Where are the headquarters of humanitarian organizations? • gdho

# read in libraries
library(gdho)
library(tidyverse)
library(sf) # for geolocation 
library(rnaturalearth) # for geolocation 
library(viridis)

In this demo, we will show you how to plot a map to reflect the headquarter concentration of the humanitarian headquarters around the world. At the end of the demo, you will learn how to:

Summarize headquarter information from gdho data
Combine gdho data with external geolocation data
Visualize a choropleth map about headquarter location distribution

1. Data processing: summarize headquarters

First, we want to get a table showing how many headquarters each country has. We transform the column hq_location to get the information:

gdho_hq <- gdho |>
  filter(!is.na(hq_location)) |> # remove NA data 
  group_by(hq_location) |> # group by countries
  summarise(count = n()) |> # count the number of headquarters of each country
  arrange(desc(count)) # sort by descending order
  
gdho_hq |>
  head()
#> # A tibble: 6 × 2
#>   hq_location   count
#>   <chr>         <int>
#> 1 United States   324
#> 2 Somalia         266
#> 3 Afghanistan     241
#> 4 Pakistan        177
#> 5 Colombia        169
#> 6 Ukraine         165

We can see that the top six countries having the most humanitarian organization headquarters are United States, Somalia, Afgahnistan, Pakistan, Colombia and Ukraine.

2. Data combination: join with geodata

To plot a choropleth map, we also need to acquire geolocation information of the countries (e.g. the borders). Here we use the package rnaturalearth and sf to help. The package rnaturalearth contains geodata about the countries in the world, and we simply import this dataset and combine it with our gdho data.

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

ho_map <- left_join(world, gdho_hq, by = c("name_long" = "hq_location"))

3. Map visualization: concentration of the headquarters

Now, we have a data table that contains all information we need: the counts of humanitarian organization headquarters each country has, and the geo-information of each country. We start by plotting the world view of the headquarter concentration.

ggplot() +
  theme_void() +
  geom_sf(data = ho_map, aes(fill = count), color = "white", lwd = 0) + # plot map
    scale_fill_viridis(trans = "log", breaks=c(1,10,50,100,300), 
                       name = "Num. Headquarters",
                       guide = guide_legend(keyheight = unit (2, units = "mm"),
                                            label.position = "bottom", 
                                            title.position = 'top', nrow = 1)) + 
  labs(
    title = "Humanitarian Organization Headquarters Concentration",
    subtitle = "Number of headquarters per country"
  ) + # add metadata
  theme(
    plot.background = element_rect(fill = "#f5f5f2", color = NA),
    legend.position = c(0.13, 0.15)
  ) + # polish the appearance
  coord_sf(ylim = c(-50, 90)) # Remove antarctic
#> Warning: A numeric `legend.position` argument in `theme()` was deprecated in ggplot2
#> 3.5.0.
#> ℹ Please use the `legend.position.inside` argument of `theme()` instead.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.

Now we have seen how the headquarters are distributed globally, it may worth zoom in to see in detail. For example, let’s check out Africa:

af_map <- ho_map |> filter(continent == "Africa") # Filter data with only African countries
ggplot() +
  theme_void() +
  geom_sf(data = af_map, aes(fill = count), color = "white", lwd = 0.5) +
    scale_fill_viridis(trans = "log", breaks=c(1,5,10,20,50,100)) +
  labs(
    title = "Humanitarian Organization Headquarters Concentration",
    subtitle = "Number of headquarters per country in Africa"
  )

References:

[1] Choropleth map with R and ggplot2