Horror Films Are Lottery Tickets

USD return on investment (ROI) distribution for Horror and Crime films (1980–2020, US box office). Horror shows a much wider spread of outcomes, with fewer losses overall but a handful of extreme wins.

A half-eye distribution chart comparing return on investment (ROI) for Horror and Crime films (1980–2020). Plotted on a log scale, the chart reveals Horror as Hollywood’s highest-variance genre — most films lose money, but a few deliver extraordinary returns. Crime films cluster near break-even, making them the most predictable bet in the dataset.

Figure 1: Half-eye distribution chart comparing return on investment (ROI) for Horror and Crime films (1980–2020, US box office, log scale). Horror films (n=254) show a wide, right-skewed distribution with a median ROI of 2x and a long tail of extreme outliers — including Paranormal Activity (2007), which returned 12,889x on a $15,000 budget. Crime films (n=400) cluster tightly near break-even with a median ROI of 0.3x and few breakout returns. The chart shows that Horror is Hollywood’s highest-variance genre, while Crime is its least volatile.

Steps to Create this Graphic

1. Load Packages & Setup

```{r}
#| label: load
#| warning: false
#| message: false      
#| results: "hide"     

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
pacman::p_load(
  tidyverse, ggtext, showtext,  
  janitor, scales, glue, ggdist
  )
})

### |- figure size ----
camcorder::gg_record(
  dir    = here::here("temp_plots"),
  device = "png",
  width  = 10,
  height = 7,
  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

movies_raw<- read_csv(
  here::here("data/30DayChartChallenge/2026/movies.csv"),
  show_col_types = FALSE
  ) |>
  clean_names() 
```

3. Examine the Data

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

glimpse(movies_raw)
```

4. Tidy Data

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

### |- filter and compute ROI ----
movies_clean <- movies_raw |>
  filter(
    genre %in% c("Horror", "Crime"),
    budget > 0,
    gross > 0
  ) |>
  mutate(
    roi       = (gross - budget) / budget,
    roi_log1p = log1p(roi),
    genre     = factor(genre, levels = c("Crime", "Horror"))
  )

### |- summary stats ----
summary_stats <- movies_clean |>
  group_by(genre) |>
  summarise(
    n          = n(),
    median_roi = median(roi),
    p25        = quantile(roi, 0.25),
    p75        = quantile(roi, 0.75),
    pct_loss   = mean(roi < 0) * 100,
    .groups    = "drop"
  )

### |- axis labels with counts ----
genre_labels <- summary_stats |>
  mutate(
    genre = as.character(genre),
    label = glue("{genre}\n(n = {scales::comma(n)})")
  ) |>
  select(genre, label) |>
  tibble::deframe()

### |- top Horror outlier ----
top_horror <- movies_clean |>
  filter(genre == "Horror") |>
  slice_max(roi, n = 1, with_ties = FALSE)

### |- pre-compute positions ----
break_even_log  <- log1p(0)
median_hor_log  <- log1p(summary_stats$median_roi[summary_stats$genre == "Horror"])
median_cri_log  <- log1p(summary_stats$median_roi[summary_stats$genre == "Crime"])
pct_loss_horror <- summary_stats$pct_loss[summary_stats$genre == "Horror"]
pct_loss_crime  <- summary_stats$pct_loss[summary_stats$genre == "Crime"]

### |- x-axis breaks and labels ----
roi_breaks  <- c(-0.75, 0, 1, 5, 10, 50, 100)
log_breaks  <- log1p(roi_breaks)
axis_labels <- c("−75%", "0x\n(break-even)", "1x", "5x", "10x", "50x", "100x")
```

5. Visualization Parameters

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

### |- plot aesthetics ----
colors <- get_theme_colors(
  palette = list(
    horror     = "#8B1A2A",
    crime      = "#6B84A6",   
    median_pt  = "#1A1A1A",
    annotation = "#444444",
    bg         = "#FAFAF8" 
  )
)

### |- titles and caption ----
title_text <- "Horror Films Are Lottery Tickets"

subtitle_text <- glue(
  "USD return on investment (ROI) distribution for ",
  "<span style='color:{colors$palette$horror};font-weight:700'>Horror</span> and ",
  "<span style='color:{colors$palette$crime};font-weight:700'>Crime</span> films ",
  "(1980–2020, US box office).<br>",
  "Horror shows a much wider spread of outcomes, with fewer losses overall but a handful of extreme wins."
)

caption_text <- create_dcc_caption(
  dcc_year    = 2026,
  dcc_day     = 10,
  source_text = "Kaggle — Movie Industry dataset (Daniel Grijalva)"
)

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

font_body    <- fonts$text    %||% ""
font_title   <- fonts$title   %||% ""
font_caption <- fonts$caption %||% ""

### |- theme ----
base_theme <- create_base_theme(colors)

weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    plot.background = element_rect(fill = colors$palette$bg, color = NA),
    panel.background = element_rect(fill = colors$palette$bg, color = NA),
    panel.grid.major.x = element_line(color = "gray90", linewidth = 0.3),
    panel.grid.major.y = element_blank(),
    panel.grid.minor = element_blank(),
    axis.title.x = element_text(
      size = 9, color = colors$palette$annotation, margin = margin(t = 8)
    ),
    axis.title.y = element_blank(),
    axis.text.y = element_text(
      size = 12, face = "bold",
      color = c(colors$palette$crime, colors$palette$horror)
    ),
    axis.text.x = element_text(
      size = 8.5,
      color = colors$palette$annotation,
      lineheight = 1.2
    ),
    axis.ticks = element_blank(),
    plot.title = element_text(
      family = fonts$title,
      face   = "bold",
      size   = 22,
      color  = "#1A1A1A",
      margin = margin(b = 6)
    ),
    plot.subtitle = element_markdown(
      family     = fonts$text,
      size       = 10,
      color      = "#444444",
      lineheight = 1.45,
      margin     = margin(b = 20)
    ),
    plot.caption = element_markdown(
      family = fonts$text,
      size   = 7.5,
      color  = "gray50",
      hjust  = 0,
      margin = margin(t = 14)
    ),
    plot.margin = margin(20, 24, 14, 20)
  )
)

theme_set(weekly_theme)
```

6. Plot

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

### |- main plot ----
p <- movies_clean |>
  ggplot(aes(x = roi_log1p, y = genre, fill = genre, color = genre)) +
  geom_vline(
    xintercept = break_even_log,
    color = "gray50",
    linewidth = 0.5,
    linetype = "dashed"
  ) +
  stat_halfeye(
    .width = c(0.50, 0.90),
    point_size = 3.5,
    point_color = colors$palette$median_pt,
    interval_color = colors$palette$median_pt,
    slab_alpha = 0.3,
    normalize = "groups",
    scale = 0.72,
    adjust = 1.2
  ) +
  annotate(
    "text",
    x = break_even_log + 0.05,
    y = 0.58,
    label = "0x ROI\n(break-even)",
    size = 2.9,
    color = "gray40",
    hjust = 0,
    fontface = "bold",
    lineheight = 1.2,
    family = fonts$text
  ) +
  annotate(
    "text",
    x = break_even_log - 0.15,
    y = 2.38,
    label = glue("{round(pct_loss_horror, 0)}% of Horror\nfilms lose money"),
    size = 2.8,
    color = colors$palette$horror,
    hjust = 1,
    lineheight = 1.2,
    family = fonts$text
  ) +
  annotate(
    "text",
    x = break_even_log - 0.15,
    y = 1.38,
    label = glue("{round(pct_loss_crime, 0)}% of Crime\nfilms lose money"),
    size = 2.8,
    color = colors$palette$crime,
    hjust = 1,
    lineheight = 1.2,
    family = fonts$text
  ) +
  annotate(
    "text",
    x = median_hor_log + 0.08,
    y = 2.44,
    label = glue("Median: {round(summary_stats$median_roi[summary_stats$genre == 'Horror'], 1)}x"),
    size = 2.9,
    color = colors$palette$horror,
    hjust = 0,
    family = fonts$text
  ) +
  annotate(
    "text",
    x = median_cri_log + 0.08,
    y = 1.44,
    label = glue("Median: {round(summary_stats$median_roi[summary_stats$genre == 'Crime'], 1)}x"),
    size = 2.9,
    color = colors$palette$crime,
    hjust = 0,
    family = fonts$text
  ) +
  annotate(
    "text",
    x = log1p(20),
    y = 2.20,
    label = "A handful of films\ndeliver extreme returns",
    size = 2.8,
    color = colors$palette$horror,
    hjust = 0.5,
    lineheight = 1.2,
    family = fonts$text
  ) +
  annotate(
    "text",
    x = log1p(top_horror$roi) - 0.35,
    y = 2.18,
    label = glue("{top_horror$name} ({top_horror$year})\n$15k budget → 12,889x ROI"),
    size = 2.6,
    color = colors$palette$horror,
    hjust = 1,
    lineheight = 1.2,
    family = fonts$text
  ) +
  annotate(
    "point",
    x = log1p(top_horror$roi),
    y = 2.08,
    size = 2,
    color = colors$palette$horror,
    shape = 21,
    fill = "white"
  ) +
  scale_fill_manual(values = c(
    "Horror" = colors$palette$horror,
    "Crime"  = colors$palette$crime
  )) +
  scale_color_manual(values = c(
    "Horror" = colors$palette$horror,
    "Crime"  = colors$palette$crime
  )) +
  scale_x_continuous(
    name = "Return on Investment (USD)  —  log scale  [ (Gross − Budget) / Budget ]",
    breaks = log_breaks,
    labels = axis_labels
  ) +
  scale_y_discrete(labels = genre_labels) +
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text
  )
```

7. Save

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

### |-  plot image ----  
save_plot(
  p, 
  type = "30daychartchallenge", 
  year = 2026, 
  day = 10, 
  width = 10, 
  height = 7
  )
```

8. Session Info

TipExpand for Session Info

R version 4.3.1 (2023-06-16 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
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 utils     datasets  methods   base     

other attached packages:
 [1] here_1.0.2      ggdist_3.3.3    glue_1.8.0      scales_1.4.0   
 [5] janitor_2.2.1   showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9 
 [9] ggtext_0.1.2    lubridate_1.9.5 forcats_1.0.1   stringr_1.6.0  
[13] dplyr_1.2.0     purrr_1.2.1     readr_2.2.0     tidyr_1.3.2    
[17] tibble_3.2.1    ggplot2_4.0.2   tidyverse_2.0.0

loaded via a namespace (and not attached):
 [1] gtable_0.3.6         xfun_0.56            htmlwidgets_1.6.4   
 [4] tzdb_0.5.0           vctrs_0.7.1          tools_4.3.1         
 [7] generics_0.1.4       curl_7.0.0           parallel_4.3.1      
[10] gifski_1.32.0-2      pacman_0.5.1         pkgconfig_2.0.3     
[13] RColorBrewer_1.1-3   S7_0.2.0             distributional_0.7.0
[16] lifecycle_1.0.5      compiler_4.3.1       farver_2.1.2        
[19] textshaping_1.0.4    codetools_0.2-19     snakecase_0.11.1    
[22] litedown_0.9         htmltools_0.5.9      yaml_2.3.12         
[25] pillar_1.11.1        crayon_1.5.3         camcorder_0.1.0     
[28] magick_2.8.6         commonmark_2.0.0     tidyselect_1.2.1    
[31] digest_0.6.39        stringi_1.8.7        labeling_0.4.3      
[34] rsvg_2.6.2           rprojroot_2.1.1      fastmap_1.2.0       
[37] grid_4.3.1           cli_3.6.5            magrittr_2.0.3      
[40] withr_3.0.2          bit64_4.6.0-1        timechange_0.4.0    
[43] rmarkdown_2.30       bit_4.6.0            otel_0.2.0          
[46] ragg_1.5.0           hms_1.1.4            evaluate_1.0.5      
[49] knitr_1.51           markdown_2.0         rlang_1.1.7         
[52] gridtext_0.1.6       Rcpp_1.1.1           xml2_1.5.2          
[55] svglite_2.1.3        rstudioapi_0.18.0    vroom_1.7.0         
[58] jsonlite_2.0.0       R6_2.6.1             systemfonts_1.3.2   

9. GitHub Repository

TipExpand for GitHub Repo

The complete code for this analysis is available in 30dcc_2026_10.qmd.

For the full repository, click here.

10. References

TipExpand for References

1. Data Sources:
   • Grijalva, D. (2021). Movie Industry [Dataset]. Kaggle. Retrieved April 10, 2026 from https://www.kaggle.com/datasets/danielgrijalvas/movies

11. Custom Functions Documentation

Note📦 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