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About gglorenz

The goal of gglorenz is to plot Lorenz Curves with the blessing of ggplot2.

Installation

# Install the CRAN version
install.packages("gglorenz")

# Install the development version from GitHub:
# install.packages("remotes")
remotes::install_github("jjchern/gglorenz")

Example

Suppose you have a vector with each element representing the amount of income or wealth of an individual, and you are interested in knowing how much of that is produced by the top x% of the population, then the gglorenz::stat_lorenz(desc = TRUE) would make a ggplot2 graph for you.

library(tidyverse)
#> ── Attaching packages ─────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
#> ✓ ggplot2 3.3.0     ✓ purrr   0.3.4
#> ✓ tibble  3.0.1     ✓ dplyr   1.0.0
#> ✓ tidyr   1.1.0     ✓ stringr 1.4.0
#> ✓ readr   1.3.1     ✓ forcats 0.4.0
#> ── Conflicts ────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
#> x dplyr::filter() masks stats::filter()
#> x dplyr::lag()    masks stats::lag()
library(gglorenz)

billionaires
#> # A tibble: 500 x 6
#>    Rank  Name            Total_Net_Worth Country       Industry      TNW
#>    <chr> <chr>           <chr>           <chr>         <chr>       <dbl>
#>  1 1     Jeff Bezos      $118B           United States Technology  118  
#>  2 2     Bill Gates      $91.3B          United States Technology   91.3
#>  3 3     Warren Buffett  $86.1B          United States Diversified  86.1
#>  4 4     Mark Zuckerberg $74.3B          United States Technology   74.3
#>  5 5     Amancio Ortega  $71.7B          Spain         Retail       71.7
#>  6 6     Bernard Arnault $65.0B          France        Consumer     65  
#>  7 7     Carlos Slim     $64.7B          Mexico        Diversified  64.7
#>  8 8     Larry Ellison   $54.7B          United States Technology   54.7
#>  9 9     Larry Page      $52.6B          United States Technology   52.6
#> 10 10    Sergey Brin     $51.2B          United States Technology   51.2
#> # … with 490 more rows

billionaires %>%
    ggplot(aes(TNW)) +
    stat_lorenz(desc = TRUE) +
    coord_fixed() +
    geom_abline(linetype = "dashed") +
    theme_minimal() +
    hrbrthemes::scale_x_percent() +
    hrbrthemes::scale_y_percent() +
    hrbrthemes::theme_ipsum_rc() +
    labs(x = "Cumulative Percentage of the Top 500 Billionaires",
         y = "Cumulative Percentage of Total Net Worth",
         title = "Inequality Among Billionaires",
         caption = "Source: https://www.bloomberg.com/billionaires/ (accessed February 8, 2018)")

billionaires %>%
    filter(Industry %in% c("Technology", "Real Estate")) %>%
    ggplot(aes(x = TNW, colour = Industry)) +
    stat_lorenz(desc = TRUE) +
    coord_fixed() +
    geom_abline(linetype = "dashed") +
    theme_minimal() +
    hrbrthemes::scale_x_percent() +
    hrbrthemes::scale_y_percent() +
    hrbrthemes::theme_ipsum_rc() +
    labs(x = "Cumulative Percentage of Billionaires",
         y = "Cumulative Percentage of Total Net Worth",
         title = "Real Estate is a Relatively Equal Field",
         caption = "Source: https://www.bloomberg.com/billionaires/ (accessed February 8, 2018)")

If you have a data frame with columns indicating the wealth and number of individuals at that level you can use the n aesthetic like so: ggplot(freqdata, aes(x = value, n = freq) + stat_lorenz().

In addition, the annotate_ineq() function allows you to label the chart with inequality statistics such as the Gini coefficient:

billionaires %>%
    ggplot(aes(TNW)) +
    stat_lorenz(desc = TRUE) +
    coord_fixed() +
    geom_abline(linetype = "dashed") +
    theme_minimal() +
    hrbrthemes::scale_x_percent() +
    hrbrthemes::scale_y_percent() +
    hrbrthemes::theme_ipsum_rc() +
    labs(x = "Cumulative Percentage of the Top 500 Billionaires",
         y = "Cumulative Percentage of Total Net Worth",
         title = "Inequality Among Billionaires",
         caption = "Source: https://www.bloomberg.com/billionaires/ (accessed February 8, 2018)") +
    annotate_ineq(billionaires$TNW)

You can also use other geoms such as area or polygon and arranging population in ascending order:

billionaires %>%
    filter(Industry %in% c("Technology", "Real Estate")) %>% 
    add_row(Industry = "Perfect Equality", TNW = 1) %>% 
    ggplot(aes(x = TNW, fill = Industry)) +
    stat_lorenz(geom = "area", alpha = 0.65) +
    coord_fixed() +
    hrbrthemes::scale_x_percent() +
    hrbrthemes::scale_y_percent() +
    hrbrthemes::theme_ipsum_rc() +
    theme(legend.title = element_blank()) +
    labs(x = "Cumulative Percentage of Billionaires",
         y = "Cumulative Percentage of Total Net Worth",
         title = "Real Estate is a Relatively Equal Field",
         caption = "Source: https://www.bloomberg.com/billionaires/ (accessed February 8, 2018)")

billionaires %>%
    filter(Industry %in% c("Technology", "Real Estate")) %>% 
    mutate(Industry = forcats::as_factor(Industry)) %>% 
    ggplot(aes(x = TNW, fill = Industry)) +
    stat_lorenz(geom = "polygon", alpha = 0.65) +
    geom_abline(linetype = "dashed") +
    coord_fixed() +
    hrbrthemes::scale_x_percent() +
    hrbrthemes::scale_y_percent() +
    hrbrthemes::theme_ipsum_rc() +
    theme(legend.title = element_blank()) +
    labs(x = "Cumulative Percentage of Billionaires",
         y = "Cumulative Percentage of Total Net Worth",
         title = "Real Estate is a Relatively Equal Field",
         caption = "Source: https://www.bloomberg.com/billionaires/ (accessed February 8, 2018)")

Acknowledgement

The package came to exist solely because Bob Rudis was generous enough to write a chapter that demystifies ggplot2.