bio-data-visualization-interactive-visualization
Build interactive HTML/web visualizations with plotly (Python/R), bokeh (Python), and gganimate/plotly frames for animation, with awareness of current Kaleido static-export model (post-orca-EOL), HTML file-size bloat, and the limits of interactive-only output for journal submission. Use when producing zoomable/hoverable plots for notebook EDA, supplementary HTML, dashboards, or animated time-course / iteration visualizations.
npx skills add BioTender-max/awesome-bio-agent-skills --skill interactive-visualization --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: plotly 5.24+, plotly R 4.10+, bokeh 3.4+, kaleido 1.0+ (note: v1 dropped bundled Chrome), gganimate 1.0.9+, altair 5.4+, htmlwidgets 1.6+. Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` - R: `packageVersion('<pkg>')` then `?function_name` If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Interactive Visualization **"Build an interactive plot"** -> Render a zoomable, hoverable, panable HTML/web visualization, knowing that interactive output is a SUPPLEMENT to (not replacement for) the static figure needed for journal submission. Choose plotly for fastest onboarding and ggplot2 conversion (`ggplotly`); bokeh for streaming/server-side; altair for grammar-of-graphics; D3.js for full custom. - Python: `plotly.graph_objects`, `plotly.express`, `bokeh`, `altair` - R: `plotly` (via `ggplotly`), `htmlwidgets` ecosystem (leaflet, networkD3, DT) ## The Single Most Important Modern Insight -- Kaleido v1 and the Static-Export Pipeline In
- Version Compatibility
- Interactive vs Static — The Reproducibility Cost
- plotly (Python) — Standard Interactive
- plotly (R) — ggplotly Conversion
- bokeh (Python) — Server-Side / Streaming
- Animation — gganimate (R) and plotly frames (Python)
- htmlwidgets Ecosystem (R)
- Per-Method Failure Modes
- plotly static export silently fails
- orca dependency in older code
- EPS export needed but Kaleido v1 dropped it
- HTML file > 10 MB
- gganimate slow on large frames
- Interactive plot shown as figure in paper
What does the bio-data-visualization-interactive-visualization skill do?
Build interactive HTML/web visualizations with plotly (Python/R), bokeh (Python), and gganimate/plotly frames for animation, with awareness of current Kaleido static-export model (post-orca-EOL), HTML file-size bloat, and the limits of interactive-only output for journal submission. Use when producing zoomable/hoverable plots for notebook EDA, supplementary HTML, dashboards, or animated time-course / iteration visualizations.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill interactive-visualization --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.