HW-01-AI-Aashima Pokharel
Aashima Pokharel
2024-09-17
Introduction
This report contains exploratory data anlysis on the data that are collected on accidental cases.
options(repos = c(CRAN = “https://cran.rstudio.com”))
#Install the packages
install.packages(“ggplot2”)
install.packages(“readxl”)
install.packages(“dplyr”)
install.packages(“reshape2”)
#Load the libraries
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary("ggplot2")
library(readxl)
library(dplyr)
library(reshape2)##
## Attaching package: 'reshape2'
##
## The following object is masked from 'package:tidyr':
##
## smiths#Step1: Load the data
mydata1<-read.csv(file.choose(),header=T)#Step:2 Summary
head(mydata1)## Wthr_Cond_ID Light_Cond_ID Road_Type_ID Road_Algn_ID SurfDry
## 1 Clear Dark, not lighted 2 lane, 2 way Straight, level 1
## 2 Clear Dark, not lighted 2 lane, 2 way Straight, level 1
## 3 Clear Daylight 2 lane, 2 way Straight, level 1
## 4 Clear Daylight 2 lane, 2 way Straight, level 1
## 5 Clear Dark, not lighted 2 lane, 2 way Straight, grade 1
## 6 Clear Daylight Unknown Straight, level 1
## Traffic_Cntl_ID Harm_Evnt_ID Intrsct_Relat_ID
## 1 Marked lanes Motor vehicle in transport Non intersection
## 2 Center stripe/divider Motor vehicle in transport Non intersection
## 3 Marked lanes Motor vehicle in transport Intersection
## 4 Center stripe/divider Fixed object Non intersection
## 5 None Motor vehicle in transport Non intersection
## 6 None Motor vehicle in transport Driveway access
## FHE_Collsn_ID Road_Part_Adj_ID Road_Cls_ID
## 1 Sd both going straight-rear end Main/proper lane Farm to market
## 2 Sd both going straight-rear end Main/proper lane Us & state highways
## 3 Other Main/proper lane Farm to market
## 4 Omv vehicle going straight Main/proper lane Us & state highways
## 5 Sd both going straight-rear end Main/proper lane Farm to market
## 6 Other Main/proper lane County road
## Pop_Group_ID Crash_Speed_LimitCat Veh_Body_Styl_ID
## 1 10,000 - 24,999 pop 30-40 mph Farm equipment
## 2 Rural 65-70 mph Farm equipment
## 3 Other 45-60 mph Farm equipment
## 4 Rural 65-70 mph Farm equipment
## 5 Rural 45-60 mph Farm equipment
## 6 Rural 30-40 mph Farm equipment
## Prsn_Ethnicity_ID GenMale TrafVol Prsn_Age
## 1 White 1 23681 25-54 years
## 2 White 1 1384 25-54 years
## 3 White 1 16838 Other
## 4 White 1 13870 25-54 years
## 5 Other 1 1953 25-54 years
## 6 White 1 1324 55-64 years
## Prsn_Injry_Sev_ID
## 1 O
## 2 O
## 3 O
## 4 O
## 5 O
## 6 Osummary(mydata1)## Wthr_Cond_ID Light_Cond_ID Road_Type_ID Road_Algn_ID
## Length:1295 Length:1295 Length:1295 Length:1295
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## SurfDry Traffic_Cntl_ID Harm_Evnt_ID Intrsct_Relat_ID
## Min. :0.0000 Length:1295 Length:1295 Length:1295
## 1st Qu.:1.0000 Class :character Class :character Class :character
## Median :1.0000 Mode :character Mode :character Mode :character
## Mean :0.9143
## 3rd Qu.:1.0000
## Max. :1.0000
## FHE_Collsn_ID Road_Part_Adj_ID Road_Cls_ID Pop_Group_ID
## Length:1295 Length:1295 Length:1295 Length:1295
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Crash_Speed_LimitCat Veh_Body_Styl_ID Prsn_Ethnicity_ID GenMale
## Length:1295 Length:1295 Length:1295 Min. :0.0000
## Class :character Class :character Class :character 1st Qu.:1.0000
## Mode :character Mode :character Mode :character Median :1.0000
## Mean :0.8842
## 3rd Qu.:1.0000
## Max. :1.0000
## TrafVol Prsn_Age Prsn_Injry_Sev_ID
## Min. : 202 Length:1295 Length:1295
## 1st Qu.: 8188 Class :character Class :character
## Median :15426 Mode :character Mode :character
## Mean :15039
## 3rd Qu.:22049
## Max. :28993#Step:3 Checking the structure of the data
str(data)## function (..., list = character(), package = NULL, lib.loc = NULL, verbose = getOption("verbose"),
## envir = .GlobalEnv, overwrite = TRUE)#Step:4 Count Missing Values
sum(is.na(mydata1))## [1] 0#Step:5 Adding column for traffictype
newdata<-mutate(mydata1, Traffictype=factor(TrafVol>5000,labels=c("Low","High")))
head(newdata)## Wthr_Cond_ID Light_Cond_ID Road_Type_ID Road_Algn_ID SurfDry
## 1 Clear Dark, not lighted 2 lane, 2 way Straight, level 1
## 2 Clear Dark, not lighted 2 lane, 2 way Straight, level 1
## 3 Clear Daylight 2 lane, 2 way Straight, level 1
## 4 Clear Daylight 2 lane, 2 way Straight, level 1
## 5 Clear Dark, not lighted 2 lane, 2 way Straight, grade 1
## 6 Clear Daylight Unknown Straight, level 1
## Traffic_Cntl_ID Harm_Evnt_ID Intrsct_Relat_ID
## 1 Marked lanes Motor vehicle in transport Non intersection
## 2 Center stripe/divider Motor vehicle in transport Non intersection
## 3 Marked lanes Motor vehicle in transport Intersection
## 4 Center stripe/divider Fixed object Non intersection
## 5 None Motor vehicle in transport Non intersection
## 6 None Motor vehicle in transport Driveway access
## FHE_Collsn_ID Road_Part_Adj_ID Road_Cls_ID
## 1 Sd both going straight-rear end Main/proper lane Farm to market
## 2 Sd both going straight-rear end Main/proper lane Us & state highways
## 3 Other Main/proper lane Farm to market
## 4 Omv vehicle going straight Main/proper lane Us & state highways
## 5 Sd both going straight-rear end Main/proper lane Farm to market
## 6 Other Main/proper lane County road
## Pop_Group_ID Crash_Speed_LimitCat Veh_Body_Styl_ID
## 1 10,000 - 24,999 pop 30-40 mph Farm equipment
## 2 Rural 65-70 mph Farm equipment
## 3 Other 45-60 mph Farm equipment
## 4 Rural 65-70 mph Farm equipment
## 5 Rural 45-60 mph Farm equipment
## 6 Rural 30-40 mph Farm equipment
## Prsn_Ethnicity_ID GenMale TrafVol Prsn_Age
## 1 White 1 23681 25-54 years
## 2 White 1 1384 25-54 years
## 3 White 1 16838 Other
## 4 White 1 13870 25-54 years
## 5 Other 1 1953 25-54 years
## 6 White 1 1324 55-64 years
## Prsn_Injry_Sev_ID Traffictype
## 1 O High
## 2 O Low
## 3 O High
## 4 O High
## 5 O Low
## 6 O Low#step:6 Creating new data with two columns only
newdata<-newdata %>% transmute(ScaledTrafficVolume=scale(TrafVol),Personage=Prsn_Age)
head(newdata)## ScaledTrafficVolume Personage
## 1 1.0468830 25-54 years
## 2 -1.6543011 25-54 years
## 3 0.2178834 Other
## 4 -0.1416768 25-54 years
## 5 -1.5853692 25-54 years
## 6 -1.6615698 55-64 years#Step:7 Arranging the data in ascending order
newdata<-arrange(newdata,ScaledTrafficVolume)
head(newdata)## ScaledTrafficVolume Personage
## 1 -1.797495 25-54 years
## 2 -1.788530 25-54 years
## 3 -1.785744 25-54 years
## 4 -1.783685 55-64 years
## 5 -1.778354 25-54 years
## 6 -1.776900 15-24 yearsStep:7 Visualization
Barplot of Light_Condn_ID
lighting<-table(mydata1$Light_Cond_ID)
barplot(lighting, main="Lighting Condition During Crashes", col= "purple")
## Barplot of Rode_Type_ID
Roadtype<-table(mydata1$Road_Type_ID)
barplot(Roadtype, main="Road Type in which the crash occured", col= "Yellow")
## Histogram of Crash_Speed_LimitCat
ggplot(mydata1,aes(x=Crash_Speed_LimitCat))+geom_histogram(binwidth=400, stat="count",col="brown")+labs(title = "Histogram for Crash Speed Limit", x="Crash Speed Limit",y="frequency")## Warning in geom_histogram(binwidth = 400, stat = "count", col = "brown"):
## Ignoring unknown parameters: `binwidth`, `bins`, and `pad`
## Histogram of Traffic Volume
hist(mydata1$TrafVol,main="Traffic Volume During Crashes",xlab="Traffic",col="pink",border="black")
## Histogram of GenMale
hist(mydata1$GenMale,main="Gender of Person Driving During Crashes",xlab="Gender",col="green",border="black")
##Boxplot for Road_Type_ID vs TrafVol
ggplot(mydata1, aes(x=Road_Type_ID,y=TrafVol))+geom_boxplot(fill="red")+labs(title="Traffic Volume by Road Type", x="Road Type",y="Traffic Volume")
##Box plot of Crash_Speed_LimitCat and Wthr_Condn_ID
ggplot(mydata1,aes(x=Wthr_Cond_ID,y=as.numeric(gsub("[^0-9]","",Crash_Speed_LimitCat))))+geom_boxplot(fill="purple",color="pink")+theme_bw()## Warning: Removed 62 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Scatter plot for GenMale versus TrafVol
ggplot(mydata1,aes(x=Road_Cls_ID,y=TrafVol, ))+geom_point(color="blue",alpha=0.7)+labs(title="scatter plot of Gender vs Traffic Volume ", x="Road_Cls_ID",y="Traffic Volume",)
## Heatmap of Road_Algn versus Harm_Event_ID
heatmap_file<-table(mydata1$Harm_Evnt_ID,mydata1$Road_Algn_ID)
heatmap_dataframe<-melt(heatmap_file)
ggplot(heatmap_dataframe,aes(x=Var1,y=Var2,fill=value))+geom_tile()+theme_bw()+labs(title="heatmap of Road Algn versus Harm Event", x="Harm_Event_ID",y="Injury Severity")
##Heatmap of Road_Algn_ID versus Crash_Speed_LimitCat
heatmap_file1<-table(mydata1$Road_Algn_ID,mydata1$Crash_Speed_LimitCat)
heatmap_dataframe<-melt(heatmap_file1)
ggplot(heatmap_dataframe,aes(x=Var1,y=Var2,fill=value))+geom_tile()+theme_bw()+labs(title="heatmap of Road Algn versus Crash Speed", x="Raod_Algn_ID",y="Crash_Speed_LimitCat")
#Comments:
From the data analysis we can conclude that most of the crashes occur during daylight condition and in the 2 way 2 lane roads.
Most of the crashes occurs when the vehicle is in the speed of 45-60mph.
Greater number of crashes seem to occur when the Traffic volume is around 20,000.
It can be seen from the data, that mostly male are the victim of crashes.