Height Distribution: Scottish Munros vs. Munro Tops

Peak-count pyramid by elevation band (2021)

TidyTuesday
2025
Population pyramid showing height distribution of Scottish Munros vs Munro Tops (2021). Munros dominate higher elevations (1100m+) while Munro Tops are more common in lower ranges, with peak distribution at 950-1000m.
Author

Steven Ponce

Published

August 12, 2025

Figure 1: Population pyramid showing height distribution of Scottish Munros vs Munro Tops in 2021. Green bars (right) represent 282 Munros, brown bars (left) represent 226 Munro Tops. The chart reveals that Munros dominate higher elevations above 1100m, while Munro Tops are more prevalent in lower ranges. The largest groups are found at elevations of 950-1000m (78 Munros, 73 Munro Tops) and 900-950m (68 Munros, 71 Munro Tops), indicating a relatively even distribution at lower elevations.)

Steps to Create this Graphic

1. Load Packages & Setup

Show code
```{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
    scales,      # Scale Functions for Visualization
    glue         # Interpreted String Literals
  )})

### |- figure size ----
camcorder::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
```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false

tt <- tidytuesdayR::tt_load(2025, week = 33)

munros <- tt$scottish_munros |> clean_names()

tidytuesdayR::readme(tt)
rm(tt)
```

3. Examine the Data

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

glimpse(munros)
```

4. Tidy Data

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

breaks <- seq(900, 1350, 50)
labels <- paste0(seq(900, 1300, 50), "–", seq(950, 1350, 50), " m")

pyramid_data <- munros |>
  filter(!is.na(height_m), !is.na(x2021)) |>
  mutate(
      # Bins cut
    height_bin = cut(height_m,
      breaks = breaks, labels = labels,
      include.lowest = TRUE, right = FALSE
    ),
    x2021 = factor(x2021, levels = c("Munro", "Munro Top"))
  ) |>
  filter(!is.na(height_bin)) |>
  count(height_bin, x2021, name = "n") |>
  # Complete the data to show all combinations, filling with 0
  complete(height_bin, x2021, fill = list(n = 0)) |>
  # Label placement
  mutate(
    n_signed = if_else(x2021 == "Munro", n, -n),
    big_bar = abs(n_signed) >= 8,
    tiny_bar = abs(n_signed) <= 3,
    label_pos = case_when(
      big_bar ~ n_signed / 2,
      n_signed > 0 & tiny_bar ~ n_signed + 4.5,
      n_signed > 0 ~ n_signed + 3,
      n_signed < 0 & tiny_bar ~ n_signed - 4.5,
      TRUE ~ n_signed - 3
    ),
    label_hjust = if_else(big_bar, 0.5, if_else(n_signed > 0, 0, 1)),
    label_text = if_else(n > 0, as.character(abs(n_signed)), ""),
    label_col = if_else(big_bar, "white", "gray20")
  )

# symmetric limits (nice round)
lim <- ceiling((max(pyramid_data$n) + 2) / 10) * 10

# totals for subtitle 
totals <- pyramid_data |>
  group_by(x2021) |>
  summarise(N = sum(n), .groups = "drop") |>
  pivot_wider(names_from = x2021, values_from = N)
```

5. Visualization Parameters

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

### |-  plot aesthetics ----
colors <- get_theme_colors(
    palette = c("Munro" = "#4B5945", "Munro Top" = "#9A7E6F")
    )

### |- titles and caption ----
title_text <- str_glue("Height Distribution: Scottish Munros vs. Munro Tops")

subtitle_text <- str_glue(
    "Peak-count pyramid by elevation band (2021) — ",
    "<span style='color:{colors$palette['Munro']}'><b>Munros</b></span> (n = {totals$Munro}) vs ",
    "<span style='color:{colors$palette['Munro Top']}'><b>Munro Tops</b></span> (n = {totals$`Munro Top`})"
)

caption_text <- create_social_caption(
    tt_year = 2025,
    tt_week = 33,
    source_text = "Database of British and Irish hills"
)

### |-  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$title, size = rel(1.2), color  = colors$title, margin = margin(b = 10)),
        plot.subtitle = element_text(family = fonts$subtitle, lineheight = 1.2, color = colors$subtitle, size = rel(0.78), margin = margin(b = 20)),
        
        # Axis elements
        axis.line = element_blank(), 
        axis.ticks = element_blank(), 
        
        # Grid elements
        panel.grid.major.x = element_line(color = "gray90",linetype = "solid", linewidth = 0.3),
        panel.grid.major.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        panel.grid.minor.y = element_blank(),
        
        # Axis elements
        axis.text = element_text(color = colors$text, size = rel(0.7)),
        axis.title.x = element_text(color = colors$text, face = "bold", size = rel(0.8), margin = margin(t = 15)),
        axis.title.y = element_text(color = colors$text, face = "bold", size = rel(0.8), margin = margin(r = 10)),
        
        # Legend elements
        legend.position = "plot",
        legend.title = element_text(family = fonts$tsubtitle, color = colors$text, size = rel(0.8), face = "bold"),
        legend.text = element_text(family = fonts$tsubtitle, color = colors$text, size = rel(0.7)),
        legend.margin = margin(t = 15),
        
        # Plot margin
        plot.margin = margin(t = 15, r = 15, b = 15, l = 15),
    )
)

# Set theme
theme_set(weekly_theme)
```

6. Plot

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

### |-  final plot ----
p <- ggplot(pyramid_data, aes(x = height_bin, y = n_signed, fill = x2021)) +
  # Geoms
  geom_col(width = 0.78, color = "white", linewidth = 0.3, alpha = 0.92) +
  geom_hline(yintercept = 0, color = "#5f6b73", linewidth = 0.6) +
  geom_text(
    aes(y = label_pos, label = label_text, hjust = label_hjust, color = label_col),
    fontface = "bold", size = 3.2
  ) +
  # Scales
  scale_color_identity() +
  scale_fill_manual(values = colors$palette, guide = "none") +
  scale_y_continuous(
    limits = c(-lim, lim),
    breaks = seq(-lim, lim, 20),
    labels = function(x) abs(x),
    expand = expansion(mult = c(0.02, 0.02))
  ) +
  coord_flip() +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    x = "Height range (m)",
    y = "Number of peaks"
  ) +
  # Theme
  theme(
    plot.title = element_text(
      size = rel(1.65),
      family = fonts$title,
      face = "bold",
      color = colors$title,
      lineheight = 1.1,
      margin = margin(t = 5, b = 10)
    ),
    plot.subtitle = element_markdown(
      size = rel(0.85),
      family = fonts$subtitle,
      color = alpha(colors$subtitle, 0.9),
      lineheight = 1.2,
      margin = margin(t = 0, b = 20)
    ),
    plot.caption = element_markdown(
      size = rel(0.55),
      family = fonts$caption,
      color = colors$caption,
      hjust = 1,
      margin = margin(t = 10)
    )
  )
```

7. Save

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

### |-  plot image ----  
save_plot(
  plot = p, 
  type = "tidytuesday", 
  year = 2025, 
  week = 33, 
  width  = 8,
  height = 8
  )
```

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

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       xfun_0.49          httr2_1.0.6        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      codetools_0.2-20  
[21] snakecase_0.11.1   htmltools_0.5.8.1  yaml_2.3.10        crayon_1.5.3      
[25] pillar_1.9.0       camcorder_0.1.0    magick_2.8.5       commonmark_1.9.2  
[29] tidyselect_1.2.1   digest_0.6.37      stringi_1.8.4      rsvg_2.6.1        
[33] rprojroot_2.0.4    fastmap_1.2.0      grid_4.4.0         colorspace_2.1-1  
[37] cli_3.6.4          magrittr_2.0.3     utf8_1.2.4         withr_3.0.2       
[41] rappdirs_0.3.3     bit64_4.5.2        timechange_0.3.0   rmarkdown_2.29    
[45] tidytuesdayR_1.1.2 gitcreds_0.1.2     bit_4.5.0          ragg_1.3.3        
[49] hms_1.1.3          evaluate_1.0.1     knitr_1.49         markdown_1.13     
[53] rlang_1.1.6        gridtext_0.1.5     Rcpp_1.0.13-1      xml2_1.3.6        
[57] renv_1.0.3         vroom_1.6.5        svglite_2.1.3      rstudioapi_0.17.1 
[61] jsonlite_1.8.9     R6_2.5.1           systemfonts_1.1.0

9. GitHub Repository

The complete code for this analysis is available in tt_2025_33.qmd.

For the full repository, click here.

10. References

  1. Data Sources:
  • TidyTuesday 2025 Week 33: [Scottish Munros](https://github.com/rfordatascience/tidytuesday/blob/main/data/2025/2025-08-19)
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