April 20th Fatal Crashes Compared to Yearly Average (1992-2016)

Difference between April 20th fatalities and the yearly average for each year

TidyTuesday

2025

Analyzing 25 years of traffic fatality data (1992-2016) to examine the alleged connection between April 20th (‘4/20’) and increased fatal car crashes.

Author

Steven Ponce

Published

April 20, 2025

Figure 1: Bar chart showing the difference between April 20th fatal crashes and yearly averages from 1992 to 2016. Orange bars represent years above average (9 years total), with 2007 showing the highest peak at +57. Blue bars represent years below average (16 years total), with 2004’s lowest point at -60. The visualization suggests no consistent pattern of increased crashes on April 20th compared to yearly averages.

Steps to Create this Graphic

1. Load Packages & Setup

Show code

## 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
    skimr,          # Compact and Flexible Summaries of Data
    scales,         # Scale Functions for Visualization
    glue,           # Interpreted String Literals
    here,           # A Simpler Way to Find Your Files
    camcorder       # Record Your Plot History 
    )
})

### |- figure size ----
gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  =  8,
    height =  8,
    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

tt <- tidytuesdayR::tt_load(2025, week = 16) 

daily_accidents <- tt$daily_accidents |> clean_names()
# daily_accidents_420 <- tt$daily_accidents_420 |> clean_names()

tidytuesdayR::readme(tt)
rm(tt)

3. Examine the Data

Show code

glimpse(daily_accidents)
skim(daily_accidents)

4. Tidy Data

Show code

### |-  tidy data ----
# Add date components to daily accidents data  
daily_accidents_tidy <- daily_accidents |>
    mutate(
        year = year(date),
        month = month(date),
        day = day(date),
        is_april_20 = (month == 4 & day == 20)
    )

# Calculate yearly averages
yearly_averages <- daily_accidents_tidy |>
    group_by(year) |>
    summarize(
        yearly_avg = mean(fatalities_count),
        .groups = "drop"
    )

# Extract April 20th data for each year
april_20_yearly <- daily_accidents_tidy |>
    filter(is_april_20) |>
    select(year, fatalities_count) |>
    arrange(year) |>
    # Join with yearly averages
    left_join(yearly_averages, by = "year") |>
    # Calculate difference from yearly average
    mutate(
        diff_from_avg = fatalities_count - yearly_avg,
        above_avg = diff_from_avg > 0
    )

# Find key statistics for annotations
max_year_data <- april_20_yearly |> 
    filter(diff_from_avg == max(diff_from_avg))
max_year <- max_year_data$year
max_diff <- max_year_data$diff_from_avg

min_year_data <- april_20_yearly |> 
    filter(diff_from_avg == min(diff_from_avg))
min_year <- min_year_data$year
min_diff <- min_year_data$diff_from_avg

# Count years above and below average
n_above <- sum(april_20_yearly$above_avg)
n_below <- nrow(april_20_yearly) - n_above

5. Visualization Parameters

Show code

### |-  plot aesthetics ----
colors <- get_theme_colors(
    palette = c(
        "FALSE" = "#3A6CA4", "TRUE" = "#F05E23"
    )
)

### |-  titles and caption ----
title_text <- str_glue("April 20th Fatal Crashes Compared to Yearly Average (1992-2016)")
subtitle_text <- str_glue("Difference between April 20th fatalities and the yearly average for each year")

# Create caption
caption_text <- create_social_caption(
    tt_year = 2025,
    tt_week = 16,
    source_text =  "420 raw-data via OSF" 
)

### |-  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(face = "bold", family = fonts$title, size = rel(1.14), margin = margin(b = 10)),
        plot.subtitle = element_text(family = fonts$subtitle, color = colors$text, size = rel(0.78), margin = margin(b = 20)),
        
        # Axis elements
        axis.title = element_text(color = colors$text, face = "bold", size = rel(0.8)),
        axis.text = element_text(color = colors$text, size = rel(0.7)),
        
        # Grid elements
        panel.grid.minor = element_line(color = "gray50", linewidth = 0.05),
        panel.grid.major = element_line(color = "gray50", linewidth = 0.02),
        
        # Legend elements
        legend.position = "plot",
        legend.direction = "horizontal",
        legend.title = element_text(family = fonts$text, size = rel(0.8), face = "bold"),
        legend.text = element_text(family = fonts$text, size = rel(0.7)),
        
        # two-row legend
        legend.box.spacing = unit(0.4, "cm"),
        legend.key.width = unit(1.5, "cm"),
        legend.spacing.x = unit(0.2, "cm"),
 
        legend.box = "horizontal",
        legend.box.just = "left",
        
        # Plot margins 
        plot.margin = margin(t = 20, r = 20, b = 20, l = 20),
    )
)

# Set theme
theme_set(weekly_theme)

6. Plot

Show code

### |-  Plot  ----
p <- ggplot(april_20_yearly, aes(x = year, y = diff_from_avg, fill = above_avg)) +
    # Geoms
    geom_col(width = 0.7) +
    geom_hline(yintercept = 0, linetype = "dashed", color = "gray50", linewidth = 0.5) +
    # Annotate
    annotate(
        "text", x = max_year, y = max_diff + 7, 
        label = paste0("Peak: ", max_year, " (+", round(max_diff), ")"),
        color = colors$palette[2], fontface = "bold", size = 3.5
        ) +
    annotate(
        "text", x = min_year, y = min_diff - 7, 
        label = paste0("Low: ", min_year, " (", round(min_diff), ")"),
        color = colors$palette[1], fontface = "bold", size = 3.5
        ) +
    annotate(
        "text", x = max(april_20_yearly$year) - 3, y = 43, 
        label = paste0(n_above, " years above avg"),
        color = colors$palette[2], size = 3.5, hjust = 1
        ) +
    annotate(
        "text", x = max(april_20_yearly$year) - 3, y = -43, 
        label = paste0(n_below, " years below avg"),
        color = colors$palette[1], size = 3.5, hjust = 1
        ) +
    # Scales
    scale_fill_manual(
        values = colors$palette,
        labels = c("Below Average", "Above Average")
    ) +
    scale_x_continuous(
        breaks = seq(
            min(april_20_yearly$year),
            max(april_20_yearly$year), 
            by = 5)
        ) +
    # Labs
    labs(
        title = title_text,
        subtitle = subtitle_text,
        caption = caption_text,
        x = NULL,
        y = "Difference from Yearly Average",
    ) +
    # Theme
    theme(
        plot.title = element_text(
            size = rel(1.4),
            family = fonts$title,
            face = "bold",
            color = colors$title,
            lineheight = 1.1,
            margin = margin(t = 5, b = 5)
        ),
        plot.subtitle = element_text(
            size = rel(0.85),
            family = fonts$subtitle,
            color = alpha(colors$subtitle, 0.9),
            lineheight = 1.2,
            margin = margin(t = 5, b = 10)
        ),
        plot.caption = element_markdown(
            size = rel(0.65),
            family = fonts$caption,
            color = colors$caption,
            hjust = 0.5,
            margin = margin(t = 10)
        )
    )

7. Save

Show code

### |-  plot image ----  
save_plot(
  plot = p, 
  type = "tidytuesday", 
  year = 2025, 
  week = 16, 
  width = 8,
  height = 8
)

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 22631)

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] camcorder_0.1.0 here_1.0.1      glue_1.8.0      scales_1.3.0   
 [5] skimr_2.1.5     janitor_2.2.0   showtext_0.9-7  showtextdb_3.0 
 [9] sysfonts_0.8.9  ggtext_0.1.2    lubridate_1.9.3 forcats_1.0.0  
[13] stringr_1.5.1   dplyr_1.1.4     purrr_1.0.2     readr_2.1.5    
[17] tidyr_1.3.1     tibble_3.2.1    ggplot2_3.5.1   tidyverse_2.0.0
[21] pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       httr2_1.0.6        xfun_0.49          htmlwidgets_1.6.4 
 [5] gh_1.4.1           tzdb_0.5.0         vctrs_0.6.5        tools_4.4.0       
 [9] generics_0.1.3     parallel_4.4.0     curl_6.0.0         gifski_1.32.0-1   
[13] fansi_1.0.6        pkgconfig_2.0.3    lifecycle_1.0.4    farver_2.1.2      
[17] compiler_4.4.0     textshaping_0.4.0  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       magick_2.8.5       commonmark_1.9.2  
[29] tidyselect_1.2.1   digest_0.6.37      stringi_1.8.4      labeling_0.4.3    
[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        rappdirs_0.3.3     bit64_4.5.2       
[45] timechange_0.3.0   rmarkdown_2.29     tidytuesdayR_1.1.2 gitcreds_0.1.2    
[49] bit_4.5.0          ragg_1.3.3         hms_1.1.3          evaluate_1.0.1    
[53] knitr_1.49         markdown_1.13      rlang_1.1.6        gridtext_0.1.5    
[57] Rcpp_1.0.13-1      xml2_1.3.6         renv_1.0.3         vroom_1.6.5       
[61] svglite_2.1.3      rstudioapi_0.17.1  jsonlite_1.8.9     R6_2.5.1          
[65] systemfonts_1.1.0

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in tt_2025_16.qmd.

For the full repository, click here.

10. References

Expand for References

Data Sources:
- TidyTuesday 2025 Week 16: Fatal Car Crashes on 4/20

--- title: "April 20th Fatal Crashes Compared to Yearly Average (1992-2016)" subtitle: "Difference between April 20th fatalities and the yearly average for each year" description: "Analyzing 25 years of traffic fatality data (1992-2016) to examine the alleged connection between April 20th ('4/20') and increased fatal car crashes." author: "Steven Ponce" date: "2025-04-20" categories: ["TidyTuesday", "2025"] tags: [ "fatal crashes", "road safety", "4/20", "traffic fatalities", "data myths", "statistical analysis", "time series", "seasonal patterns", "vehicle accidents", "public health", "holiday effects", "temporal analysis" ] image: "thumbnails/tt_2025_16.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 # filters: # - social-share # share: # permalink: "https://stevenponce.netlify.app/data_visualizations/TidyTuesday/2025/tt_2025_16.html" # description: "My visualization of 25 years of traffic data challenges the '4/20 effect' on fatal crashes. Data shows April 20th had fewer crashes than yearly average in 16 of 25 years studied. #DataScience #TidyTuesday" # # twitter: true # linkedin: true # email: true # facebook: false # reddit: false # stumble: false # tumblr: false # mastodon: true # bsky: true --- ![Bar chart showing the difference between April 20th fatal crashes and yearly averages from 1992 to 2016. Orange bars represent years above average (9 years total), with 2007 showing the highest peak at +57. Blue bars represent years below average (16 years total), with 2004's lowest point at -60. The visualization suggests no consistent pattern of increased crashes on April 20th compared to yearly averages.](tt_2025_16.png){#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 skimr, # Compact and Flexible Summaries of Data scales, # Scale Functions for Visualization glue, # Interpreted String Literals here, # A Simpler Way to Find Your Files camcorder # Record Your Plot History ) }) ### |- figure size ---- gg_record( dir = here::here("temp_plots"), device = "png", width = 8, height = 8, 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 tt <- tidytuesdayR::tt_load(2025, week = 16) daily_accidents <- tt$daily_accidents |> clean_names() # daily_accidents_420 <- tt$daily_accidents_420 |> clean_names() tidytuesdayR::readme(tt) rm(tt) ``` #### 3. Examine the Data ```{r} #| label: examine #| include: true #| eval: true #| results: 'hide' #| warning: false glimpse(daily_accidents) skim(daily_accidents) ``` #### 4. Tidy Data ```{r} #| label: tidy #| warning: false ### |- tidy data ---- # Add date components to daily accidents data daily_accidents_tidy <- daily_accidents |> mutate( year = year(date), month = month(date), day = day(date), is_april_20 = (month == 4 & day == 20) ) # Calculate yearly averages yearly_averages <- daily_accidents_tidy |> group_by(year) |> summarize( yearly_avg = mean(fatalities_count), .groups = "drop" ) # Extract April 20th data for each year april_20_yearly <- daily_accidents_tidy |> filter(is_april_20) |> select(year, fatalities_count) |> arrange(year) |> # Join with yearly averages left_join(yearly_averages, by = "year") |> # Calculate difference from yearly average mutate( diff_from_avg = fatalities_count - yearly_avg, above_avg = diff_from_avg > 0 ) # Find key statistics for annotations max_year_data <- april_20_yearly |> filter(diff_from_avg == max(diff_from_avg)) max_year <- max_year_data$year max_diff <- max_year_data$diff_from_avg min_year_data <- april_20_yearly |> filter(diff_from_avg == min(diff_from_avg)) min_year <- min_year_data$year min_diff <- min_year_data$diff_from_avg # Count years above and below average n_above <- sum(april_20_yearly$above_avg) n_below <- nrow(april_20_yearly) - n_above ``` #### 5. Visualization Parameters ```{r} #| label: params #| include: true #| warning: false ### |- plot aesthetics ---- colors <- get_theme_colors( palette = c( "FALSE" = "#3A6CA4", "TRUE" = "#F05E23" ) ) ### |- titles and caption ---- title_text <- str_glue("April 20th Fatal Crashes Compared to Yearly Average (1992-2016)") subtitle_text <- str_glue("Difference between April 20th fatalities and the yearly average for each year") # Create caption caption_text <- create_social_caption( tt_year = 2025, tt_week = 16, source_text = "420 raw-data via OSF" ) ### |- fonts ---- setup_fonts() fonts <- get_font_families() ### |- plot theme ---- # Start with base theme base_theme <- create_base_theme(colors) # Add weekly-specific theme elements weekly_theme <- extend_weekly_theme( base_theme, theme( # Text styling plot.title = element_text(face = "bold", family = fonts$title, size = rel(1.14), margin = margin(b = 10)), plot.subtitle = element_text(family = fonts$subtitle, color = colors$text, size = rel(0.78), margin = margin(b = 20)), # Axis elements axis.title = element_text(color = colors$text, face = "bold", size = rel(0.8)), axis.text = element_text(color = colors$text, size = rel(0.7)), # Grid elements panel.grid.minor = element_line(color = "gray50", linewidth = 0.05), panel.grid.major = element_line(color = "gray50", linewidth = 0.02), # Legend elements legend.position = "plot", legend.direction = "horizontal", legend.title = element_text(family = fonts$text, size = rel(0.8), face = "bold"), legend.text = element_text(family = fonts$text, size = rel(0.7)), # two-row legend legend.box.spacing = unit(0.4, "cm"), legend.key.width = unit(1.5, "cm"), legend.spacing.x = unit(0.2, "cm"), legend.box = "horizontal", legend.box.just = "left", # Plot margins plot.margin = margin(t = 20, r = 20, b = 20, l = 20), ) ) # Set theme theme_set(weekly_theme) ``` #### 6. Plot ```{r} #| label: plot #| warning: false ### |- Plot ---- p <- ggplot(april_20_yearly, aes(x = year, y = diff_from_avg, fill = above_avg)) + # Geoms geom_col(width = 0.7) + geom_hline(yintercept = 0, linetype = "dashed", color = "gray50", linewidth = 0.5) + # Annotate annotate( "text", x = max_year, y = max_diff + 7, label = paste0("Peak: ", max_year, " (+", round(max_diff), ")"), color = colors$palette[2], fontface = "bold", size = 3.5 ) + annotate( "text", x = min_year, y = min_diff - 7, label = paste0("Low: ", min_year, " (", round(min_diff), ")"), color = colors$palette[1], fontface = "bold", size = 3.5 ) + annotate( "text", x = max(april_20_yearly$year) - 3, y = 43, label = paste0(n_above, " years above avg"), color = colors$palette[2], size = 3.5, hjust = 1 ) + annotate( "text", x = max(april_20_yearly$year) - 3, y = -43, label = paste0(n_below, " years below avg"), color = colors$palette[1], size = 3.5, hjust = 1 ) + # Scales scale_fill_manual( values = colors$palette, labels = c("Below Average", "Above Average") ) + scale_x_continuous( breaks = seq( min(april_20_yearly$year), max(april_20_yearly$year), by = 5) ) + # Labs labs( title = title_text, subtitle = subtitle_text, caption = caption_text, x = NULL, y = "Difference from Yearly Average", ) + # Theme theme( plot.title = element_text( size = rel(1.4), family = fonts$title, face = "bold", color = colors$title, lineheight = 1.1, margin = margin(t = 5, b = 5) ), plot.subtitle = element_text( size = rel(0.85), family = fonts$subtitle, color = alpha(colors$subtitle, 0.9), lineheight = 1.2, margin = margin(t = 5, b = 10) ), plot.caption = element_markdown( size = rel(0.65), family = fonts$caption, color = colors$caption, hjust = 0.5, margin = margin(t = 10) ) ) ``` #### 7. Save ```{r} #| label: save #| warning: false ### |- plot image ---- save_plot( plot = p, type = "tidytuesday", year = 2025, week = 16, width = 8, height = 8 ) ``` #### 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_2025_16.qmd`](https://github.com/poncest/personal-website/blob/master/data_visualizations/TidyTuesday/2025/tt_2025_16.qmd). For the full repository, [click here](https://github.com/poncest/personal-website/). ::: #### 10. References ::: {.callout-tip collapse="true"} ##### Expand for References 1. Data Sources: - TidyTuesday 2025 Week 16: [Fatal Car Crashes on 4/20](https://github.com/rfordatascience/tidytuesday/blob/main/data/2025/2025-04-01) :::