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Visualizing missing cases and variables

To get a clear picture of the missingness across variables and cases, use gg_miss_var() and gg_miss_case(). These are the visual counterpart to miss_var_summary() and miss_case_summary().

These can be split up into multiple plots with one for each category by choosing a variable to facet by.

Cet exercice fait partie du cours

Dealing With Missing Data in R

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Instructions

Using the riskfactors dataset:

  • Visualize the number of missings in cases using gg_miss_case().
  • Explore the number of missings in cases using gg_miss_case() and facet by the variable education.
  • Visualize the number of missings in variables using gg_miss_var().
  • Explore the number of missings in variables using gg_miss_var() and facet by the variable education.

What do you notice in the visualizations of the whole data compared to the faceting?

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# Visualize the number of missings in cases using `gg_miss_case()`
gg_miss_case(___)

# Explore the number of missings in cases using `gg_miss_case()` 
# and facet by the variable `education`
gg_miss_case(___, facet = ___)

# Visualize the number of missings in variables using `gg_miss_var()`
gg_miss_var(___)

# Explore the number of missings in variables using `gg_miss_var()` 
# and facet by the variable `education`
___(___, facet = ___)
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