Assignment_1
Mathew Katz
2022-09-04
Overview: In the dataset below we’re going to look at police misconduct settlements. More specifically, we’re going to look at settlements in New York City where the amount awarded was $100,000 or more from 2010-2019. In the article, “Cities Spend Millions On Police Misconduct Every Year. Here’s Why It’s So Difficult to Hold Departments Accountable.” by Amelia Thomson-DeVeaux, Laura Bronner and Damini Sharma, “(https://fivethirtyeight.com/features/police-misconduct-costs-cities-millions-every-year-but-thats-where-the-accountability-ends/) it talks about how these settlements are one of the only ways families can receive justice due to the law protecting police officers so very well.
Here is a brief look at the data:
df <- read.csv('https://raw.githubusercontent.com/fivethirtyeight/police-settlements/main/new_york_ny/final/new_york_edited.csv')
head(df)## plaintiff_name claim_number summary_allegations incident_date
## 1 Kathy Batka 2009PI023313 CIVIL RIGHTS CLAIMS 2008-07-26
## 2 Ingrid Conway 2009PI014930 PEACE OFFICER/POLICE ACTION (PI) 2009-05-01
## 3 Melvin Rennock 2009PI000487 CIVIL RIGHTS CLAIMS 2005-09-05
## 4 Peter Mcenaney 2008PI024680 CIVIL RIGHTS CLAIMS 2007-12-31
## 5 Hector Galvez 2008PI022264 PEACE OFFICER/POLICE ACTION (PI) 2008-08-23
## 6 Dwight Trotman 2008PI007921 PEACE OFFICER/POLICE ACTION (PI) 2008-01-03
## filed_date plaintiff_attorney closed_date amount_awarded
## 1 2009-08-24 Matthew Flamm 2010-01-04 00:00:00 5000
## 2 2009-06-12 Richard J Cardinale 2010-01-04 00:00:00 15000
## 3 2008-11-06 Anthony C Ofodile 2010-01-04 00:00:00 17500
## 4 2008-09-18 Richard Cardinale 2010-01-04 00:00:00 11000
## 5 2008-09-18 Rosenberg Minc Falkoff & Wolff 2010-01-04 00:00:00 25000
## 6 2008-03-17 Breadbar Garfield 2010-01-04 00:00:00 5000
## location calendar_year incident_year
## 1 7 AV & 33 ST & 34 ST, MANHATTAN (NEW YORK) 2010 2008
## 2 111 BRIDGE ST, BROOKLYN (KINGS) 2010 2009
## 3 Precise location missing, BROOKLYN (KINGS) 2010 2005
## 4 Precise location missing, BROOKLYN (KINGS) 2010 2007
## 5 444 E 119 ST, MANHATTAN (NEW YORK) 2010 2008
## 6 1262 WEBSTER AV, BRONX 2010 2008
## filed_year city state other_expenses collection total_incurred court
## 1 2009 New York City NY NA NA NA NA
## 2 2009 New York City NY NA NA NA NA
## 3 2008 New York City NY NA NA NA NA
## 4 2008 New York City NY NA NA NA NA
## 5 2008 New York City NY NA NA NA NA
## 6 2008 New York City NY NA NA NA NA
## docket_number matter_name case_outcome
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 NA NA NA
## 6 NA NA NAHere is a brief summary of the dataframe columns:
summary(df)## plaintiff_name claim_number summary_allegations incident_date
## Length:32632 Length:32632 Length:32632 Length:32632
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## filed_date plaintiff_attorney closed_date amount_awarded
## Length:32632 Length:32632 Length:32632 Min. : 1
## Class :character Class :character Class :character 1st Qu.: 9000
## Mode :character Mode :character Mode :character Median : 16500
## Mean : 52222
## 3rd Qu.: 30000
## Max. :26875657
##
## location calendar_year incident_year filed_year
## Length:32632 Min. :2010 Min. :1980 Min. :2000
## Class :character 1st Qu.:2012 1st Qu.:2010 1st Qu.:2011
## Mode :character Median :2014 Median :2012 Median :2013
## Mean :2014 Mean :2012 Mean :2013
## 3rd Qu.:2017 3rd Qu.:2014 3rd Qu.:2015
## Max. :2019 Max. :2019 Max. :2019
## NA's :435
## city state other_expenses collection
## Length:32632 Length:32632 Mode:logical Mode:logical
## Class :character Class :character NA's:32632 NA's:32632
## Mode :character Mode :character
##
##
##
##
## total_incurred court docket_number matter_name case_outcome
## Mode:logical Mode:logical Mode:logical Mode:logical Mode:logical
## NA's:32632 NA's:32632 NA's:32632 NA's:32632 NA's:32632
##
##
##
##
## Let’s make the columns more clear:
df <- setNames(df, c("Plantiff", "Claim_Number", "Summary", "Incident_Date", "Filed_Date", "Plantiff_Attorney", "Closed_Date", "Money_Awarded", "Location", "Calendar_Year", "Incident_Year", "Filed_Year", "City", "State", "Other_Expenses", "Collection", "Total_Incurred","Court","Docket_Number", "Matter_Name", "Case_Outcome")) We want to look at the “big” misconducts. Let’s choose settlements of $100,000 or more:
police_misconduct <- subset(df, Money_Awarded > 100000)It seems to be a relatively constant state of 100+ police misconducts worth $100,000+ per year:
hist(police_misconduct$Calendar_Year)
Findings and Recommendations: I don’t have the answer for police brutalily. If I did, I would be a billionaire. But I’ll tell you what; this isn’t a problem that will just go away over time. The crazy thing is that the data-collectors all said that this isn’t even all of it! There’s more misconducts and settlements that aren’t public or that are just missing. YOUR tax money is being used to pay for police officers’ wrongdoings and it is being taken away from the money that could be used for better training and better officers. It is a problem that just keeps on giving.