World Castles, Fortresses and Palaces

This week we’re exploring the Castlemap dataset of 5,793 castles, fortresses, palaces and ruins in 138 countries. The data comes from Wikidata, and every landmark has verified coordinates, a Wikipedia article and a photo on Wikimedia Commons. Each also has a fame rank, based on how many languages have an article on it and how often that article is read. The Great Wall of China is first.

Which countries have the most castles?
Are palaces newer than fortresses?
Which landmarks have articles in many languages but few readers?

Thank you to Georgios Karamanis for curating this week’s dataset.

The Data

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

tuesdata <- tidytuesdayR::tt_load('2026-09-01')
## OR
tuesdata <- tidytuesdayR::tt_load(2026, week = 35)

world_castles <- tuesdata$world_castles

# Option 2: Read directly from GitHub

world_castles <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2026/2026-09-01/world_castles.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('2026-09-01')

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

world_castles = pandas.read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2026/2026-09-01/world_castles.csv')

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

using TidierTuesday

# Download datasets for the week, and load them as a NamedTuple of DataFrames
data = tt_load("2026-09-01")

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

world_castles = read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2026/2026-09-01/world_castles.csv")

# Option 3: Read directly from Github and assign without Tidier dependencies
world_castles = CSV.read("https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2026/2026-09-01/world_castles.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

`world_castles.csv`

variable	class	description
qid	character	Wikidata item ID, e.g. `Q1067425`.
name	character	Landmark name, in English where available, e.g. `Neuschwanstein Castle`.
category	character	Type of landmark: `castle`, `fortress`, `palace` or `ruin`.
country	character	Country the landmark is in, e.g. `France`. The UK constituent countries are listed separately, e.g. `Scotland`.
iso	character	ISO 3166-1 alpha-2 country code, e.g. `FR`. The UK constituent countries use ISO 3166-2 subdivision codes, e.g. `GB-SCT`.
lat	double	Latitude in WGS84 decimal degrees, e.g. `48.80472`.
lon	double	Longitude in WGS84 decimal degrees, e.g. `2.12028`.
year	double	Founding year, e.g. `1661`. Negative for BC dates, e.g. `-3000`.
year_approx	double	Whether the founding year is an estimate, `1`, or a documented date, `0`.
century	character	Century the founding year falls in, e.g. `17th century` or `30th century BC`.
wikipedia	character	URL of the Wikipedia article, e.g. `https://en.wikipedia.org/wiki/Palace_of_Versailles`.
image	character	URL of a Wikimedia Commons photo, e.g. `https://commons.wikimedia.org/wiki/Special:FilePath/Alhambra%20detail.jpg?width=1280`.
sitelinks	double	Number of Wikipedia language editions with an article on the landmark, e.g. `95`.
pageviews	double	Wikipedia pageviews over the trailing 365 days, e.g. `756430`.
fame_rank	double	Global fame rank, blending `sitelinks` and `pageviews`, where `1` is the most famous.

Cleaning Script

# Data from <https://thecastlemap.com/castles.csv>, accessed 31 July 2026. No
# cleaning was necessary.
world_castles <- readr::read_csv("https://thecastlemap.com/castles.csv")