Chicago Employee Salary: Comparison using histograms

Data

This plot uses the chi_emps data frame from package gcubed. The original source of the data is the City of Chicago’s Data Portal⁶.

First, find the 3 departments with the most salaried employees.

library(gcubed)
library(dplyr)
df <- filter(chi_emps, SalHour == "Salary") 
large_dept_names <- names(sort(table(df$Department), decreasing = TRUE))[1:3]
large_dept_names

## [1] "POLICE" "FIRE"   "OEMC"

large_depts <- df[df$Department %in% large_dept_names, ]
head(large_depts)

## # A tibble: 6 x 8
##   Name  Titles Department FullPart SalHour TypicalHours AnnualSalary
##   <chr> <chr>  <chr>      <chr>    <chr>          <dbl>        <dbl>
## 1 AARO… SERGE… POLICE     F        Salary            NA       101442
## 2 AARO… POLIC… POLICE     F        Salary            NA        94122
## 3 ABAR… POLIC… POLICE     F        Salary            NA        48078
## 4 ABBA… FIRE … FIRE       F        Salary            NA       103350
## 5 ABBA… POLIC… POLICE     F        Salary            NA        93354
## 6 ABBO… POLIC… POLICE     F        Salary            NA        68616
## # … with 1 more variable: HourlyRate <dbl>

Code for plot

library(ggplot2)
library(scales) # to add commas to axis values

chi_comp_plt <- ggplot(large_depts, aes(x = AnnualSalary, fill = Department)) + 
         geom_histogram(bins = 50, colour = "black") + 
         facet_wrap(~Department, ncol = 1, scales = "free_y") + 
  theme_bw() + 
  scale_x_continuous(label = comma) + 
  scale_y_continuous(label = comma) + 
  xlab("Annual Salary ($)") + ylab("# of Employees") + 
  ggtitle("Annual Salaries of Chicago City Employees (2019)") + 
  theme(legend.position = "none", plot.title = element_text(size = 16, face = "bold", hjust = 0.5))

chi_comp_plt

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6. The data was current as of July 2019↩