README

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

ggstratify(your_data)

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.

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("ggstratify")

# 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

Figure types

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

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

On a Dot + Error figure, Join the dots with a line under Plot options joins the points left to right – one line per colour and per panel – which is what a set of yearly estimates is usually read for. The line is computed by the same summary as the dots, so it passes through them.

Derived variables

Derive a variable makes a new variable out of one you already have, which can then be used as a layer like any other. A continuous variable becomes quantile groups, equal-width bins or your own cut points. A variable of any type becomes missing vs observed: the rows where it has a value and the rows where it does not.

A date or date-time variable gets a time resolution. Either a calendar period – the year, quarter, month, week, day, hour or minute it falls in, which keeps it a date and so keeps a trend spaced by elapsed time – or a position in the cycle, which drops the year and pools every March since the data began: month of the year, season, quarter of the year, day of the week, hour of the day. Months run January to December and days Monday to Sunday, never alphabetically; the season’s start month is yours to choose, so March gives the northern hemisphere and September the southern.

For a Line plot drawn against a date, the Appearance panel sets how far apart the ticks are and what each one reads, so a column of dates can be shown and labelled as years.

crp in the bundled epi_cohort is incomplete on purpose and admit_date spans three calendar years, so ggstratify(epi_cohort) is enough to try both.

Survey weights

Set a numeric column as the Survey weight under Describe, and each row counts for as many people as its weight – aes(weight = survey_weight) for a histogram, density, boxplot or violin, and a weighted fit for a smoother. Every N is shown twice, as rows and as the sum of the weights. The bar on a Dot + Error figure and the band on a Kaplan-Meier curve are design-based estimates from the survey package, with the sampling strata and clusters as well when you give them. The design is the whole sample’s, and every panel and figure is a subpopulation of it. svy_weight in epi_cohort is there to try it on.

ggstratify