title.position = "plot"
  )



#   
#   geom_flag(
#     mapping = aes(country = code2),
#     size = 4,
#     # pch = 1,
#     position = position_beeswarm(
#       cex = 3,
#       method = "hex",
#       priority = "density",
#       corral = "omit",
#       corral.width = 1.2
#     )
#   )
# 
#   
#   
# 
# 
# # An example label in markdown: Not to use for now
# label = glue::glue("<span style='font-size:9pt; color:black'>{short_name}</span><br><span style='color:{text_col}'>{value}</span>")

The Actual Base Plot / Graphic

ts = 90

g_base <- ggplot(
  data = plotdf,
  mapping = aes(
    x = value,
    y = y_var,
    colour = income_group)
  ) +
  
  # Points for countries
  ggflags::geom_flag(
    aes(country = code2),
    size = 12
  ) +
  # while building plot, I use geom_point() as a replacement
  # to geom_flag to save plot rendering time
  # geom_point(
  #   size = 8
  # ) +
  
  scale_x_continuous(
    limits = c(0.5, 8.3),
    breaks = 1:8,
    expand = expansion(0)
  ) +
  
  scale_y_continuous(
    limits = c(0.8,  4.5),
    expand = expansion(0)
  ) +
  
  # Labels for the countries and their values
  geom_text(
    aes(label = display_name, 
        y = y_var - 0.1),
    lineheight = 0.35,
    size = ts / 3,
    vjust = 0,
    colour = text_col,
    alpha = 0.7,
    family = "body_font"
  ) +
  
  # Basic annotations for the background
  annotate(
    geom = "text",
    label = "Public Doctor's salary equal to clerk",
    x = 0.95, 
    y = 0.9,
    angle = 90,
    hjust = 0,
    vjust = 0,
    colour = ann_col,
    alpha = 0.5,
    family = "caption_font",
    size = ts / 3
  ) +
  geom_vline(
    xintercept = 1,
    colour = ann_col,
    linetype = 1,
    linewidth = 0.5,
    alpha = 0.5
  ) +
  # annotate(
  #   geom = "text",
  #   label = "Public Doctor's salary is 4 times that of a clerk",
  #   x = 5.05, 
  #   y = 4.5,
  #   angle = -90,
  #   hjust = 0,
  #   vjust = 0,
  #   colour = ann_col,
  #   alpha = 0.5,
  #   family = "caption_font",
  #   size = ts / 3
  # ) +
  # geom_vline(
  #   xintercept = 5,
  #   colour = ann_col,
  #   linetype = 1,
  #   linewidth = 0.5,
  #   alpha = 0.5
  # ) +
  
  # Names / Labels for the income groups
  geom_text(
    data = df_labels,
    mapping = aes(
      x = 8.15,
      y = y_var,
      label = label
    ),
    hjust = "inward",
    vjust = 0,
    family = "caption_font",
    size = ts / 1.5,
    colour = text_col,
    alpha = 0.7
  ) +
  
  # Labels
  labs(
    x = "Ratio of Salary of a public sector (Government Doctor) to a clerk in the country",
    y = NULL,
    title = plot_title,
    subtitle = plot_subtitle,
    caption = plot_caption
  ) +
  
  # Themeing customization
  theme_minimal(
    base_family = "body_font",
    base_size = ts
  ) +
  
  theme(
    panel.grid.major.y = element_blank(),
    panel.grid.minor.y = element_blank(),
    legend.position = "none",
    panel.grid.minor.x = element_blank(),
    plot.caption = element_textbox(
      family = "caption_font",
      colour = text_hil,
      hjust = 0.5
    ),
    plot.title = element_markdown(
      family = "title_font",
      colour = text_hil,
      hjust = 0.5,
      size = 2.5 * ts,
      margin = margin(5,0,2,0, "mm")
    ),
    plot.subtitle = element_markdown(
      family = "body_font",
      colour = text_col,
      hjust = 0.5,
      lineheight = 0.35,
      margin = margin(0,0,0,0, "mm")
    ),
    panel.grid.major.x = element_line(
      linetype = 3,
      linewidth = 0.5,
      colour = "grey75"
    ),
    axis.text.y = element_blank(),
    axis.title.y = element_blank(),
    axis.ticks = element_blank(),
    axis.text.x = element_text(
      colour = text_col,
      hjust = 1, 
      margin = margin(0,0,0,0,"mm")
    ),
    plot.title.position = "plot"
  )

#   geom_flag(
#     mapping = aes(country = code2),
#     size = 4,
#     # pch = 1,
#     position = position_beeswarm(
#       cex = 3,
#       method = "hex",
#       priority = "density",
#       corral = "omit",
#       corral.width = 1.2
#     )
#   )

Savings the graphics

ggsave(
  filename = here::here("data_vizs", "tidy_wbi.png"),
  plot = g_base,
  width = 400,    # Best Twitter Aspect Ratio = 4:5
  height = 500,   
  units = "mm",
  bg = "white"
)

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

A4 Inforgraphic, Insets and QR Code

plot_title <- "Public  Doctors'  Salaries"

plot_subtitle <- glue::glue("Salaries of Government doctors in different countries, compared to a clerk.<br>In relative terms, <b style='color:{ann_col}'>Qatar</b> pays its doctors the least, while <b style='color:{ann_col}'>The Gambia</b> pays the<br>most. The data comes from the Worldwide Bureaucracy Indicators (WWBI) <br> provided by the World Bank - a comprehensive cross-national data-set on<br>public sector employment and wages.")

g_base <- ggplot(
  data = plotdf,
  mapping = aes(
    x = value,
    y = y_var,
    colour = income_group)
  ) +
  
  # Points for countries
  ggflags::geom_flag(
    aes(country = code2),
    size = 12
  ) +

  # Scales
  scale_x_continuous(
    limits = c(0.5, 8.3),
    breaks = 1:8,
    expand = expansion(0)
  ) +
  
  scale_y_continuous(
    limits = c(0.8,  4.5),
    expand = expansion(0)
  ) +
  
  # Labels for the countries and their values
  geom_text(
    aes(label = display_name, 
        y = y_var - 0.1),
    lineheight = 0.35,
    size = ts / 3,
    vjust = 0,
    colour = text_col,
    alpha = 0.7,
    family = "body_font"
  ) +
  
  # Basic annotations for the background
  annotate(
    geom = "text",
    label = "Public Doctor's salary equal to clerk",
    x = 0.95, 
    y = 0.9,
    angle = 90,
    hjust = 0,
    vjust = 0,
    colour = ann_col,
    alpha = 0.5,
    family = "caption_font",
    size = ts / 3
  ) +
  geom_vline(
    xintercept = 1,
    colour = ann_col,
    linetype = 1,
    linewidth = 0.5,
    alpha = 0.5
  ) +
 
  # Names / Labels for the income groups
  geom_text(
    data = df_labels,
    mapping = aes(
      x = 8.15,
      y = y_var,
      label = label
    ),
    hjust = "inward",
    vjust = 0,
    family = "caption_font",
    size = ts / 1.5,
    colour = text_col,
    alpha = 0.7
  ) +
  
  # Labels
  labs(
    x = "Ratio of Salary of a public sector (Government Doctor) to a clerk in the country",
    y = NULL,
    title = plot_title,
    subtitle = plot_subtitle,
    caption = plot_caption
  ) +
  
  # Themeing customization
  theme_minimal(
    base_family = "body_font",
    base_size = ts
  ) +
  
  theme(
    panel.grid.major.y = element_blank(),
    panel.grid.minor.y = element_blank(),
    legend.position = "none",
    panel.grid.minor.x = element_blank(),
    plot.caption = element_textbox(
      family = "caption_font",
      colour = text_hil,
      hjust = 0.5
    ),
    plot.title = element_markdown(
      family = "title_font",
      colour = text_hil,
      hjust = 0.5,
      size = 2.8 * ts,
      margin = margin(10,0,10,0, "mm")
    ),
    plot.subtitle = element_markdown(
      family = "body_font",
      colour = text_col,
      hjust = 0,
      lineheight = 0.35,
      margin = margin(3,100,15,0, "mm"),
      size = 0.95 * ts
    ),
    panel.grid.major.x = element_line(
      linetype = 3,
      linewidth = 0.75,
      colour = "grey75"
    ),
    axis.text.y = element_blank(),
    axis.title.y = element_blank(),
    axis.ticks = element_blank(),
    axis.text.x = element_text(
      colour = text_col,
      hjust = 1, 
      margin = margin(0,0,0,0,"mm")
    ),
    plot.title.position = "plot"
  )

# Text Inset (Description of the Dataset)


# QR Code for the plot
url_graphics <- paste0(
  "https://aditya-dahiya.github.io/projects_presentations/data_vizs/",
  # The file name of the current .qmd file
  "tidy_wbi",         
  ".html"
)
#
plot_qr <- ggplot(
  data = NULL, 
  aes(x = 0, y = 0, label = url_graphics)
  ) + 
  ggqr::geom_qr(
    colour = text_hil, 
    fill = bg_col,
    size = 2
    ) +
  coord_fixed() +
  theme_void() +
  theme(plot.background = element_rect(
    fill = NA, 
    colour = NA
    )
  )

g <- g_base +
  inset_element(
    p = plot_qr,
    left = 0.75, right = 1.08,
    bottom = 1, top = 1.2,
    align_to = "plot"
  ) +
  plot_annotation(
    theme = theme(
      plot.background = element_rect(
        fill = "transparent",
        colour = "transparent"
      )
    )
  )

# A4 infographic Version
ggsave(
  filename = here::here("data_vizs", "a4_tidy_wbi.png"),
  plot = g,
  width = 210 * 2,    # Best Twitter Aspect Ratio = 4:5
  height = 297 * 2,   
  units = "mm",
  bg = bg_col
)

The A4-sized Info-graphic

Info-graphics for comparison of different job-pairs:

sel_names <- wwbi_series |> 
  filter(str_detect(indicator_name, 
                    "Pay compression ratio in public sector, by occupation:")) |> 
  mutate(
    indicator_name = str_remove(
      indicator_name,
      "Pay compression ratio in public sector, by occupation: "
    )
  ) |> 
  mutate(
    indicator_name = str_remove(
      indicator_name,
      " \\(clerk as reference\\)"
    )
  )

xdf1 <- wwbi_data |> 
  filter(indicator_code %in% sel_names$indicator_code) |> 
  left_join(sel_names) |> 
  select(country_code, indicator_name, value)

xdf1
library(GGally)

xdf2 <- xdf1 |> 
  pivot_wider(
    id_cols = country_code,
    names_from = indicator_name,
    values_from = value
  )
  
# GGally::ggpairs(
#   data = xdf2,
#   columns = 2:ncol(plotdf)
# ) +
#   coord_fixed()

# Identified Combinations
# Doctors vs. Nurses
# Judges vs. Senior Officers

Government Salaries: Doctors vs. Nurses

# List of countries with flags in ggflags
flag_cons <- ggflags::lflags |> names()

bg_col <- "white"        # Background Colour
myfill <- c("#006aff",
            "#ff9900")     # Various fill colours
mycol <- "blue"           # Various colours for annotations  

# Important Countries to display
display_cons <- c(
  "India", "United States", "China",
  "United Kingdom", "Pakistan",
  "Qatar", "Thailand",
  "Australia", "Chile", "Ethiopia"
  )

# Plot rectangles for background
rectangle1 <- tibble(
  x = c(0, 0, 1, 1, 0),
  y = c(0, 1, 1, 0, 0)
)

rectangle2 <- tibble(
  x = c(0, 1, 1, 0, 0),
  y = c(1, 1, 5, 5, 1)
)

# Text for the Plot
plot_title <- "Govt. Salaries : Doctors vs. Nurses"

plot_caption <- paste0(
  "Data: **World Bank (2017):** Worldwide Bureaucracy Indicators", 
  " |  **Code:** ", 
  social_caption_1, 
  " |  **Graphics:** ", 
  social_caption_2
  )

plot_subtitle <- glue::glue("
In every country, public-sector doctors are paid more than nurses, and in <b style='color:{mycol}'>some countries</b>,<br>doctors get more than twice. This graph compares their salaries relative to those of clerks<br>in the same country. Countries further away from the diagonal line have a bigger wage<br>gap between doctors and nurses. Those countries higher up along the diagonal line pay<br>both doctors and nurses much more than they pay their clerks. The number in brackets<br>is ratio of doctors to nurses’ salaries.
")

# Base Test Size
ts = 80


# Drop some overplotted countries
cn_to_drop <- c(
  "tc", "iq", "dk", "sk", "ec",
  "ps", "by", "es", "eg", "gw",
  "ky", "bt"
)

# Getting final data-set ready
plotdf <- xdf1 |> 
  filter(str_detect(indicator_name, "Hospital")) |> 
  mutate(
    indicator_name = snakecase::to_snake_case(indicator_name)) |>
  pivot_wider(
    id_cols = country_code,
    names_from = indicator_name,
    values_from = value
  ) |> 
  mutate(
    display_ratio = round(hospital_doctor / hospital_nurse, 1)
  ) |> 
  left_join(
    wwbi_country |> 
              select(country_code, 
                     short_name,
                     code2 = x2_alpha_code)
    ) |> 
  mutate(code2 = str_to_lower(code2)) |> 
  filter(code2 %in% flag_cons) |> 
  mutate(
    size_var = if_else(
      short_name %in% display_cons,
      24,
      12)
    ) |> 
  arrange(desc(size_var), hospital_doctor) |> 
  mutate(col_var = if_else(
    display_ratio > 2.1,
    "a",
    "b"
  )) |> 
  filter(hospital_doctor < 4.2) |> 
  filter(!(code2 %in% cn_to_drop))

# Checking overplotted countries
# plotdf |> 
#   ggplot(aes(x = hospital_nurse, y = hospital_doctor,
#              label = code2)) + 
#   geom_text(alpha = 0.5) +
#   coord_fixed(xlim = c(1, 2),
#               ylim = c(1.5, 2.5))

# The actual graphic -------------------------------------------

g_base <- plotdf |> 
  ggplot(
    mapping = aes(
      x = hospital_nurse,
      y = hospital_doctor
    )
  ) +
  
  # Annotations --------------------------------------
  geom_abline(
    slope = 1,
    colour = text_col,
    alpha = 0.5,
    linewidth = 1
  ) +
  
  annotate(
    geom = "text",
    x = 3.3, y = 3.33,
    hjust = 1, vjust = 0,
    label = "Line of equality: Doctors and Nurses get paid equally",
    angle = 45,
    size = 24, 
    family = "body_font",
    colour = text_col
  ) +
  
  annotate(
    geom = "text",
    x = 1.03, y = 0.8,
    hjust = 0, vjust = 1,
    label = str_wrap("Countries in the light-blue rectangle pay doctors and nurses less than clerks.", 30),
    lineheight = 0.25,
    angle = 0,
    size = 24, 
    family = "body_font",
    colour = myfill[1] |> darken(0.4)
  ) +
  
  annotate(
    geom = "text",
    x = 0.23, y = 4,
    hjust = 1, vjust = 1,
    label = str_wrap("Countries in the light-orange rectangle pay their nurses less than clerks, but doctors more than clerks.", 80),
    lineheight = 0.25,
    angle = 90,
    size = 24, 
    family = "body_font",
    colour = myfill[2] |> darken(0.4)
  ) +
  
  # Rectangles for the background
  geom_polygon(
    data = rectangle1, 
    aes(x = x, y = y), 
    fill = myfill[1], 
    color = "transparent",
    alpha = 0.25
    ) +
  
  geom_polygon(
    data = rectangle2, 
    aes(x = x, y = y), 
    fill = myfill[2], 
    color = "transparent",
    alpha = 0.25
    ) +
  
  # The actual data to be plotted
  ggflags::geom_flag(
    mapping = aes(
      country = code2
    ),
    size = 10
  ) +
  # During trials, used geom_point instead of geom_flag
  # geom_point(
  #   alpha = 0.2,
  #   size = 5,
  #   pch = 16
  # ) +
  
  # Names of Countries and Ratio
  geom_text(
    mapping = aes(
      label = paste0(short_name, "\n(", display_ratio, ")"),
      size = size_var,
      colour = col_var
    ),
    nudge_y = -0.04,
    family = "body_font",
    check_overlap = TRUE,
    lineheight = 0.25,
    vjust = 1
  ) +
  scale_size_identity() +
  scale_colour_manual(values = c(mycol, text_col)) +
  
  
  # Scales and Coordinates
  coord_fixed(
    ylim = c(0.5, 4.2),
    xlim = c(0.2, 3.6)
    
  ) +
  scale_y_continuous(
    breaks = 1:4,
    expand = expansion(0)
  ) +
  scale_x_continuous(
    breaks = 1:3,
    expand = expansion(0)
  ) +
  
  # Lables
  labs(
    x = "Public Nurses' Salary (times that of a clerk)",
    y = "Public Doctors' Salary (times that of a clerk)",
    title = plot_title,
    subtitle = plot_subtitle,
    caption = plot_caption
  ) +
  
  # Themeing customization
  theme_minimal(
    base_family = "body_font",
    base_size = ts
  ) +
  
  theme(
    panel.grid.minor.y = element_blank(),
    legend.position = "none",
    panel.grid.minor.x = element_blank(),
    plot.caption = element_textbox(
      family = "caption_font",
      colour = text_hil,
      hjust = 1
    ),
    plot.title = element_markdown(
      family = "title_font",
      colour = text_hil,
      hjust = 0,
      size = 2.3 * ts,
      margin = margin(10,0,5,0, "mm")
    ),
    plot.subtitle = element_markdown(
      family = "body_font",
      colour = text_col,
      hjust = 0,
      lineheight = 0.35,
      margin = margin(0,0,10,0, "mm")
    ),
    panel.grid.major = element_line(
      linetype = 3,
      linewidth = 0.5,
      colour = "grey75"
    ),
    axis.ticks = element_line(
      colour = text_col,
      linewidth = 0.75
    ),
    axis.title = element_text(
      colour = text_col,
      hjust = 1,
      margin = margin(0,0,0,0, "mm"),
      size = 1.5 * ts
    ),
    axis.text = element_text(
      colour = text_col,
      hjust = 1, 
      size = ts,
      margin = margin(0,0,0,0,"mm")
    ),
    axis.line = element_line(
      linetype = 1,
      colour = text_col,
      linewidth = 0.75,
      arrow = arrow()
    ),
    plot.title.position = "plot"
  )

# Saving the plot
ggsave(
  filename = here::here("data_vizs", "tidy_wbi_doctors_nurses.png"),
  plot = g_base,
  width = 210 * 2,    # Best Twitter Aspect Ratio = 4:5
  height = 297 * 2,   
  units = "mm",
  bg = bg_col
)

A scatterplot comparing the salaries of Doctors and Nurses in the public sector in different countries. Every country pays doctors more than nurses, but some pay doctors much more than their nurses.