ggstratify

A one-function, point-and-click Shiny interface for the descriptive analysis.

ggstratify(your_data)

That is the only function you need to remember.

ggstratify demo

Overview

Descriptive analysis is essential in every research study. Humans (not AI) need to understand the data before making decisions, such as choosing an appropriate statistical model.

In particular, understanding how variables are distributed across strata defined by other variables is often critical.

• Visually inspect your data without repeatedly writing code.

• ggstratify runs entirely locally and requires neither a network connection nor a language model.

• Easily export figures to share with collaborators.

Before you start: decide the variable types

This package describes what it is given. It does not guess what you meant. Convert each column to the type you intend before handing it over.

dat <- transform(
  dat,
  sex       = factor(sex, levels = c("Male", "Female")),   # groups as factors
  severity  = factor(severity, levels = c("Mild", "Moderate", "Severe")),
  age       = as.numeric(age)                              # measurements as numeric
)
ggstratify(dat)

A grouping variable left as 1, 2, 3 will be described as a number. The order of a factor’s levels becomes the order of the panels, the figures and the axis.

Installation

# install.packages("remotes")
remotes::install_github("AkiShiroshita/ggstratify")

Usage

library(ggstratify)

ggstratify(epi_cohort)           # the example data that ships with the package
ggstratify(iris)                 # data.frame / tibble / data.table / matrix

The screen

Tab	Contents
Plot	Either every figure at once as panels (fastest) or one at a time, enlarged. The variables you chose decide which (see below)
Data	The first 200 rows
Strata	Every stratum: variable, level, N and the file name it will be written to. Strata with no file name get no figure. Plus the rows excluded for missing values
R-code	The ggplot2 code for the figure on screen. One button copies it

Figure types

Boxplot / Density / Dot + Error / Dotplot / Histogram / Kaplan-Meier curve / Line / Scatter / Violin

Acknowledgements

• Claude Code (Anthropic’s Claude Opus 5) assisted with adding notes, testing and English-language proofreading. The design, decisions and final responsibility remain the author’s.
• The package design was inspired by ggplotgui (Gert Stulp, GPL-3).

License

GPL-3