jjstatsplot

A statistical visualization bridge for jamovi based on ggstatsplot and modern R plotting packages

Statistical Visualization Made Simple

jjstatsplot brings the power of ggstatsplot and modern statistical plotting tools to jamovi, making publication-ready statistical visualizations accessible through an intuitive point-and-click interface. It automatically integrates hypothesis tests, effect sizes, confidence intervals, sample sizes, and distribution diagnostics directly onto your figures—without writing a single line of code.

With 19 distinct analysis types, jjstatsplot covers everything from baseline univariate distributions to multi-group comparisons, network arcs, ridgeline density plots, and automated intelligent plot selection.

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✨ Key Features & Analysis Suite (19 Analyses)

Category	Analysis	Function	Statistical Details & Clinical Applications
Continuous Distributions	Histogram	jjhistostats	Distribution visualization with Shapiro-Wilk normality testing, parametric/robust/Bayesian central tendency, and density overlays.
Continuous vs Continuous	Scatter Plot	jjscatterstats	Pairwise association with Pearson/Spearman/robust correlation coefficients, regression fits, and marginal distribution plots.
Continuous vs Continuous	Correlation Matrix	jjcorrmat	Multi-variable correlation matrices displaying pairwise correlation strength, significance markers, and clustering.
Continuous vs Continuous	Hull Plot	hullplot	Bivariate scatter with convex polygonal hull boundaries highlighting distinct clinical clusters and group separation.
Group Comparisons	Between-Groups Box-Violin	jjbetweenstats	Group comparison with violin plots, boxplots, raw data points, one-way ANOVA / Kruskal-Wallis, post-hoc tests, and effect sizes (eta-squared, Cohen's d).
Group Comparisons	Within-Subjects Box-Violin	jjwithinstats	Repeated measures and matched-pair comparison using repeated measures ANOVA or Friedman tests with paired trajectory lines.
Group Comparisons	Horizontal Dot Plot	jjdotplotstats	Horizontal box-violin mean comparison across categorical factors with detailed effect sizes and confidence intervals.
Group Comparisons	Dot Chart	jjdotchart	Cleveland-style dot charts comparing observed group summaries against reference values or predefined clinical benchmarks.
Categorical Associations	Bar Charts	jjbarstats	Frequency comparisons with Pearson Chi-square, Fisher's exact test, Cramer's V effect size, and natural language summary annotations.
Categorical Associations	Pie Charts	jjpiestats	Proportion visualization with chi-square goodness-of-fit testing for composition analysis.
Categorical Associations	Segmented Total Bar	jjsegmentedtotalbar	Stacked proportion bars reporting both segment-level and aggregate total category statistics.
Categorical Associations	Waffle Charts	jwaffle	Square icon/matrix waffle charts for intuitive patient-level proportion and ratio visualization.
Advanced Distributions	Raincloud Plot	raincloud	Integrated distribution display combining raw scatter points (jitter), boxplot summary, and smoothed half-density cloud.
Advanced Distributions	Advanced Raincloud	advancedraincloud	Enhanced raincloud plot supporting longitudinal tracking, multi-group stratification, and custom orientation.
Advanced Distributions	Ridgeline Plot	jjridges	Staggered multi-group density ridges (joyplots) for comparing biomarker distribution shifts across stages or cohorts.
Network & Flows	Arc Diagram	jjarcdiagram	Network arc diagrams displaying connections and co-occurrence strength between discrete pathological entities.
Trends & Time Series	Line Chart	linechart	Longitudinal trajectory and trend plots with error bars, confidence intervals, and slope change annotations.
Ranked Data	Lollipop Chart	lollipop	Clean, high-data-to-ink ratio lollipop plots for comparing ranked numerical values across extensive categories.
Automated Selection	Automatic Plot Selection	statsplot2	Intelligent plotting engine that automatically inspects selected variable types and renders the optimal statistical plot.

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🚀 Advanced Capabilities

• Statistical Paradigms: Switch seamlessly between parametric, non-parametric, robust, and Bayesian statistical frameworks.
• Grouped & Faceted Analysis: Automatic multi-panel faceting by secondary clinical factors.
• Theme & Aesthetic Control: Choose between native jamovi styling or ggstatsplot's color palettes and typography.
• Reproducibility: All GUI interactions generate clean, reproducible R code using underlying tidyverse and ggstatsplot idioms.

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📦 Installation

In jamovi (Recommended)

1. Open jamovi (>= 2.6).
2. Click Modules (top right) → jamovi library.
3. Search for jjstatsplot.
4. Click Install.

As an R Package

# Install development version from GitHub
remotes::install_github("sbalci/jjstatsplot")

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🏃 Quick Start (R Interface)

library(jjstatsplot)

# 1. Distribution analysis with statistical test
jjstatsplot::jjhistostats(
  data = iris,
  dep = "Sepal.Length",
  xlab = "Sepal Length (cm)",
  results.subtitle = TRUE
)

# 2. Between-group box-violin comparison with ANOVA/Kruskal-Wallis
jjstatsplot::jjbetweenstats(
  data = mtcars,
  dep = "mpg",
  group = "cyl",
  type = "nonparametric"
)

# 3. Correlation matrix with significance levels
jjstatsplot::jjcorrmat(
  data = mtcars,
  vars = vars(mpg, hp, wt, qsec)
)

# 4. Raincloud plot combining density, boxplot, and points
jjstatsplot::raincloud(
  data = iris,
  dep = "Sepal.Width",
  group = "Species"
)

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📚 Documentation & Resources

• Module Documentation: https://www.serdarbalci.com/jjstatsplot/
• ClinicoPath Umbrella Ecosystem: https://www.serdarbalci.com/ClinicoPathJamoviModule/
• ggstatsplot Upstream Package: https://www.indrapatil.com/ggstatsplot/
• Issue Tracker & Feature Requests: GitHub Issues

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📄 Citation

If you use jjstatsplot in your research, please cite:

@manual{balci2026clinicopath,
  title  = {ClinicoPath: jamovi Module for Clinicopathological Research},
  author = {Serdar Balci},
  year   = {2026},
  url    = {https://www.serdarbalci.com/ClinicoPathJamoviModule/},
  doi    = {10.5281/zenodo.3997188}
}

Please also cite the underlying package:

• Patil, I. (2021). Visualizations with statistical details: The 'ggstatsplot' approach. Journal of Open Source Software, 6(61), 3167.

📝 License

GPL (>= 2) — see the LICENSE.md file for details.