MSDS R Bridge Final Project
2022-07-26
Guns and Crime
Overview
This report provides an analysis on the AER dataset “More Guns, less Crime?”, containing data from 50 US states and the District of Columbia between 1977 and 1999, specifically looking at the relationship between crime rates, laws, and population density.
Install packages:
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2)# read in the data from GitHub
url <- "https://raw.githubusercontent.com/josh1den/MSDS-R-Bridge/main/Guns.csv"
GunData <- read.csv(url)
colnames(GunData)## [1] "X" "year" "violent" "murder" "robbery"
## [6] "prisoners" "afam" "cauc" "male" "population"
## [11] "income" "density" "state" "law"# view unique states
unique(GunData$state)## [1] "Alabama" "Alaska" "Arizona"
## [4] "Arkansas" "California" "Colorado"
## [7] "Connecticut" "Delaware" "District of Columbia"
## [10] "Florida" "Georgia" "Hawaii"
## [13] "Idaho" "Illinois" "Indiana"
## [16] "Iowa" "Kansas" "Kentucky"
## [19] "Louisiana" "Maine" "Maryland"
## [22] "Massachusetts" "Michigan" "Minnesota"
## [25] "Mississippi" "Missouri" "Montana"
## [28] "Nebraska" "Nevada" "New Hampshire"
## [31] "New Jersey" "New Mexico" "New York"
## [34] "North Carolina" "North Dakota" "Ohio"
## [37] "Oklahoma" "Oregon" "Pennsylvania"
## [40] "Rhode Island" "South Carolina" "South Dakota"
## [43] "Tennessee" "Texas" "Utah"
## [46] "Vermont" "Virginia" "Washington"
## [49] "West Virginia" "Wisconsin" "Wyoming"View structure of the data:
str(GunData)## 'data.frame': 1173 obs. of 14 variables:
## $ X : int 1 2 3 4 5 6 7 8 9 10 ...
## $ year : int 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 ...
## $ violent : num 414 419 413 448 470 ...
## $ murder : num 14.2 13.3 13.2 13.2 11.9 10.6 9.2 9.4 9.8 10.1 ...
## $ robbery : num 96.8 99.1 109.5 132.1 126.5 ...
## $ prisoners : int 83 94 144 141 149 183 215 243 256 267 ...
## $ afam : num 8.38 8.35 8.33 8.41 8.48 ...
## $ cauc : num 55.1 55.1 55.1 54.9 54.9 ...
## $ male : num 18.2 18 17.8 17.7 17.7 ...
## $ population: num 3.78 3.83 3.87 3.9 3.92 ...
## $ income : num 9563 9932 9877 9541 9548 ...
## $ density : num 0.0746 0.0756 0.0762 0.0768 0.0772 ...
## $ state : chr "Alabama" "Alabama" "Alabama" "Alabama" ...
## $ law : chr "no" "no" "no" "no" ...The 14 variables in the data frame are in the following format, source: https://rdrr.io/cran/AER/man/Guns.html
- X: index
- year: year
- violent: violent crime incidents per 100k population (pop.)
- murder: murder rate per 100k pop
- robbery: rate per 100k pop.
- prisoners: previous year per 100k pop.
- afam: percent of African-Americam state pop, ages 10-64
- cauc: percent of Caucasian state pop, ages 10-64
- male: percent of male state pop, ages 10-29
- population: population in millions
- income: per capita personal income
- density: population per sq. mile x 1000
- law: was a shall carry law in effect that year
View summary statistics:
summary(GunData)## X year violent murder
## Min. : 1 Min. :1977 Min. : 47.0 Min. : 0.200
## 1st Qu.: 294 1st Qu.:1982 1st Qu.: 283.1 1st Qu.: 3.700
## Median : 587 Median :1988 Median : 443.0 Median : 6.400
## Mean : 587 Mean :1988 Mean : 503.1 Mean : 7.665
## 3rd Qu.: 880 3rd Qu.:1994 3rd Qu.: 650.9 3rd Qu.: 9.800
## Max. :1173 Max. :1999 Max. :2921.8 Max. :80.600
## robbery prisoners afam cauc
## Min. : 6.4 Min. : 19.0 Min. : 0.2482 Min. :21.78
## 1st Qu.: 71.1 1st Qu.: 114.0 1st Qu.: 2.2022 1st Qu.:59.94
## Median : 124.1 Median : 187.0 Median : 4.0262 Median :65.06
## Mean : 161.8 Mean : 226.6 Mean : 5.3362 Mean :62.95
## 3rd Qu.: 192.7 3rd Qu.: 291.0 3rd Qu.: 6.8507 3rd Qu.:69.20
## Max. :1635.1 Max. :1913.0 Max. :26.9796 Max. :76.53
## male population income density
## Min. :12.21 Min. : 0.4027 Min. : 8555 Min. : 0.000707
## 1st Qu.:14.65 1st Qu.: 1.1877 1st Qu.:11935 1st Qu.: 0.031911
## Median :15.90 Median : 3.2713 Median :13402 Median : 0.081569
## Mean :16.08 Mean : 4.8163 Mean :13725 Mean : 0.352038
## 3rd Qu.:17.53 3rd Qu.: 5.6856 3rd Qu.:15271 3rd Qu.: 0.177718
## Max. :22.35 Max. :33.1451 Max. :23647 Max. :11.102120
## state law
## Length:1173 Length:1173
## Class :character Class :character
## Mode :character Mode :character
##
##
## Interpreting the Summary Statistics
Many of the columns have outliers (violent, murder, robbery, prisoners, afam, population).
Due to the categorical nature of the “Law” column, the relationship between laws and rates is unclear.
boxplot(GunData[,3:5])
Data Wrangling
This analysis is focusing on the distribution and relationship of crime rates and income, density, and shall carry laws, using the following techniques:
- Subset the data to include only the desired columns
- Create a new column “crime” representing total crime rate
- Drop violent, murder, and robbery columns
- Convert law column yes/no values to numeric 1=yes, 0=no
- Reorder the columns for better viewing
- Subset the data into desired columns:
# subset the data
# desired columns to retain
cols <- c("year", "violent","murder", "robbery", "density", "state", "law")
# create new dataframe from subset data
guns <- GunData[cols]
head(guns)## year violent murder robbery density state law
## 1 1977 414.4 14.2 96.8 0.0745524 Alabama no
## 2 1978 419.1 13.3 99.1 0.0755667 Alabama no
## 3 1979 413.3 13.2 109.5 0.0762453 Alabama no
## 4 1980 448.5 13.2 132.1 0.0768288 Alabama no
## 5 1981 470.5 11.9 126.5 0.0771866 Alabama no
## 6 1982 447.7 10.6 112.0 0.0773185 Alabama no- Create a new column “crime” representing total crime rate
guns$crime <- guns$violent + guns$murder + guns$robbery
head(guns)## year violent murder robbery density state law crime
## 1 1977 414.4 14.2 96.8 0.0745524 Alabama no 525.4
## 2 1978 419.1 13.3 99.1 0.0755667 Alabama no 531.5
## 3 1979 413.3 13.2 109.5 0.0762453 Alabama no 536.0
## 4 1980 448.5 13.2 132.1 0.0768288 Alabama no 593.8
## 5 1981 470.5 11.9 126.5 0.0771866 Alabama no 608.9
## 6 1982 447.7 10.6 112.0 0.0773185 Alabama no 570.3- Drop violent, murder, and robbery columns
# define columns to drop
drop <- c("violent","murder","robbery")
# keep only the columns not in drop
guns <- guns[,!(names(guns) %in% drop)]
head(guns)## year density state law crime
## 1 1977 0.0745524 Alabama no 525.4
## 2 1978 0.0755667 Alabama no 531.5
## 3 1979 0.0762453 Alabama no 536.0
## 4 1980 0.0768288 Alabama no 593.8
## 5 1981 0.0771866 Alabama no 608.9
## 6 1982 0.0773185 Alabama no 570.3- Convert law column from yes/no to numeric 1=yes, 0=no
guns$law <- ifelse(guns$law=="yes",1,0)
head(guns)## year density state law crime
## 1 1977 0.0745524 Alabama 0 525.4
## 2 1978 0.0755667 Alabama 0 531.5
## 3 1979 0.0762453 Alabama 0 536.0
## 4 1980 0.0768288 Alabama 0 593.8
## 5 1981 0.0771866 Alabama 0 608.9
## 6 1982 0.0773185 Alabama 0 570.3- Reorder columns for better viewing
order <- c(3, 1, 5, 2, 4)
guns <- guns[, order]
head(guns)## state year crime density law
## 1 Alabama 1977 525.4 0.0745524 0
## 2 Alabama 1978 531.5 0.0755667 0
## 3 Alabama 1979 536.0 0.0762453 0
## 4 Alabama 1980 593.8 0.0768288 0
## 5 Alabama 1981 608.9 0.0771866 0
## 6 Alabama 1982 570.3 0.0773185 0Visualization
Distribution of Crime Rates
m <- ggplot(guns, aes(x = crime)) + geom_histogram(binwidth=15)
title <- ggtitle("Distribution of Crime Rates")
center <- theme(plot.title = element_text(hjust = 0.5))
xl <- xlab("Crime Rate")
yl <- ylab("Count")
m + title + xl + yl + center
Most of the crime rates are between 100-1000 per 100k. The presence of outliers is in line with the summary statistics and boxplot.
Average crime rate by year:
crime_law <- aggregate(cbind(crime, law) ~ year, guns, mean)
c1 <- ggplot(crime_law, aes(x=year, y=crime)) +
geom_line()
title <- ggtitle("Average Crime Rates")
center <- theme(plot.title = element_text(hjust = 0.5))
xl <- xlab("Year")
yl <- ylab("per 100k")
c1 + title + xl + yl + center
Crimes have risen and fallen since 1977, rising in the late 70s/early 80s, then decreasing, before rising again in the late 80s, reaching a peak in the early 90s, and steadily decreasing since 1993.
Let’s see if there is a noticeable pattern with gun laws during this time.
c2 <- ggplot(crime_law, aes(x=year, y=law)) +
geom_line()
title <- ggtitle("Percent of States with Gun Laws")
center <- theme(plot.title = element_text(hjust = 0.5))
xl <- xlab("Year")
yl <- ylab("Percentage")
c2 + title + xl + yl + center
Less than 10% of states had gun laws in 1977, with the figure steadily increasing since 1985, and dramatically increasing since 1994.
Let’s look at the average crime rate with and without laws.
crimelaw2 <- aggregate(crime ~ law, guns, FUN = function(x) round(mean(x)))
crimelaw2$law <- gsub(0, "No", as.character(crimelaw2$law))
crimelaw2$law <- gsub(1, "Yes", as.character(crimelaw2$law))
p <- ggplot(crimelaw2, aes(x=law, y=crime)) +
geom_bar(stat="identity", fill="steelblue")+
theme_minimal() +
geom_text(aes(label=crime), vjust=1.6, color="white",
position = position_dodge(0.9), size=3.5)
labels <- labs(title="Average Crime Rates With/Without Shall Carry Laws", x="Law?", y="Avg Crime Rate")
center <- theme(plot.title = element_text(hjust = 0.5))
p + labels + center
States without shall carry laws on average experience 41% higher crime rates than states with laws.
Lastly, lets take a look at the relationship between density and crime rates:
g1 <- ggplot(guns, aes(x = density, y = crime)) + geom_point()
labels <- labs(title="Crime Rates by Density", x="Population per Sq Mile (x 1000)", y="Crime Rate per 100k")
center <- theme(plot.title = element_text(hjust = 0.5))
g1 + labels + center
Each of the highest crimes rates occur in the highest density populations. Do these populations have gun laws?
# subset dataframe by density, crime, and law: cols, dcl
cols <- c("crime","density","law")
dcl <- guns[cols]
dcl$law <- gsub(0, "No Law", as.character(dcl$law))
dcl$law <- gsub(1, "Law", as.character(dcl$law))
q <- ggplot(dcl, aes(x=density, y=crime, color=law)) +
geom_point(size = 3, alpha=0.6) +
labs(title = "Crime Rate by Population Density and Shall Carry Laws",
x="Population per Sq Mile (x 1000)", y="Crime Rate per 100k")
q
Conclusions
The conclusions of this analysis are:
Crime rates fluctuated between between 1977 and 1993, peaked in 1993, and decreased steadily until 1999.
In 1977, less than 10% states had shall carry laws in effect. In 1993, less than 35% of states had passed shall carry laws. By 1999, the figure was nearly 60%.
States with active shall carry laws on average had crime rates 41% lower than states without laws.
The states with the highest population density had the highest crime rates. None of those states had shall carry laws.