2023-03-24
Importing Libraries
Packages provide a comprehensive set of tools for data visualization and data manipulation. Each of these packages contain various pre-written functions that can assist us in cleaning, reshaping, and visualizing our data.
library(ggplot2) library(tidyverse) library(tidyr) library(dplyr) library(ggmap) library(htmltools)
Importing the Data
We will refer to our data set as MotorVehicleCrashes. Note: that this dataset contains nearly two million entries, and most entries contain at least one missing value for a column.
MotorVehicleCrashes <- read.csv("~/Downloads/Motor_Vehicle_Collisions_-_Crashes.csv")
This dataset contains information about all the motor vehicle collisions that occurred in New York City from 2013-2022 reported by NYPD. It is required of the police department to report these collisions only if there is an injury, fatality, or a minimum of $1,000 in damage.
The following data contains information including: Crash date and time, location (borough, zip code, latitude/longitude, street, cross streets), persons injured or killed (recorded for persons involved, pedestrians, cyclists, and motorists), contributing factors for each vehicle involved, vehicle type.
More information can be found at the link below. https://data.cityofnewyork.us/Public-Safety/Motor-Vehicle-Collisions-Crashes/h9gi-nx95
By analyzing the dataset and defining the problems it presents, we can help improve public safety by reducing the number of collisions on the road.
It is important to identify trends and patterns in the occurrence of motor vehicle collisions, understand the underlying causes of these collisions, and develop interventions to prevent them. The data can help us identify high-risk areas, specific factors contributing to collisions, and types of vehicles involved.
Additionally, the injury and fatality data can give us a clear understanding of the severity of collisions and their impact on public health and safety. By defining these problems, policymakers, transportation agencies, and community leaders can develop targeted strategies to improve road safety, reduce the number of collisions, and save lives.
How many collisions have resulted in injuries or deaths over the past ten years? How often are pedestrians, cyclists, and motorists involved in motor vehicle collisions? How often are pedestrians, cyclists, and motorists killed in motor vehicle collisions?
## totalInjuries Person ## 1 108036 Pedestrians ## 2 50384 Cyclists ## 3 423739 Motorists ## 4 587331 Total
## totalDeaths Person ## 1 1423 Pedestrians ## 2 212 Cyclists ## 3 1135 Motorists ## 4 2805 Total
Let’s first analyze collisions by borough.
## # A tibble: 5 × 4 ## BOROUGH total_collisions total_injuries total_deaths ## <chr> <int> <int> <int> ## 1 BROOKLYN 431428 137634 566 ## 2 QUEENS 365206 105477 483 ## 3 MANHATTAN 308260 61639 301 ## 4 BRONX 200794 63134 243 ## 5 STATEN ISLAND 57189 15737 88
The table above shows the total number of collisions, the total number of people injured in crashes, and the total number of deaths in motor vehicle crashes for each borough in New York City.
We can conclude from the table that Brooklyn has the most amount of motor vehicle crashes, injuries, and deaths while Staten Island has the least amount of motor vehicle collisions.
Each coordinate on the map represents a fatal vehicular accident, whether the fatality represents the driver, a pedestrian, or a cyclist.
How has the frequency of vehicular collisions changed over the past ten years?
Are motor vehicle collisions more likely to occur on a certain day?
Are motor vehicle collisions more likely to occur at a certain time of the day? 
The data set presents a hundred reasons how someone could be involved in a motor vehicle accident. What are the leading causes of motor vehicle accidents? 
Which contributing factors are most likely to result in an injury?
## # A tibble: 10 × 2 ## CONTRIBUTING.FACTOR.VEHICLE.1 total_injuries ## <chr> <int> ## 1 Driver Inattention/Distraction 123590 ## 2 Failure to Yield Right-of-Way 56913 ## 3 Following Too Closely 38767 ## 4 Traffic Control Disregarded 23141 ## 5 Unsafe Speed 16716 ## 6 Fatigued/Drowsy 12330 ## 7 Turning Improperly 10079 ## 8 Alcohol Involvement 10077 ## 9 Passing or Lane Usage Improper 9958 ## 10 Driver Inexperience 8892
Which contributing factors are most likely to result in a fatality?
## # A tibble: 10 × 2 ## CONTRIBUTING.FACTOR.VEHICLE.1 total_deaths ## <chr> <int> ## 1 Unsafe Speed 351 ## 2 Driver Inattention/Distraction 327 ## 3 Failure to Yield Right-of-Way 242 ## 4 Traffic Control Disregarded 234 ## 5 Alcohol Involvement 99 ## 6 Pedestrian/Bicyclist/Other Pedestrian Error/Confusion 91 ## 7 Illnes 53 ## 8 Driver Inexperience 51 ## 9 Passenger Distraction 48 ## 10 Lost Consciousness 43
What are unsafe driving habits that are most likely to result in a motor vehicle collision? 
What are some common vehicular problems that could cause a collision during operation? 
Help identify the most common causes of collisions, which can inform safety policies and interventions aimed at reducing collisions and improving road safety.
Help identify areas or intersections with high collision rates, allowing for more targeted allocation of resources such as traffic enforcement, traffic engineering improvements, or educational campaigns.
Help insurers and lawyers better understand the risks associated with different types of drivers, vehicles, and road conditions, which can inform insurance policies and legal decisions.
Help researchers better understand the factors that contribute to collisions, which can inform the development of new technologies and policies aimed at improving road safety.