2026 Winter Olympics

sports
olympics
2026 Winter Olympics Schedules
Published

February 10, 2026

Load Packages

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

Load Data

tuesdata <- tidytuesdayR::tt_load('2026-02-10')
schedule <- tuesdata$schedule

Data Cleaning

schedule_intervals <- schedule |> 
  mutate(length = end_datetime_local - start_datetime_local)

  
events <- schedule |> 
  select(-date, -day_of_week) |> 
  distinct(.keep_all = TRUE)

time_grid <- tibble(
  time = seq(
    from = floor_date(min(events$start_datetime_local), "hour"),
    to   = ceiling_date(max(events$end_datetime_local), "hour"),
    by   = "60 min"
  )
)

density <- events |>
  mutate(key = 1) |>
  inner_join(time_grid |> mutate(key = 1), by = "key", relationship = "many-to-many") |>
  filter(start_datetime_local <= time, end_datetime_local > time) |>
  count(time, discipline_name, name = "n_events") |> 
  mutate(total = sum(n_events), .by = discipline_name) |> 
  mutate(discipline_name = fct_reorder(discipline_name, total))

sidex <- density |> 
  summarise(total_events = sum(n_events), .by = time)

sidey <- events |> 
  summarise(total_events = n(), .by = discipline_name)

Plot

plot <- ggplot(density, aes(x = time, y = discipline_name)) +
  geom_point(aes(size = n_events, fill = discipline_name), alpha = 0.85, 
             position = position_jitter(),
             pch = 21, colour = "black") +
    geom_xsideline(
    data = sidex,
    aes(y = total_events, x = time),
    inherit.aes = FALSE,
    linewidth = 0.5,
    colour = "cadetblue4",
    alpha = 0.9) +
  geom_ysidecol(
    data = sidey,
    aes(x = total_events, y = discipline_name),
    inherit.aes = FALSE,
    fill = "cadetblue4",
    alpha = 0.9
    ) +
  scale_size_continuous(range = c(2, 5), guide = guide_legend(nrow = 1)) +
  scale_xsidey_continuous(breaks = c(2, 5, 8)) +
  scale_fill_viridis_d(option = "mako", guide = "none") +
  scale_x_datetime(
  expand = c(0.005, 0),
  date_breaks = "2 days",
  date_labels = "%b %d") +
  labs(
    title = "There are usually more curling events happening concurrently at any given time than any other Olympic sport",
    subtitle = "Event counts represented by dot size and binned by hour.Top ridge line represents the number of\nevents happening in each hour. Side ridge bar plot represents the total number of events for each sport discipline.",
    x = NULL,
    y = "Sport Discipline",
    size = "Concurrent Events") +
   theme_cole(remove_grid = TRUE) +
   theme(legend.position = "top") +
   add_caption_cwb(type = "plot") 

current_dir <- dirname(knitr::current_input())

ggsave(plot = plot, 
       dpi = "screen",
       width = 18,
       height = 13,
       device = ragg::agg_png,
       filename = file.path(current_dir, "concurrent_olympic_events.png"))


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

# 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, "concurrent_olympic_events.png")
)

References

cwb::cite_packages(format = "rmd")

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