Himalayan Mountaineering

Himalayan mountain climbing success rates
Author

Cole Baril

Published

January 21, 2025

Load Libraries

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

Load Data

tuesdata <- tidytuesdayR::tt_load('2025-01-21')
exped_tidy <- tuesdata$exped_tidy
peaks_tidy <- tuesdata$peaks_tidy

Data Cleaning

attempts <- exped_tidy |> 
  left_join(peaks_tidy, by = "PEAKID") |> 
  clean_names() |> 
  mutate(across(starts_with("success"), ~as.character(.))) |> 
  pivot_longer(cols = matches("(route|success)[1-4]"),
               names_to = c(".value", "attempt"),
               names_pattern = "(route|success)([1-4])") |> 
  select(expid, pkname, year, season_factor, route, attempt, success, heightm) |> 
  drop_na(route)

Expedition Success Rates

exp_success <- attempts |> 
  summarise(exp_success = any(as.logical(success)),
            .by = c("expid", "season_factor")) |> 
  summarise(success_rate = mean(exp_success), 
            n_expeditions = n(),
            .by = season_factor)


ggplot(exp_success, aes(season_factor, success_rate)) +
  geom_point(size = 3) +
  geom_errorbar(aes(
    ymin = success_rate - sqrt(success_rate * (1 - success_rate) / n_expeditions),
    ymax = success_rate + sqrt(success_rate * (1 - success_rate) / n_expeditions)
  ), width = 0.08) +
  geom_text(data = exp_success, 
            mapping = aes(x = season_factor, 
                          y = success_rate, 
                          label = paste0("n = ", n_expeditions)),
            nudge_x = 0.3, size = 7) +
  scale_y_continuous(labels = scales::percent) +
  labs(
    title = "Himalayan Mountain Expedition Success Rates by Season",
    y = "Success rate",
    x = "Season"
  ) +
  theme_cole(remove_grid = TRUE, base_size = 25) +
  add_caption_cwb(type = "plot", include_data_source = TRUE, data_source = "The Himalayan Database")

Does Size Matter?

attempts <- attempts %>%
  mutate(height_bin = cut(
    heightm,
    breaks = c(0, 7000, 8000, Inf),
    labels = c("<7000m", "7000–8000m", "8000m+")
  ))

height_success <- attempts %>%
  summarise(exp_success = any(as.logical(success)),
            .by = c("expid", "season_factor", "height_bin")) %>%
  summarise(success_rate = mean(exp_success),
            n_expeditions = n(),
            .by = c("season_factor", "height_bin"))

plot <- ggplot(height_success, aes(season_factor, success_rate, color = height_bin)) +
  geom_point(size = 3, position = position_dodge(width = 0.4)) +
  geom_errorbar(aes(
    ymin = success_rate - sqrt(success_rate * (1 - success_rate) / n_expeditions),
    ymax = success_rate + sqrt(success_rate * (1 - success_rate) / n_expeditions)
  ), width = 0.1, position = position_dodge(width = 0.4)) +
  scale_y_continuous(labels = scales::percent) +
  scale_colour_viridis_d(end = 0.9) +
  labs(
    title = "Himalayan Expedition Success by Season & Mountain Height",
    y = "Success rate",
    x = "Season",
    color = "Height bin"
  ) +
  theme_cole(remove_grid = TRUE, base_size = 25) +
  add_caption_cwb(type = "plot", include_data_source = TRUE, data_source = "The Himalayan Database")


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

ggsave(plot = plot, 
       dpi = "screen",
       width = 22,
       height = 15,
       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

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