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Summary and comparison of column types

Source: R/inspect_types.R
inspect_types.Rd

For a single data frame, summarise the column types. If two data frames are supplied, compare column type composition of both data frames.

Usage

inspect_types(df1, df2 = NULL, compare_index = FALSE)

Arguments

df1

A data frame.

df2

An optional second data frame for comparison.

compare_index

Whether to check column positions as well as types when comparing data frames. Defaults to FALSE.

Value

A tibble summarising the count and percentage of different column types for one or a pair of data frames.

Details

For a single data frame, 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 data frames, 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 data frame, the tibble returned is as for a single data frame, 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.

See also

show_plot

Author

Alastair Rushworth

Examples

# Load dplyr for starwars data & pipe
library(dplyr)

# Single data frame 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 data frame 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>