EasyStat
Automated Statistical Analysis, Visualization and Multi-Format Narrative Reporting
Provides automated statistical analysis, rich visualization, and multi-format narrative reporting through a unified pipeline. Descriptive statistics are available via easy_describe() and easy_group_summary(). Inferential tests with plain-language narratives are provided by easy_regression(), easy_logistic_regression(), easy_ttest(), easy_anova(), easy_chisq(), easy_ztest(), easy_ftest(), easy_correlation(), easy_wilcox(), and easy_kruskal(). Publication-ready 'ggplot2' visualizations are produced by easy_histogram(), easy_boxplot(), easy_scatter(), easy_barplot(), easy_qqplot(), easy_density(), easy_correlation_heatmap(), easy_regression_diagnostics(), and easy_odds_ratio_plot(). The core Narrative Generator Module applies conditional logic to extracted p-values, effect sizes, and model-fit metrics to produce statistically sound, human-readable explanations automatically. Results render in the 'RStudio' Viewer (HTML), the console (ASCII), or export directly to Microsoft Word via 'flextable' and 'officer'.
EasyStat 
Automated Statistical Analysis, Visualization, and Multi-Format Narrative Reporting in R
Authors: Mr. Mahesh Divakaran & Dr. Gunjan Singh (Amity School of Applied Sciences, Amity University Lucknow) - Prof. Dr. Jayadevan Shreedharan (Gulf Medical University)
Overview
EasyStat bridges the gap between statistical output and actionable insight. A single function call delivers the statistical result, a plain-language narrative interpretation, and publication-ready tables, rendered in the RStudio Viewer, the R console, or Microsoft Word.
User-facing p-values are reported as percentages rounded to 4 decimal places, while raw model objects still retain the original numeric p-values for advanced use.
Installation
# From CRAN (when available)
install.packages("EasyStat")
# Development version from GitHub
# install.packages("devtools")
devtools::install_github("itsmdivakaran/Easystat")
# From local source
install.packages("path/to/EasyStat", repos = NULL, type = "source")
Quick Start
library(EasyStat)
# Linear regression with narrative
easy_regression(mpg ~ wt + hp, data = mtcars)
# Logistic regression with odds ratios
easy_logistic_regression(am ~ mpg + wt, data = mtcars)
# t-test
easy_ttest(mpg ~ am, data = mtcars)
# One-way ANOVA
easy_anova(Sepal.Length ~ Species, data = iris)
# Descriptive statistics for multiple variables
easy_describe(mtcars, vars = c("mpg", "hp", "wt"))
# Correlation heatmap
easy_correlation_heatmap(mtcars, vars = c("mpg", "hp", "wt", "qsec", "drat"))
# Export any result to Word
result <- easy_logistic_regression(am ~ mpg + wt, data = mtcars)
export_to_word(result, file = "report.docx", title = "Transmission Model",
author = "Mahesh Divakaran, Gunjan Singh, Jayadevan Shreedharan")
Four-Step Pipeline
| Step | Module | Role |
|---|---|---|
| 1 | Core Statistical Engine | Wraps lm(), glm(), t.test(), aov(), chisq.test(), var.test(), cor.test() |
| 2 | Metric Extractor | Uses model summaries and broom helpers to extract p-values, effect sizes, CIs, and fit metrics |
| 3 | Narrative Generator Module | Applies conditional logic to produce plain-language explanations |
| 4 | Unified Result Object | Returns easystat_result S3 objects with tables, narrative, and optional plots |
Function Reference
Descriptive Statistics
| Function | Description |
|---|---|
easy_describe() |
21-statistic summary for one or more numeric variables |
easy_group_summary() |
Stratified descriptives by a grouping factor |
Regression Models
| Function | Model | Key Output |
|---|---|---|
easy_regression() |
Linear regression | R-squared, ANOVA table, diagnostics, influential observations |
easy_logistic_regression() |
Binary logistic regression | Odds ratios, OR CIs, classification table, McFadden pseudo-R2 |
Inferential Tests
| Function | Test | Effect Size |
|---|---|---|
easy_ttest() |
Independent / one-sample t-test | Cohen's d |
easy_anova() |
One-way ANOVA with post-hoc context | eta-squared |
easy_chisq() |
Chi-square independence and GOF | Cramér's V |
easy_ztest() |
One- and two-sample z-test | Cohen's d |
easy_ftest() |
F-test for equality of variances | Variance ratio + CI |
easy_correlation() |
Pearson / Spearman / Kendall correlation and matrix | r, r-squared |
easy_wilcox() |
Wilcoxon rank-sum / signed-rank test | Median comparison + CI |
easy_kruskal() |
Kruskal-Wallis test | Rank-based eta-squared |
Visualizations
| Function | Plot type |
|---|---|
easy_histogram() |
Histogram with normal-curve overlay |
easy_boxplot() |
Grouped box-and-whisker plot |
easy_scatter() |
Scatter plot with regression line and R-squared |
easy_barplot() |
Count or mean (+/- SE) bar chart |
easy_qqplot() |
Q-Q normality plot |
easy_density() |
Kernel density curve, optionally grouped |
easy_correlation_heatmap() |
Annotated pairwise correlation heatmap |
easy_regression_diagnostics() |
Fitted-vs-residuals diagnostic plot |
easy_odds_ratio_plot() |
Logistic regression odds-ratio plot |
easy_autoplot() |
Smart dispatcher that picks the right plot for a result |
Theme & Export
| Function | Description |
|---|---|
theme_easystat() |
Consistent ggplot2 theme for all plots |
export_to_word() |
Formatted .docx report with flextable and officer |
Output Modes
| Mode | Trigger |
|---|---|
| RStudio HTML Viewer | Auto-detected in interactive sessions |
| Console | Scripts, terminals, non-interactive sessions |
Word .docx |
export_to_word() |
Running the Smoke Test
source(system.file("smoke_test.R", package = "EasyStat"))
Citation
If you use EasyStat in your research, please cite:
Divakaran M., Singh G., & Shreedharan J. (2026). EasyStat: Automated Statistical Analysis, Visualization and Multi-Format Narrative Reporting in R (Version 2.0.0). Amity University Lucknow & Gulf Medical University. https://itsmdivakaran.github.io/Easystat/index.html
License
MIT (c) 2026 EasyStat Authors. See LICENSE for details.