Increase in Murder rates and Assaults across United States - R studio Assessment Answers

November 06, 2018
Author : Julia Miles

Solution Code: 1DFE

Question: Increase in Murder rates and Assaults across United States

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R Studio Assignment

crime<-read.csv("D:/Downloads/WDI_excel_2016_04/cr.csv",header=T,sep=",")

symbols(crime$murder, crime$burglary, circles=crime$population)

radius <- sqrt( crime$population/ pi )

symbols(crime$murder, crime$burglary, circles=radius, inches=0.35, fg="white", bg="red", xlab="Murder Rate", ylab="Burglary Rate")

text(crime$murder, crime$burglary, crime$state, cex=0.5)

symbols(crime$motor_vehicle_theft, crime$larceny_theft, circles=radius, inches=0.35, fg="white", bg="red", xlab="Motor ", ylab="Burglary Rate")

symbols(crime$motor_vehicle_theft, crime$larceny_theft, circles=radius, inches=0.35, fg="white", bg="red", xlab="Motor Vehicle Theft", ylab="Larceny Theft")

text(crime$motor_vehicle_theft, crime$larceny_theft, crime$state, cex=0.5)

boxplot(crime$Robbery)

radius <- sqrt( crime$population/ pi )

symbols(crime$Robbery, crime$Forcible_rate, circles=radius, inches=0.35, fg="white", bg="blue", xlab="Robbery", ylab="Forcible Rate")

text(crime$Robbery, crime$Forcible_rate, crime$state, cex=0.5)

symbols(crime$murder, crime$aggravated_assult, circles=radius, inches=0.35, fg="black", bg="green", xlab="Murder", ylab="Aggravated Assault")

text(crime$murder, crime$aggravated_assult, crime$state, cex=0.5)

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Solution: Increase in Murder rates and Assaults across United States

Introduction

The data taken into consideration explains the crime rate across different countries. For the purpose of this study, we initially consider the relationship between burglary rate and murder rate across various countries. Then, we try to determine the relationship between robbery and forcible rate. Box plots are constructed for all variables to determine the distribution of the variables. For aggravated assault and robbery, the box plot was included in the analysis to know the distribution specifically.

Round 1

Relationship between Murder rate and Burglary Rate. Initially, the symbols were constructed for each countries to define their highest percentage of crime relationships. The graph is constructed by taking Murder crime as x axis and burglary in y axis and the symbols showing the relationship between two crimes across various countries are plotted

Next, we construct the bubble chart to show the exact severity of the two crime rates across the countries. Here, California recorded the highest population with crime rates and burglary rates, followed by Texas Florida

Round 2

Determining the relationship between Motor Vehicle Theft and Larceny Theft. The mean larceny theft recorded across United States is 2295.658 with a standard deviation of 516.88. The state New Dakota recorded the lowest larceny theft with 1343.7 crimes in 2008 and Hawaii State recorded the highest larceny theft rate with 3308.7 crimes in 2008. The mean motor vehicle theft recorded across United States is 362.834 with a standard deviation of 207.85. The state Maine recorded the lowest motor vehicle theft with 102 crimes in 2008 and Nevada State recorded the highest larceny theft rate with 1115.2 crimes in 2008.

Round 3

Box plot showing the distribution of Robbery

The box plot showing the distribution of Robbery rate, indicates that there exists no outliers or extreme points in the variable Robbery rate. In addition, it is also observed that the middle line in the box (normally called as media) falls close to lower quartile, indicating that the distribution of Robbery rate is skewed right. That is, most of the robbery rates are above the mean overall robbery rates. The minimum and maximum recorded robbery rates are 7.4 (North Dakota) and 256.7 (Maryland State) respectively.

The graph showing the relationship between Robbery rate and forcible rate is given below

Round 4

Box plot showing the distribution of aggravated assault. The box plot showing the distribution of aggravated assault, indicates that there exists no outliers or extreme points in the variable aggravated assault rate. In addition, it is also observed that the middle line in the box (normally called as media) falls close to lower quartile, indicating that the distribution of aggravated assault rate is skewed right. That is, most of the robbery rates are above the mean overall aggravated assault rates. The minimum and maximum recorded robbery rates are 61.7 (Maine State) and 579 (South Carolina State) respectively.

The graph showing the relationship between aggravated assault and murder rate is given below

Data Wrangling:

1) Data representing the crime rate in 2008 is imported as csv file. Every state in United States with their respective crime rates were included

2) The first row was labelled and was named as header as it contains the variable neames

3) The first column was removed as it is of no use (first column consists of serial number)

4) The first row was labelled as header

5) An additional column representing the radius was created by dividing the population column by pi

6) No missing data was observed in the dataset. Box plot is used to determine the outliers for each and every variable included in the study and it is found that there exists no outliers in the dataset

7) The same process was repeated on a data file detailing Murder crime rates and Robbery Crime rates

R Codes

crime<-read.csv("D:/ cr.csv",header=T,sep=",")

symbols(crime$murder, crime$burglary, circles=crime$population)

radius <- sqrt( crime$population/ pi )

symbols(crime$murder, crime$burglary, circles=radius, inches=0.35, fg="white", bg="red", xlab="Murder Rate", ylab="Burglary Rate")

text(crime$murder, crime$burglary, crime$state, cex=0.5)

symbols(crime$motor_vehicle_theft, crime$larceny_theft, circles=radius, inches=0.35, fg="white", bg="red", xlab="Motor ", ylab="Burglary Rate")

symbols(crime$motor_vehicle_theft, crime$larceny_theft, circles=radius, inches=0.35, fg="white", bg="red", xlab="Motor Vehicle Theft", ylab="Larceny Theft")

text(crime$motor_vehicle_theft, crime$larceny_theft, crime$state, cex=0.5)

boxplot(crime$Robbery)

radius <- sqrt( crime$population/ pi )

symbols(crime$Robbery, crime$Forcible_rate, circles=radius, inches=0.35, fg="white", bg="blue", xlab="Robbery", ylab="Forcible Rate")

text(crime$Robbery, crime$Forcible_rate, crime$state, cex=0.5)

symbols(crime$murder, crime$aggravated_assult, circles=radius, inches=0.35, fg="black", bg="green", xlab="Murder", ylab="Aggravated Assault")

text(crime$murder, crime$aggravated_assult, crime$state, cex=0.5)

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