Chicago City Salaries Compared: Density Ridges

Data

This plot uses the chi_emps data set of the gcubed package. The original source of the data is the City of Chicago’s Data Portal⁹.

First, get the departments that have more than 500 salaried employees:

library(gcubed)

df <- chi_emps[chi_emps$SalHour == "Salary", ]
dept_counts <- table(df$Department)
large_dept_names <- names(dept_counts[dept_counts > 500])
large_dept_names

## [1] "AVIATION"       "FINANCE"        "FIRE"           "OEMC"          
## [5] "POLICE"         "PUBLIC LIBRARY"

df$Dept <- ifelse(df$Department %in% large_dept_names, df$Department, "OTHER")
table(df$Dept)

## 
##       AVIATION        FINANCE           FIRE           OEMC          OTHER 
##            583            534           4631            799           4432 
##         POLICE PUBLIC LIBRARY 
##          14060            702

Code for plot

dept_levels <- c("OTHER", rev(large_dept_names))
df$Dept <- factor(df$Dept, levels = dept_levels)

library(ggridges) # to use geom_density_ridges
library(scales) # to format axis values with commas

chi_ridge_plt <- ggplot(data = df, aes(x = AnnualSalary, y = Dept)) + 
  geom_density_ridges() + 
  scale_x_continuous(label=comma) +
  ggtitle("Annual Salaries of Chicago City Employees (2019)") + 
  theme(plot.title = element_text(size = 16, face = "bold", hjust = 0.5))

chi_ridge_plt

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9. The data was current as of July 2019↩