# 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:

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