The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Old friends

Create the ggmice equivalent of mice plots

How to re-create the output of the plotting functions from mice with ggmice. In alphabetical order of the mice functions.

First load the ggmice, mice, and ggplot2 packages, some incomplete data and a mids object into your workspace.

# load packages
library(ggmice)
library(mice)
library(ggplot2)
# load incomplete dataset from mice
dat <- boys
# generate imputations
imp <- mice(dat, method = "pmm", printFlag = FALSE)

bwplot

Box-and-whisker plot of observed and imputed data.

# original plot
mice::bwplot(imp, hgt ~ .imp)

# ggmice equivalent
ggmice(imp, aes(x = .imp, y = hgt)) +
  geom_boxplot() +
  labs(x = "Imputation number")

# extended reproduction with ggmice
ggmice(imp, aes(x = .imp, y = hgt)) +
  stat_boxplot(geom = "errorbar", linetype = "dashed") +
  geom_boxplot(outlier.colour = "grey", outlier.shape = 1) +
  labs(x = "Imputation number") +
  theme(legend.position = "none")

densityplot

Density plot of observed and imputed data.

# original plot
mice::densityplot(imp, ~hgt)

# ggmice equivalent
ggmice(imp, aes(x = hgt, group = .imp)) +
  geom_density()

# extended reproduction with ggmice
ggmice(imp, aes(x = hgt, group = .imp, size = .where)) +
  geom_density() +
  scale_size_manual(
    values = c("observed" = 1, "imputed" = 0.5),
    guide = "none"
  ) +
  theme(legend.position = "none")

fluxplot

Influx and outflux plot of multivariate missing data patterns.

# original plot
fluxplot(dat)

# ggmice equivalent
plot_flux(dat)

md.pattern

Missing data pattern plot.

# original plot
md <- md.pattern(dat)

# ggmice equivalent
plot_pattern(dat)

# extended reproduction with ggmice
plot_pattern(dat, square = TRUE) +
  theme(
    legend.position = "none",
    axis.title = element_blank(),
    axis.title.x.top = element_blank(),
    axis.title.y.right = element_blank()
  )

plot.mids

Plot the trace lines of the MICE algorithm.

# original plot
plot(imp, hgt ~ .it | .ms)

# ggmice equivalent
plot_trace(imp, "hgt")

stripplot

Stripplot of observed and imputed data.

# original plot
mice::stripplot(imp, hgt ~ .imp)

# ggmice equivalent
ggmice(imp, aes(x = .imp, y = hgt)) +
  geom_jitter(width = 0.25) +
  labs(x = "Imputation number")

# extended reproduction with ggmice (not recommended)
ggmice(imp, aes(x = .imp, y = hgt)) +
  geom_jitter(
    shape = 1,
    width = 0.1,
    na.rm = TRUE,
    data = data.frame(
      hgt = dat$hgt,
      .imp = factor(rep(1:imp$m, each = nrow(dat))),
      .where = "observed"
    )
  ) +
  geom_jitter(shape = 1, width = 0.1) +
  labs(x = "Imputation number") +
  theme(legend.position = "none")

xyplot

Scatterplot of observed and imputed data.

# original plot
mice::xyplot(imp, hgt ~ age)

# ggmice equivalent
ggmice(imp, aes(age, hgt)) +
  geom_point()

# extended reproduction with ggmice
ggmice(imp, aes(age, hgt)) +
  geom_point(size = 2, shape = 1) +
  theme(legend.position = "none")

Extensions

Interactive plots

To make ggmice visualizations interactive, the plotly package can be used. For example, an interactive influx and outflux plot may be more legible than a static one.

# load packages
library(plotly)
# influx and outflux plot
p <- plot_flux(dat)
ggplotly(p)

Plot multiple variables

You may want to create a plot visualizing the imputations of multiple variables as one object. To visualize multiple variables at once, the variable names are saved in a vector. This vector is used together with the functional programming package purrr and visualization package patchwork to map() over the variables and subsequently wrap_plots to create a single figure.

# load packages
library(purrr)
library(patchwork)
# create vector with variable names
vrb <- names(dat)

Display box-and-whisker plots for all variables.

# original plot
mice::bwplot(imp)

# ggmice equivalent
p <- map(vrb, ~ {
  ggmice(imp, aes(x = .imp, y = .data[[.x]])) +
    geom_boxplot() +
    scale_x_discrete(drop = FALSE) +
    labs(x = "Imputation number")
})
wrap_plots(p, guides = "collect") &
  theme(legend.position = "bottom")

Display density plots for all variables.

# original plot
mice::densityplot(imp)

# ggmice equivalent
p <- map(vrb, ~ {
  ggmice(imp, aes(x = .data[[.x]], group = .imp)) +
    geom_density()
})
wrap_plots(p, guides = "collect") &
  theme(legend.position = "bottom")

Display strip plots for all variables.

# original plot
mice::stripplot(imp)

# ggmice equivalent
p <- map(vrb, ~ {
  ggmice(imp, aes(x = .imp, y = .data[[.x]])) +
    geom_jitter() +
    labs(x = "Imputation number")
})
wrap_plots(p, guides = "collect") &
  theme(legend.position = "bottom")

This is the end of the vignette. This document was generated using:

sessionInfo()
#> R version 4.5.0 (2025-04-11)
#> Platform: aarch64-apple-darwin20
#> Running under: macOS Sequoia 15.5
#> 
#> Matrix products: default
#> BLAS:   /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRblas.0.dylib 
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1
#> 
#> locale:
#> [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#> 
#> time zone: Europe/Amsterdam
#> tzcode source: internal
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] patchwork_1.3.1 purrr_1.0.4     plotly_4.11.0   ggmice_0.1.1   
#> [5] ggplot2_3.5.2   mice_3.18.0    
#> 
#> loaded via a namespace (and not attached):
#>  [1] gtable_0.3.6       shape_1.4.6.1      xfun_0.52          bslib_0.9.0       
#>  [5] htmlwidgets_1.6.4  lattice_0.22-6     crosstalk_1.2.1    vctrs_0.6.5       
#>  [9] tools_4.5.0        Rdpack_2.6.4       generics_0.1.4     tibble_3.3.0      
#> [13] pan_1.9            pkgconfig_2.0.3    jomo_2.7-6         Matrix_1.7-3      
#> [17] data.table_1.17.8  RColorBrewer_1.1-3 lifecycle_1.0.4    compiler_4.5.0    
#> [21] farver_2.1.2       stringr_1.5.1      codetools_0.2-20   htmltools_0.5.8.1 
#> [25] sass_0.4.10        yaml_2.3.10        lazyeval_0.2.2     glmnet_4.1-9      
#> [29] pillar_1.11.0      nloptr_2.2.1       jquerylib_0.1.4    tidyr_1.3.1       
#> [33] MASS_7.3-65        cachem_1.1.0       reformulas_0.4.1   iterators_1.0.14  
#> [37] rpart_4.1.24       boot_1.3-31        foreach_1.5.2      mitml_0.4-5       
#> [41] nlme_3.1-168       tidyselect_1.2.1   digest_0.6.37      stringi_1.8.7     
#> [45] dplyr_1.1.4        labeling_0.4.3     splines_4.5.0      fastmap_1.2.0     
#> [49] grid_4.5.0         cli_3.6.5          magrittr_2.0.3     survival_3.8-3    
#> [53] broom_1.0.8        withr_3.0.2        scales_1.4.0       backports_1.5.0   
#> [57] rmarkdown_2.29     httr_1.4.7         nnet_7.3-20        lme4_1.1-37       
#> [61] evaluate_1.0.4     knitr_1.50         rbibutils_2.3      viridisLite_0.4.2 
#> [65] rlang_1.1.6        Rcpp_1.0.14        glue_1.8.0         rstudioapi_0.17.1 
#> [69] minqa_1.2.8        jsonlite_2.0.0     R6_2.6.1

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.