r/datavisualization

▲ 2 r/datavisualization+3 crossposts

I’ve been trying to make cleaner, more readable graphs lately and realized most default tools don’t look that great out of the box.

Excel works, but it often ends up looking… basic.

Some tools look better, but take way more effort to learn.

So I’m curious what people actually use in practice:

  • what you consistently go back to
  • what gives you good results without too much friction
  • what you’d recommend to someone who cares about how charts actually look
  • Bonus if you’ve switched tools and noticed a big difference.
reddit.com
u/Open-Ease685 — 3 days ago
▲ 89 r/datavisualization+2 crossposts

Brent crude oil since 1987: 5 major shocks that reshaped the global economy, all in one chart

u/anuveya — 4 days ago
▲ 2 r/datavisualization+1 crossposts

Please, I need some feedback on my visualization project.

Hey people. My team we made this visualisation dashboard for CO2 Emissions from 1960 to 2024. We need to improve the dashboard and wanted feedback from you guys. I know we cannot make you to actually test the dashboard by yourself, but any feedback from watching the video would be helpful. Will you please fill out the Google form and also give suggestions in the comments to improve? I will really appreciate your help.
Thanks in advance!..

Feedback google form

u/Intelligent_Ad_7754 — 3 days ago
▲ 86 r/datavisualization+1 crossposts

One Piece of Data - A Way to Explore One Piece Universe with Data and Visualization

tl;dr: I built One Piece of Data

Hello r/OnePiece

I have working on this personal project named One Piece of Data for the last 3 years. Starting as a personal and my own happiness/objective, now I try to share with my fellow nakama :)

Sichibukiai's Appearance Timeline

The idea is very simple, I want to explore One Piece from a data perspective, where I can search something quickly, filter something, aggregate something, and eventually vizualise it.

Fortunately, we have One Piece Wikia that contains this information in more structured form, but obviously still text based. With its API, I collect the data, clean it, and store it in a database. From here, the possibility is endless.

So, what is is:

Recurring and New Characters per Saga

Explore

  • Characters — sortable/filterable table (by saga, arc, chapter, time-skip, status, affiliation, etc.) with detail pages covering bio, appearances, devil fruit, haki, affiliations, and occupations
  • Devil Fruits — grouped by name + model, with type/sub-type filters and every known user
  • Affiliations & Occupations — rosters with status breakdowns (e.g. who in the Marines is alive vs. defeated)
  • Sagas / Arcs / Volumes / Chapters — cross-linked, with featured character portraits

Analytics dashboards

  • Bounty rankings, demographics, appearance counts, story pacing, data-quality stats. And the connection between them.
  • Character Compare — pick any two characters and diff them side-by-side
  • Character Network — interactive graph of who interacts with who
  • Timeline & Appearance Race — watch screen time accumulate across the run
  • Word Cloud per character
  • Chapter Release Predictor — forecasts upcoming Jump issue release dates and break weeks

Games

  • Guess the Character (image quiz) and Who Am I? (progressive hints), with local score tracking and shareable result cards

Appearance Race with 30 chapter window

There's also a global search and an AI chat assistant if you'd rather just ask questions. You need to register (and I need to enable it for you) because the LLM is expensive. It's still a beta.

Obviously, it's far from perfect or complete. The manga is still running. The Wikia is not 100% complete. And my data preparation/cleaning is not perfect. But, I believe it's good enough to see something beyond reading chapter by chapter. I will try to update it 1-2 days after a new chapter is released.

I will try to share some visualization or finding. For example: the result from World Top 100 Popularity or the interesting connection between blood type and strength.

Enjoy! and tell me your suggestion of data or vizualisation that you want to see!

PS: Dang, I am not planning to release it in the same time as u/OharaLibrarianArtur map. I admire him.

reddit.com
u/ismailsunni — 5 days ago
▲ 404 r/datavisualization+1 crossposts

Found a use for all that public LIDAR: paleo-waterfall detection in northern California

I'm trying to learn QGIS to create visualizations. This is output from a detector I built that flags candidate paleo-knickpoints in stream networks. The idea is to find places where a former waterfall has migrated upstream and left its plunge pool behind. The colored dots are sample points along auto-extracted channels, graded by score (blue/green low, red high). White X marks are the candidate knickpoint locations themselves. Basemap is the hillshade.

u/StonkOperator — 7 days ago
▲ 5 r/datavisualization+2 crossposts

[OC] I turned "Journey to the West", a 500-year-old, 100-chapter epic into a storyline data visualization

Journey to the West (西游记) is one of the greatest epics ever written and I wanted to see what the whole thing looked like as data. Every character, location, and story arc across all 100 chapters, explorable in one place.

https://journey-to-the-west.datasorbet.io/

Data Sources:

  • Wu, C. (2001). Journey to the West (W. J. F. Jenner, Trans.). Foreign Languages Press. (Original work published ca. 16th century)
  • Wu, C. (2012). Journey to the West (A. C. Yu, Trans.; Rev. ed.). University of Chicago Press. (Original work published ca. 16th century)
  • Wong, I. (n.d.). Journey to the West novel summary. Journey to the West Library. Retrieved June 19, 2025.
  • Lee, X. (n.d.). Monkey King: The Journey to the West. Retrieved June 19, 2025.
  • Wikipedia contributors. (n.d.). List of Journey to the West characters. Wikipedia. Retrieved June 19, 2025.
  • Fo Guang Shan 佛光山. (2013-2025). Journey to the West. Chinese Notes. Licensed under Creative Commons Attribution 4.0 International License. Retrieved June 19, 2025.

Tools used

Typescript, React, Next.js, OpenCode, Midjourney, Claude, Gemini

journey-to-the-west.datasorbet.io
u/kristw — 4 days ago
▲ 0 r/datavisualization+1 crossposts

[OC] Changes in church membership vs growth in adjacent social sectors since 2000

Church membership among Americans fell from 69% to 43% over this period (Gallup), while several unrelated sectors experienced large growth. Full Article Here

• Pickleball participation: +19,800%
• Podcast adoption: +2,350%
• Pet industry spending: +882%
• Therapy utilization: +185%
• Church membership: -38%

These variables were selected because they each involve places people increasingly direct time, money, identity, or community participation.

This is not intended as a causal argument. The growth of these sectors is not presented as a consequence of church decline.

The comparison is more about observing broad cultural shifts and asking whether people increasingly build rituals, communities, and identities through different institutions than in previous decades.

Curious what people think.

Data sources:

Church membership: Gallup
Pickleball participation: SFIA participation reports
Podcast adoption: Edison Research Infinite Dial
Pet industry spending: American Pet Products Association (APPA)
Therapy utilization: SAMHSA / National Survey on Drug Use and Health

Built using Python, matplotlib, NumPy, and ffmpeg.

Animation: custom bar-chart race with yearly interpolation and indexed comparison methodology.

u/Ok_Persimmon_5871 — 6 days ago
▲ 125 r/datavisualization+1 crossposts

Learn Neural Network Architecture Visualizer

Neural network architecture diagrams. Three visualization modes: fully connected networks (FCNN), convolutional networks in 2D (LeNet), and deep networks in 3D (AlexNet). try it here https://8gwifi.org/ml/nn-viz.jsp

u/anish2good — 9 days ago
▲ 2 r/datavisualization+2 crossposts

[OC] I used Claude to build a live election dashboard in 2 days. It handled 430K requests from 24K visitors for $0.

On election results day in Tamil Nadu, a dashboard I built served 24K visitors across 24 countries. 430K requests. 8.7 GB bandwidth. $0 infrastructure cost.

The stack: a Python scraper on my laptop pulling from 234 pages (no API exists), Cloudflare Workers KV on the free tier, edge caching, and vanilla JS on the frontend. 997 datastore writes for the entire day, the free tier limit is 1,000.

I used Claude as my coding partner for the entire build. It wrote the scraper, the frontend, the API. I made the architecture and product decisions. During live counting, users asked for features and I shipped them in minutes. 60+ commits on the results day alone.

Wrote a detailed blog about the architecture, the AI workflow, and what I learned (Link in Comments). Happy to answer any questions.

u/Naive-Performance-18 — 10 days ago