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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.

Este ejercicio forma parte del curso

Dealing With Missing Data in R

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Instrucciones del ejercicio

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?

Ejercicio interactivo práctico

Prueba este ejercicio y completa el código de muestra.

# 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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