inspectdf (version 0.0.12.1)

inspect_cat: Summary and comparison of the levels in categorical columns

Description

For a single dataframe, summarise the levels of each categorical column. If two dataframes are supplied, compare the levels of categorical features that appear in both dataframes. For grouped dataframes, summarise the levels of categorical features separately for each group.

Usage

inspect_cat(df1, df2 = NULL, include_int = FALSE)

Value

A tibble summarising or comparing the categorical features in one or a pair of dataframes.

Arguments

df1

A dataframe.

df2

An optional second data frame for comparing categorical levels. Defaults to NULL.

include_int

Logical flag - whether to treat integer columns as categories. Default is FALSE.

Author

Alastair Rushworth

Details

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

• col_name, character vector containing column names of df1.

• cnt integer column containing count of unique levels found in each column, including NA.

• common, a character column containing the name of the most common level.

• common_pcnt, the percentage of each column occupied by the most common level shown in common.

• levels, a named list containing relative frequency tibbles for each feature.

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

• col_name, character vector containing names of columns appearing in both df1 and df2.

• jsd, a numeric column containing the Jensen-Shannon divergence. This measures the difference in relative frequencies of levels in a pair of categorical features. Values near to 0 indicate agreement of the distributions, while 1 indicates disagreement.

• pval, the p-value corresponding to a NHT that the true frequencies of the categories are equal. A small p indicates evidence that the the two sets of relative frequencies are actually different. The test is based on a modified Chi-squared statistic.

• lvls_1, lvls_2, the relative frequency of levels 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.

See Also

inspect_imb, show_plot

Examples

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

# Single dataframe summary
inspect_cat(starwars)

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

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