Billboard Hot 100 Number Ones

music
Exploring frequency of explicit billboard hot 100 hits over time
Author

Cole Baril

Published

August 26, 2025

Load Packages

require(pacman)
p_load(tidytuesdayR, magick, trashpanda, tidyverse, janitor)

Load Data

tuesdata <- tidytuesdayR::tt_load('2025-08-26')
billboard <- tuesdata$billboard
topics <- tuesdata$topics

Explicit Songs

plot <- billboard |> 
  mutate(year = year(date)) |> 
  filter(year != "2025") |> 
  reframe(prop_explicit = mean(explicit),
          .by = year) |> 
  ggplot(aes(x = year, y = prop_explicit)) +
  geom_line(linewidth = 1) +
  geom_point(size = 3) +
  scale_y_continuous(labels = scales::label_percent()) +
  labs(y = "Proportion Explicit",
       x = "Year",
       title = "Proportion of Explicit Billboard Hot 100 Number One Songs") +
  theme_cole(remove_grid = TRUE) +
  add_caption_cwb()

# Save and display images
current_dir <- dirname(knitr::current_input())
plot_name <- "explicit_billboard_songs.png"

ggsave(plot = plot, 
       dpi = "screen",
       width = 17,
       height = 12,
       device = ragg::agg_png,
       filename = file.path(current_dir, plot_name))


# Read the big plot
img <- image_read(file.path(current_dir, plot_name)) 


# Force 16:9 aspect ratio with minimal padding
# Target size: 1200x675 px (16:9)
img_card <- image_scale(img, "1200x675")           # scale to fit inside 16:9
img_card <- image_extent(
  img_card,
  geometry = "1200x675",
  gravity = "center"
)

# Save as card preview
image_write(img_card, path = file.path(current_dir, "preview.png"))

knitr::include_graphics(
  file.path(current_dir, plot_name)
)

References

trashpanda::cite_packages(format = "rmd")

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