# IndrajeetPatil/ggstatsplot

Enhancing {ggplot2} plots with statistical analysis 📊📣

Repository: https://github.com/IndrajeetPatil/ggstatsplot
Canonical: https://ross.abutalabs.com/products/ggstatsplot
Homepage: https://www.indrapatil.com/ggstatsplot/
Language: R
License: NOASSERTION
License Family: other
Topics: ggplot-extension, dataviz, r, statistical-analysis, datascience, bayes-factors, regression-models, effect-size, non-parametric-statistics, hypothesis-testing
Last push: 2026-08-25T05:41:40+00:00

## Health v2 (maintenance only)
Score: 95/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 87, longevity 100
- inputs: {"age_days": 3159, "days_push": 8, "days_rel": 8, "gap_med": 58.0, "n_releases_24m": 11}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2197, forks 201 (observed 2026-08-28T04:06:25.285133+00:00)

## What it is
An R extension package for ggplot2 that embeds results of statistical tests (p-values, effect sizes, Bayes factors, confidence intervals) directly into plots as subtitles and annotations. It merges data visualization and statistical modeling into a single exploratory analysis step.

## Use cases
- add statistical test results to ggplot2 plots
- create boxplots with p-values and effect sizes
- visualize correlations with significance annotations
- run exploratory data analysis with stats in the plot
- make pie and bar charts with chi-squared test results
- annotate pairwise comparisons with significance brackets

## When to choose
- you use R and ggplot2 for exploratory data analysis
- you want plots that self-document their statistical tests
- you need publication-ready figures with statistical details
- you want both frequentist and Bayesian test summaries in one plot

## When to avoid
- you need interactive or web-based visualization
- you work outside R or don't use ggplot2
- you need full control over plot layout without statistical annotations
- you need a general-purpose plotting library with no stats overhead

## Facets
- artifact type: library
- maturity: stable
- function: data-visualization, charts, data-science, analytics
- domain: data-visualization, data-science
- platform: cross-platform, cli
- tags: r-package, ggplot2-extension, statistical-plotting, bayes-factors, hypothesis-testing, effect-size, exploratory-data-analysis, cran, statistics, r

## Member repositories
- IndrajeetPatil/ggstatsplot (main) score 95

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:25.285133+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:47:09.566373+00:00, confidence not recorded.
  - readme: https://github.com/IndrajeetPatil/ggstatsplot (fetched 2026-08-28T04:06:25.285133+00:00, sha 94fb465576e2)
  - homepage: https://www.indrapatil.com/ggstatsplot/ (fetched 2026-08-29T10:27:22.310104+00:00, sha 06a4d83106ad)
  - site_page: https://www.indrapatil.com/ggstatsplot/articles/web_only/faq.html (fetched 2026-08-29T10:27:22.313759+00:00, sha dc985e6217fb)
  - site_page: https://www.indrapatil.com/ggstatsplot/news/index.html (fetched 2026-08-29T10:27:22.316676+00:00, sha 1000732b23bb)
- Data as of 2026-08-30T08:39:29.467469+00:00.