Passport Power Shifts: 2006-2025

Measuring passport strength through visa-free access to global destinations.

#TidyTuesday
{ggflags}
{ggtext}
Claude Sonnet Code
Author

Aditya Dahiya

Published

September 14, 2025

About the Data

The dataset explores passport power through the Henley Passport Index, produced by Henley & Partners, which measures visa-free travel access for passport holders worldwide. The index assigns a score of 1 to destinations where no visa is required, or where travelers can obtain a visa on arrival, visitor’s permit, or electronic travel authority (ETA) without pre-departure government approval. A score of 0 is given when a traditional visa or government-approved electronic visa (e-Visa) is required before departure. The total passport score equals the number of visa-free destinations available to holders of that passport. Henley & Partners updates the Global Passport Index rankings monthly, with recent changes to US passport rankings drawing significant media attention. The data is sourced from the Henley Passport Index API and includes comprehensive information about visa requirements, visa-free access, and electronic travel authorizations across different countries and regions over multiple years, providing insights into how passport strength correlates with geopolitical relationships, economic stability, and international mobility trends.

Figure 1: Each bar represents a country’s change in Henley Passport Index ranking between 2006 and 2025. Positive values (green bars extending right) show improved rankings, while negative values (red bars extending left) indicate declined rankings. Flags mark each country, with labels positioned to avoid overlap.

How I Made This Graphic

Loading required libraries

Code 
pacman::p_load(ggforce)
# To plot geom_convex_hull()
pacman::p_load(MASS, fields)

pacman::p_load(
  tidyverse, # All things tidy

  scales, # Nice Scales for ggplot2
  fontawesome, # Icons display in ggplot2
  ggtext, # Markdown text support for ggplot2
  showtext, # Display fonts in ggplot2
  colorspace, # Lighten and Darken colours

  patchwork, # Composing Plots
  sf # Spatial Operations
)

# install.packages("ggflags", repos = c(
#   "https://jimjam-slam.r-universe.dev",
#   "https://cloud.r-project.org"))

pacman::p_load(ggflags)


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")

Visualization Parameters

Code 
# Font for titles
font_add_google("Saira",
  family = "title_font"
)

# Font for the caption
font_add_google("Saira Condensed",
  family = "body_font"
)

# Font for plot text
font_add_google("Saira Extra Condensed",
  family = "caption_font"
)

showtext_auto()

# A base Colour
bg_col <- "white"
seecolor::print_color(bg_col)

# Colour for highlighted text
text_hil <- "grey40"
seecolor::print_color(text_hil)

# Colour for the text
text_col <- "grey30"
seecolor::print_color(text_col)

line_col <- "grey30"

# Define Base Text Size
bts <- 80

# Caption stuff for the plot
sysfonts::font_add(
  family = "Font Awesome 6 Brands",
  regular = here::here("docs", "Font Awesome 6 Brands-Regular-400.otf")
)
github <- "&#xf09b"
github_username <- "aditya-dahiya"
xtwitter <- "&#xe61b"
xtwitter_username <- "@adityadahiyaias"
social_caption_1 <- glue::glue("<span style='font-family:\"Font Awesome 6 Brands\";'>{github};</span> <span style='color: {text_hil}'>{github_username}  </span>")
social_caption_2 <- glue::glue("<span style='font-family:\"Font Awesome 6 Brands\";'>{xtwitter};</span> <span style='color: {text_hil}'>{xtwitter_username}</span>")
plot_caption <- paste0(
  "**Data:**  Henley Passport Index by Henley & Partners",
  " |  **Code:** ",
  social_caption_1,
  " |  **Graphics:** ",
  social_caption_2
)
rm(
  github, github_username, xtwitter,
  xtwitter_username, social_caption_1,
  social_caption_2
)

# Add text to plot-------------------------------------------------
plot_title <- "Passport Power Shifts (2006-2025)"

plot_subtitle <- "Based on visa-free destinations accessible to passport holders, the Henley Index reveals dramatic shifts in global mobility. UAE's spectacular 39-position climb and declines by poorer, war-torn economies highlight how economic growth and diplomatic expansion are redefining travel freedom worldwide." |> str_wrap(105)

str_view(plot_subtitle)


# Caption stuff for the plot
sysfonts::font_add(
  family = "Font Awesome 6 Brands",
  regular = here::here("docs", "Font Awesome 6 Brands-Regular-400.otf")
)
github <- "&#xf09b"
github_username <- "aditya-dahiya"
xtwitter <- "&#xe61b"
xtwitter_username <- "@adityadahiyaias"
social_caption_1 <- glue::glue("<span style='font-family:\"Font Awesome 6 Brands\";'>{github};</span> <span style='color: {text_hil}'>{github_username}  </span>")
social_caption_2 <- glue::glue("<span style='font-family:\"Font Awesome 6 Brands\";'>{xtwitter};</span> <span style='color: {text_hil}'>{xtwitter_username}</span>")
plot_caption_2 <- paste0(
  "**Data:**  Henley Passport Index by Henley & Partners",
  "<br>**Code:** ",
  social_caption_1,
  "<br>**Graphics:** ",
  social_caption_2
)
rm(
  github, github_username, xtwitter,
  xtwitter_username, social_caption_1,
  social_caption_2
)

Exploratory Data Analysis and Wrangling

Code 
# pacman::p_load(summarytools)
# 
# country_lists |>
#   dfSummary() |>
#   view()
# 
# rank_by_year |>
#   dfSummary() |>
#   view()
# 
# pacman::p_unload(summarytools)
# 
# # A basic plot of changing ranks
# rank_by_year |>
#   ggplot(
#     aes(
#       x = year,
#       y = rank,
#       group = code,
#       colour = code
#     )
#   ) +
#   geom_line() +
#   theme(
#     legend.position = "none"
#   )
# 
# Credits: Claude Sonnet 4.0
# Calculate ranking changes from 2006 to 2025
ranking_changes <- rank_by_year %>%
  # Filter for the start and end years
  filter(year %in% c(2006, 2025)) %>%
  # Ensure we have both years for each country
  group_by(code, country) %>%
  filter(n() == 2) %>%
  # Calculate the change in ranking (2006 rank - 2025 rank)
  # Positive values mean improvement (lower rank number = better)
  summarise(
    rank_2006 = rank[year == 2006],
    rank_2025 = rank[year == 2025],
    rank_change = rank_2006 - rank_2025,
    .groups = "drop"
  ) %>%
  # Sort by rank change to identify top movers
  arrange(desc(rank_change))
 
# # View the biggest improvers and fallers
# print("Top 10 Countries with Most Improved Rankings:")
# head(ranking_changes, 10)
# 
# print("Top 10 Countries with Most Fallen Rankings:")
# tail(ranking_changes, 10)
# 
# # Create the change column
# rank_by_year_with_change <- rank_by_year %>%
#   left_join(
#     ranking_changes %>%
#       mutate(
#         change = case_when(
#           # Top 10 improvers (highest positive rank_change)
#           rank(-rank_change, ties.method = "first") <= 10 ~ "improved",
#           # Top 10 fallers (lowest/most negative rank_change)
#           rank(rank_change, ties.method = "first") <= 10 ~ "fallen",
#           # All others
#           TRUE ~ "others"
#         )
#       ) %>%
#       select(code, change),
#     by = "code"
#   ) %>%
#   # Handle countries that might not have data for both 2006 and 2025
#   mutate(change = ifelse(is.na(change), "others", change))
# 
# # Verify the results
# print("Summary of change categories:")
# rank_by_year_with_change %>%
#   count(change)
# 
# # Show the top improvers and fallers with their details
# print("Top 10 Improved Countries (2006 vs 2025):")
# ranking_changes %>%
#   head(10) %>%
#   select(country, rank_2006, rank_2025, rank_change)
# 
# print("Top 10 Fallen Countries (2006 vs 2025):")
# ranking_changes %>%
#   tail(10) %>%
#   select(country, rank_2006, rank_2025, rank_change)
# 
# # Optional: Create a visualization of the biggest changes
# library(ggplot2)

top_movers <- ranking_changes %>%
  slice(c(1:10, (nrow(.)-9):nrow(.))) %>%
  mutate(
    direction = ifelse(rank_change > 0, "Improved", "Fallen"),
    country_ordered = fct_reorder(country, rank_change),
    # Convert country codes to lowercase for ggflags
    flag_code = str_to_lower(code),
    # Create modified rank_change that leaves gap for flags
    base_x = case_when(
      rank_change > 0 ~ 1.5,
      rank_change < 0 ~ -1.5,
      .default = 0
    ),
    # Position for country labels
    label_x = case_when(
      direction == "Improved" ~ -3,
      direction == "Fallen" ~ 3,
      TRUE ~ 0
    ),
    # Hjust for country labels
    label_hjust = case_when(
      direction == "Improved" ~ 1,
      direction == "Fallen" ~ 0,
      TRUE ~ 0.5
    )
  )

country_lists |> 
  select(code, country, visa_free_access)

############################# Plot 2 #######################################
# Get country codes for Visa Free Access
# Inspiration: Claude AI Sonnet 4.0

library(tidyverse)
library(jsonlite)

codes_filter <- ggflags::lflags |> 
  names()

# Extract visa-free access data to long format
visa_free_long <- country_lists %>%
  select(code, country, visa_free_access) %>%
  # Filter out rows with empty or null visa_free_access
  filter(!is.na(visa_free_access) & visa_free_access != "[]" & visa_free_access != "") %>%
  # Parse JSON and extract country codes
  mutate(
    # Parse the JSON string to extract the nested array
    parsed_json = map(visa_free_access, ~{
      tryCatch({
        # Parse the JSON - it appears to be a nested array format
        parsed <- fromJSON(.x, flatten = TRUE)
        # Extract the first level of the nested structure
        if(is.list(parsed) && length(parsed) > 0) {
          parsed[[1]]
        } else {
          NULL
        }
      }, error = function(e) {
        # Return NULL if parsing fails
        NULL
      })
    })
  ) %>%
  # Filter out rows where JSON parsing failed
  filter(!map_lgl(parsed_json, is.null)) %>%
  # Unnest the parsed JSON data with name separation
  unnest(parsed_json, names_sep = "_") %>%
  # Clean and select relevant columns
  select(
    origin_code = code,
    origin_country = country,
    destination_code = parsed_json_code,  # This will be the "code" field from JSON
    destination_country = parsed_json_name  # This will be the "name" field from JSON
  ) %>%
  # Remove any rows with missing destination codes
  filter(!is.na(destination_code) & destination_code != "") |> 
  mutate(
    across(
      .cols = c(origin_code, destination_code),
      .fns = str_to_lower
    )
  )


# Show summary by origin country
visa_free_summary <- visa_free_long %>%
  group_by(origin_code, origin_country) %>%
  summarise(
    visa_free_destinations = n(),
    .groups = 'drop'
  ) %>%
  arrange(desc(visa_free_destinations))

library(tidyverse)

# Prepare the data for plotting
visa_free_plot_data <- visa_free_long %>% 
  select(origin_country, origin_code, destination_code) %>% 
  filter(destination_code %in% codes_filter) |> 
  # Count visa-free destinations per origin country
  group_by(origin_code, origin_country) %>% 
  mutate(total_destinations = n()) %>% 
  ungroup() %>% 
  # Order origin countries by total destinations (most at top)
  mutate(
    origin_country_ordered = fct_reorder(origin_country, total_destinations, .desc = TRUE),
    origin_country_ordered = fct_rev(origin_country_ordered)
  ) %>% 
  # For each origin country, arrange destinations and create symmetric x positions
  group_by(origin_code) %>% 
  arrange(destination_code) %>% 
  mutate(
    rank_num = row_number(),
    n_destinations = n(),
    # Create symmetric positions around 0
    x_position = if (n_destinations[1] == 1) {
      0
    } else {
      seq(-((n_destinations[1] - 1) / 2), ((n_destinations[1] - 1) / 2), length.out = n_destinations[1])
    }
  ) %>% 
  ungroup()

The Plot

Code 
g <- top_movers |> 
  ggplot(
    mapping = aes(
      y = country_ordered, 
      colour = direction
    )
  ) +
  geom_vline(
    xintercept = 0,
    linewidth = 0.4,
    colour = text_hil
  ) +
  geom_segment(
    mapping = aes(
      x = base_x,
      xend = rank_change
    ),
    linewidth = 12,
    alpha = 0.6
  ) +
  # Add flags
  ggflags::geom_flag(
    aes(country = flag_code), 
    x = 0, 
    size = 20
    ) +
  # Add country labels
  geom_text(
    aes(
      x = label_x, 
      label = country, 
      hjust = label_hjust
    ), 
    size = bts / 3,
    colour = text_hil,
    family = "caption_font"
  ) +
  scale_colour_manual(
    values = c(
      "Fallen" = "#d62728", 
      "Improved" = "#2ca02c"
      )
  ) +
  
  geom_richtext(
    aes(
      x = case_when(
        direction == "Improved" ~ 5,
        direction == "Fallen" ~ -5,
        TRUE ~ 0
      ),
      y = country_ordered,
      label = paste0(
        "<span style='color:grey20'>", rank_2006, "</span>",
        " → ",
        "<span style='color:black'>", rank_2025, "</span>"
      ),
      hjust = case_when(
        direction == "Improved" ~ 0,
        direction == "Fallen" ~ 1,
        TRUE ~ 0.5
      )
    ),
    fill = NA,
    label.color = NA,
    size = bts / 5,
    family = "body_font"
  ) +
  
  # Add legend for rank explanation
  annotate(
    geom = "richtext",
    x = 40, y = 2,
    label = paste0(
      "<span style='color:grey20'>Rank (2006)</span>",
      " → ",
      "<span style='color:black'>Rank (2025)</span>"
    ),
    hjust = 0.5,
    vjust = 0.5,
    fill = NA,
    label.color = NA,
    size = bts / 3,
    family = "body_font"
  ) +
  annotate(
    geom = "text",
    x = 40, y = 1.5,
    label = "Lower rank numbers indicate stronger passports",
    hjust = 0.5,
    vjust = 0.5,
    size = bts / 4,
    family = "body_font",
    color = "grey40"
  ) +
  scale_x_continuous(
    breaks = seq(-40, 60, 10)
  ) +
  labs(
    title = plot_title,
    subtitle = plot_subtitle,
    caption = plot_caption,
    x = "Change in Rankings (2025 rank - 2006 rank)",
    y = NULL,
    colour = NULL,
    fill = NULL
  ) +
  theme_minimal(
    base_family = "body_font",
    base_size = bts
  ) +
  theme(
    legend.position = "none",
    
    # Overall
    text = element_text(
      margin = margin(0, 0, 0, 0, "mm"),
      colour = text_col,
      lineheight = 0.3
    ),
    
    # Axes
    axis.text.y = element_blank(),
    axis.ticks.y = element_blank(),
    axis.ticks.length.y = unit(0, "mm"),
    
    axis.text.x = element_text(
      margin = margin(2,2,2,2, "mm")
    ),
    axis.ticks.length.x = unit(5, "mm"),
    axis.ticks.x = element_line(
      colour = text_hil,
      linewidth = 0.4
    ),
    axis.line.x = element_line(
      arrow = arrow(
        ends = "both"
      ),
      colour = text_hil,
      linewidth = 0.4
    ),
    axis.title.x = element_text(
      margin = margin(2,2,2,2, "mm")
    ),
    panel.grid = element_blank(),
    panel.grid.major.x = element_line(
      linewidth = 0.3,
      linetype = 3,
      colour = alpha(text_col, 0.7)
    ),

    # Labels and Strip Text
    plot.title = element_text(
      margin = margin(5, 0, 5, 0, "mm"),
      hjust = 0.5,
      vjust = 0.5,
      colour = text_hil,
      size = 3.2 * bts,
      family = "body_font",
      face = "bold"
    ),
    plot.subtitle = element_text(
      margin = margin(5, 0, 0, 0, "mm"),
      vjust = 0.5,
      colour = text_hil,
      size = 1.3 * bts,
      hjust = 0.5,
      family = "caption_font",
      lineheight = 0.3
    ),
    plot.caption = element_textbox(
      margin = margin(5, 0, 2, 0, "mm"),
      hjust = 0.5,
      halign = 0.5,
      colour = text_hil,
      size = bts * 0.8,
      family = "caption_font"
    ),
    plot.caption.position = "plot",
    plot.title.position = "plot",
    plot.margin = margin(5, 5, 5, 5, "mm")
  )

ggsave(
  filename = here::here(
    "data_vizs",
    "tidy_henley_passport_index.png"
  ),
  plot = g,
  width = 400,
  height = 500,
  units = "mm",
  bg = bg_col
)

The Plot 2

This visualization was created using R to transform complex passport data into an intuitive flag-based display. The foundation relied on the tidyverse ecosystem for data manipulation, particularly using jsonlite to parse the nested JSON structure containing visa-free access information from the Henley Passport Index API. The core challenge involved unnesting the complex JSON data structure and creating symmetric positioning for destination flags around a central axis using conditional logic within grouped mutate() operations. The visual appeal comes from the ggflags package, which renders actual country flags instead of text labels, making patterns immediately recognizable. I used geom_flag() twice: once for visa-free destinations positioned symmetrically along each row, and again for origin country flags anchored to the left edge using x = -Inf positioning. Typography and styling leverage showtext for Google Fonts integration (Saira font family), while ggtext enables rich text formatting for the caption with embedded social media icons using Font Awesome. The annotations system replaces traditional plot titles with precisely positioned text elements using annotate(geom = "text") and annotate(geom = "richtext"), providing complete control over placement and styling.

Code 
# Create the plot

g <- visa_free_plot_data |> 
  ggplot(
    mapping = aes(
      x = x_position,
      y = origin_country_ordered,
      label = destination_code,
      country = destination_code
    )
  ) +
  geom_point(alpha = 0.1) +
  geom_flag(aes(country = destination_code), size = 2.7) +
  
  # Add origin country flags at the left end
  geom_flag(
    aes(country = origin_code, y = origin_country_ordered), 
    x = -Inf, 
    size = 3,
    inherit.aes = FALSE,
    data = visa_free_plot_data %>% 
      count(origin_code, origin_country_ordered)
  ) +
  geom_text(
    aes(
      label = paste0("(",  visa_free_destinations, ")"),
      y = origin_country_ordered
    ), 
    x = -Inf, 
    size = bts / 9,
    hjust = 1.5,
    family = "caption_font",
    colour = text_col,
    inherit.aes = FALSE,
    data = visa_free_plot_data |> 
      left_join(visa_free_summary)
  ) +
  
  # Add title annotation at bottom left
  annotate(
    geom = "text",
    x = Inf, y = -Inf,
    label = "Global\nVisa-Free\nAccess\nPatterns",
    hjust = 1.02, vjust = -1.7,
    colour = text_hil,
    size = 3.5 * bts * 0.35,  # Convert to geom_text size
    family = "body_font",
    fontface = "bold",
    lineheight = 0.25
  ) +
  
  # Add subtitle annotation below title
  annotate(
    geom = "text",
    x = Inf, y = -Inf,
    label = "Each dot represents a destination country with visa-free access for the Country's passport holders" |> str_wrap(20) |> str_view(),
    hjust = 1.02, vjust = -0.6,
    colour = text_hil,
    size = 1.7 * bts * 0.35,  # Convert to geom_text size
    family = "caption_font",
    lineheight = 0.3
  ) +
  
  # Add caption annotation at bottom right
  annotate(
    geom = "richtext",
    x = Inf, y = -Inf,
    label = plot_caption_2,  # Replace with your actual caption
    hjust = 1.02, vjust = -0.5,
    colour = text_hil,
    size = bts * 0.8 * 0.35,  # Convert to geom_richtext size
    family = "caption_font",
    fill = NA,
    label.color = NA,
    lineheight = 0.35
  ) +
  
  scale_x_continuous(
    breaks = NULL,
    expand = expansion(mult = c(0.01, 0.01))
  ) +
  coord_cartesian(clip = "off") +
  labs(
    title = NULL,     # Remove plot title
    subtitle = NULL,  # Remove plot subtitle
    x = NULL,
    y = NULL
  ) +
  theme_minimal(
    base_family = "body_font",
    base_size = bts
  ) +
  theme(
    legend.position = "none",
    
    # Overall
    text = element_text(
      margin = margin(0, 0, 0, 0, "mm"),
      colour = text_col,
      lineheight = 0.3
    ),
    
    # Axes
    axis.text.y = element_text(
      hjust = 1, 
      margin = margin(0,7.5,0,0, "mm"),
      size = bts / 3,
      family = "caption_font"
    ),
    axis.ticks.y = element_blank(),
    axis.ticks.length.y = unit(0, "mm"),
    
    axis.text.x = element_blank(),
    axis.ticks.length.x = unit(0, "mm"),
    axis.ticks.x = element_blank(),
    axis.line.x = element_blank(),
    axis.title.x = element_blank(),
    panel.grid = element_blank(),
    
    # Remove plot title and subtitle themes since they're now annotations
    plot.title = element_blank(),
    plot.subtitle = element_blank(),
    plot.caption = element_blank(),  # Remove since using annotation instead
    
    plot.caption.position = "plot",
    plot.title.position = "plot",
    plot.margin = margin(5, 5, 5, 5, "mm")
  )

ggsave(
  filename = here::here(
    "data_vizs",
    "tidy_henley_passport_index_2.png"
  ),
  plot = g,
  width = 400,
  height = 550,
  units = "mm",
  bg = bg_col
)

This visualization displays global visa-free travel patterns using the Henley Passport Index data. Each row represents a passport-holding country (shown by flags on the left), ordered from most to least visa-free destinations. The colored dots represent destination countries where visa-free travel is permitted, positioned symmetrically around the center axis. Destination countries are depicted as flag icons rather than text labels, making travel patterns immediately recognizable. Numbers in parentheses indicate the total count of visa-free destinations available to each passport holder.

Savings the thumbnail for the webpage

Code 
# Saving a thumbnail

library(magick)

# Saving a thumbnail for the webpage
image_read(here::here(
  "data_vizs",
  "tidy_henley_passport_index_2.png"
)) |>
  image_resize(geometry = "x400") |>
  image_write(
    here::here(
      "data_vizs",
      "thumbnails",
      "tidy_henley_passport_index.png"
    )
  )

Session Info

Code 
sessioninfo::session_info()$packages |>
  as_tibble() |>
  dplyr::select(package,
    version = loadedversion,
    date, source
  ) |>
  dplyr::arrange(package) |>
  janitor::clean_names(
    case = "title"
  ) |>
  gt::gt() |>
  gt::opt_interactive(
    use_search = TRUE
  ) |>
  gtExtras::gt_theme_espn()
Table 1: R Packages and their versions used in the creation of this page and graphics

Links