## ----knitr-setup, include = FALSE---------------------------------------------
knitr::opts_chunk$set(fig.align = "center",
                      fig.width = 5,
                      fig.height = 4,
                      dpi = 100)


## ----vis-miss-----------------------------------------------------------------
library(naniar)

vis_miss(airquality)

## ----gg-miss-upset------------------------------------------------------------

gg_miss_upset(airquality)


## ----upset-plot-riskfactors---------------------------------------------------

gg_miss_upset(riskfactors)


## ----gg-miss-upset-n-var-miss-------------------------------------------------
# how many missings?
n_var_miss(riskfactors)

gg_miss_upset(riskfactors, nsets = n_var_miss(riskfactors))


## ----gg-miss-upset-n-sets-----------------------------------------------------

gg_miss_upset(riskfactors, 
              nsets = 10,
              nintersects = 50)


## ----gg-miss-upset-nintersect-NA----------------------------------------------
gg_miss_upset(riskfactors, 
              nsets = 10,
              nintersects = NA)

## ----ggplot-geom-miss-point---------------------------------------------------
library(ggplot2)
# using regular geom_point()
ggplot(airquality,
       aes(x = Ozone,
           y = Solar.R)) +
geom_point()

library(naniar)

# using  geom_miss_point()
ggplot(airquality,
       aes(x = Ozone,
           y = Solar.R)) +
 geom_miss_point()

# Facets!
ggplot(airquality,
       aes(x = Ozone,
           y = Solar.R)) +
 geom_miss_point() + 
 facet_wrap(~Month)

# Themes
ggplot(airquality,
       aes(x = Ozone,
           y = Solar.R)) +
 geom_miss_point() + 
 theme_dark()



## ----gg-miss-var--------------------------------------------------------------

gg_miss_var(airquality)
library(ggplot2)
gg_miss_var(airquality) + labs(y = "Look at all the missing ones")


## ----gg-miss-var-show-pct-----------------------------------------------------
gg_miss_var(airquality, show_pct = TRUE)

## ----gg-miss-var-group--------------------------------------------------------

gg_miss_var(airquality,
            facet = Month)


## ----gg-miss-case-------------------------------------------------------------
gg_miss_case(airquality)
gg_miss_case(airquality) + labs(x = "Number of Cases")

## ----gg-miss-case-order-by-case-----------------------------------------------

gg_miss_case(airquality, order_cases = TRUE)


## ----gg-miss-case-group-------------------------------------------------------

gg_miss_case(airquality, facet = Month)


## ----gg-miss-fct--------------------------------------------------------------

gg_miss_fct(x = riskfactors, fct = marital)
library(ggplot2)
gg_miss_fct(x = riskfactors, fct = marital) + labs(title = "NA in Risk Factors and Marital status")

# using group_by
library(dplyr)
riskfactors %>%
  group_by(marital) %>%
  miss_var_summary()


## -----------------------------------------------------------------------------
gg_miss_fct(oceanbuoys, year)
# to load who data
library(tidyr)
gg_miss_fct(who, year)

## ----gg-miss-span-------------------------------------------------------------

# data method

miss_var_span(pedestrian, hourly_counts, span_every = 3000)

gg_miss_span(pedestrian, hourly_counts, span_every = 3000)
# works with the rest of ggplot
gg_miss_span(pedestrian, hourly_counts, span_every = 3000) + labs(x = "custom")
gg_miss_span(pedestrian, hourly_counts, span_every = 3000) + theme_dark()


## ----gg-miss-span-group-------------------------------------------------------

gg_miss_span(pedestrian, 
             hourly_counts, 
             span_every = 3000, 
             facet = sensor_name)


## ----gg-miss-case-cumsum------------------------------------------------------
gg_miss_case_cumsum(airquality)
library(ggplot2)
gg_miss_case_cumsum(riskfactors, breaks = 50) + theme_bw()


## ----gg-miss-var-cumsum-------------------------------------------------------
gg_miss_var_cumsum(airquality)

## ----gg-miss-which------------------------------------------------------------
gg_miss_which(airquality)