R: Summary and comparison of the rate of missingness across...

inspect_na {inspectdf} | R Documentation

## Summary and comparison of the rate of missingness across dataframe columns

### Description

For a single dataframe, summarise the rate of missingness in each column. If two dataframes are supplied, compare missingness for columns appearing in both dataframes. For grouped dataframes, summarise the rate of missingness separately for each group.

### Usage

    inspect_na(df1, df2 = NULL)

### Arguments

`df1` | A data frame
`df2` | An optional second data frame for making columnwise comparison of missingness. Defaults to `NULL`.

### Details

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

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

- `cnt`, an integer vector containing the number of missing values by column.

- `pcnt`, the percentage of records in each columns that is missing.

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

- `col_name`, the name of the columns occurring in either `df1` or `df2`.

- `cnt_1`, `cnt_2`, a pair of integer vectors containing counts of missing entries for each column in `df1` and `df2`.

- `pcnt_1`, `pcnt_2`, a pair of columns containing percentage of missing entries for each column in `df1` and `df2`.

- `p_value`, the p-value associated with test of equivalence of rates of missingness. Small values indicate evidence that the rate of missingness differs for a column occurring in both `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.

### Value

A tibble summarising the count and percentage of columnwise missingness for one or a pair of data frames.

### Author(s)

Alastair Rushworth

### See Also

`show_plot`

### Examples

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

    # Single dataframe summary
    inspect_na(starwars)

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

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

[Package inspectdf version 0.0.11 Index]