# Rows: 87 (avg missing ratio: 8.6 ± 9.1 %)
# Cols: 14 (avg missing ratig: 8.6 ± 15 %)
# suggesting $col_type_strings for reading data:  cincccnffcclll 
$df_summary# :
# A tibble: 14 × 4
   columns    col_type  n_uniq_Val nMISS
   <chr>      <chr>          <int> <int>
 1 name       character         87     0
 2 height     integer           46     6
 3 mass       numeric           39    28
 4 hair_color character         12     5
 5 skin_color character         31     0
 6 eye_color  character         15     0
 7 birth_year numeric           37    44
 8 sex        factor             5     4
 9 gender     factor             3     4
10 homeworld  character         49    10
11 species    character         38     4
12 films      list               7     0
13 vehicles   list              10     0
14 starships  list              15     0
 # $numeric_data_summary :
# A tibble: 3 × 9
    idx column      Min.   QT1 Median  Mean   QT3  Max. nMISS
  <dbl> <chr>      <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl>
1     2 height        66 167      180 175.  191     264     6
2     3 mass          15  55.6     79  97.3  84.5  1358    28
3     7 birth_year     8  35       52  87.6  72     896    44
# $category_column_summary:
       We have  2  elements with total 6 categories