What is ggstratify?
Descriptive analysis often means making the same figure repeatedly
for different subgroups. ggstratify makes this easier:
choose the variables you want to describe and stratify by, and the app
creates the figures for you. It also gives you the R code used to create
each figure.
Everything runs locally. Your data are not uploaded anywhere.
Before you start
ggstratify uses your variables as they are typed. Make
sure categorical variables are factors and that their levels are in the
order you want.
dat <- transform(
dat,
sex = factor(sex, levels = c("Male", "Female")),
severity = factor(severity, levels = c("Mild", "Moderate", "Severe")),
age = as.numeric(age)
)The app takes a data object, not a file. Read your data first, check
the variables, and then pass the object to
ggstratify():
cohort <- read.csv("cohort.csv")
str(cohort)
ggstratify(cohort)Your original data are never modified.
Launch the app
ggstratify(epi_cohort)epi_cohort is a simulated dataset included with the
package.
str(epi_cohort)
#> 'data.frame': 600 obs. of 11 variables:
#> $ id : chr "P0001" "P0002" "P0003" "P0004" ...
#> $ age : num 66 78 57 42 57 64 64 67 72 69 ...
#> $ sex : Factor w/ 2 levels "Male","Female": 1 1 2 2 2 1 2 2 2 1 ...
#> $ site : Factor w/ 4 levels "Site A","Site B",..: 1 2 1 1 1 1 1 1 2 1 ...
#> $ treatment: Factor w/ 3 levels "Control","Low dose",..: 1 3 3 2 1 2 2 1 3 1 ...
#> $ severity : Factor w/ 3 levels "Mild","Moderate",..: 1 2 1 2 2 1 1 1 1 1 ...
#> $ bmi : num 23.6 23.1 24.9 20.1 25.3 21.2 23 25.6 27.6 26 ...
#> $ crp : num 4.4 4.6 8.8 21.8 18.1 17.6 4.4 6.3 1.7 3.4 ...
#> $ los_days : num 14 10 5 15 16 12 9 10 8 10 ...
#> $ fu_days : num 229 65 258 289 141 4 235 365 40 365 ...
#> $ death : int 1 1 0 0 1 0 0 0 0 0 ...The same function works with any data frame, tibble, data.table, or matrix.
Stratifying your figures
The Layers panel controls how figures are split.
- No layer: one figure.
- One panel variable: several panels in one figure.
- Additional stratification variables: separate figures.
For example, you could describe D, show panels by
C, and create separate figures by A and
B.
When several stratification variables are selected, you can either:
- Separate: make figures for each variable independently.
- Cross: make a figure for every combination of their levels.
The Strata tab shows the figures that will be produced and their sample sizes before you export them.
Missing values and small strata
Rows missing a value for a stratification variable are excluded because they cannot be assigned to a figure or panel. The number of excluded rows is shown in the app and in the generated code.
Strata with fewer observations than the selected Minimum N
per figure are listed but not plotted. Set the minimum to
0 if you want to include small strata. Empty factor levels
are never plotted.
Categorizing a continuous variable
The Categorize panel lets you turn a continuous variable into groups using:
- Quantiles
- Equal-width bins
- Custom cut points
For example, you can create an age group at 65 years:
The new variable can then be used like any other categorical variable.
Figure types
ggstratify supports several common descriptive plots,
including:
- Boxplots
- Histograms
- Bar plots
- Scatter plots
- Line plots
- Kaplan-Meier curves
For line plots, choose a time variable and measurement, and optionally specify an ID to draw one line per subject. A LOWESS smoother can also be added.
For Kaplan-Meier curves, select the follow-up time and event variables. Optional confidence intervals, censoring marks, and risk tables are available.
All plots are calculated within the selected panels and strata.
Changing the axis range
Appearance lets you set the X- and Y-axis ranges.
The range only zooms the figure; it does not remove observations from the analysis. This means that summary statistics such as boxplot medians remain unchanged.
Generated R code
The R-code tab shows the code used to create the figure currently shown.
The code includes the necessary data preparation, stratification, and
ggplot2 commands, so you can copy it into your own
analysis.
For example:
library(data.table)
library(ggplot2)
dt <- as.data.table(epi_cohort)
d <- dt[sex == "Male"]
p <- ggplot(d, aes(x = treatment, y = los_days)) +
geom_boxplot() +
theme_bw()
pThe preview, exported figures, and R-code tab all use the same code generator, so the generated code reproduces the figure shown in the app.