bio-data-visualization-distribution-plots
Plot per-group distributions of continuous data using boxplots, violins, beeswarms, quasirandom jitter, and raincloud plots with sample-size honesty (Weissgerber 2015), KDE-bandwidth awareness, and N-aware encoding choices. Use when comparing distributions across a small number of groups — expression per cluster, biomarker per arm, scores per condition — and the bar-of-mean default is misleading.
npx skills add BioTender-max/awesome-bio-agent-skills --skill distribution-plots --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: ggplot2 3.5+, ggbeeswarm 0.7+, ggdist 3.3+, gghalves 0.1.4+, seaborn 0.13+, matplotlib 3.8+, ptitprince 0.3+ (Python raincloud). Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures - R: `packageVersion('<pkg>')` then `?function_name` to verify parameters If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Distribution Plots **"Plot the distribution per group"** -> Render boxplot, violin, beeswarm, or raincloud calibrated to N per group, the underlying distribution shape, and the audience's ability to read each encoding. The default `geom_bar(stat='summary')` is the canonical misleading choice — Weissgerber 2015 *PLOS Biol* documented that 703 top physiology papers use bar-of-mean despite multiple distinct distributions producing identical bars. - R: `ggplot2::geom_boxplot`, `ggplot2::geom_violin`, `ggbeeswarm::geom_quasirandom`, `ggdist::stat_halfeye`, `gghalves::geom_half_violin` - Python: `seaborn.boxplot/viol
- Version Compatibility
- The Single Most Important Modern Insight -- Bars of Means Lie
- Decision Tree by N per Group
- Box, Violin, Beeswarm, Raincloud -- The Four Standard Encodings
- Boxplot (Tukey 1977) -- summary only
- Violin -- density + summary
- Beeswarm / quasirandom -- every point shown deterministically
- Raincloud (Allen 2019) -- distribution + summary + raw
- Letter-value plot (Hofmann-Wickham 2017)
- Stacked / split violin (paired comparisons)
- Per-Method Failure Modes
- Bar of mean with SEM
- Violin with default Silverman bandwidth oversmooths bimodality
- Notched boxplot with too-small N
What does the bio-data-visualization-distribution-plots skill do?
Plot per-group distributions of continuous data using boxplots, violins, beeswarms, quasirandom jitter, and raincloud plots with sample-size honesty (Weissgerber 2015), KDE-bandwidth awareness, and N-aware encoding choices. Use when comparing distributions across a small number of groups — expression per cluster, biomarker per arm, scores per condition — and the bar-of-mean default is misleading.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill distribution-plots --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.