Visualizing temporal trends in country of citizenship for people arrested by ICE from Sep. 1, 2023 to Jun. 26, 2025

ice_arrests_country.R

#############
## Dataset on ICE enforcement from Sep. 1, 2023 to Jun. 26, 2025
## Source: https://deportationdata.org/data/ice.html
#############

############# 
## Read in data
#############

## Arrest data
arrests <- read_excel('2025-ICLI-00019_2024-ICFO-39357_ERO Admin Arrests_raw.xlsx', skip=6, sheet=1)
head(arrests)

############# 
## Clean data 
#############

library(tidyverse)
library(dplyr)

arrests_df <- data.frame(
  date = arrests$`Apprehension Date`, 
  citizenship = arrests$`Citizenship Country`)

# Aggregate results by month
arrests_df <- arrests_df %>%
  filter(year(date) <= 2025) %>% # <-- clean up typo date of 2115 in some sheets
  mutate(month = floor_date(date, unit = "month"))
# Count methods per month
arrests_df_summary <- arrests_df %>%
  group_by(month, citizenship) %>%
  summarise(count = n(), .groups = 'drop')
head(arrests_df_summary)
range(arrests_df_summary$count)

# Cherry pick few countries to highlight
arrests_df_summary %>% filter(year(arrests_df_summary$month) == 2025) %>%
  filter(count > quantile(count, 0.9, na.rm=TRUE)) %>%
  summarize(sum(count), .by=citizenship)
# 1 BRAZIL                                     1693
# 2 COLOMBIA                                   3704
# 3 CUBA                                       2933
# 4 DOMINICAN REPUBLIC                         1919
# 5 ECUADOR                                    3086
# 6 EL SALVADOR                                5579
# 7 GUATEMALA                                 16191
# 8 HONDURAS                                  13473
# 9 MEXICO                                    43861
# 10 NICARAGUA                                  3992
# 11 PERU                                       1407
# 12 VENEZUELA                                  8197
# 13 CHINA, PEOPLES REPUBLIC OF                  861
# 14 INDIA                                       547
# 15 HAITI                                       341
# 16 IRAN                                        181
# 17 VIETNAM                                     142

# Clean up for display later
arrests_df_summary$citizenship[arrests_df_summary$citizenship == "CHINA, PEOPLES REPUBLIC OF"] <- 'CHINA'

# Cherry pick a few countries for plotting
topcitizens <- rev(c('MEXICO',  'GUATEMALA', 'HONDURAS', 'VENEZUELA', 'EL SALVADOR', 
                     'CHINA', 'INDIA', 'HAITI', 'RUSSIA'))
arrests_df_summary_sub <- arrests_df_summary[arrests_df_summary$citizenship %in% topcitizens,]

############# 
## Plot
#############

library(ggplot2)
library(gganimate)
library(dplyr)
library(tidyr)
library(lubridate)
library(ggrepel)

# Prepare data
df_long <- arrests_df_summary_sub %>%
  pivot_longer(cols = count,
               names_to = "metric",
               values_to = "count") %>%
  filter(metric == "count") %>%
  mutate(
    metric = recode(metric, count = "Arrests"),
    month = as.Date(month),
    citizenship = factor(citizenship, levels = topcitizens)
  ) %>%
  arrange(citizenship, month)

# Create a numeric time index for animation reveal
df_long <- df_long %>%
  mutate(reveal_order = match(citizenship, topcitizens)) %>%
  group_by(citizenship) %>%
  arrange(month) %>%
  mutate(ind = row_number() + (reveal_order - 1) * n()) %>%
  ungroup()

# Plot
p <- ggplot(df_long, aes(x = month, y = count, group = citizenship, color = citizenship)) +
  geom_line() +
  geom_point() +
  # Label the moving point
  geom_text_repel(aes(label = citizenship), hjust = -0.2, vjust = -0.5, size = 5, show.legend = FALSE) +
  scale_x_date(date_breaks = "3 months", date_labels = "%b %Y") +
  labs(
    title = "People arrested by ICE: What countries are they from?",
    subtitle = "Number of ICE arrests from Sep. 1, 2023 to Jun. 26, 2025 per month highlights 
    recent increased number of arrests for immigrants from many countries.",
    caption = "'Arrest' = An event where an ICE officer apprehended someone,
    whether or not that person has a criminal record.
    Select countries of citizenship shown.
    Data source: https://deportationdata.org/data/ice.html",
    x = "",
    y = "Number of People Arrested by ICE"
  ) +
  theme_bw(base_size = 15) +
  theme(
    plot.title = element_text(size = 18, lineheight = 1.9, vjust = 3),
    axis.title = element_text(size = 13),
    axis.text = element_text(size = 10),
    plot.subtitle = element_text(size = 13, lineheight = 0.9),
    plot.caption = element_text(size = 10, vjust = -35, hjust=0),
    legend.position = "none",
    axis.text.x = element_text(angle = 45, hjust = 1),
    plot.margin = margin(t = 30, r = 20, b = 90, l = 20)
  )

# Animate
panim <- p +
  transition_reveal(along = ind) +  # Moves point and label along
  enter_fade() +
  exit_fade() +
  view_follow(fixed_y = FALSE)

# Render
animate(panim,
        width = 600,
        height = 600,
        fps = 20,
        duration = 20,
        end_pause = 100)