Basic Setup
Set up libraries
library(stringr)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(tidyr)
library(ggplot2)
Read Data and Clean up headers
AirlineData <- read.csv("AirlineData1.csv", header=TRUE, blank.lines.skip = TRUE, )
colnames(AirlineData)[1]<-"Airline"
colnames(AirlineData)[2]<-"Status"
AirlineData
## Airline Status Los.Angeles Phoenix San.Diego San.Francisco Seattle
## 1 ALASKA on time 497 221 212 503 1841
## 2 ALASKA delayed 62 12 20 102 305
## 3 AM WEST on time 694 4840 383 320 201
## 4 AM WEST delayed 117 415 65 129 61
Tidy the data
AirlineDataTidy1 <- gather(AirlineData, Destination, Number, Los.Angeles:Seattle)
AirlineDataTidy <- spread(AirlineDataTidy1, Status, Number)
colnames(AirlineDataTidy)[3:4]<- c('Delayed','OnTime')
AirlineDataTidy
## Airline Destination Delayed OnTime
## 1 ALASKA Los.Angeles 62 497
## 2 ALASKA Phoenix 12 221
## 3 ALASKA San.Diego 20 212
## 4 ALASKA San.Francisco 102 503
## 5 ALASKA Seattle 305 1841
## 6 AM WEST Los.Angeles 117 694
## 7 AM WEST Phoenix 415 4840
## 8 AM WEST San.Diego 65 383
## 9 AM WEST San.Francisco 129 320
## 10 AM WEST Seattle 61 201
Analysis of Data
Analyse the data for sector specific delays and overall delays
Sector-wise comparison
delaySectorWise <- mutate(group_by(AirlineDataTidy, Airline), DelayPercent=(Delayed/(Delayed+OnTime))*100)
delaySectorWise <- arrange(delaySectorWise, Destination)
delaySectorWise
## Source: local data frame [10 x 5]
## Groups: Airline [2]
##
## Airline Destination Delayed OnTime DelayPercent
## <fctr> <chr> <int> <int> <dbl>
## 1 ALASKA Los.Angeles 62 497 11.091234
## 2 AM WEST Los.Angeles 117 694 14.426634
## 3 ALASKA Phoenix 12 221 5.150215
## 4 AM WEST Phoenix 415 4840 7.897241
## 5 ALASKA San.Diego 20 212 8.620690
## 6 AM WEST San.Diego 65 383 14.508929
## 7 ALASKA San.Francisco 102 503 16.859504
## 8 AM WEST San.Francisco 129 320 28.730512
## 9 ALASKA Seattle 305 1841 14.212488
## 10 AM WEST Seattle 61 201 23.282443
ggplot(data=delaySectorWise, aes(x=Destination, y=DelayPercent, group=Airline, color=Airline)) + geom_line() +
geom_point()
Conclusion: ALASKA airlines has a lower delay in all sectors compared to AM WEST. This is also confirmed by the chart.
Sector-wise comparison
OverallDelays<-mutate(summarise(group_by(AirlineDataTidy, Airline), DelayedAll=sum(Delayed), OnTimeAll=sum(OnTime)), DelayPercentAll=100*DelayedAll/(DelayedAll+OnTimeAll))
OverallDelays
## # A tibble: 2 × 4
## Airline DelayedAll OnTimeAll DelayPercentAll
## <fctr> <int> <int> <dbl>
## 1 ALASKA 501 3274 13.27152
## 2 AM WEST 787 6438 10.89273
Conclusion: AM WEST has a lower delay percent compared to ALASKA when all flights are considered. This is because they have some sectors where their volume is very high and the percentage of delays in those sectors is lower than average delay.