day 8: circular

library(tidyverse)
library(tidytuesdayR)
library(tidyplots)
library(janitor)
library(ggeasy)
library(here)
library(patchwork)


# choosing a dataset randomly

set.seed(8888)
ttyears <- c(2018:2025)
ttweeks <- c(1:52)

# choose a year at random
chosen_year <- sample(ttyears, size = 1)

# choose at week at random
chosen_week <- sample(ttweeks, size = 1)

# read the data from that year/week

df <- tidytuesdayR::tt_load(2023, 34)

# print dataset
print(df)

pop <- df[[1]]


options(scipen = 999)

The data for this plot comes from a 2023 TidyTuesday challenge. The Week 34 data that year was about refugee populations between 2010 and 2022. I was interested in the relative numbers of refugees, asylum seekers and displaced persons from Iraq and Afghanistan during this period.

prep

countries <- c("Afghanistan", "Iraq")

# filter conflict zone
conflict <- pop %>%
  select(year, coo_name, coo, coa_name, coa, refugees, asylum_seekers, idps) %>%
  filter(coo_name %in% countries)

# extract internally displaced persons

idps <- conflict %>%
  filter(idps > 0) %>%
  select(year, coo_name, coo_name, idps) %>%
  arrange(coo_name)

# summarise total refugees and asylum seeker per year by country pof origin
total <- conflict %>%
  group_by(coo_name, year) %>%
  summarise(totalrefugees = sum(refugees), 
            totalasylum = sum(asylum_seekers))


joined <- left_join(total, idps, by= c("coo_name", "year")) %>%
  pivot_longer(names_to = "type", values_to = "number", totalrefugees:idps) %>%
  group_by(coo_name, year) %>%
  mutate(total = sum(number)) %>%
  rowwise() %>%
  mutate(proportion = number/total) %>%
  mutate(type = dplyr::recode(type,
    "totalrefugees" = "Refugees",
    "totalasylum" = "Asylum seekers",
    "idps" = "Internally-Displaced Persons"
  )) %>%
  mutate(type = as_factor(type)) %>%
  mutate(coo_name = as_factor(coo_name))

ggplot

a <- joined %>% 
  filter(coo_name == "Afghanistan", year > 2010) %>%
  ggplot(aes(x = 2, y = proportion, fill = type)) +
  geom_col(width = 1) +
  coord_polar(theta = "y") +
  xlim(0.5, 2.5) +
  facet_wrap(~ year, nrow = 3) +
  theme_void(base_family = "Lato") +
  easy_remove_legend() +
  scale_fill_manual(values = c("#313657", "#6C90B9", "#DD3C51")) +
  theme(
    plot.margin = margin(t = 1, r = 30, b = 1, l = 30),
  ) +
  labs(title = "As conflict wears on, the number of people who are displaced within \ntheir own country increases.", 
       subtitle = "Afghanistan") +
  theme(
  plot.title = element_text(margin = margin(b = 10)),  
   plot.subtitle = element_text(margin = margin(b = 10)), # space below subtitle # space below title
  )


i <- joined %>% 
  filter(coo_name == "Iraq", year > 2010) %>%
  ggplot(aes(x = 2, y = proportion, fill = type)) +
  geom_col(width = 1) +
  coord_polar(theta = "y") +
  xlim(0.5, 2.5) +
  facet_wrap(~ year, nrow = 3) +
  theme_void(base_family = "Lato") +
  easy_remove_legend_title() +
  scale_fill_manual(values = c("#313657", "#6C90B9", "#DD3C51")) +
  theme(
    plot.margin = margin(t = 1, r = 30, b = 1, l = 30),
  ) +
  labs(subtitle = "Iraq", caption = "TidyTuesday 2023 Week 34 | Data from `refugees` R package") +
   theme(plot.caption = element_text(margin = margin(t = 10)),
    legend.position = "bottom",
  legend.margin = margin(t = 10, b = 10) # more space around legend
   )

a / i 

tidyplot

Tidyplots makes it super intuitive to make donut plots. Sadly it isn’t working with patchwork at the moment, so I couldn’t work out how to combine these plots. Also it doesn’t seem to have a way to add both a title and subtitle.

joined %>% 
  filter(coo_name == "Afghanistan", year > 2010) %>%
  tidyplot(y = proportion, colour = type) %>% 
    add_donut() %>% 
    adjust_size(width = 25, height = 25) |>
  split_plot(by = year) %>%
  adjust_font(fontsize = 12, family = "Lato") %>%
  adjust_title("Afghanistan", fontsize = 16) %>%
  adjust_colors(new_colors = c("#313657", "#6C90B9", "#DD3C51")) %>%
  adjust_legend_position(position = "bottom")  %>%
  remove_legend_title() %>%
  adjust_caption("TidyTuesday 2023 Week 34 | Data from `refugees` R package") %>%
  save_plot(here::here("charts26", "2026-04-08_circular", "tidyA.png"))

joined %>% 
  filter(coo_name == "Iraq", year > 2010) %>%
  tidyplot(y = proportion, colour = type) %>% 
    add_donut() %>% 
    adjust_size(width = 25, height = 25) |>
  split_plot(by = year) %>%
  adjust_font(fontsize = 12, family = "Lato") %>%
  adjust_title("Iraq", fontsize = 16) %>%
  adjust_colors(new_colors = c("#313657", "#6C90B9", "#DD3C51")) %>%
  adjust_legend_position(position = "bottom")  %>%
    remove_legend_title() %>%
  adjust_caption("TidyTuesday 2023 Week 34 | Data from `refugees` R package") %>%
  save_plot(here::here("charts26", "2026-04-08_circular", "tidyI.png"))