Understanding the Rise in Domestic Terrorism: Context Matters

While raw attack counts increased 53% (1994-2001 vs 2020-2024), population growth explains half this increase. Per capita rates show a 26% rise—significant but less dramatic.

A four-panel analytical dashboard examining US domestic terrorism trends (1994-2024) with population adjustment. By integrating US Census Bureau data, this makeover reveals that population growth explains half of the apparent 53% increase in attacks—the per capita rate rose only 26%.

Published

November 19, 2025

Original

The original visualization comes from Left-Wing Terrorism and Political Violence in the United States: What the Data Tells Us

Makeover

Figure 1: Four-panel dashboard showing US domestic terrorism trends from 1994-2024. Panel A shows total annual attacks increased 53% from early period to recent years. Panel B displays per capita rates (attacks per million people) increased 26%, demonstrating that population growth explains half the raw increase. Panel C presents smoothed trend lines by ideology, with right-wing attacks (orange line) rising from ~8 attacks in mid-2000s to ~25 in recent years, while jihadist (teal) and left-wing (light blue) attacks remain lower. Panel D shows right-wing share of total attacks, typically 60-90%, with a notable dip to ~50% in the late 2000s. Key finding: while raw attack counts rose substantially, population-adjusted rates show a more modest increase, and right-wing attacks consistently dominate throughout the entire period.

Steps to Create this Graphic

1. Load Packages & Setup

Show code

```{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'
    janitor,       # Simple Tools for Examining and Cleaning Dirty Data
    skimr,         # Compact and Flexible Summaries of Data
    scales,        # Scale Functions for Visualization
    ggtext,        # Improved Text Rendering Support for 'ggplot2'
    showtext,      # Using Fonts More Easily in R Graphs
    glue,          # Interpreted String Literals
    patchwork,     # The Composer of Plots
    ggrepel,       # Automatically Position Non-Overlapping Text Labels
    zoo            # For rollmean function
)
})

### |- figure size ----
camcorder::gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  = 16,
    height = 11,
    units  = "in",
    dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))
```

2. Read in the Data

Show code

```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false
#|

terrorism_enriched <- read_csv(
  here::here("data/MakeoverMonday/2025/terrorism_enriched.csv")) |>
  mutate(ideology = factor(
    ideology,
    levels = c("Right", "Left", "Jihadist", "Ethnonationalist", "Other")
  ))

terrorism_yearly <- read_csv(
  here::here("data/MakeoverMonday/2025/terrorism_yearly.csv"))

# Filter to 1994-2024 (exclude partial 2025)
data_1994_2024 <- terrorism_enriched |> filter(year <= 2024)
```

3. Examine the Data

Show code

```{r}
#| label: examine
#| include: true
#| eval: true
#| results: 'hide'
#| warning: false

glimpse(terrorism_enriched)
glimpse(terrorism_yearly)
```

4. Tidy Data

Show code

```{r}
#| label: tidy
#| warning: false

### |-  Data Notes ----
# The datasets loaded below have been preprocessed via `data_prep.R`
# 
# Preprocessing script: https://github.com/poncest/MakeoverMonday/blob/master/2025/Week_45/data_prep.R
# Download data_prep.R from the GitHub link above and run locally
#
# Key preprocessing steps:
# 1. Original data: terrorism_by_ideology_1994-2025.csv (raw attack counts)
# 2. Population data: Fetched from US Census Bureau via tidycensus
#    - 1994-2009: Intercensal estimates (official reconciled data)
#    - 2010-2019: Vintage 2019 estimates via API
#    - 2020-2023: Vintage 2023 estimates
#    - 2024-2025: Linear projection (excluded from analysis)
# 3. Calculated metrics:
#    - attacks_per_million: Population-adjusted rates
#    - pct_of_year: Percentage composition
#    - 3-year rolling averages (for smoothing)
#
# Loaded datasets (from Section 2):
# - terrorism_enriched: Full dataset with all calculated metrics
# - terrorism_yearly: Annual summaries
# - data_1994_2024: Filtered dataset excluding partial 2025 data
```

5. Visualization Parameters

Show code

```{r}
#| label: params
#| include: true
#| warning: false

### |-  plot aesthetics ----
# Get base colors with custom palette
colors <- get_theme_colors(
  palette = list(
    "Right"            = "#A08843",
    "Left"             = "#4F6D78",
    "Jihadist"         = "#73896C",
    "Ethnonationalist" = "#8E7E9E",
    "Other"            = "#9B9B9B",
    "total"            = "#2C3E4C",
    "per_capita"       = "#6A5882",
    "breakdown"        = "#A08843"
  )
)   
 
### |-  Main titles ----
title_text <- "Understanding the Rise in Domestic Terrorism: Context Matters"

subtitle_text <- str_glue(
  "While raw attack counts increased 53% (1994-2001 vs 2020-2024), <b>population growth explains half this increase</b>.<br>",
  "Per capita rates show a 26% rise—significant but less dramatic."
)

### |-  Data source caption ----
caption_text <- create_social_caption_02_mm(
  mm_year = 2025,
  mm_week = 45,
  note_text = str_glue(
    "**Note:** 2025 data excluded because it represents a partial year.<br>",
    "Per capita rates are calculated as attacks per million people.<br>",
    "The use of the 2010 Census as a population baseline may produce a slight discontinuity.<br>"
  ),
  source_text = str_glue(
    "Terrorism incidents: CSIS Warfare, Irregular Threats & Terrorism Program (1994–2024).<br>",
    "U.S. population estimates:<br>",
    "&nbsp;&nbsp;• Intercensal estimates (1994–2009) ",
    "&nbsp;&nbsp;• Vintage 2019 estimates (2010–2019) ",
    "&nbsp;&nbsp;• Vintage 2023 estimates (2020–2024)<br>"
  )
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    # # Text styling
    plot.title = element_text(
      size = rel(1.5), family = fonts$title, face = "bold",
      color = colors$title, lineheight = 1.1, hjust = 0,
      margin = margin(t = 5, b = 10)
    ),
    plot.subtitle = element_markdown(
      size = rel(0.9), family = fonts$subtitle, face = "italic",
      color = alpha(colors$subtitle, 0.9), lineheight = 1.1,
      margin = margin(t = 0, b = 20)
    ),

    # Legend formatting
    legend.position = "plot",
    legend.justification = "top",
    legend.margin = margin(l = 12, b = 5),
    legend.key.size = unit(0.8, "cm"),
    legend.box.margin = margin(b = 10),
    # legend.title = element_text(face = "bold"),

    # Axis formatting
    axis.ticks.y = element_blank(),
    axis.ticks.x = element_line(color = "gray", linewidth = 0.5),
    axis.title.x = element_text(
      face = "bold", size = rel(0.85),
      margin = margin(t = 10)
    ),
    axis.title.y = element_text(
      face = "bold", size = rel(0.85),
      margin = margin(r = 10)
    ),
    axis.text.x = element_text(
      size = rel(0.85), family = fonts$subtitle,
      color = colors$text
    ),
    axis.text.y = element_text(
      size = rel(0.85), family = fonts$subtitle,
      color = colors$text
    ),

    # Grid lines
    panel.grid.minor = element_line(color = "#ecf0f1", linewidth = 0.2),
    panel.grid.major = element_line(color = "#ecf0f1", linewidth = 0.4),

    # Margin
    plot.margin = margin(20, 20, 20, 20)
  )
)

# Set theme
theme_set(weekly_theme)
```

6. Plot

Show code

```{r}
#| label: plot
#| warning: false

### |-  Panel A: Total Attacks (Raw Counts) ----
pa <-
  terrorism_yearly |>
  filter(year <= 2024) |>
  ggplot(aes(x = year, y = total_attacks)) +
  geom_hline(
    yintercept = 23.8,
    linetype = "dashed",
    color = "grey60",
    linewidth = 0.4
  ) +
  geom_line(linewidth = 1.2, color = colors$palette$total, alpha = 0.9) +
  geom_point(size = 2.2, color = colors$palette$total) +
  annotate(
    "text",
    x = 2001,
    y = 27,
    label = "Mean: 23.8",
    size = 2.8,
    color = "grey50",
    hjust = 0,
    family = fonts$subtitle
  ) +
  scale_x_continuous(breaks = seq(1995, 2025, 5)) +
  scale_y_continuous(breaks = seq(0, 50, 10), limits = c(0, 50)) +
  labs(
    title = "A. Total Attacks",
    subtitle = "Raw count increased 53% (1994-2001 to 2020-2024)",
    y = "Annual attacks",
    x = NULL
  ) +
  theme(
    panel.grid.major.x = element_blank()
  )

### |-  Panel B: Per Capita Rate ----
pb <-
  terrorism_yearly |>
  filter(year <= 2024) |>
  ggplot(aes(x = year, y = attacks_per_million)) +
  geom_hline(
    yintercept = 0.082,
    linetype = "dashed",
    color = "grey60",
    linewidth = 0.4
  ) +
  geom_line(linewidth = 1.2, color = colors$palette$per_capita, alpha = 0.9) +
  geom_point(size = 2.2, color = colors$palette$per_capita) +
  annotate(
    "segment",
    x = 2010, xend = 2010,
    y = 0, yend = 0.155,
    linetype = "dotted",
    color = "grey70",
    linewidth = 0.35
  ) +
  annotate(
    "text",
    x = 2010,
    y = 0.158,
    label = "Census 2010\nbase year",
    size = 2.4,
    color = "grey60",
    hjust = 0.5,
    lineheight = 0.9,
    family = fonts$subtitle
  ) +
  annotate(
    "text",
    x = 2001,
    y = 0.093,
    label = "Mean: 0.082",
    size = 2.8,
    color = "grey50",
    hjust = 0,
    family = fonts$subtitle
  ) +
  scale_x_continuous(breaks = seq(1995, 2025, 5)) +
  scale_y_continuous(
    breaks = seq(0, 0.16, 0.04),
    limits = c(0, 0.16)
  ) +
  labs(
    title = "B. Per Capita Rate",
    subtitle = "Population-adjusted: 26% increase (1994-2001 to 2020-2024)",
    y = "Attacks per million people",
    x = NULL
  ) +
  theme(
    panel.grid.major.x = element_blank()
  )

### |-  Panel C: Attacks by Ideology (Top 3, 3-year avg)  ----
data_smoothed <- data_1994_2024 |>
  filter(ideology %in% c("Right", "Left", "Jihadist")) |>
  arrange(ideology, year) |>
  group_by(ideology) |>
  mutate(attacks_smooth = rollmean(attacks, k = 3, fill = NA, align = "center")) |>
  ungroup()

label_data <- data_smoothed |>
  filter(!is.na(attacks_smooth)) |>
  group_by(ideology) |>
  filter(year == max(year)) |>
  ungroup() |>
  mutate(
    label = case_when(
      ideology == "Right" ~ "Right-wing",
      ideology == "Left" ~ "Left-wing",
      ideology == "Jihadist" ~ "Jihadist"
    )
  )

pc <-
  data_smoothed |>
  ggplot(aes(x = year, y = attacks_smooth, color = ideology)) +
  geom_line(linewidth = 1.2, alpha = 0.95) +
  geom_vline(
    xintercept = 2014,
    linetype = "dotted",
    alpha = 0.4,
    color = "grey40"
  ) +
  geom_text_repel(
    data = label_data,
    aes(label = label),
    hjust = 0,
    nudge_x = 1.2,
    direction = "y",
    segment.size = 0.3,
    segment.color = "grey60",
    size = 3,
    fontface = "bold",
    family = fonts$subtitle,
    show.legend = FALSE,
    min.segment.length = 0
  ) +
  annotate(
    "text",
    x = 2014,
    y = 32,
    label = "2014",
    size = 2.5,
    color = "grey40",
    family = fonts$subtitle
  ) +
  scale_x_continuous(breaks = seq(1995, 2025, 5)) +
  scale_y_continuous(breaks = seq(0, 35, 10), limits = c(0, 35)) +
  scale_color_manual(
    values = c(
      "Right"    = colors$palette$Right,
      "Left"     = colors$palette$Left,
      "Jihadist" = colors$palette$Jihadist
    ),
    labels = c("Right-wing", "Left-wing", "Jihadist")
  ) +
  labs(
    title = "C. Breakdown by Ideology",
    subtitle = "Right-wing attacks increased notably after 2014 (3-year rolling average)",
    y = "Annual attacks (3-year avg)",
    x = "Year"
  ) +
  theme(
    legend.position = "none",
    panel.grid.major.x = element_blank()
  )

### |-  Panel D: Right-Wing Share of Total (3-year avg) ----
pd <-
  data_1994_2024 |>
  group_by(year) |>
  mutate(
    year_total = sum(attacks),
    pct = attacks / year_total * 100
  ) |>
  ungroup() |>
  filter(ideology == "Right") |>
  arrange(year) |>
  mutate(pct_smooth = rollmean(pct, k = 3, fill = NA, align = "center")) |>
  ggplot(aes(x = year, y = pct_smooth)) +
  annotate(
    "rect",
    xmin = 1994, xmax = 2024,
    ymin = 50, ymax = 100,
    fill = "#FFF7EB",
    alpha = 0.7
  ) +
  geom_hline(
    yintercept = 50,
    linetype   = "dashed",
    color      = "grey50",
    linewidth  = 0.4
  ) +
  geom_hline(
    yintercept = 75,
    linetype   = "dotted",
    color      = "grey60",
    linewidth  = 0.35
  ) +
  geom_line(
    linewidth = 1.2,
    color = colors$palette$Right,
    alpha = 0.95
  ) +
  annotate(
    "text",
    x = 1995,
    y = 52,
    label = "Majority (50%) threshold",
    size = 2.5,
    color = "grey50",
    hjust = 0,
    family = fonts$subtitle
  ) +
  annotate(
    "text",
    x = 1995,
    y = 77,
    label = "Supermajority (75%) threshold",
    size = 2.5,
    color = "grey50",
    hjust = 0,
    family = fonts$subtitle
  ) +
  scale_x_continuous(breaks = seq(1995, 2025, 5)) +
  scale_y_continuous(
    breaks = seq(0, 100, 25),
    limits = c(0, 100),
    labels = label_percent(scale = 1)
  ) +
  labs(
    title = "D. Right-Wing Share of Total",
    subtitle = "Typically 60–90%, with a dip to ~50% in the late 2000s (3-year rolling average)",
    y = "Right-wing % of annual attacks",
    x = "Year"
  ) +
  theme(
    legend.position    = "none",
    panel.grid.major.x = element_blank()
  )

### |-  combined plot ----
combined_plots <- (pa | pb) / (pc | pd) +
  plot_layout(heights = c(1, 1.05))

combined_plots <- combined_plots +
  plot_annotation(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    theme = theme(
      plot.title = element_text(
        size = rel(2.3),
        family = fonts$title,
        face = "bold",
        color = colors$title,
        lineheight = 1.1,
        margin = margin(t = 5, b = 5)
      ),
      plot.subtitle = element_markdown(
        size = rel(1.2),
        family = fonts$subtitle,
        color = alpha(colors$subtitle, 0.9),
        lineheight = 1.2,
        margin = margin(t = 5, b = 15)
      ),
      plot.caption = element_markdown(
        size = rel(0.65),
        family = fonts$caption,
        color = colors$caption,
        hjust = 0,
        lineheight = 1.2,
        margin = margin(t = 10)
      )
    )
  )
```

7. Save

Show code

```{r}
#| label: save
#| warning: false

### |-  plot image ----  
save_plot_patchwork(
  plot = combined_plots, 
  type = "makeovermonday", 
  year = current_year,
  week = current_week,
  width = 16, 
  height = 11
  )
```

8. Session Info

Expand for Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] here_1.0.1      zoo_1.8-12      ggrepel_0.9.6   patchwork_1.3.0
 [5] glue_1.8.0      showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9 
 [9] ggtext_0.1.2    scales_1.3.0    skimr_2.1.5     janitor_2.2.0  
[13] lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1   dplyr_1.1.4    
[17] purrr_1.0.2     readr_2.1.5     tidyr_1.3.1     tibble_3.2.1   
[21] ggplot2_3.5.1   tidyverse_2.0.0 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       xfun_0.49          htmlwidgets_1.6.4  lattice_0.22-6    
 [5] tzdb_0.5.0         yulab.utils_0.1.8  vctrs_0.6.5        tools_4.4.0       
 [9] generics_0.1.3     curl_6.0.0         parallel_4.4.0     gifski_1.32.0-1   
[13] fansi_1.0.6        pkgconfig_2.0.3    ggplotify_0.1.2    lifecycle_1.0.4   
[17] compiler_4.4.0     farver_2.1.2       munsell_0.5.1      repr_1.1.7        
[21] codetools_0.2-20   snakecase_0.11.1   htmltools_0.5.8.1  yaml_2.3.10       
[25] crayon_1.5.3       pillar_1.9.0       camcorder_0.1.0    magick_2.8.5      
[29] commonmark_1.9.2   tidyselect_1.2.1   digest_0.6.37      stringi_1.8.4     
[33] rsvg_2.6.1         rprojroot_2.0.4    fastmap_1.2.0      grid_4.4.0        
[37] colorspace_2.1-1   cli_3.6.4          magrittr_2.0.3     base64enc_0.1-3   
[41] utf8_1.2.4         withr_3.0.2        bit64_4.5.2        timechange_0.3.0  
[45] rmarkdown_2.29     bit_4.5.0          hms_1.1.3          evaluate_1.0.1    
[49] knitr_1.49         markdown_1.13      gridGraphics_0.5-1 rlang_1.1.6       
[53] gridtext_0.1.5     Rcpp_1.0.13-1      xml2_1.3.6         renv_1.0.3        
[57] vroom_1.6.5        svglite_2.1.3      rstudioapi_0.17.1  jsonlite_1.8.9    
[61] R6_2.5.1           fs_1.6.5           systemfonts_1.1.0

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in mm_2025_45.qmd.

For the full repository, click here.

10. References

Expand for References

Data Sources:

Terrorism Data:
- Makeover Monday 2025 Week 45: Terrorism and Political Violence in the United States
- Original dataset: terrorism_by_ideology_1994-2025.csv
Population Data:
- US Census Bureau, Population Estimates Program (PEP)
- Retrieved via tidycensus R package
- Intercensal estimates (1994-2009): 2000-2010 Intercensal Estimates
- Vintage 2019 estimates (2010-2019): API access via tidycensus
- Vintage 2023 estimates (2020-2024): API access via tidycensus

Data Preprocessing:

Script: data_prep.R
- Integrates terrorism incidents with US Census population data
- Calculates per capita rates (attacks per million population)
- Computes 3-year rolling averages for trend smoothing
- Generates period classifications and summary statistics
- See script comments for full methodology

Citations:

Terrorism Data:
- Center for Strategic and International Studies (CSIS). (2024). Terrorism and Political Violence in the United States: What the Data Tells Us. CSIS Warfare, Irregular Threats, and Terrorism Program. Retrieved from https://www.csis.org/programs/transnational-threats-project/terrorism-and-political-violence
Population Data:
- US Census Bureau. (Various years). Population Estimates Program (PEP). Retrieved via tidycensus R package. https://www.census.gov/programs-surveys/popest.html
- US Census Bureau. (2012). Intercensal Estimates of the Resident Population for the United States: April 1, 2000 to July 1, 2010. https://www.census.gov/data/tables/time-series/demo/popest/intercensal-2000-2010-national.html

Methodology Notes:

2025 data excluded from analysis (partial year)
Per capita rates calculated as attacks per million population
3-year rolling averages applied to reduce year-to-year volatility
Census 2010 base year may cause slight discontinuity in time series

Reproducibility:

All analysis code available in the project repository:

Data preprocessing: data_prep.R
Visualization code: mm_2025_45_final.R
Publication document: mm_2025_45.qmd

11. Custom Functions Documentation

📦 Custom Helper Functions

This analysis uses custom functions from my personal module library for efficiency and consistency across projects.

Functions Used:

fonts.R: setup_fonts(), get_font_families() - Font management with showtext
social_icons.R: create_social_caption() - Generates formatted social media captions
image_utils.R: save_plot() - Consistent plot saving with naming conventions
base_theme.R: create_base_theme(), extend_weekly_theme(), get_theme_colors() - Custom ggplot2 themes

Why custom functions?
These utilities standardize theming, fonts, and output across all my data visualizations. The core analysis (data tidying and visualization logic) uses only standard tidyverse packages.

Source Code:
View all custom functions → GitHub: R/utils

--- title: "Understanding the Rise in Domestic Terrorism: Context Matters" subtitle: "While raw attack counts increased 53% (1994-2001 vs 2020-2024), population growth explains half this increase. Per capita rates show a 26% rise—significant but less dramatic." description: "A four-panel analytical dashboard examining US domestic terrorism trends (1994-2024) with population adjustment. By integrating US Census Bureau data, this makeover reveals that population growth explains half of the apparent 53% increase in attacks—the per capita rate rose only 26%." date: "2025-11-19" categories: ["MakeoverMonday", "Data Visualization", "R Programming", "2025"] tags: [ "makeover-monday", "terrorism-analysis", "data-visualization", "ggplot2", "patchwork", "per-capita-analysis", "population-adjustment", "time-series", "multi-panel-dashboard", "rolling-averages", "us-census-data", "tidycensus", "analytical-rigor", "csis-data", "domestic-terrorism", "ideology-analysis", "data-literacy" ] image: "thumbnails/mm_2025_45.png" format: html: toc: true toc-depth: 5 code-link: true code-fold: true code-tools: true code-summary: "Show code" self-contained: true theme: light: [flatly, assets/styling/custom_styles.scss] dark: [darkly, assets/styling/custom_styles_dark.scss] editor_options: chunk_output_type: inline execute: freeze: true cache: true error: false message: false warning: false eval: true --- ```{r} #| label: setup-links #| include: false # CENTRALIZED LINK MANAGEMENT ## Project-specific info current_year <- 2025 current_week <- 45 project_file <- "mm_2025_45.qmd" project_image <- "mm_2025_45.png" ## Data Sources data_main <- "https://data.world/makeovermonday/2025w45-terrorism-and-political-violence-in-the-usa" data_secondary <- "https://data.world/makeovermonday/2025w45-terrorism-and-political-violence-in-the-usa" ## Repository Links repo_main <- "https://github.com/poncest/personal-website/" repo_file <- paste0("https://github.com/poncest/personal-website/blob/master/data_visualizations/MakeoverMonday/", current_year, "/", project_file) ## External Resources/Images chart_original <- "https://raw.githubusercontent.com/poncest/MakeoverMonday/refs/heads/master/2025/Week_45/original_chart.png" ## Organization/Platform Links org_primary <- "https://www.csis.org/analysis/left-wing-terrorism-and-political-violence-united-states-what-data-tells-us" org_secondary <- "https://www.csis.org/analysis/left-wing-terrorism-and-political-violence-united-states-what-data-tells-us" # Helper function to create markdown links create_link <- function(text, url) { paste0("[", text, "](", url, ")") } # Helper function for citation-style links create_citation_link <- function(text, url, title = NULL) { if (is.null(title)) { paste0("[", text, "](", url, ")") } else { paste0("[", text, "](", url, ' "', title, '")') } } ``` ### Original The original visualization comes from `r create_link("Left-Wing Terrorism and Political Violence in the United States: What the Data Tells Us", data_secondary)` ![Original visualization](https://raw.githubusercontent.com/poncest/MakeoverMonday/refs/heads/master/2025/Week_45/original_chart.png) ### Makeover ![Four-panel dashboard showing US domestic terrorism trends from 1994-2024. Panel A shows total annual attacks increased 53% from early period to recent years. Panel B displays per capita rates (attacks per million people) increased 26%, demonstrating that population growth explains half the raw increase. Panel C presents smoothed trend lines by ideology, with right-wing attacks (orange line) rising from \~8 attacks in mid-2000s to \~25 in recent years, while jihadist (teal) and left-wing (light blue) attacks remain lower. Panel D shows right-wing share of total attacks, typically 60-90%, with a notable dip to \~50% in the late 2000s. Key finding: while raw attack counts rose substantially, population-adjusted rates show a more modest increase, and right-wing attacks consistently dominate throughout the entire period.](mm_2025_45.png){#fig-1} ### **Steps to Create this Graphic** #### 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' janitor, # Simple Tools for Examining and Cleaning Dirty Data skimr, # Compact and Flexible Summaries of Data scales, # Scale Functions for Visualization ggtext, # Improved Text Rendering Support for 'ggplot2' showtext, # Using Fonts More Easily in R Graphs glue, # Interpreted String Literals patchwork, # The Composer of Plots ggrepel, # Automatically Position Non-Overlapping Text Labels zoo # For rollmean function ) }) ### |- figure size ---- camcorder::gg_record( dir = here::here("temp_plots"), device = "png", width = 16, height = 11, units = "in", dpi = 320 ) # Source utility functions suppressMessages(source(here::here("R/utils/fonts.R"))) source(here::here("R/utils/social_icons.R")) source(here::here("R/utils/image_utils.R")) source(here::here("R/themes/base_theme.R")) ``` #### 2. Read in the Data ```{r} #| label: read #| include: true #| eval: true #| warning: false #| terrorism_enriched <- read_csv( here::here("data/MakeoverMonday/2025/terrorism_enriched.csv")) |> mutate(ideology = factor( ideology, levels = c("Right", "Left", "Jihadist", "Ethnonationalist", "Other") )) terrorism_yearly <- read_csv( here::here("data/MakeoverMonday/2025/terrorism_yearly.csv")) # Filter to 1994-2024 (exclude partial 2025) data_1994_2024 <- terrorism_enriched |> filter(year <= 2024) ``` #### 3. Examine the Data ```{r} #| label: examine #| include: true #| eval: true #| results: 'hide' #| warning: false glimpse(terrorism_enriched) glimpse(terrorism_yearly) ``` #### 4. Tidy Data ```{r} #| label: tidy #| warning: false ### |- Data Notes ---- # The datasets loaded below have been preprocessed via `data_prep.R` # # Preprocessing script: https://github.com/poncest/MakeoverMonday/blob/master/2025/Week_45/data_prep.R # Download data_prep.R from the GitHub link above and run locally # # Key preprocessing steps: # 1. Original data: terrorism_by_ideology_1994-2025.csv (raw attack counts) # 2. Population data: Fetched from US Census Bureau via tidycensus # - 1994-2009: Intercensal estimates (official reconciled data) # - 2010-2019: Vintage 2019 estimates via API # - 2020-2023: Vintage 2023 estimates # - 2024-2025: Linear projection (excluded from analysis) # 3. Calculated metrics: # - attacks_per_million: Population-adjusted rates # - pct_of_year: Percentage composition # - 3-year rolling averages (for smoothing) # # Loaded datasets (from Section 2): # - terrorism_enriched: Full dataset with all calculated metrics # - terrorism_yearly: Annual summaries # - data_1994_2024: Filtered dataset excluding partial 2025 data ``` #### 5. Visualization Parameters ```{r} #| label: params #| include: true #| warning: false ### |- plot aesthetics ---- # Get base colors with custom palette colors <- get_theme_colors( palette = list( "Right" = "#A08843", "Left" = "#4F6D78", "Jihadist" = "#73896C", "Ethnonationalist" = "#8E7E9E", "Other" = "#9B9B9B", "total" = "#2C3E4C", "per_capita" = "#6A5882", "breakdown" = "#A08843" ) ) ### |- Main titles ---- title_text <- "Understanding the Rise in Domestic Terrorism: Context Matters" subtitle_text <- str_glue( "While raw attack counts increased 53% (1994-2001 vs 2020-2024), population growth explains half this increase. ", "Per capita rates show a 26% rise—significant but less dramatic." ) ### |- Data source caption ---- caption_text <- create_social_caption_02_mm( mm_year = 2025, mm_week = 45, note_text = str_glue( "**Note:** 2025 data excluded because it represents a partial year. ", "Per capita rates are calculated as attacks per million people. ", "The use of the 2010 Census as a population baseline may produce a slight discontinuity. " ), source_text = str_glue( "Terrorism incidents: CSIS Warfare, Irregular Threats & Terrorism Program (1994–2024). ", "U.S. population estimates: ", "  • Intercensal estimates (1994–2009) ", "  • Vintage 2019 estimates (2010–2019) ", "  • Vintage 2023 estimates (2020–2024) " ) ) ### |- fonts ---- setup_fonts() fonts <- get_font_families() ### |- plot theme ---- # Start with base theme base_theme <- create_base_theme(colors) # Add weekly-specific theme elements weekly_theme <- extend_weekly_theme( base_theme, theme( # # Text styling plot.title = element_text( size = rel(1.5), family = fonts$title, face = "bold", color = colors$title, lineheight = 1.1, hjust = 0, margin = margin(t = 5, b = 10) ), plot.subtitle = element_markdown( size = rel(0.9), family = fonts$subtitle, face = "italic", color = alpha(colors$subtitle, 0.9), lineheight = 1.1, margin = margin(t = 0, b = 20) ), # Legend formatting legend.position = "plot", legend.justification = "top", legend.margin = margin(l = 12, b = 5), legend.key.size = unit(0.8, "cm"), legend.box.margin = margin(b = 10), # legend.title = element_text(face = "bold"), # Axis formatting axis.ticks.y = element_blank(), axis.ticks.x = element_line(color = "gray", linewidth = 0.5), axis.title.x = element_text( face = "bold", size = rel(0.85), margin = margin(t = 10) ), axis.title.y = element_text( face = "bold", size = rel(0.85), margin = margin(r = 10) ), axis.text.x = element_text( size = rel(0.85), family = fonts$subtitle, color = colors$text ), axis.text.y = element_text( size = rel(0.85), family = fonts$subtitle, color = colors$text ), # Grid lines panel.grid.minor = element_line(color = "#ecf0f1", linewidth = 0.2), panel.grid.major = element_line(color = "#ecf0f1", linewidth = 0.4), # Margin plot.margin = margin(20, 20, 20, 20) ) ) # Set theme theme_set(weekly_theme) ``` #### 6. Plot ```{r} #| label: plot #| warning: false ### |- Panel A: Total Attacks (Raw Counts) ---- pa <- terrorism_yearly |> filter(year <= 2024) |> ggplot(aes(x = year, y = total_attacks)) + geom_hline( yintercept = 23.8, linetype = "dashed", color = "grey60", linewidth = 0.4 ) + geom_line(linewidth = 1.2, color = colors$palette$total, alpha = 0.9) + geom_point(size = 2.2, color = colors$palette$total) + annotate( "text", x = 2001, y = 27, label = "Mean: 23.8", size = 2.8, color = "grey50", hjust = 0, family = fonts$subtitle ) + scale_x_continuous(breaks = seq(1995, 2025, 5)) + scale_y_continuous(breaks = seq(0, 50, 10), limits = c(0, 50)) + labs( title = "A. Total Attacks", subtitle = "Raw count increased 53% (1994-2001 to 2020-2024)", y = "Annual attacks", x = NULL ) + theme( panel.grid.major.x = element_blank() ) ### |- Panel B: Per Capita Rate ---- pb <- terrorism_yearly |> filter(year <= 2024) |> ggplot(aes(x = year, y = attacks_per_million)) + geom_hline( yintercept = 0.082, linetype = "dashed", color = "grey60", linewidth = 0.4 ) + geom_line(linewidth = 1.2, color = colors$palette$per_capita, alpha = 0.9) + geom_point(size = 2.2, color = colors$palette$per_capita) + annotate( "segment", x = 2010, xend = 2010, y = 0, yend = 0.155, linetype = "dotted", color = "grey70", linewidth = 0.35 ) + annotate( "text", x = 2010, y = 0.158, label = "Census 2010\nbase year", size = 2.4, color = "grey60", hjust = 0.5, lineheight = 0.9, family = fonts$subtitle ) + annotate( "text", x = 2001, y = 0.093, label = "Mean: 0.082", size = 2.8, color = "grey50", hjust = 0, family = fonts$subtitle ) + scale_x_continuous(breaks = seq(1995, 2025, 5)) + scale_y_continuous( breaks = seq(0, 0.16, 0.04), limits = c(0, 0.16) ) + labs( title = "B. Per Capita Rate", subtitle = "Population-adjusted: 26% increase (1994-2001 to 2020-2024)", y = "Attacks per million people", x = NULL ) + theme( panel.grid.major.x = element_blank() ) ### |- Panel C: Attacks by Ideology (Top 3, 3-year avg) ---- data_smoothed <- data_1994_2024 |> filter(ideology %in% c("Right", "Left", "Jihadist")) |> arrange(ideology, year) |> group_by(ideology) |> mutate(attacks_smooth = rollmean(attacks, k = 3, fill = NA, align = "center")) |> ungroup() label_data <- data_smoothed |> filter(!is.na(attacks_smooth)) |> group_by(ideology) |> filter(year == max(year)) |> ungroup() |> mutate( label = case_when( ideology == "Right" ~ "Right-wing", ideology == "Left" ~ "Left-wing", ideology == "Jihadist" ~ "Jihadist" ) ) pc <- data_smoothed |> ggplot(aes(x = year, y = attacks_smooth, color = ideology)) + geom_line(linewidth = 1.2, alpha = 0.95) + geom_vline( xintercept = 2014, linetype = "dotted", alpha = 0.4, color = "grey40" ) + geom_text_repel( data = label_data, aes(label = label), hjust = 0, nudge_x = 1.2, direction = "y", segment.size = 0.3, segment.color = "grey60", size = 3, fontface = "bold", family = fonts$subtitle, show.legend = FALSE, min.segment.length = 0 ) + annotate( "text", x = 2014, y = 32, label = "2014", size = 2.5, color = "grey40", family = fonts$subtitle ) + scale_x_continuous(breaks = seq(1995, 2025, 5)) + scale_y_continuous(breaks = seq(0, 35, 10), limits = c(0, 35)) + scale_color_manual( values = c( "Right" = colors$palette$Right, "Left" = colors$palette$Left, "Jihadist" = colors$palette$Jihadist ), labels = c("Right-wing", "Left-wing", "Jihadist") ) + labs( title = "C. Breakdown by Ideology", subtitle = "Right-wing attacks increased notably after 2014 (3-year rolling average)", y = "Annual attacks (3-year avg)", x = "Year" ) + theme( legend.position = "none", panel.grid.major.x = element_blank() ) ### |- Panel D: Right-Wing Share of Total (3-year avg) ---- pd <- data_1994_2024 |> group_by(year) |> mutate( year_total = sum(attacks), pct = attacks / year_total * 100 ) |> ungroup() |> filter(ideology == "Right") |> arrange(year) |> mutate(pct_smooth = rollmean(pct, k = 3, fill = NA, align = "center")) |> ggplot(aes(x = year, y = pct_smooth)) + annotate( "rect", xmin = 1994, xmax = 2024, ymin = 50, ymax = 100, fill = "#FFF7EB", alpha = 0.7 ) + geom_hline( yintercept = 50, linetype = "dashed", color = "grey50", linewidth = 0.4 ) + geom_hline( yintercept = 75, linetype = "dotted", color = "grey60", linewidth = 0.35 ) + geom_line( linewidth = 1.2, color = colors$palette$Right, alpha = 0.95 ) + annotate( "text", x = 1995, y = 52, label = "Majority (50%) threshold", size = 2.5, color = "grey50", hjust = 0, family = fonts$subtitle ) + annotate( "text", x = 1995, y = 77, label = "Supermajority (75%) threshold", size = 2.5, color = "grey50", hjust = 0, family = fonts$subtitle ) + scale_x_continuous(breaks = seq(1995, 2025, 5)) + scale_y_continuous( breaks = seq(0, 100, 25), limits = c(0, 100), labels = label_percent(scale = 1) ) + labs( title = "D. Right-Wing Share of Total", subtitle = "Typically 60–90%, with a dip to ~50% in the late 2000s (3-year rolling average)", y = "Right-wing % of annual attacks", x = "Year" ) + theme( legend.position = "none", panel.grid.major.x = element_blank() ) ### |- combined plot ---- combined_plots <- (pa | pb) / (pc | pd) + plot_layout(heights = c(1, 1.05)) combined_plots <- combined_plots + plot_annotation( title = title_text, subtitle = subtitle_text, caption = caption_text, theme = theme( plot.title = element_text( size = rel(2.3), family = fonts$title, face = "bold", color = colors$title, lineheight = 1.1, margin = margin(t = 5, b = 5) ), plot.subtitle = element_markdown( size = rel(1.2), family = fonts$subtitle, color = alpha(colors$subtitle, 0.9), lineheight = 1.2, margin = margin(t = 5, b = 15) ), plot.caption = element_markdown( size = rel(0.65), family = fonts$caption, color = colors$caption, hjust = 0, lineheight = 1.2, margin = margin(t = 10) ) ) ) ``` #### 7. Save ```{r} #| label: save #| warning: false ### |- plot image ---- save_plot_patchwork( plot = combined_plots, type = "makeovermonday", year = current_year, week = current_week, width = 16, height = 11 ) ``` #### 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 `r create_link(project_file, repo_file)`. For the full repository, `r create_link("click here", repo_main)`. ::: #### 10. References ::: {.callout-tip collapse="true"} ##### Expand for References **Data Sources:** 1. **Terrorism Data:** - Makeover Monday `r current_year` Week `r current_week`: `r create_link("Terrorism and Political Violence in the United States", data_main)` - Original dataset: `terrorism_by_ideology_1994-2025.csv` 2. **Population Data:** - US Census Bureau, Population Estimates Program (PEP) - Retrieved via tidycensus R package - Intercensal estimates (1994-2009): `r create_link("2000-2010 Intercensal Estimates", "https://www2.census.gov/programs-surveys/popest/tables/2000-2010/intercensal/national/us-est00int-01.csv")` - Vintage 2019 estimates (2010-2019): API access via tidycensus - Vintage 2023 estimates (2020-2024): API access via tidycensus **Data Preprocessing:** - **Script:** `r create_link("data_prep.R", "https://github.com/poncest/MakeoverMonday/blob/master/2025/Week_45/data_prep.R")` - Integrates terrorism incidents with US Census population data - Calculates per capita rates (attacks per million population) - Computes 3-year rolling averages for trend smoothing - Generates period classifications and summary statistics - See script comments for full methodology **Citations:** 1. **Terrorism Data:** - Center for Strategic and International Studies (CSIS). (2024). *Terrorism and Political Violence in the United States: What the Data Tells Us*. CSIS Warfare, Irregular Threats, and Terrorism Program. Retrieved from https://www.csis.org/programs/transnational-threats-project/terrorism-and-political-violence 2. **Population Data:** - US Census Bureau. (Various years). *Population Estimates Program (PEP)*. Retrieved via tidycensus R package. https://www.census.gov/programs-surveys/popest.html - US Census Bureau. (2012). *Intercensal Estimates of the Resident Population for the United States: April 1, 2000 to July 1, 2010*. https://www.census.gov/data/tables/time-series/demo/popest/intercensal-2000-2010-national.html **Methodology Notes:** - 2025 data excluded from analysis (partial year) - Per capita rates calculated as attacks per million population - 3-year rolling averages applied to reduce year-to-year volatility - Census 2010 base year may cause slight discontinuity in time series **Reproducibility:** All analysis code available in the `r create_link("project repository", "https://github.com/poncest/MakeoverMonday/tree/master/2025/Week_45")`: - Data preprocessing: `data_prep.R` - Visualization code: `mm_2025_45_final.R` - Publication document: `mm_2025_45.qmd` ::: #### 11. Custom Functions Documentation ::: {.callout-note collapse="true"} ##### 📦 Custom Helper Functions This analysis uses custom functions from my personal module library for efficiency and consistency across projects. **Functions Used:** - **`fonts.R`**: `setup_fonts()`, `get_font_families()` - Font management with showtext - **`social_icons.R`**: `create_social_caption()` - Generates formatted social media captions - **`image_utils.R`**: `save_plot()` - Consistent plot saving with naming conventions - **`base_theme.R`**: `create_base_theme()`, `extend_weekly_theme()`, `get_theme_colors()` - Custom ggplot2 themes **Why custom functions?**\ These utilities standardize theming, fonts, and output across all my data visualizations. The core analysis (data tidying and visualization logic) uses only standard tidyverse packages. **Source Code:**\ View all custom functions → [GitHub: R/utils](https://github.com/poncest/personal-website/tree/master/R) :::