Enhance a mice imputation workflow with visualizations for incomplete and/or imputed data. The ggmice functions produce ggplot objects which may be easily manipulated or extended. Use ggmice to inspect missing data, develop imputation models, evaluate algorithmic convergence, or compare observed versus imputed data.

You can install the latest ggmice release from CRAN with:

install.packages("ggmice")

Alternatively, you could install the development version of ggmice from GitHub with:

# install.packages("devtools")
devtools::install_github("amices/ggmice")

Inspect the missing data in an incomplete dataset and subsequently evaluate the imputed data points against observed data. See the Get started vignette for an overview of all functionalities. Example data from mice.

# load packages
library(ggplot2)
library(mice)
library(ggmice)
# load some data
dat <- boys
# visualize the incomplete data
ggmice(dat, aes(age, bmi)) + geom_point()

# impute the incomplete data
imp <- mice(dat, m = 1, seed = 1, printFlag = FALSE)
# visualize the imputed data
ggmice(imp, aes(age, bmi)) + geom_point() 

The ggmice package is developed with guidance and feedback from Gerko Vink, Stef van Buuren, Thomas Debray, Valentijn de Jong, Johanna Muñoz, Thom Volker, Mingyang Cai and Anaïs Fopma. The ggmice hex is based on designs from the ggplot2 hex and the mice hex (by Jaden Walters).

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under ReCoDID grant agreement No 825746.

You are invited to join the improvement and development of ggmice. Please note that the project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.