Tidy Tuesday Customs and Border Protection

library(tidyverse)
library(janitor)

cbp_resp <- bind_rows(
  read_csv("https://www.cbp.gov/sites/default/files/assets/documents/2023-Nov/nationwide-encounters-fy20-fy23-aor.csv", show_col_types = FALSE),
  read_csv("https://www.cbp.gov/sites/default/files/2024-10/nationwide-encounters-fy21-fy24-aor.csv", show_col_types = FALSE)
) |>
  janitor::clean_names() |>
  unique()

cbp_state <- bind_rows(
  read_csv("https://www.cbp.gov/sites/default/files/assets/documents/2023-Nov/nationwide-encounters-fy20-fy23-state.csv", show_col_types = FALSE),
  read_csv("https://www.cbp.gov/sites/default/files/2024-10/nationwide-encounters-fy21-fy24-state.csv", show_col_types = FALSE)
) |>
  janitor::clean_names() |>
  unique()

cbp_resp |>
  glimpse()

table(cbp_resp$encounter_type)

table(cbp_resp$component)

table(cbp_resp$title_of_authority)

table(cbp_resp$month_grouping)

library(kableExtra)

expulsions <- cbp_resp %>%
  filter(title_of_authority == "Title 42") %>%
  group_by(citizenship, fiscal_year) %>%
  summarise(total = sum(encounter_count), .groups = "drop") %>%
  pivot_wider(names_from = fiscal_year, values_from = total, names_prefix = "FY ") %>%
  arrange(desc(`FY 2023`)) %>%
  mutate(across(starts_with("FY"), ~ format(., big.mark = ",", scientific = FALSE)))

expulsions %>%
  kbl(
    caption = "Title 42 Expulsions by Citizenship and Fiscal Year",
    format = "html",
    digits = 0
  ) %>%
  kable_styling(
    bootstrap_options = c("striped", "hover", "condensed", "responsive"),
    full_width = TRUE,
    font_size = 12
  ) %>%
  add_header_above(c(" " = 1, "Fiscal Years" = ncol(expulsions) - 1)) %>%
  column_spec(1, width = "10em", bold = TRUE) %>% 
  column_spec(2:ncol(expulsions), width = "8em") 

inadmissibles <- cbp_resp |>
  filter(title_of_authority == "Title 8", encounter_type == "Inadmissibles") |>
  group_by(citizenship, fiscal_year) |>
  summarise(total = sum(encounter_count), .groups = "drop") |>
  pivot_wider(names_from = fiscal_year, values_from = total, names_prefix = "FY ") %>%
  arrange(desc(`FY 2023`)) %>%
  mutate(across(starts_with("FY"), ~ format(., big.mark = ",", scientific = FALSE)))

inadmissibles %>%
  kbl(
    caption = "Title 8 Inadmissibles by Citizenship and Fiscal Year",
    format = "html",
    digits = 0
  ) %>%
  kable_styling(
    bootstrap_options = c("striped", "hover", "condensed", "responsive"),
    full_width = TRUE,
    font_size = 12
  ) %>%
  add_header_above(c(" " = 1, "Fiscal Years" = ncol(inadmissibles) - 1))

apprehensions <- cbp_resp |>
  filter(title_of_authority == "Title 8", encounter_type == "Apprehensions") |>
  group_by(citizenship, fiscal_year) |>
  summarise(total = sum(encounter_count), .groups = "drop") |>
  pivot_wider(names_from = fiscal_year, values_from = total, names_prefix = "FY ") %>%
  arrange(desc(`FY 2023`)) %>%
  mutate(across(starts_with("FY"), ~ format(., big.mark = ",", scientific = FALSE)))

apprehensions %>%
  kbl(
    caption = "Title 8 Apprehensions by Citizenship and Fiscal Year",
    format = "html",
    digits = 0
  ) %>%
  kable_styling(
    bootstrap_options = c("striped", "hover", "condensed", "responsive"),
    full_width = TRUE,
    font_size = 12
  ) %>%
  add_header_above(c(" " = 1, "Fiscal Years" = ncol(apprehensions) - 1))

plot_data <- cbp_resp %>%
  group_by(citizenship, fiscal_year, encounter_type) %>%
  summarise(total = sum(encounter_count, na.rm = TRUE), .groups = 'drop') %>%
  mutate(fiscal_year = as.factor(fiscal_year))

ggplot(plot_data, aes(x = fiscal_year, y = total, fill = encounter_type)) +
  geom_bar(stat = "identity", position = "stack") +  
  facet_wrap(~ citizenship, scales = "free_y") +     
  labs(
    title = "Encounters by Citizenship and Year",
    x = "Fiscal Year",
    y = "Total Encounters",
    fill = "Encounter Type"
  ) +
  theme_minimal() +
  theme(
    strip.text = element_text(size = 8, face = "bold"),  
    axis.text.x = element_text(angle = 45, hjust = 1)    
  )

library(ggplot2)
library(dplyr)
library(maps)

library(grid)
library(sf)

# Get world map data and convert to sf object
world_map <- map_data("world")
world_sf <- st_as_sf(map("world", plot = FALSE, fill = TRUE))

# Create a data frame of unique country centroids for positioning the bar charts
country_coords <- world_map %>%
  group_by(region) %>%
  summarise(
    long = mean(range(long)),
    lat = mean(range(lat))
  ) %>%
  rename(citizenship = region)

# Merge plot data with country coordinates
plot_data_coords <- plot_data %>%
  inner_join(country_coords, by = "citizenship")

# Create a base world map using ggplot2
world_base <- ggplot() +
  geom_sf(data = world_sf, fill = "lightgray", color = "white") +
  theme_void() +
  labs(
    title = "Title 42 Encounters by Citizenship and Year",
    subtitle = "Country-specific encounters visualized as bar charts over each country"
  ) +
  coord_sf()

# Debugging Step: Plot text labels for each country to verify coordinates
world_base <- world_base +
  geom_text(
    data = plot_data_coords,
    aes(x = long, y = lat, label = citizenship),
    size = 3, color = "red"
  )

# Plot the world base map with text labels to verify coordinates
print(world_base)

# After verifying coordinates, proceed with adding bar charts

# Add bar charts for each country using annotation_custom()
for (country in unique(plot_data_coords$citizenship)) {
  
  # Filter data for each country
  country_data <- plot_data_coords %>% filter(citizenship == country)
  
  # Create a mini bar plot for each country
  mini_bar <- ggplot(country_data, aes(x = fiscal_year, y = total, fill = encounter_type)) +
    geom_bar(stat = "identity", position = "stack") +
    theme_void() +
    theme(
      legend.position = "none",
      axis.text = element_blank(),
      axis.ticks = element_blank(),
      plot.margin = margin(0, 0, 0, 0)
    ) +
    scale_y_continuous(limits = c(0, max(country_data$total) * 1.1))  # Scale y to be proportional
  
  # Convert the mini bar plot into a grob object
  mini_bar_grob <- ggplotGrob(mini_bar)
  
  # Get the longitude and latitude for placing the mini bar
  country_long <- country_data$long[1]
  country_lat <- country_data$lat[1]
  
  # Add the mini bar plot to the world map
  world_base <- world_base + 
    annotation_custom(
      grob = mini_bar_grob,
      xmin = country_long - 5,
      xmax = country_long + 5,
      ymin = country_lat - 5,
      ymax = country_lat + 5
    )
}

# Print the final map with bar charts
print(world_base)