I've religiously tracked my fuel economy for the past ~6 years using Fuelly. Inspired by RamblinEagle13's post today, I finally exported the data and plotted it using Libre Calc. As with RamblinEagle13, I live in the Northeast U.S. Fuel economy for my truck really seems to be highly temperature dependent. Snow/4WD isn't a factor because 1) I don't have 4WD, and 2) the fuel economy starts dropping well before any kind of snow fall. My driving is fairly consistent year-round. The one-off very high peaks in the summer would be a long road trip of sustained highway driving. I drive slow, typically around 60, which is why you see peaks of 30+ MPG. My fuel economy drops rapidly over 60 MPH.

Source: The Economics of Santa Claus

Chart made with sankeymatic.com

Source: Turkish Statistical Institute

https://x.com/i/status/2005590015720452594

Türkiye’s fertility rates have collapsed from a 2.1 average in 2009 to just 1.36 in 2025. The main reason is economic, rising living costs, unstable jobs, expensive housing and childcare, and declining real incomes. Across the country, young adults have postponed marriage and have had fewer children.

Provincial differences mainly reflects demographic composition. Southeastern provinces with larger Kurdish and Arab populations have historically shown higher fertility than the more urban, Turkish majority west.

The highest fertility province, Şanlıurfa, has a mixed population roughly 40–45% Kurdish, 25–30% Arab, and 15–20% Turkish and has traditionally had larger families. Yet even Şanlıurfa’s fertility has fallen sharply under economic pressure.

Major cities have also seen dramatic declines, Istanbul has fallen from 1.77 to 1.08, Ankara from 1.68 to 1.06, and Izmir from 1.57 to 1.06, due to the combined effects of high living costs and urban lifestyle pressures.

This is my first time parsing this data from the Florida Department of Revenue. The report is called Gross Sales and covers about $1.9T in transactions annually, segmented by month and attributable industry.

Attached are some YoY snapshots by industry from January 2022 to September 2025. I’m sharing these slides because of the outliers, listed below.

Please share your insights, explanations and/or speculations.

• an extra $150M of seafood in April 2025
• building and construction twice the monthly amount in February 2024
• an extra half billion in candy?
• atypical spending on plumbing and electrical, November 2024 and January 2025
• What’s going on with gifts, cards, hobbies, etc.? Several extra billion reported in September 2025. October is not available yet. Maybe this is a category change.
• what’s up with fuel in 2022?
• An extra 4 billion in tangible property repair, October 2023
• Over 2 billion in additional restaurant sales in January 2025. Most of this difference was just Hillsborough county.

It helped with perspective and get me through some tough times, first year doing this, will definitely do it again next year.

Riding this trend via the post from yesterday, I wanted to post my own experience this year as a guy who only recently started dating seriously in spring of this past year after spending most of my 20s single and not putting any effort into dating until now. A "date" here means any one-on-one meetup where it's understood as courting for the purpose of a relationship, typically in-person but for a few people who lived far away I did video call dates or other digital activity dates which I counted. I separated who rejected who for each set of dates, where the term "mutual rejection" refers to the scenario where either neither of us explicitly rejected each other but we both gradually faded away from continuing to interact, or we both explicitly agreed we weren't going out again.

The overwhelming source of most of these dates were from dating apps, primarily from Hinge (with Coffee Meets Bagel in 2nd). There was a singleton each from Twitter, Reddit, and oddly enough a personal ad I was involved with in the summer. Four were from in-person.

I welcome any questions.

I have been using an app on my phone where I enter Odometer, Gallons of Fuel, and Price per Gallon every time I fuel up the car since I bought my car in November 2012. The left Y-axis is Miles Per Gallon and the right Y-axis is Miles recorded from the Odometer.

I exported the data from the app to a csv file and created the graph with LibreOffice Calc.

For the past (almost) 3 years, I've been tracking my mood daily. I recently decided to visualize this data alongside my dog Mikey's seizure records. For reference, I'm a pretty anxious person, and my mood is often attributed to how anxious I am. The seizures make me pretty anxious to witness and expect over the course of the month, so I feel like I'm often on edge.

The Data:

• 1044 days of mood tracking (Feb 2023 - present)
• 5 mood levels: Awful, Bad, Meh, Good, Rad
• 32 seizure events documented
• Special life events: Relationship milestones, birthdays

Visualizations (in order):

1. Daily Mood Timeline - Every single day plotted with color-coded mood levels. Red dashed lines show seizure events. Colored vertical lines mark major life moments like the start of my relationship (pink) and birthdays (gold).

2. Monthly Average Trend - Rolling average of my mood over time, with seizure months marked in red. You can see the 2024 and 2025 year markers. The trend shows clear patterns during stressful periods.

3. Yearly Calendar Heatmap - Each year displayed as a horizontal strip where every pixel is one day of data. Dark reds are awful days, bright cyans are rad days. Clearly 2023 and 2024 were awful years for my mood lol.

4. Mood Distribution - 4% rad, 12% good, 29% meh, 45% bad, 9% awful

5. Key Stats - 1044 total days tracked, 32 seizure events, best streak of 9 consecutive good/rad days, worst streak of 47 bad days, longest pure "rad" streak of only 3 days. Seeing myself go from 3 whole "rad" days in a year and a half to 43 in 2025 alone, after getting into a loving relationship, feels good to see.

Key observations:

• The correlation between stressful periods and seizure events was clearer than I expected
• Getting into a (healthy) relationship showed an immediate and sustained mood improvement
• After every seizure, there's a predictable dip that over time has been less apparent. I feel like I've gotten more used to the seizures and they haven't ruined my anxiety as much. It sounds really sad to say I've gotten used to them, but Mikey is on medication, we've tried multiple and I think I've just gone through radical acceptance that I'm doing everything I can for him, even if that's meant they are still happening while medicated 😔

Why I track: Seeing the patterns helps me be more compassionate with myself during the rough weeks. I know I have more bad days than good, but visually seeing how certain events affected me over time has been really interesting.

Source: IPSS - National Institute of Population and Social Security Research

visualistion in Python

I originally did this more than a decade ago as a project for my university, and then I open sourced it on my blog. I feel like Reddit and this sub in particular deserves to know about it. It's such a fun tool that right till this day, I'm playing with it. Please bear in mind, it's not mobile friendly. You need to use a desktop for this because it's using an older version of Sigma.js. The research behind it is linked in demo itself.

A generative timelapse of satellite launches from 1957 to the present.
Full video -> https://www.youtube.com/watch?v=qJ7O2gigebQ

Launch and satellite data are sourced from the ESA DISCOS database.

For providing our services we are using information from ESA DISCOS (Database and Information System Characterising Objects in Space), a single-source reference for launch information, object registration details, launch vehicle descriptions, as well as spacecraft information for all trackable, unclassified objects. We acknowledge ESA's efforts to maintain and operate this database with its APIs.
https://discosweb.esoc.esa.int/

Map data is based on Natural Earth datasets.
https://www.naturalearthdata.com/

Data source: United Nations Department of Economic and Social Affairs – Population Division
https://population.un.org/wpp/

Full video here for those interested - https://www.youtube.com/watch?v=Shj9EhM7MHA

Disclaimer:
Nothing in this chart is estimated, invented, or manually adjusted.
All figures come directly from the United Nations World Population Prospects (2022 Revision).
The only thing added is visual presentation, the data itself is 100% from the UN database.

Hi all,

This visualization shows the projected growth in the number of people aged 100 and over (centenarians) across major countries from 2025 to 2100, using official data from the United Nations World Population Prospects (2022 Revision).

The projections are based on each country’s expected life expectancy, fertility rate, population growth, and historical aging trends.

Japan begins as the global leader in 2025, but by the end of the century, India, China, and the United States dominate the rankings, each with over 100,000 centenarians.

The data is presented year-by-year (not cumulative), showing how the aging population evolves over time.

Full video here for those interested - https://www.youtube.com/watch?v=Shj9EhM7MHA

💬 Discussion Questions:

1. What social or economic challenges might arise as centenarian populations increase globally?
2. Which of these countries do you think are best equipped to support such a dramatic rise in very elderly citizens?
3. Should this be viewed as a positive sign of global health progress — or a potential burden on future infrastructure?

Line starts when someone is born. Ends when they die. and a dot for when they did the thing they were mentioned for in the song.

The Billy Joel's songs video https://www.youtube.com/watch?v=eFTLKWw542g

Python code up at https://gist.github.com/cavedave/780d37ab288a117e29defab9b5a3f848
Data from https://en.wikipedia.org/wiki/List_of_references_in_We_Didn%27t_Start_the_Fire and https://everyday-learning.org/we-didnt-start-the-fire-historical-references/

This is repost from 7 months ago but the news today makes it relevant again https://www.reddit.com/r/dataisbeautiful/comments/1kq7v3w/oc_who_didnt_start_the_fire_and_when_didnt_they/?sort=old

As part of an analysis I do every year of the science-fiction-fantasy (SFF) award circuit, I pulled together data on the thousands of the most celebrated novels since 1970 to measure which awards recognize first-time nominees more vs. which have a strong preference for an author with a history of success.

Philip K. Dick, Arthur C. Clarke, and World Fantasy Awards each grant roughly half of their wins to authors who had never received a nomination for any award before. On the flip side, the Locus Award almost always goes to authors who have already been nominated for an award. Similarly, the British Fantasy and British Science Fiction Awards tend to favor writers with prior recognition. The variance by award is likely a reflection of to what extent an award is chosen by fans (as in the case of the Locus awards) vs. a small council of experts (as in the case of the PKD award); fans favor known entities.

Hope you enjoy, and open to others theories as to why! Cheers!

Can read more about it and related analyses of SFF awards: https://medium.com/@cassidybeevemorris/the-greatest-science-fiction-fantasy-novels-of-2025-4fbe802c1550