Exploratory Data Analysis of Airline Accidents

OVERVIEW
INTRODUCTION
ABOUT DATASET
KEY TERMS
DATA IMPORTATION
Data Cleaning
Data Description
DESCRIPTIVE SUMMARY STATISTICS
TREND ANALYSIS
Incident Distribution Per Airline
CODE APPENDIX

OVERVIEW

This task focuses on the Exploratory Data Analysis (EDA) of airline accidents, aiming to uncover patterns and trends related to total incidents, fatal accidents, and total fatalities. By analyzing and visualizing the data, we can gain valuable insights into the factors that contribute to these accidents and their impact on the aviation industry.The analysis primarily revolves around the visualization of data, using various charts and graphs to present the information in a clear and concise manner. By examining the frequency and distribution of airline accidents over time, we can identify any significant changes or trends. Additionally, we explore the relationship between fatal accidents and total fatalities, shedding light on the severity of different incidents.

INTRODUCTION

Airline accidents have always been a matter of concern for both the aviation industry and the general public. Understanding the patterns and trends of these accidents is crucial for improving safety measures and preventing future incidents. Exploratory Data Analysis (EDA) provides valuable insights into the characteristics and factors associated with airline accidents. By analyzing data related to total incidents, fatal accidents, and total fatalities, we can gain a deeper understanding of the risks and challenges faced by the aviation industry.

ABOUT DATASET

The dataset is from Kaggle: https://www.kaggle.com/datasets/khaledshawky/airline-accidents

KEY TERMS

Incidents and Accidents: Tracking the frequency and severity of accidents involving airline passengers
Fatal Accidents: The count of accidents resulting in fatalities
Total Incidents: The sum of of incidents recorded in the dataset
Total Fatalities: The total number of fatalities across all recorded accidents

DATA IMPORTATION

Data Cleaning

Total Number of missing values in each column
	x
airline	0
incidents_85_99	0
fatal_accidents_85_99	0
fatalities_85_99	0
incidents_00_14	0
fatal_accidents_00_14	0
fatalities_00_14	0

## [1] 0

Data Description

## tibble [56 × 7] (S3: tbl_df/tbl/data.frame)
##  $ airline              : chr [1:56] "Aer Lingus" "Aeroflot" "Aerolineas Argentinas" "Aeromexico" ...
##  $ incidents_85_99      : num [1:56] 2 76 6 3 2 14 2 3 5 7 ...
##  $ fatal_accidents_85_99: num [1:56] 0 14 0 1 0 4 1 0 0 2 ...
##  $ fatalities_85_99     : num [1:56] 0 128 0 64 0 79 329 0 0 50 ...
##  $ incidents_00_14      : num [1:56] 0 6 1 5 2 6 4 5 5 4 ...
##  $ fatal_accidents_00_14: num [1:56] 0 1 0 0 0 2 1 1 1 0 ...
##  $ fatalities_00_14     : num [1:56] 0 88 0 0 0 337 158 7 88 0 ...

airline	incidents_85_99	fatal_accidents_85_99	fatalities_85_99	incidents_00_14	fatal_accidents_00_14	fatalities_00_14
Length:56	Min. : 0.000	Min. : 0.000	Min. : 0.0	Min. : 0.000	Min. :0.0000	Min. : 0.00
Class :character	1st Qu.: 2.000	1st Qu.: 0.000	1st Qu.: 0.0	1st Qu.: 1.000	1st Qu.:0.0000	1st Qu.: 0.00
Mode :character	Median : 4.000	Median : 1.000	Median : 48.5	Median : 3.000	Median :0.0000	Median : 0.00
NA	Mean : 7.179	Mean : 2.179	Mean :112.4	Mean : 4.125	Mean :0.6607	Mean : 55.52
NA	3rd Qu.: 8.000	3rd Qu.: 3.000	3rd Qu.:184.2	3rd Qu.: 5.250	3rd Qu.:1.0000	3rd Qu.: 83.25
NA	Max. :76.000	Max. :14.000	Max. :535.0	Max. :24.000	Max. :3.0000	Max. :537.00

56 oberservations and 7 columns
1 character column and 6 numerical variables

DESCRIPTIVE SUMMARY STATISTICS

Total Incidents

## [1] 402

## [1] 231

## [1] 633

There is a decrease in the number of incidents from the first 14 years (1985 - 1999) and the second 14 years period (2000-2014)

Fatal Accidents

Count of fatal accidents by airlines
Var1	Freq
0	17
1	16
2	4
3	8
4	3
5	3
6	1
7	1
8	1
12	1
14	1

Count of fatal accidents by airlines
Var1	Freq
0	32
1	12
2	11
3	1

Most airlines didn’t cause fatal accidents from 1985 to 1999
Most airlines didn’t cause fatal accidents from 2000 to 2014 showing an improvement

## fatal_accidents_85_99 fatal_accidents_00_14 
##                   122                    37

122 serious accidents happened from 1985 to 1999.
37 serious accidents happened from 2000 to 2014 showing a sharp decrease in the number of fatal accidents.

Total Fatalities

## fatalities_85_99 fatalities_00_14 
##             6295             3109

6295 deaths occured from 1985 to 1999
3109 deaths occured from 2000 upto 2014 showing
A sharp decrease in the number of people killed

## [1] 9404

9404 deaths were recorded from 1985 up to 2014

Comparison analysis

## [1] 159

Out of the total incidents only 20% fatal accidents happened from 1985 to 2014

TREND ANALYSIS

Incidents

Observation

As the years approach 2014, the number of incidents are decreasing showing a decreasing trend

Fatal Accidents

Observation

Decreasing trend in the number of fatal accidents

Fatalities

Observation

From 2000 to 2014, most airlines were recording less deaths.

Incident Distribution Per Airline

only a few airlines had 0 count of incident ratios

Airlines such Acer Lingus have lowest ratio of incidents as compared to other airlines from 2000 to 2014.
More airline have zero count of incident ratio showing an improvement as from 1985 to 2014

Airlines such as Acer Lingus were the safest airlines since they didn’t have an accident from 1985 to 2014

CODE APPENDIX

knitr::opts_chunk$set(echo = F,warning = F, message = F)
library(readxl)
ac=read_xlsx(file.choose())

#Cleaning variable names

library(janitor)

ac=clean_names(ac)
#checking for missing values

library(knitr)

kable(colSums(is.na(ac)),caption="Total Number of missing values in each column")

#No missing values

#Checking for duplicated entries

anyDuplicated(ac)

#No duplicated entries in the dataset

str(ac)

kable(summary(ac),format = "pipe") #Summary Statistics

total_incidents_1=sum(ac$incidents_85_99) 

total_incidents_1 #402 incidents from 1985 up to 1999

total_incidents_2=sum(ac$incidents_00_14) 

total_incidents_2 #231 incidents from 2000 up to 2014

total=total_incidents_1+total_incidents_2 

total #633 total incidents recorded in the dataset


#Fatal accidents

table(ac$fatal_accidents_85_99) %>%
  kable(caption = "Count of fatal accidents by airlines") 

table(ac$fatal_accidents_00_14) %>%
  kable(caption = "Count of fatal accidents by airlines") 
library(dplyr)
ac %>% dplyr::select(fatal_accidents_85_99,fatal_accidents_00_14) %>%
  colSums()
#Total fatalities
ac %>% dplyr::select(fatalities_85_99,fatalities_00_14) %>%
  colSums()

ac %>% dplyr::select(fatalities_85_99,fatalities_00_14) %>%
  sum()

library(MASS)

sum(ac$fatal_accidents_85_99)+sum(ac$fatal_accidents_00_14) #159

kpi1=c("total accidents","total incidents")

values=c(159,633)

pct=round(values/sum(values)*100)

kpi1=paste(kpi1,pct,"%",sep=" ")

pie(values,labels = kpi1,col=c("blue","green"),
    main = "Proportion of fatal accidents within total incidents")

#trend analysis
library(ggplot2)

ggplot(ac,aes(x=airline))+
  geom_line(aes(y=ac$incidents_85_99,
                color="incidents from 1985 to 1999"),
            group=1,show.legend = T)+
  geom_line(aes(y=ac$incidents_00_14,
                color="incidents from 2000 to 2014"),group=1,
            show.legend = T)+
  theme_bw()+
  theme(axis.text.x = element_text(size = 8,hjust=1,angle=90))+
  theme(legend.position="bottom")+
  labs(colour="",
       title = "Incident Trend Analysis Over Time Per Airline",
       y="Incidents",x="Airlines")

ggplot(ac,aes(x=airline))+
  geom_line(aes(y=ac$fatal_accidents_85_99,
                color="fatal accidents from 1985 to 1999"),
            group=1,show.legend = T)+
  geom_line(aes(y=ac$fatal_accidents_00_14,
                color="fatal accidents from 2000 to 2014"),group=1,
            show.legend = T)+
  theme_bw()+
  theme(axis.text.x = element_text(size = 8,hjust=1,angle=90))+
  theme(legend.position="bottom")+
  labs(colour="",
       title = "Fatal Accidents Trend Analysis Over Time Per Airline",
       y="Fatal Accidents",x="Airlines")
ggplot(ac,aes(x=airline))+
  geom_line(aes(y=ac$fatalities_85_99,
                color="fatalities from 1985 to 1999"),
            group=1,show.legend = T)+
  geom_line(aes(y=ac$fatalities_00_14,
                color="fatalities from 2000 to 2014"),group=1,
            show.legend = T)+
  theme_minimal()+
  theme(axis.text.x = element_text(size = 8,hjust=1,angle=90))+
  labs(title = "Fatalities Trend Analysis Over Time Per Airline",
       y="Fatalities",x="Airlines")
id2=ggplot(ac,aes(airline,incidents_85_99,))+
  geom_segment(aes(x=airline,xend=airline,y=0,yend=incidents_85_99),
               color="skyblue",size=1)+
  geom_point(size=2,color="black")+
  coord_flip()+ theme_bw()+
  theme(axis.text.y = element_text(size = 6,angle=0))+
  labs(title = "Incident Distribution Per Airline",
       subtitle = "1985-1999",
       y="Incidents",x="Airlines")

id2

id3=ggplot(ac,aes(airline,incidents_00_14,))+
  geom_segment(aes(x=airline,xend=airline,y=0,yend=incidents_00_14),
               color="orange",size=1, show.legend = F)+
  geom_point(size=2,color="black")+
  coord_flip()+ theme_bw()+
  theme(axis.text.y = element_text(size = 6,angle=0))+
  labs(title = "Incident Distribution Per Airline",
       subtitle = "2000-2014",
       y="Incidents",x="Airlines")

id3


library(dplyr)

safe= ac %>%
  dplyr::select(airline,fatal_accidents_85_99,fatal_accidents_00_14)

ggplot(safe,aes(airline, fatal_accidents_85_99))+
  geom_bar(aes(fill="fatal accident from 1985 to 1999"),
           stat = "identity",
           show.legend = T)+
  geom_bar(aes(y=fatal_accidents_00_14,
           fill="fatal accident from 2000 to 2014"),
           stat = "identity",show.legend = T)+
  theme_bw()+
  theme(axis.text.x = element_text(size = 8,vjust=-0,hjust=1,angle=90))+
  theme(legend.position = "bottom")+
  labs(fill="",title = "Safest Airline from 1985 to 2014",
       y="frequency",x="airlines")

Exploratory Data Analysis of Airline Accidents

Data Analyst - Pythias C

2024-02-25

OVERVIEW

INTRODUCTION

ABOUT DATASET

KEY TERMS

DATA IMPORTATION

Data Cleaning

Data Description

DESCRIPTIVE SUMMARY STATISTICS

Total Incidents

Fatal Accidents

Total Fatalities

Comparison analysis

TREND ANALYSIS

Incidents

Fatal Accidents

Fatalities

Incident Distribution Per Airline

CODE APPENDIX