Nothing
## ----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)
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