In ian-flores/chicagoDA: Facilitate access to the Cook County State Attorney’s Office data

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  message = FALSE,
  warning = FALSE
)

library(chicagoDA)
library(tidyverse)
library(lubridate)

Datasets

There are 4 datasets included in the package. All of them are filtered for cases received by the State Attorney's Office during 2019. If you wish to extract cases from before or after 2019 you'll need to use the get_all_data() function. This is given size constraints for packages.

Intake

• Observational Unit: Case
• Rows: 36,190
• Variables: 17
• Description: Arrests brought in for review by the SAO

intake %>%
  group_by(date = ymd(received_date)) %>%
  count() %>%
  ggplot(aes(x = date, y = n)) +
    geom_point() +
    geom_smooth() +
    labs(x = 'Date',
         y = 'Received Cases',
         title = 'Is the number of received cases constant through out the year?')

Initiation

• Obersvational Unit: Charge
• Rows: 73,029
• Variables: 27
• Description: Arrests that turned into cases

library(forcats)

initiation %>%
  filter(law_enforcement_agency == 'CHICAGO PD',
         race != '') %>%
  add_count(updated_offense_category, race) %>%
  select(updated_offense_category, race, n) %>%
  unique() %>%
  group_by(updated_offense_category) %>%
  mutate(crime_count = sum(n)) %>%
  ungroup() %>%
  group_by(race) %>%
  top_n(20) %>%
  ungroup() %>%
  ggplot(aes(x = fct_reorder(updated_offense_category, crime_count, .desc = F), 
             y = log(n),
             colour = race, 
             fill = race)) +
    geom_bar(stat = 'identity') +
    coord_flip() +
    labs(y = 'Log of the number of charges initiated by arrests made by Chicago PD',
         x = 'Type of Charge',
         title = 'In what cases does Chicago PD focuses?',
         subtitle = 'Showing top 20 charges brought up, per race')

Dispositions

• Observational Unit: Charge
• Rows: 20,234
• Variables: 31
• Description: How the charges turned out (guilty, not guilty, dismissed, etc)

dispositions %>%
  group_by(race, charge_disposition) %>%
  count() %>%
  ungroup() %>%
  group_by(charge_disposition) %>%
  mutate(charge_n = sum(n)) %>%
  filter(race != '') %>%
  ggplot(aes(x = fct_reorder(charge_disposition, charge_n), y = log(n), fill = race)) +
    geom_bar(stat = 'identity') +
    coord_flip() +
    labs(y = 'Log of the number of dispositions',
         x = 'Disposition Status',
         title = 'Are the differences by race in the disposition of charges?',
         subtitle = 'Data for all Cook County')

Sentencing

• Observational Unit: Charge
• Rows: 4,329
• Variables: 39
• Description: Sentence imposed to a charge

sentencing %>%
  filter(commitment_type == 'Illinois Department of Corrections',
         race != '', race != 'Unknown') %>%
  ggplot(aes(x = age_at_incident, y = as.numeric(commitment_term))) +
    geom_point() +
    geom_smooth(method = 'lm') +
    labs(x = 'Age at Incident',
         y = 'Sentence (Years)',
         title = 'Do younger prisioners get harsher sentences?') +
    facet_wrap(~ race)

ian-flores/chicagoDA documentation built on Dec. 28, 2019, 7:03 p.m.