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.
partition(data)
bind(data)
yank(data, skim_type)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 top_counts
#> 1 Species 0 1 FALSE 3 set: 50, ver: 50, vir:…