missingness_patterns: missingness patterns
In grebbel/formr_test: formr survey framework

this function shows how common possible missingness patterns are. Emulates misschk in stata.

excludes any variables that don't have any missings, so as not to clutter output. Disable using omit_complete
sorts variables by number of missings, so that the usual suspects show up at the front.
displays number of missings accounted for by each pattern

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missingness_patterns(df, min_freq = ifelse(relative, 1/nrow(df), 1),
  long_pattern = FALSE, print_legend = ifelse(long_pattern, FALSE, TRUE),
  show_culprit = TRUE, relative = FALSE, omit_complete = TRUE)

`df`	dataset
`min_freq`	show only patterns that occur at least this often. Defaults to 1 observation.
`long_pattern`	by default (FALSE) only shows column indices for space and legibility reasons.
`print_legend`	prints a legend for the column indices, defaults to FALSE if long_pattern is set
`show_culprit`	defaults to TRUE. In case a missingness pattern boils down to one variable, it will be shown here.
`relative`	defaults to FALSE. If true, percentages are shown (relative to total before excluding minimum frequency).
`omit_complete`	defaults to TRUE. Columns that don't have any missings are excluded.

data(ChickWeight)
ChickWeight[1:2,c('weight','Chick')] = NA
ChickWeight[3:5,'Diet'] = NA
names(ChickWeight); nrow(ChickWeight)
missingness_patterns(ChickWeight)