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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 incomesYour 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.