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Calculate the Lorenz Curve and Gini Coefficient in a Person Query

Source: R/create_lorenz.R
create_lorenz.Rd

This function computes the Gini coefficient and plots the Lorenz curve based on a selected metric from a Person Query data frame. It provides a way to measure inequality in the distribution of the selected metric.This function can be integrated into a larger analysis pipeline to assess inequality in metric distribution.

Usage

create_lorenz(data, metric, return = "plot")

Arguments

data

Data frame containing a Person Query.

metric

Character string identifying the metric to be used for the Lorenz curve and Gini coefficient calculation.

return

Character string identifying the return type. Options are:

  • "gini" - Numeric value representing the Gini coefficient.

  • "table" - Data frame containing a summary table of population share and value share.

  • "plot" (default) - ggplot object representing a plot of the Lorenz curve.

Gini coefficient

The Gini coefficient is a measure of statistical dispersion most commonly used to represent income inequality within a population. It is calculated as the ratio of the area between the Lorenz curve and the line of perfect equality (the 45-degree line) to the total area under the line of perfect equality. It has a range of 0 to 1, where 0 represents perfect equality and 1 represents perfect inequality. It can be applied to any Viva Insights metric where inequality is of interest.

Examples

create_lorenz(data = pq_data, metric = "Emails_sent", return = "gini")
#> [1] 0.2523283

create_lorenz(data = pq_data, metric = "Emails_sent", return = "plot")


create_lorenz(data = pq_data, metric = "Emails_sent", return = "table")
#> # A tibble: 11 × 2
#>    population_share value_share
#>               <dbl>       <dbl>
#>  1              0      0.000142
#>  2              0.1    0.0425  
#>  3              0.2    0.101   
#>  4              0.3    0.170   
#>  5              0.4    0.246   
#>  6              0.5    0.331   
#>  7              0.6    0.426   
#>  8              0.7    0.528   
#>  9              0.8    0.643   
#> 10              0.9    0.780   
#> 11              1      1