Plot incomplete or imputed data

Plot incomplete or imputed data

Usage

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

Arguments

data

An incomplete dataset (of class data.frame), or an object of class mice::mids.

mapping

A list of aesthetic mappings created with ggplot2::aes().

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

library(ggplot2)
# minimal example: scatterplot of incomplete and imputed data
dat <- mice::nhanes
ggmice(dat, aes(x = age, y = chl)) + geom_point()

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


# more advanced functionality for incomplete data
# edit variable type for mixed incomplete data
dat$hyp <- factor(dat$hyp, levels = (1:2), labels = c("no hypertension", "hypertension"))
# scatterplot with categorical incomplete data
  ggmice(dat, aes(hyp, chl)) + geom_jitter(width = 0.1)

# incomplete data scatterplot faceted by categorical variable
ggmice(dat, aes(age, chl)) + geom_point() +
  facet_wrap(~ hyp, labeller = label_both)

# incomplete data scatterplot faceted by missing data indicator
ggmice(dat, aes(age, chl)) + geom_point() +
  facet_wrap(~ factor(is.na(hyp) == 0, labels = c("hyp observed", "hyp missing")))


# more advanced functionality for imputed data
# stripplot by imputation
ggmice(imp, aes(x = .imp, y = chl)) + geom_jitter(width = 0.25) +
  labs(x = "Imputation number")

# box plot by imputation
ggmice(imp, aes(x = .imp, y = chl)) + geom_boxplot() +
  labs(x = "Imputation number")

# density plot by imputation
ggmice(imp, aes(x = chl, group = .imp)) + geom_density()

# scatterplot faceted by imputation number
ggmice(imp, ggplot2::aes(x = age, y = bmi)) + ggplot2::geom_point() +
  facet_wrap(~ .imp)