inst/doc/getting-started-w-naniar.R

In naniar: Data Structures, Summaries, and Visualisations for Missing Data

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


## ----vis-dat, echo = TRUE-----------------------------------------------------
library(visdat)
vis_dat(airquality)


## ----vis-miss-cluster-vanilla-------------------------------------------------

vis_miss(airquality)


## ----example-geom-point, echo = TRUE, fig.width = 5, fig.height=3-------------
library(ggplot2)
ggplot(airquality, 
       aes(x = Solar.R, 
           y = Ozone)) + 
  geom_point()

## ----geom-miss-point, echo = TRUE, out.width = "50%", fig.show='hold'---------

ggplot(airquality, 
       aes(x = Solar.R, 
           y = Ozone)) + 
  geom_point()

library(naniar)

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


## ----ggmissing-facet, echo = TRUE, out.width = "70%"--------------------------

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


## ----ggmissing-theme, echo = TRUE, out.width = "70%"--------------------------

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


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

gg_miss_var(airquality)


## ----gg-miss-var-theme--------------------------------------------------------
gg_miss_var(airquality) + theme_bw() 

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


## ----gg-miss-var-facet--------------------------------------------------------
gg_miss_var(airquality, facet = Month)

## ----mds-fig, out.width = "800px", echo = F-----------------------------------
knitr::include_graphics("missingness-data-structures.png")

## ----as-shadow----------------------------------------------------------------

as_shadow(airquality)


## ----bind-shadow, echo = TRUE-------------------------------------------------

aq_shadow <- bind_shadow(airquality)
aq_nab <- nabular(airquality)

library(dplyr)

glimpse(aq_shadow)
glimpse(aq_nab)

all.equal(aq_shadow, aq_nab)


## ----bind-shadow-pattern------------------------------------------------------

airquality %>%
  bind_shadow() %>%
  group_by(Ozone_NA) %>%
  summarise_at(.vars = "Solar.R",
               .funs = c("mean", "sd", "var", "min", "max"),
               na.rm = TRUE)

## ----shadow-plot, echo = TRUE, out.width = "60%"------------------------------

ggplot(aq_shadow,
       aes(x = Temp,
           colour = Ozone_NA)) + 
  geom_density()


## ----temp-humidity-explore----------------------------------------------------
# what if we explore the value of air temperature and humidity based on
# the missingness of each
  oceanbuoys %>%
    bind_shadow() %>%
    ggplot(aes(x = air_temp_c,
               fill = humidity_NA)) +
        geom_histogram()

  oceanbuoys %>%
    bind_shadow() %>%
    ggplot(aes(x = humidity,
               fill = air_temp_c_NA)) +
        geom_histogram()


## ----simpute-invisible, echo = TRUE, fig.height = 3---------------------------

library(simputation)
library(dplyr)

airquality %>%
  impute_lm(Ozone ~ Temp + Wind) %>%
  ggplot(aes(x = Temp,
             y = Ozone)) + 
  geom_point()
  

## ----simpute-visible, echo = TRUE, fig.height = 3-----------------------------

aq_shadow %>%
  as.data.frame() %>% 
  impute_lm(Ozone ~ Temp + Wind) %>%
  ggplot(aes(x = Temp,
             y = Ozone,
             colour = Ozone_NA)) + 
  geom_point()
  

## ----helpers-n-miss, echo = TRUE----------------------------------------------

dplyr::n_distinct(airquality)
dplyr::n_distinct(airquality$Ozone)

n_miss(airquality)
n_miss(airquality$Ozone)

n_complete(airquality)
n_complete(airquality$Ozone)


## ----prop-miss-case-----------------------------------------------------------
prop_miss_case(airquality)
pct_miss_case(airquality)

## ----miss-case-summary--------------------------------------------------------

miss_case_summary(airquality)


## ----miss-case-table----------------------------------------------------------

miss_case_table(airquality)


## ----prop-miss-var------------------------------------------------------------
prop_miss_var(airquality)
pct_miss_var(airquality)

## ----miss-var-summary---------------------------------------------------------

miss_var_summary(airquality)


## ----miss-var-table-----------------------------------------------------------

miss_var_table(airquality)


## ----miss-var-run-------------------------------------------------------------
miss_var_run(pedestrian,
             hourly_counts)

## ----ped-miss-var-span--------------------------------------------------------

miss_var_span(pedestrian,
              hourly_counts,
              span_every = 100)


## ----ped-miss-var-sum---------------------------------------------------------

pedestrian %>% miss_var_summary()


## ----ped-group-by-miss-var-sum------------------------------------------------

pedestrian %>%
 group_by(month) %>%
 miss_var_summary() %>%
 filter(variable == "hourly_counts")


## ----add-prop-miss, echo = TRUE-----------------------------------------------

airquality %>%
  add_prop_miss() %>%
  head()


## ----rpart-miss, echo = TRUE--------------------------------------------------

library(rpart)
library(rpart.plot)

airquality %>%
  add_prop_miss() %>%
  rpart(prop_miss_all ~ ., data = .) %>%
  prp(type = 4, extra = 101, prefix = "Prop. Miss = ")