R: Summary and comparison of the most common levels in...

inspect_imb {inspectdf} | R Documentation

## Summary and comparison of the most common levels in categorical columns

### Description

For a single dataframe, summarise the most common level in each categorical column. If two dataframes are supplied, compare the most common levels of categorical features appearing in both dataframes. For grouped dataframes, summarise the levels of categorical columns in the dataframe split by group.

### Usage

    inspect_imb(df1, df2 = NULL, include_na = FALSE)

### Arguments

`df1` | A dataframe.
`df2` | An optional second data frame for comparing columnwise imbalance. Defaults to `NULL`.
`include_na` | Logical flag, whether to include missing values as a unique level. Default is `FALSE` - to ignore `NA` values.

### Details

For a single dataframe, the tibble returned contains the columns:

- `col_name`, a character vector containing column names of `df1`.

- `value`, a character vector containing the most common categorical level in each column of `df1`.

- `pcnt`, the relative frequency of each column's most common categorical level expressed as a percentage.

- `cnt`, the number of occurrences of the most common categorical level in each column of `df1`.

For a pair of dataframes, the tibble returned contains the columns:

- `col_name`, a character vector containing names of the unique columns in `df1` and `df2`.

- `value`, a character vector containing the most common categorical level in each column of `df1`.

- `pcnt_1`, `pcnt_2`, the percentage occurrence of `value` in the column `col_name` for each of `df1` and `df2`, respectively.

- `cnt_1`, `cnt_2`, the number of occurrences of of `value` in the column `col_name` for each of `df1` and `df2`, respectively.

- `p_value`, p-value associated with the null hypothesis that the true rate of occurrence is the same for both dataframes. Small values indicate stronger evidence of a difference in the rate of occurrence.

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.

### Value

A tibble summarising and comparing the imbalance for each categorical column in one or a pair of dataframes.

### Author(s)

Alastair Rushworth

### See Also

`inspect_cat`, `show_plot`

### Examples

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

    # Single dataframe summary
    inspect_imb(starwars)

    # Paired dataframe comparison
    inspect_imb(starwars, starwars[1:20, ])

    # Grouped dataframe summary
    starwars %>% group_by(gender) %>% inspect_imb()

[Package inspectdf version 0.0.11 Index]