Tidy Tuesday 1

Code

library(tidyverse)

cia_factbook <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2024/2024-10-22/cia_factbook.csv')

cia_factbook |>
  select(country, area) |>
  arrange(desc(area)) |> slice_max(area, n = 10)

plot_area <- cia_factbook |>
  mutate(area_million_km2 = area / 1e6) |>
  slice_max(area_million_km2, n = 10) |>
  ggplot() +
  aes(x = area_million_km2, y = reorder(country, area_million_km2)) +
  geom_bar(stat = "identity", fill = "steelblue") +
  scale_x_continuous(position = "top", labels = scales::label_number(scale = 1, suffix = "M")) +
  labs(alt = "",
       title="Top 10 countries by geographical size",
       subtitle = "Total land area (in million km²) ",
       caption = "Source: CIA Factbook | Code: Joe Chelladurai",
       x = NULL, y = NULL) +
  theme_minimal() +
  theme(text = element_text(family = "PT Sans"),
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        plot.background = element_rect(fill = "white"),
        plot.title.position = "plot",
        plot.caption.position = "plot",
        plot.title = element_text(face = "bold", size = 12),
        plot.subtitle = element_text(face = "italic", size = 10),
        axis.text = element_text(size = 9))

ggsave("tidy_tuesday_oct_25.png", plot_area, width = 3, height = 3)

alt_text = "A bar chart displaying the top ten countries by geographical size, with the following areas in millions of square kilometers: Russia (17.1), Canada (9.9), United States (9.8), China (9.6), Brazil (8.5), Australia (7.7), India (3.3), Argentina (2.8), Kazakhstan (2.7), and Algeria (2.4)."