Title:	Visualizations for 'mice' with 'ggplot2'
Version:	0.1.1
Description:	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.
License:	GPL (≥ 3)
URL:	https://amices.org/ggmice/
BugReports:	https://github.com/amices/ggmice/issues
Imports:	cli, dplyr, ggplot2, magrittr, mice, purrr, rlang, scales, stats, stringr, tidyr, tidyselect, utils
Suggests:	covr, knitr, lifecycle, patchwork, plotly, rmarkdown, testthat (≥ 3.0.0)
VignetteBuilder:	knitr
Config/testthat/edition:	3
Copyright:	'ggmice' authors
Encoding:	UTF-8
RoxygenNote:	7.3.2
NeedsCompilation:	no
Packaged:	2025-07-30 13:21:49 UTC; 4216318
Author:	Hanne Oberman [aut, cre], Utrecht University [fnd], University Medical Centre Utrecht [fnd], Thom Volker [ctb], Gerko Vink [ctb], Pepijn Vink [ctb], Jamie Wallis [ctb], Kyle Lang [ctb]
Maintainer:	Hanne Oberman <h.i.oberman@uu.nl>
Repository:	CRAN
Date/Publication:	2025-07-30 13:50:02 UTC

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

Value

The result of calling rhs(lhs).

Box-and-whisker plot of observed and imputed data

Description

Box-and-whisker plot of observed and imputed data

Usage

bwplot(...)

Arguments

Value

The output of mice::bwplot and a message about the ggmice equivalent.

Examples

imp <- mice::mice(mice::nhanes, maxit = 1, printFlag = FALSE)
bwplot(imp)

Densityplot of observed and imputed data

Description

Densityplot of observed and imputed data

Usage

densityplot(...)

Arguments

Value

The output of mice::densityplot and a message about the ggmice equivalent.

Examples

imp <- mice::mice(mice::nhanes, maxit = 1, printFlag = FALSE)
densityplot(imp)

Plot incomplete or imputed data

Description

Plot incomplete or imputed data

Usage

ggmice(data = NULL, mapping = ggplot2::aes())

Arguments

Value

An object of class ggplot2::ggplot. The ggmice function returns output equivalent to ggplot2::ggplot output, with a few important exceptions:

• The theme is set to theme_mice.

• The color scale is set to the mice::mdc colors.

• The colour aesthetic is set to .where, an internally defined variable which distinguishes observed data from missing data or imputed data (for incomplete and imputed data, respectively).

See Also

See the ggmice vignette to use the ggmice() function on incomplete data or imputed data.

Examples

dat <- mice::nhanes
ggmice(dat, ggplot2::aes(x = age, y = bmi)) + ggplot2::geom_point()
imp <- mice::mice(dat, print = FALSE)
ggmice(imp, ggplot2::aes(x = age, y = bmi)) + ggplot2::geom_point()

Plot correlations between (incomplete) variables

Description

Plot correlations between (incomplete) variables

Usage

plot_corr(
  data,
  vrb = "all",
  label = FALSE,
  square = TRUE,
  diagonal = FALSE,
  rotate = FALSE,
  caption = TRUE
)

Arguments

Value

An object of class ggplot2::ggplot.

Examples

# plot correlations for all columns
plot_corr(mice::nhanes)

# plot correlations for specific columns by supplying a character vector
plot_corr(mice::nhanes, c("chl", "hyp"))

# plot correlations for specific columns by supplying unquoted variable names
plot_corr(mice::nhanes, c(chl, hyp))

# plot correlations for specific columns by passing an object with variable names
# from the environment, unquoted with `!!`
my_variables <- c("chl", "hyp")
plot_corr(mice::nhanes, !!my_variables)
# object with variable names must be unquoted with `!!`
try(plot_corr(mice::nhanes, my_variables))

Plot the influx and outflux of a multivariate missing data pattern

Description

Plot the influx and outflux of a multivariate missing data pattern

Usage

plot_flux(data, vrb = "all", label = TRUE, caption = TRUE)

Arguments

Value

An object of class ggplot2::ggplot.

Examples

# plot flux for all columns
plot_flux(mice::nhanes)

# plot flux for specific columns by supplying a character vector
plot_flux(mice::nhanes, c("chl", "hyp"))

# plot flux for specific columns by supplying unquoted variable names
plot_flux(mice::nhanes, c(chl, hyp))

# plot flux for specific columns by passing an object with variable names
# from the environment, unquoted with `!!`
my_variables <- c("chl", "hyp")
plot_flux(mice::nhanes, !!my_variables)
# object with variable names must be unquoted with `!!`
try(plot_flux(mice::nhanes, my_variables))

Plot missingness in a dataset

Description

Usage

plot_miss(
  data,
  vrb = "all",
  ordered = FALSE,
  rotate = FALSE,
  grid = FALSE,
  square = FALSE
)

Arguments

Value

An object of class ggplot2::ggplot.

Examples

# plot correlations for all columns
plot_miss(mice::nhanes)

# plot correlations for specific columns by supplying a character vector
plot_miss(mice::nhanes, c("chl", "hyp"))

# plot correlations for specific columns by supplying unquoted variable names
plot_miss(mice::nhanes, c(chl, hyp))

# plot correlations for specific columns by passing an object with variable names
# from the environment, unquoted with `!!`
my_variables <- c("chl", "hyp")
plot_miss(mice::nhanes, !!my_variables)
# object with variable names must be unquoted with `!!`
try(plot_miss(mice::nhanes, my_variables))

# plot larger dataset
plot_miss(mice::boys)
plot_miss(mice::boys, ordered = TRUE)

Plot the missing data pattern of an incomplete dataset

Description

Plot the missing data pattern of an incomplete dataset

Usage

plot_pattern(
  data,
  vrb = "all",
  square = TRUE,
  rotate = FALSE,
  cluster = NULL,
  npat = NULL,
  caption = TRUE
)

Arguments

Value

An object of class ggplot2::ggplot.

Examples

# plot missing data pattern for all columns
plot_pattern(mice::nhanes)

# plot missing data pattern for specific columns by supplying a character vector
plot_pattern(mice::nhanes, c("chl", "hyp"))

# plot missing data pattern for specific columns by supplying unquoted variable names
plot_pattern(mice::nhanes, c(chl, hyp))

# plot missing data pattern for specific columns by passing an object with variable names
# from the environment, unquoted with `!!`
my_variables <- c("chl", "hyp")
plot_pattern(mice::nhanes, !!my_variables)
# object with variable names must be unquoted with `!!`
try(plot_pattern(mice::nhanes, my_variables))

Plot the predictor matrix of an imputation model

Description

Plot the predictor matrix of an imputation model

Usage

plot_pred(
  data,
  vrb = "all",
  method = NULL,
  label = TRUE,
  square = TRUE,
  rotate = FALSE
)

Arguments

Details

The predictor matrix in mice::mice determines the role an imputation model predictor takes in the imputation model. The rows correspond to incomplete target variables, and the columns to imputation model predictors.

A value of 1 indicates that the column variable is a predictor to impute the target (row) variable. The value 0 means that it is not used as predictor.

Imputation methods for multilevel data use other codes than 0 and 1:

• Methods ⁠2l.bin⁠, ⁠2l.lmer⁠, ⁠2l.norm⁠, ⁠2l.pan⁠, ⁠2lonly.mean⁠, ⁠2lonly.norm⁠ and ⁠2lonly.pmm⁠ use code -2 to indicate the class variable;

• Methods ⁠2l.bin⁠, ⁠2l.lmer⁠, ⁠2l.norm⁠ and ⁠2l.pan⁠ use code 2 to indicate the random effects;

• Method ⁠2l.pan⁠ uses codes 3 and 4 to add class means to codes 1 and 2 respectively.

Value

An object of class ggplot2::ggplot.

References

van Buuren, S. (2018). Flexible imputation of missing data. Chapman and Hall/CRC. stefvanbuuren.name/fimd

Examples

# generate a predictor matrix
pred <- mice::quickpred(mice::nhanes)

# plot predictor matrix for all columns
plot_pred(pred)

# plot predictor matrix for specific columns by supplying a character vector
plot_pred(pred, c("chl", "hyp"))

# plot predictor matrix for specific columns by supplying unquoted variable names
plot_pred(pred, c(chl, hyp))

# plot predictor matrix for specific columns by passing an object with variable names
# from the environment, unquoted with `!!`
my_variables <- c("chl", "hyp")
plot_pred(pred, !!my_variables)
# object with variable names must be unquoted with `!!`
try(plot_pred(pred, my_variables))

# plot predictor matrix of mids object
imp <- mice::mice(mice::nhanes, print = FALSE)
plot_pred(imp)

Plot the trace lines of the imputation algorithm

Description

Plot the trace lines of the imputation algorithm

Usage

plot_trace(data, vrb = "all", trend = FALSE, legend = TRUE)

Arguments

Details

The vrb argument is "quoted" via rlang::enexpr() and evaluated according to tidy evaluation principles. In practice, this technical nuance only affects users when passing an object from the environment (e.g., a vector of variable names) to the vrb argument. In such cases, the object must be "unquoted" via the ⁠!!⁠ prefix operator.

Value

An object of class ggplot2::ggplot.

Examples

# create [mice::mids] object with [mice::mice()]
imp <- mice::mice(mice::nhanes, print = FALSE)

# plot trace lines for all imputed columns
plot_trace(imp)

# plot trace lines for specific columns by supplying a string or character vector
plot_trace(imp, "chl")
plot_trace(imp, c("chl", "hyp"))
# plot trace lines for specific columns by supplying unquoted variable names
plot_trace(imp, chl)
plot_trace(imp, c(chl, hyp))

# plot trace lines for specific columns by passing an object with variable names
# from the environment, unquoted with `!!`
my_variables <- c("chl", "hyp")
plot_trace(imp, !!my_variables)
# object with variable names must be unquoted with `!!`
try(plot_trace(imp, my_variables))

Stripplot of observed and imputed data

Description

Stripplot of observed and imputed data

Usage

stripplot(...)

Arguments

Value

The output of mice::stripplot and a message about the ggmice equivalent.

Examples

imp <- mice::mice(mice::nhanes, maxit = 1, printFlag = FALSE)
stripplot(imp)

Theme for mice::mice style ggplot2::ggplot objects

Description

Theme for mice::mice style ggplot2::ggplot objects

Usage

theme_mice()

Value

A ggplot2::ggplot theme.

Minimal theme for mice::mice style ggplot2::ggplot objects

Description

Minimal theme for mice::mice style ggplot2::ggplot objects

Usage

theme_minimice()

Value

A ggplot2::ggplot theme.

Scatterplot of observed and imputed data

Description

Scatterplot of observed and imputed data

Usage

xyplot(...)

Arguments

Value

The output of mice::xyplot and a message about the ggmice equivalent.

Examples

imp <- mice::mice(mice::nhanes, maxit = 1, printFlag = FALSE)
xyplot(imp, bmi ~ age)