For a single dataframe, summarise the column types. If two dataframes are supplied, compare column type composition of both dataframes.
inspect_types(df1, df2 = NULL, compare_index = FALSE)A dataframe.
An optional second dataframe for comparison.
Whether to check column positions as well as types when comparing dataframes.
Defaults to FALSE.
A tibble summarising the count and percentage of different column types for one or a pair of data frames.
For a single dataframe, the tibble returned contains the columns:
type, a character vector containing the column types in df1.
cnt, integer counts of each type.
pcnt, the percentage of all columns with each type.
col_name, the names of columns with each type.
For a pair of dataframes, the tibble returned contains the columns:
type, a character vector containing the column types in
df1 and df2.
cnt_1, cnt_2, pair of integer columns containing counts of each type -
in each of df1 and df2
For a grouped dataframe, the tibble returned is as for a single dataframe, but where
the first k columns are the grouping columns. There will be as many rows in the result
as there are unique combinations of the grouping variables.
# Load dplyr for starwars data & pipe
library(dplyr)
# Single dataframe summary
inspect_types(starwars)
#> # A tibble: 4 × 4
#> type cnt pcnt col_name
#> <chr> <int> <dbl> <named list>
#> 1 character 8 57.1 <chr [8]>
#> 2 list 3 21.4 <chr [3]>
#> 3 numeric 2 14.3 <chr [2]>
#> 4 integer 1 7.14 <chr [1]>
# Paired dataframe comparison
inspect_types(starwars, starwars[1:20, ])
#> # A tibble: 4 × 6
#> type equal cnt_1 cnt_2 columns issues
#> <chr> <chr> <int> <int> <named list> <list>
#> 1 character ✔ 8 8 <tibble [16 × 2]> <NULL>
#> 2 list ✔ 3 3 <tibble [6 × 2]> <NULL>
#> 3 numeric ✔ 2 2 <tibble [4 × 2]> <NULL>
#> 4 integer ✔ 1 1 <tibble [2 × 2]> <NULL>