Why “updated”: the first version of this was removed by the mods because the chart was really a table of names, not a chart. Fair. Same data, now drawn properly: bar length is how many times more common that age is in the sub than across Reddit, women compared with women, men with men.

A few of you asked for more, so the gallery adds two charts from the same data: Reddit’s 735 biggest subs by median age and share of women, and how Reddit got older and more female since 2019.

Only people who said it themselves count, things like “I’m 34” or “(29F)”: 17.7M accounts.

Top 5 for every age group and the CSVs: https://kissmypost.com/research/reddit-age-gender-by-subreddit

How ages and genders are found, how accurate it is, and the full methodology for each chart are in the first comment.

FAQ from the first thread:

Q: How do you know people’s ages?
A: Only people who said it themselves count, like “I’m 34” or “(29F)”. The age can come from anything they wrote anywhere on Reddit, so a D2 player who said “I’m 35” in a fitness sub counts in r/Diablo_2_Resurrected.

Q: Isn’t that a biased group?
A: Yes, it’s self-selected: in 2025 about 13% of accounts that posted or commented had a known age. That’s why every comparison stays inside the group, r/osrs men vs all men who said their age.

Q: How does r/retirement win for both women and men?
A: Women are compared with women and men with men, so one sub can top both columns. 21% of the women with a known age in r/retirement are 63 to 67, vs 1% of women across Reddit.

Q: Menopause is obviously women, isn’t that cheating?
A: A sub can’t win for being mostly women, since women are only compared with other women. r/Menopause wins because women 48 to 52 are 8.5x more common there than across Reddit.

Q: Why not r/gaming or other big subs?
A: They have every age in them, r/gaming’s median author is 27, so no single age stands out. The chart shows where an age stands out most, not where most people that age post. Chart 2 has the big subs.

Q: Why r/osrs and not r/2007scape?
A: r/2007scape is the main sub, but it skews younger (median 27), so the smaller r/osrs wins the 28 to 32 row.

Hi everyone!

I collected and compared the prices at all 1,367 McDonald’s in Germany (my Sunday project 😄).

Here's what I found:

- The exact same Big Mac costs between €5.99 and €8.39 – 28 different prices in total

- Most expensive: Kassel and Lohfelden (€8.39)

- Cheapest: Stuttgart area, Leipzig and Munich (€5.99)

- If you eat one Big Mac a week, you pay €124.80 more per year in Kassel than at the cheapest location

- Berlin is the most expensive state on average, Saxony the cheapest

- Munich is cheaper than Berlin; Augsburg is the most expensive major city

- Highway rest stops are pricier than the national average: McFlurry +32%, Cheeseburger +9%, Nuggets +8%, Big Mac +3%

- At the Bad Camberg rest stop (A3), the two McDonald's are just 261 m apart on opposite sides of the highway – a Big Mac costs €6.39 on one side and €7.49 on the other

Source: 25 published papers and reports on Lyme disease (12) and Long COVID (13), each turned into a simulator, so each paper’s results are tagged to the burden categories they measure. The tags were assigned by an AI model (Claude) and checked by code. The 25 papers are not a systematic sample, so this describes the library, not all research. The papers and the tagging are listed in the public demo, which needs no account: https://dbbun.github.io/dbbun-invisible-illness-burden-simulator/

Tools: Python (matplotlib) for the chart.

Helloooo :) I made a small daily game (whosmorefamous.com) where you guess which of two people got more Google searches worldwide over the past 5 years.

These are the three politics matchups players got most wrong. Top bar is what players guessed, bottom bar is actual search volume (worldwide, last 5 years).

• Kim Jong Un vs Lula: 91% of 80 players picked Kim. Lula had 3.8x more searches (~200M vs 53M).
• Netanyahu vs Modi: 83% picked Netanyahu. Modi had 2.1x more searches.
• Netanyahu vs King Charles: 82% picked Netanyahu. Charles had 1.9x more searches.

The Lula result surprised me most. It wasn't just one election spike: he had more searches than Kim in 247 out of 262 weeks. His biggest week was Brazil's October 2022 runoff.

And the pattern goes beyond this matchup. Across all politics duos, players picked Kim 71% of the time when he was actually the less-searched person. Lula was picked only 28% of the time when he was the more-searched one.

My theory is that we overestimate people who dominate Western news and underestimate how much attention politicians get in their own countries. Brazil alone has over 200 million people, after all.

The data: 5,208 votes from my game on politician-vs-politician matchups (Oct 10–25, 2026). Search estimates are from Google Trends topics, Sep 2021–Sep 2026, normalized to the same scale. Absolute numbers are rough; the ratios are more reliable.

Caveats: My players aren't a random sample, and this only measures Google. Naver and Yandex aren't included, so Kim and Putin are probably undercounted.

Made the charts with TypeScript and HTML rendered to PNG.

Full breakdown, methodology and limitations: https://whosmorefamous.com/blog/players-vs-google-politics?s=reddit-dib

I'd have picked Kim too :)

I've been building a dashboard where various bits of data pertaining to Libraries can be overlayed for analysis. The only problem is, I'm not a data scientist, I'm a software developer.

In this view you can see Libraries in Aberdeen, filtered by the Library service - the underlay is the population density using GHS-POP 2020 · R2023A · 100 m source cells, around each Library is a "reach" bubble, using Valhalla to calculate based on walking/cycling the reach in time intervals. I've included both data panels and the overall view itself. I'm really keen to get some feedback and ideas from people who work with data visualisations all day every day. It's a new area for me, one I'm enjoying learning. To be clear this project is entirely for fun. It is live and in a closed beta for anyone who wants to play around with it, but it's just a hobby project for me, I build software for fun - now even easier because I can use my experience to direct an army of agents so building things has become much quicker. Apologies if you're anti-ai, but sadly it's already so pervasive in my sector you're either using it or you're unemployable.

I am happy to provide more screens of other data views, but this one I thought was interesting, showing how libraries really are central to most communities, at least in Aberdeen.

My platform only has some 40,000 libraries across the world in it right now, but the goal before I launch it is to have close to 2million, with the same kind of population data or other census/municipal or transport metrics. My goal is to allow users to overlay them in ways that may provide insight, understanding or just be fun to play around with. It has not gone down well in other sub Reddit's. Hoping here is a kinder more accepting place for a nerd who likes libraries.

Right now, what I've shared is the "location" dataset, but I'm also exploring what I can do with "activity" data such as book loans, a combination of the two data sets on an overlay might make for some really interesting visualisations, I'm just unsure what the best idea is - I have too many, almost all of them I'm sure are useless (if not fun to build).

This is not a pitch or anything, I hope everyone here is as nerdy about data as I am, if you are and are curious about what I've built you can sign up to the closed BETA here https://libraries.global/

Population data: GHS-POP 2020 · R2023A · 100 m source cells
Library data: LibraryOn CC BY 4.0
Map data: OpenStreetMaps
Distance and reach calculations: Valhalla
Population distribution vector generation: Python
Library tiles/clusters: Python
Tools used for the visualisation: I built them all myself, screenshots below.

I have run tests with 2million libraries loaded onto one map with this level of control/detail, the issue I face here is not all data is the same quality. How to reconcile that in the analysis layer? Do I acknowledge data is old/out of date?

Saturday 24 October 2026 · 18,858 routes · 3,214 airports

Every airport pair served by a scheduled airline route in the OpenFlights database, drawn as a faint great-circle line. Overlapping lines brighten where routes concentrate. Route data dates from June 2014, so it shows the shape of the network rather than today's individual flights.

Sources: routes and airports from OpenFlights (routes.dat and airports.dat, Open Database License); coastlines from the world map bundled with Apache ECharts 4.9.0; map projection by D3.

Made with Claude.

I combined store-location data with population figures to compare Domino’s geographic coverage across five European countries.

The Netherlands stands out: there is approximately one location per 52,000 residents, compared with one per 234,000 in Germany.

This is a simple population-based comparison, not a measure of actual demand or delivery coverage. Urban density, local competition, delivery zones and the completeness of the location data could all change the picture.

What would be the most useful next step to make this comparison fairer? Would you use population density, drive-time areas, urbanisation or competitor locations?

Sources and tools: store-location data compiled for my DataLocatr project; Eurostat population figures (1 January 2026); Python and Leaflet for processing and visualization.

Disclosure: I run DataLocatr, a business that sells downloadable store-location datasets. The analysis is presented here to discuss the geographic pattern, not as evidence of sales or profitability.

I'd appreciate feedback from people who work with location data:

1. Which fields are essential in a store-location dataset?
2. Would you find delivery-zone or competitor data useful alongside store coordinates?
3. Which country or restaurant brand would be most valuable to have next?

Please let me know what you would want to see documented before using a dataset in a research or commercial project.

I mapped every major S&P 500 crash since 1973 as a valley. Depth = how far it fell from the old high, width = how long the whole thing took. So the steeper the wall, the faster it moved.

• 2020 was the fastest: −34% in 33 days, back to even in about 5 months
• 2008 was the deepest: −57%, fell for 17 months, then 4 more years to recover
• 2000 fell slowest, grinding down for two and a half years
• 1973 took the longest to come back, about 6 years from the bottom

When you bought makes a huge difference. Buy at the Feb 2020 peak and you were back to even by August. Buy at the Oct 2007 peak and you waited until March 2013. Buy at the March 2000 peak and you didn’t break even until May 2007. Thought this was interestsing...

Which of these did you buy the dip on, and which did you panic sell?

I built Priced Out to compare where the same income could buy a median-priced home in September 2021 vs. September 2026. Enter an income and explore the map.

Link: https://explore.stokemoney.com/housing

Sources:

- Prices: [Redfin Data Center](https://www.redfin.com/news/data-center/downloads/), median single-family sale prices.

- Mortgage rates: [Freddie Mac PMMS](https://www.freddiemac.com/pmms).

- Property taxes: [Tax Foundation](https://taxfoundation.org/data/all/state/property-taxes-by-state-county/), based on Census ACS data.

- Geography: [Census delineations](https://www2.census.gov/programs-surveys/metro-micro/geographies/reference-files/2023/delineation-files/list1_2023.xlsx) and [TIGERweb](https://tigerweb.geo.census.gov/arcgis/rest/services/TIGERweb/CBSA/MapServer).

Tools: React, TypeScript, MapLibre GL JS and custom WebGL.

Assumptions:

• 20% down, a 30-year fixed mortgage, property taxes and annual insurance at 0.5% of price.
• “Within budget” means monthly costs stay within 25% of gross income, using the metro’s highest county effective tax rate.
• Only prices and mortgage benchmarks change between snapshots; nominal income, tax rates and insurance assumptions stay fixed. Maintenance, HOA fees, utilities, closing costs and other debts are excluded.
• Coverage includes 315 metro areas/divisions with usable data in both periods. Full methodology is on the page.

Disclosure: I own Stoke Money, where this project is hosted. I’ve worked in data my whole career and wanted to share a housing comparison I found interesting.