Agencies from the FBI Crime Data

Figure 1: This map was created using ggplot2, cowplot, and other R packages to visualize California’s county-level NIBRS participation. The FBI Crime Data was processed by filtering for California, wrangling the data to extract agency counts and proportions, and then joining it with county map data, resulting in 73.7% of agencies reporting to NIBRS, while 26.3% do not. This analysis highlights the extent of NIBRS participation across California, offering a clearer picture of crime data reporting at the county level.

How This Graphic Was Made

1. 📦 Load Packages & Setup

# Load necessary packages using pacman for easier dependency management
pacman::p_load(
  tidyverse,   # Collection of R packages for data science, includes ggplot2 (visualization), dplyr (data manipulation), readr (data import)
  showtext,    # Enables custom fonts in ggplot2, allowing for enhanced typography in plots
  ggtext,      # Adds support for Markdown and HTML formatting in ggplot2 text elements (e.g., bold, italic, colored text)
  skimr,       # Provides detailed summary statistics in a readable and concise format for data exploration
  glue,        # Constructs strings efficiently using variable interpolation
  janitor,     # Simplifies data cleaning (e.g., renaming columns, removing duplicates, formatting variable names)
  paletteer,   # Provides access to a wide range of color palettes for consistent and visually appealing plots
  cowplot      # Enhances ggplot2 with better layout and annotation options, useful for combining multiple plots
)

# Add Google fonts
font_add_google("Oswald", family = "Oswald")
font <- "Oswald"

# Add local font
font_add("Font Awesome 6 Brands", here::here("fonts/otfs/Font Awesome 6 Brands-Regular-400.otf"))

# Automatically enable the use of showtext for all plots
showtext_auto()

# Set DPI for high-resolution text rendering
showtext_opts(dpi = 300)

2. 📖 Read in the Data

# Load the TidyTuesday dataset for Week 07 of 2025
tuesdata <- tidytuesdayR::tt_load(2025, week = 07)

# Extract and clean column names in the agencies dataset
agencies <- tuesdata$agencies %>%
  clean_names()

# Display dataset information from TidyTuesday
tidytuesdayR::readme(tuesdata)

# Remove the original loaded list to free memory
rm(tuesdata)

3. 🕵️ Examine the Data

# Display the structure of the agencies dataset, including column types and sample values
glimpse(agencies)

# Generate a detailed summary of the agencies dataset, including distribution and missing values
skim(agencies)

4. 🤼 Wrangle Data

# Get California county map data
ca_counties <- subset(map_data("county"), region == "california")

# Filter agencies for California and format county names
california <- agencies %>%
  filter(state_abbr == "CA") %>%
  mutate(county = str_to_lower(county)) %>%
  group_by(county, is_nibrs) %>%
  count(is_nibrs) %>%
  ungroup()

# Calculate the percentage of agencies reporting to NIBRS in California
perc <- california %>%
  group_by(is_nibrs) %>%
  summarise(count = sum(n)) %>%
  mutate(proportion = round(count / sum(count) * 100, 1)) %>%
  ungroup() %>%
  pull(proportion)
  
# Merge California county map data with agency data
california <- ca_counties %>%
  left_join(california, by = join_by(subregion == county))

# Extract data for Los Angeles County
los_angeles <- california %>%
  filter(subregion == "los angeles")

5. 🔤 Text

# Define the main title for the visualization
title <- "California Agencies on the FBI’s\nNational Incident-Based Reporting System (NIBRS)"

# Create a short description of the dataset focus
st <- "The FBI Crime Data Explorer provides access to crime data from\nlaw enforcement agencies nationwide. This dataset focuses on California agencies\nthat report through the National Incident-Based Reporting System (NIBRS)."

# Highlight key insight about agency participation in Los Angeles County
text <- "Los Angeles County has the highest number\nof agencies participating in NIBRS, with a total of 61."

# Generate the formatted text
cal_perc <- glue("In California, {perc[2]}% of agencies report to NIBRS, while {perc[1]}% do not.")

# Generate a social media caption with custom colors and font styling
social <- andresutils::social_caption(font_family = font, icon_color = "#b2d8ee", bg_color = "gray10", font_color = "white") 

# Construct the final plot caption with TidyTuesday details, data source, and social caption
cap <- paste0(
  "#TidyTuesday: Week 07, 2025 | **Source**: FBI Crime Data API | **Graphic**: ", social
)

6. 📊 Plot

# Create a base map of California showing NIBRS-participating agencies
p <- california %>%
  filter(is_nibrs == "TRUE") %>%
  ggplot() +
  geom_polygon(aes(x = long, y = lat, group = group, fill = n), color = "gray50") +
  geom_polygon(data = los_angeles, aes(x = long, y = lat, group = group), color = "#EFBF04", fill = NA, size = 1.5) +
  scale_fill_paletteer_c("ggthemes::Blue") +
  labs(caption = cap) +
  theme_void() +
  theme(
    legend.position = c(0.15, 0.2),
    legend.key.width = unit(1.2, "cm"),
    legend.key.height = unit(1.8, "cm"),
    legend.title = element_blank(),
    legend.text = element_text(color="white", size = 22, family = font, face = "bold"),
    plot.background = element_rect(fill = "gray10", color = "transparent"),
    plot.caption = element_textbox_simple(color = "white", family = font, size = 10)
  )

# Add text, labels, and annotations to the map
p1 <- ggdraw(p) +
  draw_text(text = title, x = 0.72, y = 0.9, size = 20, color = "white", family = font, fontface = "bold") +
  draw_text(text = "Number of Agencies", color = "white", x = 0.09, y = 0.2, size = 21, family = font, fontface = "bold", angle = 90) +
  draw_text(text = text, x = 0.43, y = 0.12, color = "white", family = font) +
  annotate("curve", x = 0.45, y = 0.14, xend = 0.57, yend = 0.21, color = "white", curvature = -0.2, linewidth = 0.7, arrow = arrow(length = unit(0.4, 'cm'))) +
  draw_text(text = st, x = 0.72, y = 0.82, color = "white", family = font) +
  draw_text(text = cal_perc, x= 0.68, y = 0.5, color = "white", family = font, angle = -45, size = 18)

7. 💾 Save

# Save the plot for TidyTuesday 2025, Week 07 with specified dimensions.
andresutils::save_plot(p1, type = "tidytuesday", year = 2025, week = 7, width = 11, height = 14)

8. 🚀 GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in tt_07_2025.qmd.

For the full repository, click here.