# Calculating a Gini Coefficients for a Number of Locales at Once in R

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The Gini coefficient is a measure of the inequality of a distribution, most commonly used to compare inequality in income or wealth among countries.

Let’s first generate some random data to analyze. You can download my random data or use the code below to generate your own. Of course, if you generate your own, your graphs and results will be different from those shown below.

city <- c("A", "B", "C", "D", "E", "F", "G", "H", "I", "J") income <- sample(1:100000, 100, replace = TRUE) cities <- data.frame(city, income)

Next, let's graph our data:

library(ggplot2) ggplot(cities, aes(income)) + stat_density(geom = "path", position = "identity") + facet_wrap(~ city, ncol = 2)

Histogram of each city's incomes Your results will differ if using random data |

The Gini coefficient is easy enough to calculate in R for a single locale using the *gini* function from the **reldist** package.

library(reldist) gini(cities[which(cities$city == "A"), ]$income)

But we don't want to replicate this code over and over to calculate the Gini coefficient for a large number of locales. We also want the coefficients to be in a data frame for easy use in R or for export for use in another program.

There are many ways to automate a function to run over many subsets of a data frame. The most straightforward in our particular case is

*aggregate*:ginicities <- aggregate(income ~ city, data = cities, FUN = "gini") names(ginisec) <- c("city", "gini")

> ginisec city gini 1 A 0.2856827 2 B 0.3639070 3 C 0.3288934 4 D 0.1863783 5 E 0.3565739 6 F 0.2587475 7 G 0.3022642 8 H 0.3795288 9 I 0.3311034 10 J 0.2496933

And finally, let's go ahead and export our data using

*write.csv*:write.csv(ginicities, "gini.csv", row.names = FALSE)

While you're at it, you might want to try using other functions on your dataset, such as

*mean*,*median*, and*length*.The full code is available in a gist.

### Citations and Further Reading

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