Assignment 4 week 5

Assignment 4 Week 5

In order to tidy and transforming the given dataset a csv file was created using all the information with a wide structure.The CSV file is stored in GitHub.

[link] (https://github.com/maliat-hossain/Air-lane-Data-607)

library(wesanderson)

## Warning: package 'wesanderson' was built under R version 4.0.4

csv file:

Flight_Information<-read.csv("https://raw.githubusercontent.com/maliat-hossain/Air-lane-Data-607/main/Flight%20Information1.csv",sep = ",")
Flight_Information

##        X     X.1 Los.Angeles Phoenix San.Diego San.Francisco Seattle
## 1 Alaska On Time         497     221       212           503    1841
## 2        Delayed          62      12        20           102     305
## 3                         NA      NA        NA            NA      NA
## 4 AmWest On Time         694    4840       383           320     201
## 5        Delayed         117     415        65           129      61

Column names are added to first columns.Empty cells were filled with the needed value.

colnames(Flight_Information)[1:2] <- c("airline","status") 
Flight_Information[[1]][2] <- Flight_Information[[1]][1] 
Flight_Information[[1]][5] <- Flight_Information[[1]][4] 
Flight_Information %>%
  knitr::kable(caption = "Table 1:Flight Information Table")%>%
  kableExtra::kable_styling(bootstrap_options = "striped")

Table 1:Flight Information Table

airline	status	Los.Angeles	Phoenix	San.Diego	San.Francisco	Seattle
Alaska	On Time	497	221	212	503	1841
Alaska	Delayed	62	12	20	102	305
		NA	NA	NA	NA	NA
AmWest	On Time	694	4840	383	320	201
AmWest	Delayed	117	415	65	129	61

Gather function is implemented to tidy the data.

Flight_Information <- gather(Flight_Information,city,number.airlines,Los.Angeles:Seattle)

Airline data has been converted to numeric data to perform analysis,unnecessary punctuation marks were removed to make data more accessible for analysis.

Flight_Information <- filter(Flight_Information,status!="") 
i <- 1

while(i <= length(Flight_Information$city)) {
  Flight_Information $city[i] <- gsub("\\."," ",Flight_Information $city[i]) 
  Flight_Information $number.airlines[i] <- gsub(",","",Flight_Information$number.airlines[i]) 
  i <- i + 1
} 

Flight_Information $number.airlines <- as.numeric(Flight_Information$number.airlines) 

Flight_Information %>%
  knitr::kable(caption = "Table 2:Flight Information Preparation for Analysis")%>%
  kableExtra::kable_styling(bootstrap_options = "striped")

Table 2:Flight Information Preparation for Analysis

airline	status	city	number.airlines
Alaska	On Time	Los Angeles	497
Alaska	Delayed	Los Angeles	62
AmWest	On Time	Los Angeles	694
AmWest	Delayed	Los Angeles	117
Alaska	On Time	Phoenix	221
Alaska	Delayed	Phoenix	12
AmWest	On Time	Phoenix	4840
AmWest	Delayed	Phoenix	415
Alaska	On Time	San Diego	212
Alaska	Delayed	San Diego	20
AmWest	On Time	San Diego	383
AmWest	Delayed	San Diego	65
Alaska	On Time	San Francisco	503
Alaska	Delayed	San Francisco	102
AmWest	On Time	San Francisco	320
AmWest	Delayed	San Francisco	129
Alaska	On Time	Seattle	1841
Alaska	Delayed	Seattle	305
AmWest	On Time	Seattle	201
AmWest	Delayed	Seattle	61

The rate of On Time and Delayed flights are calculated. The comparison of the rate of delay and on time flights bteween two airlines is necessary to quantify their performances.

Flight_Information_Analysis <- Flight_Information %>% group_by(airline,city) %>% summarise(total.airlines = sum(number.airlines)) 

## `summarise()` regrouping output by 'airline' (override with `.groups` argument)

i <- 1
rate.status <- vector()

while(i <= length(Flight_Information$number.airlines)){ 
  ap <- Flight_Information$airline[i]
  ct <- Flight_Information$city[i]
  rate.status[i] <- round(Flight_Information$number.airlines[i]/Flight_Information_Analysis$total.airlines[Flight_Information_Analysis$airline==ap & Flight_Information_Analysis$city == ct],3)
  i <- i + 1 
}

Flight_Information <- cbind(Flight_Information,rate.status)

Flight_Information%>%
  knitr::kable(caption = "Table 3:Flight Information Scenario")%>%
  kableExtra::kable_styling(bootstrap_options = "striped")

Table 3:Flight Information Scenario

airline	status	city	number.airlines	rate.status
Alaska	On Time	Los Angeles	497	0.889
Alaska	Delayed	Los Angeles	62	0.111
AmWest	On Time	Los Angeles	694	0.856
AmWest	Delayed	Los Angeles	117	0.144
Alaska	On Time	Phoenix	221	0.948
Alaska	Delayed	Phoenix	12	0.052
AmWest	On Time	Phoenix	4840	0.921
AmWest	Delayed	Phoenix	415	0.079
Alaska	On Time	San Diego	212	0.914
Alaska	Delayed	San Diego	20	0.086
AmWest	On Time	San Diego	383	0.855
AmWest	Delayed	San Diego	65	0.145
Alaska	On Time	San Francisco	503	0.831
Alaska	Delayed	San Francisco	102	0.169
AmWest	On Time	San Francisco	320	0.713
AmWest	Delayed	San Francisco	129	0.287
Alaska	On Time	Seattle	1841	0.858
Alaska	Delayed	Seattle	305	0.142
AmWest	On Time	Seattle	201	0.767
AmWest	Delayed	Seattle	61	0.233

On time flights are extracted

OnTime_Flights<-filter(Flight_Information,status =="On Time")
 OnTime_Flights %>%
  knitr::kable(caption = "Table 4:Flight Information Scenario with On Time Flights ")%>%
  kableExtra::kable_styling(bootstrap_options = "striped")

Table 4:Flight Information Scenario with On Time Flights

airline	status	city	number.airlines	rate.status
Alaska	On Time	Los Angeles	497	0.889
AmWest	On Time	Los Angeles	694	0.856
Alaska	On Time	Phoenix	221	0.948
AmWest	On Time	Phoenix	4840	0.921
Alaska	On Time	San Diego	212	0.914
AmWest	On Time	San Diego	383	0.855
Alaska	On Time	San Francisco	503	0.831
AmWest	On Time	San Francisco	320	0.713
Alaska	On Time	Seattle	1841	0.858
AmWest	On Time	Seattle	201	0.767

Delayed flights are extracted

Delayed_Flights<-filter(Flight_Information,status=="Delayed")
Delayed_Flights %>%
  knitr::kable(caption = "Table 5:Flight Information Scenario with Delayed Flights ")%>%
  kableExtra::kable_styling(bootstrap_options = "striped")

Table 5:Flight Information Scenario with Delayed Flights

airline	status	city	number.airlines	rate.status
Alaska	Delayed	Los Angeles	62	0.111
AmWest	Delayed	Los Angeles	117	0.144
Alaska	Delayed	Phoenix	12	0.052
AmWest	Delayed	Phoenix	415	0.079
Alaska	Delayed	San Diego	20	0.086
AmWest	Delayed	San Diego	65	0.145
Alaska	Delayed	San Francisco	102	0.169
AmWest	Delayed	San Francisco	129	0.287
Alaska	Delayed	Seattle	305	0.142
AmWest	Delayed	Seattle	61	0.233

ggplot(data=Delayed_Flights, aes(x=city,y=rate.status,fill=airline)) +
    scale_fill_manual(values = wes_palette("FantasticFox1",5)[4:5])+
    geom_bar(stat="identity", position=position_dodge()) +
    ggtitle(" Analizing Rate of Delay by Airlines and City") +
    xlab("city") + 
    ylab("rate") #create a ggplot compare delay rate for each airline and each city

ggplot(data=OnTime_Flights, aes(x=city,y=rate.status,fill=airline)) +
    scale_fill_manual(values = wes_palette("Darjeeling2",5)[1:3])+
    geom_bar(stat="identity", position=position_dodge()) +
    ggtitle(" Analizing Rate of OnTime Flights by Airlines and City") +
    xlab("city") + 
    ylab("rate") #create a ggplot compare delay rate for each airline and each city

The plot and rate refers that Alaska is performing better than Amwest Airlines. Amwest has a significantly higher delay rate. However the time of the data collected is not disclosed. It can be further analyzed if Amwest delayed its flight on the same day when Alaska airlines flights were on time. Weather can be an attribute to the delay of Amwest’s flights. The date needs to be added to understand the data better. However,with the present data it can be referred that technical difficulties such as issues in their engine, or unskilled crews may cause the delay to Amwest Flights. In terms of City Amwest Airlines did not perform well in San Francisco compared to Alaska Airlines.