Gg

Ggplot2 chart playground – in the browser with WebAssembly

Hacker News

Ggplot2 chart playground – in the browser with WebAssembly

Hi HN, I've always been a fan of ggplot2. It's a fantastic charting library with a powerful grammar that encodes best practices for data visualization. My goal was to make it easily accessible to anyone, without requiring any installation. Inspired by tools like JSFiddle, I created an interactive environment for R's dplyr (for data preparation) and ggplot2. It's a purely frontend application, meaning everything runs directly in your browser. There's no backend for computation, and it works on both desktop and mobile. This project is an open-source offshoot of Quesma Charts. We initially used WebAssembly to sandbox AI-generated code, and while we've since moved to a different solution (you can read about the "why" here: https://quesma.com/blog/sandboxing-ai-generated-code-why-we-... ), I wanted to share the non-AI part with the community. The stack is WebR (R compiled to WebAssembly) and Vue.js. Much of the code was written with assistance from Claude, though the WebR integration required significant manual effort due to its novelty. Give it a try! Playground: https://quesmaorg.github.io/webr-ggplot-playground/ Repo (MIT): https://github.com/QuesmaOrg/webr-ggplot-playground

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, visual, code · Missing: mac, agents, macos
84%84% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
69%69% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Strong signals: created, para · Missing: supports, reddit linkedin, podcasting
68%68% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: way, para · Missing: mobile apps, ios, personal
50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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