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[OC] How income correlates with anxiety or depression
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[OC] How income correlates with anxiety or depression

Data sources:
GDP per capita - Wellcome, The Gallup Organization Ltd. (2021). Wellcome Global Monitor, 2020. Processed by Our World in Data
https://ourworldindata.org/grapher/gdp-per-capita-maddison-project-database
Gini Coefficient - World Bank Poverty and Inequality Platform (2025) with major processing by Our World in Data
https://ourworldindata.org/grapher/economic-inequality-gini-index
% share of lifetime anxiety or depression - Bolt and van Zanden – Maddison Project Database 2023 with minor processing by Our World in Data
https://ourworldindata.org/grapher/share-who-report-lifetime-anxiety-or-depression

Data graphed using matplotlib with Python, code written with the help of codex.

u/lasushin — 2 hours ago
[OC] Mapping of every Microsoft product named 'Copilot'
🔥 Hot ▲ 1.6k r/dataisbeautiful

[OC] Mapping of every Microsoft product named 'Copilot'

I got curious about how many things Microsoft has named 'Copilot' and couldn't find a single source that listed them all. So I created one.

The final count as of March 2026: 78 separately named, separately marketed products, features, and services.

The visualisation groups them by category with dot size approximating relative prominence based on Google search volume and press coverage. Lines show where products overlap, bundle together, or sit inside one another.

Process: Used a web scraper + deep research to systematically comb through Microsoft press releases and product documentation. Then deduplication and categorisation. Cross-referencing based on a Python function which identifies where product documentation references another product either functioning within or being a sub-product of another.

Interactive version: https://teybannerman.com/strategy/2026/03/31/how-many-microsoft-copilot-are-there.html

Data sources: Microsoft product documentation, press releases, marketing pages, and launch announcements. March 2026.

Tools: Flourish

u/Embarrassed-Part7933 — 23 hours ago
🔥 Hot ▲ 131 r/dataisbeautiful

I have built a real-time tracker for the Strait of Hormuz to monitor tanker flow and threat levels before tomorrow's deadline.

I've been struggling to find a single place that combines actual AIS tanker data with the reported strike zones and oil reserve counts, so I spent the last few days putting this dashboard together

All code is open-source. Connect your own AIS feed if you have one — the dashboard supports live data integration.

With 4 days until the Trump April 6 deadline, tracking what's actually happening, not speculation!! And it is critical. ~20% of global oil flows through a 33km chokepoint.

The dashboard includes:

- Live tanker map and queue data

- "Days Until Dark" — how many days 10 major economies have of oil cover if the Strait closes (the number is dropping in real-time)

- Strike & Threat Alerts sourced from OSINT, with animated replay of strike trajectories

- Real-time countdown to April 6

- 24-hour transit volume trends

I would appreciate your guys opinion ! All feedback would help! Thanks a lot

xadon108.github.io
u/we93 — 14 hours ago
[OC] Life expectancy increased across all countries of the world between 1960 and 2020 -- an interactive d3 version of the slope plot
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[OC] Life expectancy increased across all countries of the world between 1960 and 2020 -- an interactive d3 version of the slope plot

u/ikashnitsky — 8 hours ago
[OC] Strait of Hormuz: 50% of tankers anchored during Iran war — 4-day live AIS vessel surveillance, Apr 1-4 2026
🔥 Hot ▲ 54 r/dataisbeautiful

[OC] Strait of Hormuz: 50% of tankers anchored during Iran war — 4-day live AIS vessel surveillance, Apr 1-4 2026

u/SashSail — 18 hours ago
[OC] Mapping the age of oceanic crust, overlayed with the locations of the world's volcanoes
▲ 12 r/dataisbeautiful+1 crossposts

[OC] Mapping the age of oceanic crust, overlayed with the locations of the world's volcanoes

u/symmy546 — 18 hours ago
Earnings Season Baaaabbbyyyy
▲ 7 r/dataisbeautiful+3 crossposts

Earnings Season Baaaabbbyyyy

Built this visual using prediction market data on upcoming bank earnings.

Y-axis = implied probability of beating earnings expectations
X-axis = liquidity backing that view

The basic idea: high probability is more interesting when there’s also decent liquidity behind it. That helps separate stronger signals from thinner, noisier markets.

A few things that stood out to me:

  • JPM looks like the clearest high-probability / high-liquidity name
  • GS and Morgan Stanley also screen well, though with a bit less liquidity
  • Some names show decent beat odds, but not as much capital behind the view
  • Citi / M&T / WF look favorable on probability, but less compelling than the top-right names if you care about signal strength

Any thoughts?

u/BadBoyBrando — 15 hours ago
[OC] 71% of 7,500 smartphone users are classified as addicted. Of all the behavioural data collected, only social media predicted phone addiction.
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[OC] 71% of 7,500 smartphone users are classified as addicted. Of all the behavioural data collected, only social media predicted phone addiction.

Dataset: Kaggle Smartphone Usage & Addiction Analysis

A few other findings that surprised me:

  • Low stress users are marginally more addicted than high stress ones
  • Notification volume has virtually zero correlation with addiction
  • 100% of users watching 11+ hours on weekends are classified as addicted
  • Young males lead addiction rates at 18–22 but females overtake them by 32–35

I built the dashboard in Tableau to look and feel like an actual iPhone! Each insight is its own app that you tap to open. My dashboard is published on Tableau Public here:

https://public.tableau.com/views/AlwaysOnTheImpactsofPhoneAddiction/AddictionProfile?:language=en-GB&:sid=&:redirect=auth&:display_count=n&:origin=viz_share_link

It's only my second ever dashboard, so please be kind :)

u/spaghettio12 — 23 hours ago
Week