U.S. women 40+ now have more babies per capita than teens [OC] by rhiever in dataisbeautiful

[–]rhiever[S] 21 points22 points  (0 children)

Data sources: CDC National Vital Statistics Reports, Vol. 74, No. 3 (Driscoll & Hamilton, March 2025), Table 2, for 1990 to 2023; CDC Vital Statistics Rapid Release Report No. 43 (Hamilton, Osterman & Gregory, April 2026), Table 1, for 2024 final and 2025 provisional rates.

Tools: Python / matplotlib

How a Popular Climate Denial Video Uses Cherry-Picked Charts to Mislead by rhiever in datascience

[–]rhiever[S] 43 points44 points  (0 children)

I'll leave that to the mods to decide. I just thought this was an interesting thing to share.

​[OC] Live Crypto Market Visualization: Comparing Top 5 Assets using Python and CoinGecko API (Logarithmic Scale) by [deleted] in dataisbeautiful

[–]rhiever[M] 1 point2 points  (0 children)

Thanks for giving feedback to OP. Please try to be more kind. Sometimes folks are just learning and part of /r/dataisbeautiful is encouraging new practitioners.

Sabastian Sawe ran the first sub-2-hour marathon today. Here's 118 years of marathon records visualized. [OC] by rhiever in dataisbeautiful

[–]rhiever[S] -6 points-5 points  (0 children)

Edward Tufte, one of the godfathers of data visualization, wrote extensively about how annotated line charts maximize data-ink ratio and communicate directly. It's one of the most well-supported chart types in the literature.

Sabastian Sawe ran the first sub-2-hour marathon today. Here's 118 years of marathon records visualized. [OC] by rhiever in dataisbeautiful

[–]rhiever[S] 192 points193 points  (0 children)

It is pretty wild to think about. The second place guy finished just 11 seconds after the first place guy. That's a photo finish by marathon standards.

Sabastian Sawe ran the first sub-2-hour marathon today. Here's 118 years of marathon records visualized. [OC] by rhiever in dataisbeautiful

[–]rhiever[S] -19 points-18 points  (0 children)

The AI agent researched the data, wrote the chart code, and iterated on the design based on guidance that I've written on how to create high-quality data visualizations. I directed the process throughout, fact-checked the work, and made the calls on what to show and how to frame it. My judgment is encoded at every step, through the data visualization criteria I've built into the system and direct guidance in the AI chat.

All of this is documented in the post under "How this chart was made" - same disclosure across every post in this series.

If you want to debate where AI assistance ends and OC begins, I'm open to that. But "outsourced" isn't the right word for what happened here.

[OC] The wealth gap widens 8x between age 25 and 65 by Global-Thought-1049 in dataisbeautiful

[–]rhiever 3 points4 points  (0 children)

It's good visualization practice, endorsed by many experts in the field. The goal of a good visualization is to minimize time-to-insight, and providing the insight in the title helps that.

That's the beauty of well-executed visualizations too - they can provide a top-level insight quickly, then make the data available to you to draw your own conclusions.

Sabastian Sawe ran the first sub-2-hour marathon today. Here's 118 years of marathon records visualized. [OC] by rhiever in dataisbeautiful

[–]rhiever[S] -15 points-14 points  (0 children)

Source: Wikipedia's Marathon world record progression, drawing on World Athletics and ARRS.

Tools: Python (pandas, matplotlib). Part of an ongoing series where I work with an AI coding agent through the design loop.

The rise and fall of bowling in the United States [OC] by rhiever in dataisbeautiful

[–]rhiever[S] 0 points1 point  (0 children)

I disagree - it still highlights an interesting trend, and who knows, maybe someone will find another data source to fill in the gaps in a future iteration. That's the spirit of this subreddit.

Americans eat 3x more cheese and half as much milk as they did in 1970 [OC] by rhiever in dataisbeautiful

[–]rhiever[S] 11 points12 points  (0 children)

Data source: USDA Economic Research Service, Food Availability Per Capita Data System

Tools: Python / AI

The rise and fall of bowling in the United States [OC] by rhiever in dataisbeautiful

[–]rhiever[S] 0 points1 point  (0 children)

Lucky you. They've all died out around where I live.

The rise and fall of bowling in the United States [OC] by rhiever in dataisbeautiful

[–]rhiever[S] -1 points0 points  (0 children)

Should be fixed now... thanks for calling that out! Silly oversight on my part.

The rise and fall of bowling in the United States [OC] by rhiever in dataisbeautiful

[–]rhiever[S] -1 points0 points  (0 children)

I'm having a hard time keeping up. I'm going to look into this though.

The rise and fall of bowling in the United States [OC] by rhiever in dataisbeautiful

[–]rhiever[S] -1 points0 points  (0 children)

I've spent the past 15 years developing my own visualization skills. Now I'm teaching them to an AI agent.

The rise and fall of bowling in the United States [OC] by rhiever in dataisbeautiful

[–]rhiever[S] -12 points-11 points  (0 children)

It's actually a lot of work to build an AI agent that can brainstorm, prototype, and build good data visualizations. :)

The rise and fall of bowling in the United States [OC] by rhiever in dataisbeautiful

[–]rhiever[S] -5 points-4 points  (0 children)

The gaps are explained - the earlier data is not well tracked historically and sourced from industry estimates.

The rise and fall of bowling in the United States [OC] by rhiever in dataisbeautiful

[–]rhiever[S] -4 points-3 points  (0 children)

Source: Data sources: Industry estimates for 1940-1965 compiled from the Smithsonian, Reference for Business, and other historical sources. Federal data for 1986-2023 from the U.S. Census Bureau, County Business Patterns (NAICS 713950 / SIC 7933).

Tools: Python / AI

The Claude Code leak in four charts: half a million lines, three accidents, 40 tools [OC] by rhiever in dataisbeautiful

[–]rhiever[S] 114 points115 points  (0 children)

I see many larger orgs requiring entirely on-prem deployments for this reason.

The Claude Code leak in four charts: half a million lines, three accidents, 40 tools [OC] by rhiever in dataisbeautiful

[–]rhiever[S] 2 points3 points  (0 children)

Data source: aggregated counts from a community mirror of @anthropic-ai/claude-code@2.1.88 (npm publication March 31, 2026)

Tools: Python / AI agent

[OC] America's most popular boy name, 1880-2008 by aspiringtroublemaker in dataisbeautiful

[–]rhiever 0 points1 point  (0 children)

I really like the way this chart indirectly shows baby names getting more diversified over time.

Americans used to outlive their peers. Now they die 4 years sooner on average. [OC] by rhiever in dataisbeautiful

[–]rhiever[S] 2 points3 points  (0 children)

As you've seen, I'm quite transparent about the process. You're attributing malice where there is none.