Odds Ratio Tools for Logistic Regression

Produces odds ratio analyses with comprehensive reporting tools. Generates plots, summary tables, and diagnostic checks for logistic regression models fitted with 'glm()' using binomial family. Provides visualisation methods, formatted reporting tables via 'gt', and tools to assess logistic regression model assumptions.

README

plotor

plotor makes it easy to generate clear, publication-ready odds-ratio plots and tables from logistic regression models.

If you work with binary outcomes, plotor helps you go from model → interpretation in seconds.

What plotor does

Produces clean, ggplot2-based odds ratio plots
Generates tidy odds ratio tables (with optional mini-plots)
Provides automated assumption checks for logistic regression
Works seamlessly with the tidyverse

Example outputs

Odds ratio plot

A single function call gives you a polished OR plot:

Odds ratio plot showing likelihood of survival of the Titanic disaster by class, sex and age

See full plot options →

Odds ratio table with mini-plot

Perfect for reports and publications:

Odds ratio table showing likelihood of survival of the Titanic disaster by class, sex and age

See full table output options →

Automated assumption checks

plotor includes built-in checks to help validate your logistic regression model:

Console output showing the results of logistic regression assumption checks

See full assumption check output →

Installation

Stable release (CRAN):

install.packages("plotor")

Using {pak}:

# install.packages("pak")
pak::pak("plotor")

Development version:

# install.packages("pak")
pak::pak("craig-parylo/plotor")

Minimal example

library(plotor)

# load the titanic dataset from the package
df_titanic <- get_df_titanic()

# make a logistic regression model using this data
model <- stats::glm(
  data = df_titanic,
  formula = Survived ~ Class + Sex + Age,
  family = "binomial"
)

# plot the odds ratios
plot_or(model)