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This is a visual analogue to miss_var_summary
. It draws a ggplot of the
number of missings in each variable, ordered to show which variables have
the most missing data. A default minimal theme is used, which can be
customised as normal for ggplot.
gg_miss_var(x, facet, show_pct = FALSE)
a dataframe
(optional) bare variable name, if you want to create a faceted plot.
logical shows the number of missings (default), but if set to TRUE, it will display the proportion of missings.
a ggplot object depicting the number of missings in a given column
geom_miss_point()
gg_miss_case()
gg_miss_case_cumsum gg_miss_fct()
gg_miss_span()
gg_miss_var()
gg_miss_var_cumsum()
gg_miss_which()
# NOT RUN {
gg_miss_var(airquality)
# }
# NOT RUN {
library(ggplot2)
gg_miss_var(airquality) + labs(y = "Look at all the missing ones")
gg_miss_var(airquality, Month)
gg_miss_var(airquality, Month, show_pct = TRUE)
gg_miss_var(airquality, Month, show_pct = TRUE) + ylim(0, 100)
# }
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