Generate missingness patterns using a function borrowed from mice, with options to reduce the complexity of the output.

md_pattern(data, omit_complete = TRUE, min_freq = 0.01)

Arguments

data

the dataset

omit_complete

defaults to TRUE, omitting variables without missing values

min_freq

minimum number of rows to have this missingness pattern

Examples

data("bfi", package = 'psych') md_pattern(bfi)
#> # A tibble: 4 x 28 #> description E4 N3 O4 A1 A5 C5 E2 A4 C3 O5 C1 #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 Missing va… 1 1 1 1 1 1 1 1 1 1 1 #> 2 Missing va… 9 11 14 16 16 16 16 19 20 20 21 #> 3 Missing va… 1 1 1 1 1 1 1 1 1 1 1 #> 4 99 other, … 95 92 93 90 93 93 92 87 87 88 93 #> # … with 16 more variables: E5 <dbl>, N2 <dbl>, N1 <dbl>, O1 <dbl>, E1 <dbl>, #> # C2 <dbl>, E3 <dbl>, A3 <dbl>, C4 <dbl>, A2 <dbl>, O3 <dbl>, N5 <dbl>, #> # N4 <dbl>, education <dbl>, var_miss <dbl>, n_miss <dbl>
md_pattern(bfi, omit_complete = FALSE, min_freq = 0.2)
#> # A tibble: 3 x 31 #> description O2 gender age E4 N3 O4 A1 A5 C5 E2 A4 #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 Missing va… 1 1 1 1 1 1 1 1 1 1 1 #> 2 Missing va… 0 0 0 9 11 14 16 16 16 16 19 #> 3 100 other,… 100 100 100 96 93 94 91 94 94 93 88 #> # … with 19 more variables: C3 <dbl>, O5 <dbl>, C1 <dbl>, E5 <dbl>, N2 <dbl>, #> # N1 <dbl>, O1 <dbl>, E1 <dbl>, C2 <dbl>, E3 <dbl>, A3 <dbl>, C4 <dbl>, #> # A2 <dbl>, O3 <dbl>, N5 <dbl>, N4 <dbl>, education <dbl>, var_miss <dbl>, #> # n_miss <dbl>