ggmice

Visualize incomplete and imputed data with the R package `ggmice`

https://github.com/amices/ggmice

Keywords

Repository

Visualize incomplete and imputed data with the R package `ggmice`

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# `ggmice` 


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## Visualizations for `mice` with `ggplot2`

Enhance a [`mice`](https://amices.org/mice/) imputation workflow with visualizations for incomplete and/or imputed data. The `ggmice` functions produce [`ggplot`](https://ggplot2.tidyverse.org/reference/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. 

## Installation

You can install the latest `ggmice` release from [CRAN](https://CRAN.R-project.org/package=ggmice) with:

``` r
install.packages("ggmice")
```

Alternatively, you could install the development version of `ggmice` from [GitHub](https://github.com/amices) with:

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

## Example

Inspect the missing data in an incomplete dataset and subsequently evaluate the imputed data points against observed data. See the [Get started](https://amices.org/ggmice/articles/ggmice.html) vignette for an overview of all functionalities. Example data from [`mice`](https://amices.org/mice/reference/boys), showing height (in cm) by age (in years).

```{r example, message=FALSE, warning=FALSE, echo=TRUE, results='hide', fig.keep='all'}
# load packages
library(ggplot2)
library(mice)
library(ggmice)
# load some data
dat <- boys
# visualize the incomplete data
ggmice(dat, aes(age, hgt)) + geom_point()
# impute the incomplete data
imp <- mice(dat, m = 1, seed = 1)
# visualize the imputed data
ggmice(imp, aes(age, hgt)) + geom_point()
```


## Acknowledgements

The `ggmice` package is developed with guidance and feedback from the [Amices](https://github.com/amices) team. The `ggmice` hex is based on the [`ggplot2`](https://github.com/tidyverse/ggplot2/) and  [`mice`](https://github.com/amices/mice) hex designs.

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


## Code of Conduct

You are invited to join the improvement and development of `ggmice`. Please note that the project is released with a [Contributor Code of Conduct](https://amices.org/ggmice/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.

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Owner

Citation (CITATION.cff)

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: ggmice
message: Please cite this software using these metadata.
type: software
authors:
  - given-names: Hanne Ida
    family-names: Oberman
    email: h.i.oberman@uu.nl
    affiliation: Utrecht University
    orcid: 'https://orcid.org/0000-0003-3276-2141'
repository-code: 'https://github.com/amices/ggmice'
url: 'https://amices.org/ggmice'
repository: 'https://CRAN.R-project.org/package=ggmice'
abstract: >-
  Enhance a 'mice' imputation workflow with
  visualizations for incomplete and/or imputed data.
  The plotting 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.
keywords:
  - mice
  - visualization
  - ggplot2
  - missing data
  - r
license: GPL-3.0+
commit: >-
  https://github.com/amices/ggmice/commit/fc5bb471a65307e8942a613f0f79423c893db1e2
version: v0.0.1
date-released: '2022-03-17'

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