Scottish Munros

A Munro is a Scottish mountain with an elevation of over 3,000 feet, whereas a Munro Top is a subsidiary summit of a Munro that also exceeds 3,000 feet in height but is not considered a distinct mountain in its own right. The most famous Munro is Ben Nevis.

In 1891, Sir Hugh Munro produced the first list of these hills. However, unlike other classification schemes in Scotland which require a peak to have a prominence of at least 500 feet for inclusion, the Munros lack a rigid set of criteria for inclusion. And so, re-surveying can lead to changes in which peaks are included on the list.

How many peaks currently listed as Munros have always been included on the list?
Which year saw the largest number of changes to the classification?
Which Munro is the most remote?

The Database of British and Irish Hills is licensed under a Creative Commons Attribution 4.0 International Licence. Please reference The Database of British and Irish Hills v18.2 and link to www.hills-database.co.uk.

Thank you to Nicola Rennie for curating this week’s dataset.

The Data

# Using R
# Option 1: tidytuesdayR R package 
## install.packages("tidytuesdayR")

tuesdata <- tidytuesdayR::tt_load('2025-08-19')
## OR
tuesdata <- tidytuesdayR::tt_load(2025, week = 33)

scottish_munros <- tuesdata$scottish_munros

# Option 2: Read directly from GitHub

scottish_munros <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-08-19/scottish_munros.csv')

# Using Python
# Option 1: pydytuesday python library
## pip install pydytuesday

import pydytuesday

# Download files from the week, which you can then read in locally
pydytuesday.get_date('2025-08-19')

# Option 2: Read directly from GitHub and assign to an object

scottish_munros = pandas.read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-08-19/scottish_munros.csv')

# Using Julia
# Option 1: TidierTuesday.jl library
## Pkg.add(url="https://github.com/TidierOrg/TidierTuesday.jl")

using TidierTuesday

# Download files from the week, which you can then read in locally
download_dataset('2025-08-19')

# Option 2: Read directly from GitHub and assign to an object with TidierFiles

scottish_munros = read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-08-19/scottish_munros.csv")

# Option 3: Read directly from Github and assign without Tidier dependencies
scottish_munros = CSV.read("https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-08-19/scottish_munros.csv", DataFrame)

How to Participate

Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data. There are various moderating variables that affect all data, many of which might not have been captured in these datasets. As such, our suggestion is to use the data provided to practice your data tidying and plotting techniques, and to consider for yourself what nuances might underlie these relationships.
Create a visualization, a model, a Quarto report, a shiny app, or some other piece of data-science-related output, using R, Python, or another programming language.
Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.
Submit your own dataset!

PydyTuesday: A Posit collaboration with TidyTuesday

Exploring the TidyTuesday data in Python? Posit has some extra resources for you! Have you tried making a Quarto dashboard? Find videos and other resources in Posit’s PydyTuesday repo.
Share your work with the world using the hashtags #TidyTuesday and #PydyTuesday so that Posit has the chance to highlight your work, too!
Deploy or share your work however you want! If you’d like a super easy way to publish your work, give Connect Cloud a try.

Data Dictionary

`scottish_munros.csv`

variable	class	description
DoBIH_number	character	ID Number in the Database of British and Irish hills
Name	character	Name of the Munro.
Height_m	double	The height of the Munro in metres.
Height_ft	double	The height of the Munro in feet.
xcoord	double	x-coordinate of Munro, using British National Grid (OSGB36) projection which uses easting/northing in metres.
ycoord	double	y-coordinate of Munro, using British National Grid (OSGB36) projection which uses easting/northing in metres.
1891	character	Classification of the Munro in 1891. Either Munro, Munro Top, or `NA`.
1921	character	Classification of the Munro in 1921. Either Munro, Munro Top, or `NA`.
1933	character	Classification of the Munro in 1933. Either Munro, Munro Top, or `NA`.
1953	character	Classification of the Munro in 1953. Either Munro, Munro Top, or `NA`.
1969	character	Classification of the Munro in 1969. Either Munro, Munro Top, or `NA`.
1974	character	Classification of the Munro in 1974. Either Munro, Munro Top, or `NA`.
1981	character	Classification of the Munro in 1981. Either Munro, Munro Top, or `NA`.
1984	character	Classification of the Munro in 1984. Either Munro, Munro Top, or `NA`.
1990	character	Classification of the Munro in 1990. Either Munro, Munro Top, or `NA`.
1997	character	Classification of the Munro in 1997. Either Munro, Munro Top, or `NA`.
2021	character	Classification of the Munro in 2021. Either Munro, Munro Top, or `NA`.
Comments	character	Free text field describing any changes to the data over time.

Cleaning Script

library(tidyverse)

raw_data <- read_csv("https://www.hills-database.co.uk/munrotab_v8.0.1.csv")

scottish_munros <- raw_data |>
  select(
    `DoBIH Number`, Name,
    `Height (m)`, `Height\n(ft)`, xcoord, ycoord,
    `1891`:`2021`, Comments
  ) |>
  drop_na(`DoBIH Number`) |> 
  rename(
    Height_m = `Height (m)`,
    Height_ft = `Height\n(ft)`,
    DoBIH_number = `DoBIH Number`
  ) |> 
  mutate(
    Comments = case_when(
      Comments %in% c("See named worksheet", "See named worksheet for old mapping") ~ NA_character_,
      TRUE ~ Comments
    )
  ) |> 
  mutate(
    across(`1891`:`2021`, ~case_when(
      .x == "MUN" ~ "Munro",
      .x == "TOP" ~ "Munro Top"
    ))
  )