R: Summary and comparison of numeric columns

inspect_num {inspectdf} | R Documentation

## Summary and comparison of numeric columns

### Description

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

### Usage

    inspect_num(df1, df2 = NULL, breaks = 20, include_int = TRUE)

### Arguments

`df1` | A dataframe.
`df2` | An optional second dataframe for comparing categorical levels. Defaults to `NULL`.
`breaks` | Integer number of breaks used for histogram bins, passed to `graphics::hist()`. Defaults to 20.
`include_int` | Logical flag, whether to include integer columns in numeric summaries. Defaults to `TRUE`. `hist(..., breaks)`. See `?hist` for more details.

### Details

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

- `col_name`, a character vector containing the column names in `df1`

- `min`, `q1`, `median`, `mean`, `q3`, `max` and `sd`, the minimum, lower quartile, median, mean, upper quartile, maximum and standard deviation for each numeric column.

- `pcnt_na`, the percentage of each numeric feature that is missing

- `hist`, a named list of tibbles containing the relative frequency of values falling in bins determined by `breaks`.

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

- `col_name`, a character vector containing the column names in `df1` and `df2`

- `hist_1`, `hist_2`, a list column for histograms of each of `df1` and `df2`. Where a column appears in both dataframe, the bins used for `df1` are reused to calculate histograms for `df2`.

- jsd, a numeric column containing the Jensen-Shannon divergence. This measures the difference in distribution of a pair of binned numeric 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 histogram bins 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.

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` containing statistical summaries of the numeric columns of `df1`, or comparing the histograms of `df1` and `df2`.

### Author(s)

Alastair Rushworth

### See Also

`show_plot`

### Examples

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

    # Single dataframe summary
    inspect_num(starwars)

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

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

[Package inspectdf version 0.0.11 Index]