---
title: "Border Encounters by Demographic and Authority Type"
subtitle: "Comparison of Title 8 vs Title 42 processing (in millions)"
description: "Analysis of U.S. Border Encounters (2020-2024) using R and ggplot2, featuring demographic trends, geographic distribution, and seasonal patterns"
author: "Steven Ponce"
date: "2024-11-24"
categories: ["TidyTuesday", "2024"]
tags: [ggplot2, tidyverse, patchwork, border-data, data-analysis, usmap]
image: "thumbnails/tt_2024_48.png"
format:
html:
toc: true
toc-depth: 5
code-link: true
code-fold: true
code-tools: true
code-summary: "Show code"
editor_options:
chunk_output_type: inline
execute:
freeze: true
cache: true
error: false
message: false
warning: false
eval: true
# share:
# permalink: "https://stevenponce.netlify.app/data_visualizations/tt_2024_48.png"
# description: "Visualization of U.S. Border Encounters showing demographic trends, geographic patterns, and processing types"
# linkedin: true
# twitter: true
# email: true
---
{#fig-1}
### <mark> __Steps to Create this Graphic__ </mark>
#### 1. Load Packages & Setup
```{r}
#| label: load
#| warning: false
#| message: false
#| results: "hide"
## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
if (!require("pacman")) install.packages("pacman")
pacman::p_load(
tidyverse, # Easily Install and Load the 'Tidyverse'
ggtext, # Improved Text Rendering Support for 'ggplot2'
showtext, # Using Fonts More Easily in R Graphs
janitor, # Simple Tools for Examining and Cleaning Dirty Data
scales, # Scale Functions for Visualization
glue, # Interpreted String Literals
here, # A Simpler Way to Find Your Files
patchwork, # The Composer of Plots
usmap # US Maps Including Alaska and Hawaii
)
})
suppressMessages(source(here::here("_setup.R")))
### |- figure size ----
camcorder::gg_record(
dir = here::here("temp_plots"),
device = "png",
width = 16,
height = 10,
units = "in",
dpi = 320
)
### |- resolution ----
showtext_opts(dpi = 320, regular.wt = 300, bold.wt = 800)
```
#### 2. Read in the Data
```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false
tt <- tidytuesdayR::tt_load(2024, week = 48)
cbp_resp <- tt$cbp_resp |> clean_names()
cbp_state <- tt$cbp_state |> clean_names()
tidytuesdayR::readme(tt)
rm(tt)
```
#### 3. Examine the Data
```{r}
#| label: examine
#| include: true
#| eval: true
#| results: 'hide'
#| warning: false
glimpse(cbp_resp)
glimpse(cbp_state)
```
#### 4. Tidy Data
```{r}
#| label: tidy
#| warning: false
### |- tidy data ----
# 1. Demographic Evolution Data
demographic_trends_df <- cbp_resp |>
group_by(fiscal_year, demographic) |>
summarise(total_encounters = sum(encounter_count), .groups = 'drop') |>
arrange(fiscal_year, demographic)
# Get end points for direct labeling
label_data <- demographic_trends_df |>
filter(fiscal_year == max(fiscal_year))
# 2. State Map Data
state_map_df <- cbp_state |>
group_by(state) |>
summarise(encounter_count = sum(encounter_count), .groups = 'drop')
# 3. Demographic Authority Data
demographic_authority_df <- cbp_resp |>
group_by(demographic, title_of_authority) |>
summarise(total_encounters = sum(encounter_count), .groups = 'drop') |>
pivot_wider(
names_from = title_of_authority,
values_from = total_encounters
) |>
mutate(
demographic = fct_reorder(demographic, `Title 8`),
`Title 8` = `Title 8` / 1e6,
`Title 42` = `Title 42` / 1e6
)
# 4. Seasonal Citizenship Data
# Get correct month order (Fiscal Year)
month_order <- c("OCT", "NOV", "DEC", "JAN", "FEB", "MAR",
"APR", "MAY", "JUN", "JUL", "AUG", "SEP")
top_10_citizenship <- cbp_resp |>
group_by(citizenship) |>
summarise(total_encounters = sum(encounter_count), .groups = 'drop') |>
arrange(desc(total_encounters)) |>
slice_head(n = 10) |>
pull(citizenship)
# Get top 5 countries instead of 10 to reduce visual complexity
top_5_citizenship_df <- head(top_10_citizenship, 5)
seasonal_citizenship_df <- cbp_resp |>
filter(citizenship %in% top_10_citizenship) |>
group_by(month_abbv, citizenship) |>
summarise(avg_encounters = mean(encounter_count), .groups = 'drop') |>
mutate(
month_abbv = factor(month_abbv, levels = month_order),
citizenship = factor(citizenship, levels = top_10_citizenship)
)
```
#### 5. Visualization Parameters
```{r}
#| label: params
#| include: true
#| warning: false
### |- plot aesthetics ----
bkg_col <- "#f5f5f2"
title_col <- "gray20"
subtitle_col <- "gray20"
caption_col <- "gray30"
text_col <- "gray30"
### |- titles and caption ----
# icons
tt <- str_glue("#TidyTuesday: { 2024 } Week { 48 } • Source: U.S. Customs and Border Protection (CBP)<br>")
li <- str_glue("<span style='font-family:fa6-brands'></span>")
gh <- str_glue("<span style='font-family:fa6-brands'></span>")
bs <- str_glue("<span style='font-family:fa6-brands'> </span>")
# text
title_text <- str_glue("U.S. Border Encounters Analysis (FY2020-2024)")
subtitle_text <- "Evolution, Geographic Distribution, Processing Types, and Seasonal Patterns"
caption_text <- str_glue("{tt} {li} stevenponce • {bs} sponce1 • {gh} poncest • #rstats #ggplot2")
### |- fonts ----
setup_fonts()
### |- plot theme ----
theme_set(theme_minimal(base_size = 14, base_family = "text"))
theme_update(
plot.background = element_rect(fill = bkg_col, color = bkg_col),
panel.background = element_rect(fill = bkg_col, color = bkg_col),
legend.position = "bottom",
plot.margin = margin(t = 10, r = 20, b = 10, l = 20),
axis.title.x = element_text(margin = margin(10, 0, 0, 0), size = rel(1.1),
color = text_col, family = "text", face = "bold", hjust = 0.5),
axis.title.y = element_text(margin = margin(0, 10, 0, 0), size = rel(1.1),
color = text_col, family = "text", face = "bold", hjust = 0.5),
axis.line.x = element_line(color = "#252525", linewidth = .2),
plot.title = element_text(size = rel(1.14), face = "bold", hjust = 0.5, color = title_col),
plot.subtitle = element_text(size = rel(0.86), hjust = 0.5, color = subtitle_col),
axis.title = element_text(size = rel(0.93), face = "bold", color = text_col),
axis.text = element_text(size = rel(0.79), color = text_col),
legend.title = element_blank(),
legend.text = element_text(size = rel(0.71), color = text_col),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_line(color = "gray90", linewidth = 0.2),
panel.grid.minor = element_blank()
)
```
#### 6. Plot
```{r}
#| label: plot
#| warning: false
### |- plot 1 ----
demographic_evolution <- demographic_trends_df |>
ggplot(aes(x = fiscal_year, y = total_encounters, color = demographic)) +
# Geoms
geom_line(size = 1.2) +
geom_point(size = 3) +
# Scales
scale_color_manual(
values = c(
"#E64B35", "#4DBBD5", "#91D1C2", "#8491B4", "#F39B7F"
)
) +
scale_y_continuous(
labels = label_number(scale = 1e-6, suffix = "M"),
breaks = seq(0, 2000000, by = 500000),
expand = expansion(mult = c(0.02, 0.1))
) +
scale_x_continuous(
breaks = 2020:2024,
limits = c(2020, 2024)
) +
# Labs
labs(
title = "Evolution of Border Encounters by Demographic Group",
subtitle = "Trends in total encounters from FY2020 to FY2024",
x = NULL,
y = NULL,
color = NULL
) +
# Theme
theme(
legend.position = "top",
plot.margin = margin(t = 20, r = 20, b = 20, l = 20)
)
### |- plot 2 ----
state_encounters_map <- plot_usmap(
data = state_map_df,
values = "encounter_count",
color = "#d9d9d9",
size = 0.25
) +
# Scales
coord_sf(clip = "off") +
scale_fill_gradientn(
colors = c(
"#f7f7f7",
"#67a9cf",
"#2166ac",
"#feb24c"
),
breaks = c(500000, 1000000, 2000000),
labels = c("500K", "1M", "2M"),
guide = guide_colorbar(
title = "Encounter Count",
title.position = "top",
title.hjust = 0.5,
barwidth = 12,
barheight = 1,
direction = "horizontal",
ticks = FALSE,
margin = margin(t = 10, b = 10)
)
) +
# Labs
labs(
title = "Geographic Distribution of Border Encounters",
subtitle = "Total encounters by state, FY2020-2024",
x = NULL,
y = NULL
) +
# Theme
theme(
axis.text = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
panel.grid = element_blank(),
axis.line.x = element_blank(),
legend.position = "bottom",
plot.background = element_rect(fill = bkg_col, color = bkg_col),
panel.background = element_rect(fill = bkg_col, color = bkg_col),
legend.background = element_rect(fill = bkg_col, color = NA),
plot.title = element_text(size = 16, face = "bold", hjust = 0.5, color = title_col),
plot.subtitle = element_text(size = 12, hjust = 0.5, color = subtitle_col)
)
### |- plot 3 ----
title_42_color <- "#E64B35"
title_8_color <- "#4DBBD5"
demographic_authority_plot <- ggplot(demographic_authority_df) +
# Geoms
geom_vline(
xintercept = seq(0, 6, by = 2),
color = "gray90",
linewidth = 0.2
) +
geom_segment(
aes(
y = demographic,
x = `Title 42`,
xend = `Title 8`,
yend = demographic
),
color = "gray80",
linewidth = 0.5
) +
geom_point(
aes(x = `Title 42`, y = demographic),
color = title_42_color,
size = 3
) +
geom_point(
aes(x = `Title 8`, y = demographic),
color = title_8_color,
size = 3
) +
geom_text(
aes(
x = `Title 42`, y = demographic,
label = sprintf("%.2fM", `Title 42`)
),
color = title_42_color,
vjust = 2,
hjust = 1.2,
size = 4,
family = "mono",
fontface = "bold"
) +
geom_text(
aes(
x = `Title 8`, y = demographic,
label = sprintf("%.2fM", `Title 8`)
),
color = title_8_color,
vjust = 2,
hjust = -0.2,
size = 4,
family = "mono",
fontface = "bold"
) +
annotate(
"text",
x = 3,
y = max(as.numeric(demographic_authority_df$demographic)) + 0.2,
label = "Title 42",
color = title_42_color,
hjust = 1,
vjust = 0,
size = 5,
fontface = "bold"
) +
annotate(
"text",
x = 4.8,
y = max(as.numeric(demographic_authority_df$demographic)) + 0.2,
label = "Title 8",
color = title_8_color,
hjust = 0,
vjust = 0,
size = 5,
fontface = "bold"
) +
# Scales
scale_x_continuous(
breaks = seq(0, 6, by = 2),
labels = function(x) paste0(x, "M"),
limits = c(0, 6),
expand = c(0.15, 0.1)
) +
scale_y_discrete() +
# Labs
labs(
title = "Border Encounters by Demographic and Authority Type",
subtitle = "Comparison of Title 8 vs Title 42 processing (in millions)",
x = NULL,
y = NULL
) +
# Theme
theme(
panel.grid = element_blank(),
axis.title.y = element_blank(),
plot.margin = margin(t = 30, r = 20, b = 20, l = 20),
axis.text.y = element_text(size = 11)
)
### |- plot 4 ----
seasonal_citizenship_plot <- seasonal_citizenship_df |>
filter(citizenship %in% top_5_citizenship_df) |>
mutate(
month_abbv = factor(month_abbv, levels = month_order),
citizenship = fct_reorder(citizenship, -avg_encounters, sum)
) |>
ggplot(aes(x = month_abbv, y = avg_encounters, color = citizenship, group = citizenship)) +
# Geoms
geom_line(alpha = 0.15, linewidth = 0.7) +
geom_point(size = 3.5) +
# Scales
scale_color_manual(
values = c(
"#E64B35", "#4DBBD5", "#91D1C2", "#8491B4", "#F39B7F"
)
) +
scale_y_continuous(
labels = label_number(scale = 1, suffix = "K"),
breaks = seq(0, 600, by = 200),
expand = expansion(mult = c(0.02, 0.1))
) +
# Labs
labs(
title = "Seasonal Patterns of Border Encounters by Citizenship",
subtitle = "Monthly average encounters for top 5 countries (Fiscal Year: October to September)",
x = NULL,
y = NULL
) +
theme(
# Legend refinements
legend.position = "top",
legend.direction = "horizontal",
legend.spacing.x = unit(0.5, "cm"),
legend.margin = margin(t = 5, b = 15),
legend.title = element_blank(),
legend.text = element_text(size = 9.5),
panel.grid.major.x = element_blank()
)
### |- combined plots ----
combined_plot <- (
demographic_evolution + state_encounters_map +
plot_layout(widths = c(1, 1))
) / (
demographic_authority_plot + seasonal_citizenship_plot
)
combined_plot <- combined_plot +
plot_annotation(
title = title_text,
subtitle = subtitle_text,
caption = caption_text,
theme = theme(
plot.title = element_text(
family = "title",
size = rel(2.5),
face = "bold",
hjust = 0.5,
color = title_col,
margin = margin(b = 10)
),
plot.subtitle = element_text(
family = "text",
size = rel(1.3),
hjust = 0.5,
color = subtitle_col,
margin = margin(b = 20)
),
plot.caption = element_markdown(
family = "caption",
size = rel(0.75),
color = caption_col,
hjust = 0.5,
margin = margin(t = 20)
),
plot.margin = margin(10, 10, 10, 10)
)
)
```
#### 7. Save
```{r}
#| label: save
#| warning: false
### |- plot image ----
source(here::here("R/image_utils.R"))
save_plot_patchwork(combined_plot, type = "tidytuesday", year = 2024, week = 48, height = 10, width = 16)
```
#### 8. Session Info
::: {.callout-tip collapse="true"}
##### Expand for Session Info
```{r, echo = FALSE}
#| eval: true
#| warning: false
sessionInfo()
```
:::
#### 9. GitHub Repository
::: {.callout-tip collapse="true"}
##### Expand for GitHub Repo
The complete code for this analysis is available in [`tt_2024_48.qmd`](https://github.com/poncest/personal-website/blob/master/data_visualizations/TidyTuesday/2024/tt_2024_48.qmd).
For the full repository, [click here](https://github.com/poncest/personal-website/).
:::
