day 3 circular

Author

Jen Richmond

Published

April 3, 2025

load packages

library(tidyverse)
library(janitor)
library(scales)
library(ggeasy)
library(ggannotate)
library(plotly)

This plot illustrates changing representation of women in government leadership. It is great that in 2024 fewer than 2% of governments have no women in leadership, but it is not amazing that fewer than 2% have 50% or more. I am wondering if I can look at this pattern across different areas of the world by making circular graphs in ggplot.

read in the data

I am mostly interested in the current state of affairs so I read in the data from the owidapi, filter the data to only include 2024 and rename variables to make them more friendly.

share <- read_csv("https://ourworldindata.org/grapher/countries-by-share-of-women-in-parliament.csv?v=1&csvType=full&useColumnShortNames=true") %>%
  clean_names()



share2024 <- share %>% 
  filter(year == 2024) %>%
  select(-code) %>%
  rename(number_0pct = num_countries_wom_parl_category_0pct_women, 
         number_0_10pct = num_countries_wom_parl_category_0_10pct_women,
         number_10_20pct = num_countries_wom_parl_category_10_20pct_women, 
           number_20_30pct = num_countries_wom_parl_category_20_30pct_women, 
           number_30_40pct = num_countries_wom_parl_category_30_40pct_women, 
           number_40_50pct = num_countries_wom_parl_category_40_50pct_women,
           number_50pluspct = num_countries_wom_parl_category_50pctplus_women) 

glimpse(share2024)
Rows: 7
Columns: 9
$ entity           <chr> "Africa", "Asia", "Europe", "North America", "Oceania…
$ year             <dbl> 2024, 2024, 2024, 2024, 2024, 2024, 2024
$ number_0pct      <dbl> 0, 2, 0, 0, 0, 0, 2
$ number_0_10pct   <dbl> 4, 7, 0, 0, 4, 0, 15
$ number_10_20pct  <dbl> 17, 16, 5, 1, 0, 1, 40
$ number_20_30pct  <dbl> 15, 11, 11, 6, 0, 3, 46
$ number_30_40pct  <dbl> 10, 5, 15, 3, 1, 5, 39
$ number_40_50pct  <dbl> 5, 2, 10, 2, 1, 3, 23
$ number_50pluspct <dbl> 1, 0, 0, 2, 0, 0, 3

clean it up

I want to plot data for the World and each region separately so create separate dataframes here using filter(). I also want to make the data long using pivot_longer, mutate new variables that represent the number of countries as percent, improve the labels on the percent categories and make them a factor so that they order correctly in my plot.

# create world dataframe

share2024_world <- share2024 %>%
  filter(entity == "World") %>%
  pivot_longer(names_to = "category", values_to = "number", number_0pct:number_50pluspct) %>%
    mutate(total = sum(number), percent = number/total) %>%
           mutate(percent_women = case_when(category == "number_0pct" ~ "no women", 
                           category == "number_0_10pct" ~ "less than 10%",
                           category == "number_10_20pct" ~ "10-20%", 
                           category == "number_20_30pct" ~ "20-30%", 
                           category == "number_30_40pct" ~ "30-40%", 
                           category == "number_40_50pct" ~ "40-50%",
                           category == "number_50pluspct" ~ "50+%")) %>%
    mutate(percent_women = fct_relevel(percent_women, 
                            c("no women" , "less than 10%" ,"10-20%" ,
                              "20-30%" ,"30-40%" , "40-50%" ,
                              "50+%"))) 

# create regions dataframe

share2024_regions <- share2024 %>%
  filter(entity != "World") %>%
  pivot_longer(names_to = "category", values_to = "number", number_0pct:number_50pluspct) %>%
  group_by(entity) %>%
    mutate(total = sum(number), percent = number/total) %>%
           mutate(percent_women = case_when(category == "number_0pct" ~ "no women", 
                           category == "number_0_10pct" ~ "less than 10%",
                           category == "number_10_20pct" ~ "10-20%", 
                           category == "number_20_30pct" ~ "20-30%", 
                           category == "number_30_40pct" ~ "30-40%", 
                           category == "number_40_50pct" ~ "40-50%",
                           category == "number_50pluspct" ~ "50+%")) %>%
    mutate(percent_women = fct_relevel(percent_women, 
                            c("no women" , "less than 10%" ,"10-20%" ,
                              "20-30%" ,"30-40%" , "40-50%" ,
                              "50+%")))

plot

world

Starting with world and using inspo from the R Graph gallery here I make an ugly pie chart. There is no pie geom in ggplot, so the workaround is to make a column chart and use coord_polar() to make it circular.

share2024_world %>%
  ggplot(aes(x = "", y = percent, fill = percent_women)) +
  geom_col(colour = "white") +
 coord_polar("y", start = 0) +
    theme_void() +
  easy_add_legend_title("Percent seats") +
  scale_fill_manual(values = c("#a2559b", "#00847d", "#4b6a9c", "#e56e59", "#38aaba", "#6b3d8d",  "#986d39" )) +
  labs(title = "Percent of countries by share of women in parliament in 2024, World", 
       subtitle = "Percent of seats in lower or single chamber of the legislature held by women", 
       caption = "Data source: V-Dem (2025) via OurWorldInData. \nNote: Only countries with at least one legislative chamber included.")

The more interesting question is does this pattern of representation differ across regions?

regions

share2024_regions %>%
  ggplot(aes(x = "", y = percent, fill = percent_women)) +
  geom_col(colour = "white") +
 coord_polar("y", start = 0) +
  facet_wrap(~entity) +
    scale_fill_manual(values = c("#a2559b", "#00847d", "#4b6a9c", "#e56e59", "#38aaba", "#6b3d8d",  "#986d39" )) +
    theme_void() +
   easy_add_legend_title("Percent seats") +
labs(title = "Percent of countries by share of women in parliament in 2024, Regions", 
       subtitle = "Percent of seats in lower or single chamber of the legislature held by women", 
       caption = "Data source: V-Dem (2025) via OurWorldInData. \nNote: Only countries with at least one legislative chamber included.")

There is a lot that surprises me about this plot. Oceania is not doing well; the majority of countries have less than 10% of government seats held by women. This dataset only includes the number of countries in each region that have women represented in each of the percent categories. This includes 6 Oceanic countries, but it doesn’t include information about which 6 countries are included. I am interested to dig into the country level data here.

I was also surprised that Asia is the only region that still has 2 countries that have no women in government. Again, I am curious about which 2; Afghanistan maybe? Perhaps there are Middle East countries that do not have women in government.