TidyTuesday2025-Week36

Code

library(dplyr)
library(ggplot2)
country_lists <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-09-09/country_lists.csv')
rank_by_year <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-09-09/rank_by_year.csv')

Code

#data exploration on missingness
colSums(is.na(rank_by_year))

           code         country          region            rank visa_free_count 
             20               0               0               0               0 
           year 
              0

Code

#cleaning data and summarizing it
rank_by_year_clean <- rank_by_year %>%
  filter(year==2025) %>%
  group_by(region) %>%
  summarize(visa = mean(visa_free_count)) %>%
  arrange(desc(visa)) %>%
  mutate(region = factor(region, levels = region))

#Research Question: Which regions of the world are most difficut to depart from, based on free visa policy within that regions’ countries?

Each country in the world has a list of other countries that are allowed to enter without visa, or only with visa. Additionally, certain country are allowed in with a visa, but can obtain the visa in the airport. I wanted to use the data in the Week 36 TidyTuesday dataset to explore which regions of the world were most leniant and strict about these flight conditions.

Code

#final visualization
ggplot(rank_by_year_clean, aes(x=region, y=visa, fill=region)) +
  geom_col() +
  scale_fill_manual(values = c(
  "#1A9850",
  "#66BD63",
  "#A6D96A",
  "#FEE08B",
  "#FDAE61",
  "#F46D43",
  "#D73027"
  )) +
  labs(title = "Regions Ranked by Simple Departure Policy for 2025",
       subtitle = "Based on average number of free or simple visa policies within country, higher values mean an easier time leaving the country",
       caption = "Source: https://github.com/rfordatascience/tidytuesday/blob/main/data/2025/2025-09-09/readme.md \nNo NA values in data",
       x = "",
       y = "Average free visa count for departures") +
  theme_bw() +
  theme(
    legend.position = "none",
    plot.title = element_text(face = "bold", size = 22),
    plot.subtitle = element_text(size = 10),
    axis.title = element_text(face = "bold", size = 10),
    plot.caption = element_text(color = "gray40")
  ) +
  coord_flip()