Exploring the naniar package

Sean Raleigh (Westminster College)
January 16, 2020

R Users Group, Salt Lake City, UT

Preliminaries

library(tidyverse)
library(naniar)

my_data <- read_csv("my_data.csv",
                    col_types = "ccicciici")

dim(my_data)

[1] 5340    9

Preliminaries

cat(names(my_data), sep = "\n")

gender
affective_motivation
cognitive_motivation
satisfaction
effort
OQ_admit
OQ_discharge
facility_type
age_at_admit

Visualizing missing data

vis_miss(my_data) + slide_theme_1

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Visualizing missing data

gg_miss_var(my_data) +
  slide_theme_1

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Visualizing missing data

gg_miss_var(my_data, facet = facility_type) +
  slide_theme_1

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Visualizing missing data

gg_miss_fct(x = my_data, fct = facility_type) +
  slide_theme_1

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Visualizing missing data

gg_miss_case(my_data) +
  slide_theme_1

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Visualizing missing data

gg_miss_case(my_data, facet = facility_type) +
  slide_theme_1

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Visualizing missing data

ggplot(my_data, aes(y = OQ_discharge,
                    x = OQ_admit)) +
  geom_miss_point() + slide_theme_1

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Visualizing missing data

ggplot(my_data, aes(y = OQ_discharge,
                    x = OQ_admit)) +
  geom_miss_point() +
  facet_grid(. ~ facility_type) +
  slide_theme_1

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Visualizing missing data

gg_miss_upset(my_data, text.scale = 3)

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Fin