The data frames produced by skim() are wide and sparse, filled with columns that are mostly NA. For that reason, it can be convenient to work with "by type" subsets of the original data frame. These smaller subsets have their NA columns removed.



yank(data, skim_type)



A skim_df.


A character scalar. The subtable to extract from a skim_df.


A skim_list of skim_df's, by type.


partition() creates a list of smaller skim_df data frames. Each entry in the list is a data type from the original skim_df. The inverse of partition() is bind(), which takes the list and produces the original skim_df. While partition() keeps all of the subtables as list entries, yank() gives you a single subtable for a data type.


  • bind: The inverse of a partition(). Rebuild the original skim_df.

  • yank: Extract a subtable from a skim_df with a particular type.


# Create a wide skimmed data frame (a skim_df) skimmed <- skim(iris) # Separate into a list of subtables by type separate <- partition(skimmed) # Put back together identical(bind(separate), skimmed)
#> [1] TRUE
# > TRUE # Alternatively, get the subtable of a particular type yank(skimmed, "factor")
#> #> ── Variable type: factor ─────────────────────────────────────────────────────── #> skim_variable n_missing complete_rate ordered n_unique #> 1 Species 0 1 FALSE 3 #> top_counts #> 1 set: 50, ver: 50, vir: 50