How do we visualize missing values?

Dealing With Missing Data in R

Nicholas Tierney

Statistician

Introduction to missing data visualizations in naniar

Visualization can quickly capture an idea or thought.
naniar provides a friendly family of missing data visualization functions.
Each visualization corresponds to a data summary.
Visualizations help you operate closer to the speed of thought.

Lesson overview

How to get a bird's eye view of the data
How to look at missings in the variables and cases
How to generate visualizations for missing spans and across groups in the data.

Get a bird's eye view of the missing data

vis_miss(airquality)

Get a bird's eye view of the missing data

vis_miss(airquality, cluster = TRUE)

Look at missings in variables and cases

gg_miss_var(airquality)

gg_miss_case(airquality)

Look at missings in variables and cases

gg_miss_var(airquality, facet = Month)

Visualizing missingness patterns

gg_miss_upset(airquality)

Visualizing factors of missingness

gg_miss_fct(x = airquality, fct = Month)

Visualizing spans of missingness

gg_miss_span(pedestrian, hourly_counts, span_every = 3000)

Let's practice!

Dealing With Missing Data in R

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