Each call produces a skim_df
, which is a fundamentally a tibble with a
special print method. One unusual feature of this data frame is pseudo-
namespace for columns. skim()
computes statistics by data type, and it
stores them in the data frame as <type>.<statistic>
. These types are
stripped when printing the results. The "base" skimmers (n_missing
and
complete_rate
) are the only columns that don't follow this behavior.
See skim_with()
for more details on customizing skim()
and
get_default_skimmers()
for a list of default functions.
If you just want to see the printed output, call skim_tee()
instead.
This function returns the original data. skim_tee()
uses the default
skim()
, but you can replace it with the skim
argument.
The data frame produced by skim
is wide and sparse. To avoid type coercion
skimr
uses a type namespace for all summary statistics. Columns for numeric
summary statistics all begin numeric
; for factor summary statistics
begin factor
; and so on.
See partition()
and yank()
for methods for transforming this wide data
frame. The first function splits it into a list, with each entry
corresponding to a data type. The latter pulls a single subtable for a
particular type from the skim_df
.
skim()
is designed to operate in pipes and to generally play nicely with
other tidyverse
functions. This means that you can use tidyselect
helpers
within skim
to select or drop specific columns for summary. You can also
further work with a skim_df
using dplyr
functions in a pipeline.