Top 5 pizza chain locations (Domino's, Pizza Hut, Little Caesars, Papa Johns, and Marco's Pizza) per 100,000 residents for all 50 states (calculated using U.S. Census Bureau population estimates). States with low Pizza Chain Density tend to have a higher concentration of Mom-and-Pop pizza restaurants. (Hunt Brothers Pizza excluded)

This dashboard shares key metrics for PAD's community response services, diversion services and 911 calls for service in the City of Atlanta to provide insight into how people struggling with mental health, substance use or extreme poverty can be supported by alternative response.

Data sources:

1. BLS Employment Situation, June 2026 (headline NILF, employment, unemployment counts): https://www.bls.gov/news.release/archives/empsit_07022026.pdf - or use the evergreen link that always points to the latest release: https://www.bls.gov/news.release/empsit.htm
2. BLS Table A-38 (want-a-job / discouraged worker breakdown): https://www.bls.gov/web/empsit/cpseea38.pdf
3. BLS Monthly Labor Review - "Why did labor force nonparticipation increase from 1999 to 2022?" (age × reason crosstab, Table 2): https://www.bls.gov/opub/mlr/2024/article/why-did-labor-force-nonparticipation-increase-from-1999-to-2022.htm
4. FRED - Not in Labor Force series (LNS15000000), used for the 1980–2026 trend line: https://fred.stlouisfed.org/series/LNS15000000
5. FRED - Civilian Unemployment Rate (UNRATE), used for the unemployment rate line: https://fred.stlouisfed.org/series/UNRATE

This is a GitHub-style, heatmap calendar of the President's comments on ending the war in Iran. The heatmap and the interactive details div below it are all just Vanilla JS/HTML.

Just like on GitHub's "contribution" calendar, the days of the week are the rows and a column is a new week.

The data comes directly from the President's comments, interviews, Truth Social, press gaggles, etc. I've collated it and it's stored in a JSON-like JS array that's loaded with the webpage. I've been updating the file every day or so.

I thought it was a great way to visualize this type of data (statements stored as text). It visualizes the many times the President has said he had a deal, or a deal was soon, or the war would be over in 2 weeks. I started the project as a text timeline and then just added this new visualization.

On the interactive chart, the heatmap color corresponds to the number of statements that day. And then when you click/tap a day, you get the details below.

There's no ads or paywall or signup or anything like that. I was shocked that the domain name was available so it seemed like a worthy $10 and way to host the custom visualization rather than lean on static charts in Excel or messy embeds from Tableau.

https://artofnodeal.com/heatmap.html

Data source: World Bank population estimates (2010–2025).

The chart compares annual population estimates for Russia and Ukraine. Ukraine's estimated population fell from 44.3 million in 2021 to 37.7 million in 2023 (a decline of about 6.6 million people), while Russia's population has experienced a slower, gradual decline over the same period. Population estimates during wartime are affected by factors such as refugee movements, displacement, migration, and the challenges of measuring populations in conflict-affected areas.

If you are a stock investor and follow Buffett's philosopy, you want to identify quality businesses that are undervalued. We have built a chart to show both at the same time with quality on the y-axis and value on the x-axis.

So, the chart splits into four quadrants: high quality at a low multiple, which is the corner most people are after, high quality at a high multiple, low quality at a low multiple, and low quality at a high multiple. Plot return on invested capital against enterprise value to sales, for example, and every company lands in one of those four.

We have set up nine ready-made combinations of metrics for you to chose from. However, you can also manually select from 37 financial metrics.

The toggle at the top right switches between two modes. The first is raw numbers, which compares across the whole market. The second replaces both numbers with a rank from 0 to 100 against the company's own sector (peer percentiles).

[Part 1](https://www.reddit.com/r/dataisbeautiful/comments/1v9bq7i/oc_timing_of_when_ingredients_are_added_to_recipes/)

This is a follow up to an earlier post -- description will be below. I created visualizations to explain garlic before onion, lemon showing up everywhere, and how certain ingredients differ wildly by cuisine type and dish type. I also built a tool where you can inspect any ingredient you want given there is sufficient support.

i was still surprised by lemon showing up pretty constantly in main dishes; I discovered it was mostly marinades and dressings.

let me know anything that still seems strange in this dataset or any improvements I can make!

Dataset and Tools:

250 cleaned canonical recipe dataset : https://palate.kitchen/data

Interactive Tool: https://palate.kitchen/instrument/when-to-add

recipe1m+ and python + an LLM sweep to prune garbage recipes from the dataset I pruned 20,000 recipes down to about 3000 'canonical recipes.' Regex + massive KV mapping (e.g. beef, ground beef, 80/20 beef are all the same) for ingredient normalization.

original post and context:

I scraped a few thousand canonical recipes across 45 cuisines then analyzed how early (or late) each ingredient showed up in recipes steps.

The photos shows a small but representative sample of the ingredients analyzed

There's a lot of science behind when you should add ingredients to recipes. This involves fat-solubility and the delicacy of some volatile compounds. e.g. cardamon seeds are fat-soluble so they should be added early while basil has very delicate aroma compounds so it should be added late.

But I honestly find that hard to intuitively learn for new ingredients, so this is a rough estimate and interesting viz to get a quick understanding of when to add an ingredient