Visualizing missingness patterns
Let's practice a few different ways to visualize patterns of missingness using:
gg_miss_upset()
to give an overall pattern of missingness.gg_miss_fct()
for a dataset that has a factor of interest: marriage.- and
gg_miss_span()
to explore the missingness in a time series dataset.
What do you notice with the missingness and the faceting in the data?
This exercise is part of the course
Dealing With Missing Data in R
Exercise instructions
- Explore missingness pattern of the
airquality
dataset withgg_miss_upset()
. - Explore how the missingness changes in the
riskfactors
dataset across themarital
variable usinggg_miss_fct()
- Explore how the missingness changes in the
pedestrian
dataset across thehourly_counts
variable over a span of 3000 (you can also try different spans from 2000-5000). - Explore the impact of
month
onhourly_counts
by including it in thefacet
argument, with a span of 1000.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Using the airquality dataset, explore the missingness pattern using gg_miss_upset()
gg_miss_upset(airquality)
# With the riskfactors dataset, explore how the missingness changes across the marital variable using gg_miss_fct()
gg_miss_fct(x = riskfactors, fct = marital)
# Using the pedestrian dataset, explore how the missingness of hourly_counts changes over a span of 3000
gg_miss_span(pedestrian, var = ___, span_every = ___)
# Using the pedestrian dataset, explore the impact of month by faceting by month
# and explore how missingness changes for a span of 1000
____(___, var = ___ , span_every = ___, facet = ___)