Reading the dataset
airline.df<-read.csv(paste("SixAirlinesDataV2.csv",sep=""))
Viewing the Dataset
View(airline.df)
Summarizing the Dataset
summary(airline.df)
## Airline Aircraft FlightDuration TravelMonth
## AirFrance: 74 AirBus:151 Min. : 1.250 Aug:127
## British :175 Boeing:307 1st Qu.: 4.260 Jul: 75
## Delta : 46 Median : 7.790 Oct:127
## Jet : 61 Mean : 7.578 Sep:129
## Singapore: 40 3rd Qu.:10.620
## Virgin : 62 Max. :14.660
## IsInternational SeatsEconomy SeatsPremium PitchEconomy
## Domestic : 40 Min. : 78.0 Min. : 8.00 Min. :30.00
## International:418 1st Qu.:133.0 1st Qu.:21.00 1st Qu.:31.00
## Median :185.0 Median :36.00 Median :31.00
## Mean :202.3 Mean :33.65 Mean :31.22
## 3rd Qu.:243.0 3rd Qu.:40.00 3rd Qu.:32.00
## Max. :389.0 Max. :66.00 Max. :33.00
## PitchPremium WidthEconomy WidthPremium PriceEconomy
## Min. :34.00 Min. :17.00 Min. :17.00 Min. : 65
## 1st Qu.:38.00 1st Qu.:18.00 1st Qu.:19.00 1st Qu.: 413
## Median :38.00 Median :18.00 Median :19.00 Median :1242
## Mean :37.91 Mean :17.84 Mean :19.47 Mean :1327
## 3rd Qu.:38.00 3rd Qu.:18.00 3rd Qu.:21.00 3rd Qu.:1909
## Max. :40.00 Max. :19.00 Max. :21.00 Max. :3593
## PricePremium PriceRelative SeatsTotal PitchDifference
## Min. : 86.0 Min. :0.0200 Min. : 98 Min. : 2.000
## 1st Qu.: 528.8 1st Qu.:0.1000 1st Qu.:166 1st Qu.: 6.000
## Median :1737.0 Median :0.3650 Median :227 Median : 7.000
## Mean :1845.3 Mean :0.4872 Mean :236 Mean : 6.688
## 3rd Qu.:2989.0 3rd Qu.:0.7400 3rd Qu.:279 3rd Qu.: 7.000
## Max. :7414.0 Max. :1.8900 Max. :441 Max. :10.000
## WidthDifference PercentPremiumSeats
## Min. :0.000 Min. : 4.71
## 1st Qu.:1.000 1st Qu.:12.28
## Median :1.000 Median :13.21
## Mean :1.633 Mean :14.65
## 3rd Qu.:3.000 3rd Qu.:15.36
## Max. :4.000 Max. :24.69
Summarizing Using PSYCH
attach(airline.df)
library(psych)
## Warning: package 'psych' was built under R version 3.3.3
describe(airline.df)
## vars n mean sd median trimmed mad min
## Airline* 1 458 3.01 1.65 2.00 2.89 1.48 1.00
## Aircraft* 2 458 1.67 0.47 2.00 1.71 0.00 1.00
## FlightDuration 3 458 7.58 3.54 7.79 7.57 4.81 1.25
## TravelMonth* 4 458 2.56 1.17 3.00 2.58 1.48 1.00
## IsInternational* 5 458 1.91 0.28 2.00 2.00 0.00 1.00
## SeatsEconomy 6 458 202.31 76.37 185.00 194.64 85.99 78.00
## SeatsPremium 7 458 33.65 13.26 36.00 33.35 11.86 8.00
## PitchEconomy 8 458 31.22 0.66 31.00 31.26 0.00 30.00
## PitchPremium 9 458 37.91 1.31 38.00 38.05 0.00 34.00
## WidthEconomy 10 458 17.84 0.56 18.00 17.81 0.00 17.00
## WidthPremium 11 458 19.47 1.10 19.00 19.53 0.00 17.00
## PriceEconomy 12 458 1327.08 988.27 1242.00 1244.40 1159.39 65.00
## PricePremium 13 458 1845.26 1288.14 1737.00 1799.05 1845.84 86.00
## PriceRelative 14 458 0.49 0.45 0.36 0.42 0.41 0.02
## SeatsTotal 15 458 235.96 85.29 227.00 228.73 90.44 98.00
## PitchDifference 16 458 6.69 1.76 7.00 6.76 0.00 2.00
## WidthDifference 17 458 1.63 1.19 1.00 1.53 0.00 0.00
## PercentPremiumSeats 18 458 14.65 4.84 13.21 14.31 2.68 4.71
## max range skew kurtosis se
## Airline* 6.00 5.00 0.61 -0.95 0.08
## Aircraft* 2.00 1.00 -0.72 -1.48 0.02
## FlightDuration 14.66 13.41 -0.07 -1.12 0.17
## TravelMonth* 4.00 3.00 -0.14 -1.46 0.05
## IsInternational* 2.00 1.00 -2.91 6.50 0.01
## SeatsEconomy 389.00 311.00 0.72 -0.36 3.57
## SeatsPremium 66.00 58.00 0.23 -0.46 0.62
## PitchEconomy 33.00 3.00 -0.03 -0.35 0.03
## PitchPremium 40.00 6.00 -1.51 3.52 0.06
## WidthEconomy 19.00 2.00 -0.04 -0.08 0.03
## WidthPremium 21.00 4.00 -0.08 -0.31 0.05
## PriceEconomy 3593.00 3528.00 0.51 -0.88 46.18
## PricePremium 7414.00 7328.00 0.50 0.43 60.19
## PriceRelative 1.89 1.87 1.17 0.72 0.02
## SeatsTotal 441.00 343.00 0.70 -0.53 3.99
## PitchDifference 10.00 8.00 -0.54 1.78 0.08
## WidthDifference 4.00 4.00 0.84 -0.53 0.06
## PercentPremiumSeats 24.69 19.98 0.71 0.28 0.23
Finding the datatype in the dataset
str(airline.df)
## 'data.frame': 458 obs. of 18 variables:
## $ Airline : Factor w/ 6 levels "AirFrance","British",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ Aircraft : Factor w/ 2 levels "AirBus","Boeing": 2 2 2 2 2 2 2 2 2 2 ...
## $ FlightDuration : num 12.25 12.25 12.25 12.25 8.16 ...
## $ TravelMonth : Factor w/ 4 levels "Aug","Jul","Oct",..: 2 1 4 3 1 4 3 1 4 4 ...
## $ IsInternational : Factor w/ 2 levels "Domestic","International": 2 2 2 2 2 2 2 2 2 2 ...
## $ SeatsEconomy : int 122 122 122 122 122 122 122 122 122 122 ...
## $ SeatsPremium : int 40 40 40 40 40 40 40 40 40 40 ...
## $ PitchEconomy : int 31 31 31 31 31 31 31 31 31 31 ...
## $ PitchPremium : int 38 38 38 38 38 38 38 38 38 38 ...
## $ WidthEconomy : int 18 18 18 18 18 18 18 18 18 18 ...
## $ WidthPremium : int 19 19 19 19 19 19 19 19 19 19 ...
## $ PriceEconomy : int 2707 2707 2707 2707 1793 1793 1793 1476 1476 1705 ...
## $ PricePremium : int 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 ...
## $ PriceRelative : num 0.38 0.38 0.38 0.38 0.67 0.67 0.67 1.03 1.03 0.75 ...
## $ SeatsTotal : int 162 162 162 162 162 162 162 162 162 162 ...
## $ PitchDifference : int 7 7 7 7 7 7 7 7 7 7 ...
## $ WidthDifference : int 1 1 1 1 1 1 1 1 1 1 ...
## $ PercentPremiumSeats: num 24.7 24.7 24.7 24.7 24.7 ...
Finding price differences in premium and economy and also finding the cheapest and the costliest airlines with these constraints.
aggregate(airline.df$PricePremium~airline.df$Airline, FUN=mean)
## airline.df$Airline airline.df$PricePremium
## 1 AirFrance 3065.2162
## 2 British 1937.0286
## 3 Delta 684.6739
## 4 Jet 483.3607
## 5 Singapore 1239.9250
## 6 Virgin 2721.6935
aggregate(airline.df$PriceEconomy~airline.df$Airline, FUN=mean)
## airline.df$Airline airline.df$PriceEconomy
## 1 AirFrance 2769.7838
## 2 British 1293.4800
## 3 Delta 560.9348
## 4 Jet 276.1639
## 5 Singapore 860.2500
## 6 Virgin 1603.5323
The mean premium price varied for different airlines. It was the least for Jet(483) around to max for AirFrance(3065). Jet had the lowest economic price with the highest Economy price by AirFrance at 2770.
Creating Box Plot
boxplot(airline.df$FlightDuration~airline.df$Aircraft,
xlab="Aircraft",
ylab="Flight Duration",
col=c("green","skyblue"))
Another Box Plot
boxplot(airline.df$FlightDuration~airline.df$Airline,
xlab="Airline",
ylab="Flight Duration",
col=c("grey","red","orange"))
Drawing CORRGRAM
library(corrgram)
## Warning: package 'corrgram' was built under R version 3.3.3
corrgram(airline.df, order=FALSE,
lower.panel=panel.shade,
upper.panel=panel.pie,
main="Corrgram")
Comparing Premium Economy Vs Economy Price
plot(~airline.df$PriceEconomy + airline.df$PricePremium, main="Premium Economy Price vs. Economy Price")
abline(0,1)
Pitch Difference of Premium Economy Vs Economy
library(lattice)
## Warning: package 'lattice' was built under R version 3.3.3
histogram(~airline.df$PitchDifference, main = "Distribution of Pitch Difference", xlab="Difference in Pitch")
Relative Price Between Premium Economy Vs Economy
boxplot(airline.df$PriceRelative~airline.df$PitchDifference, main="Relative Price Difference vs.Pitch", ylab="Pitch Difference", xlab="Relative Price b/w Economy and Premium Economy")
Analysisnng all fieds using scatter plot
library(car)
## Warning: package 'car' was built under R version 3.3.3
##
## Attaching package: 'car'
## The following object is masked from 'package:psych':
##
## logit
scatterplotMatrix(~PricePremium+PriceEconomy+SeatsTotal+PercentPremiumSeats+PitchDifference+WidthDifference, data=airline.df, main="Premium Economy vs. Economy Airfares")
Create a Variance-Covariance Matrix
library(lattice)
library(survival)
## Warning: package 'survival' was built under R version 3.3.3
library(Formula)
## Warning: package 'Formula' was built under R version 3.3.3
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 3.3.3
##
## Attaching package: 'ggplot2'
## The following objects are masked from 'package:psych':
##
## %+%, alpha
library(Hmisc)
## Warning: package 'Hmisc' was built under R version 3.3.3
##
## Attaching package: 'Hmisc'
## The following object is masked from 'package:psych':
##
## describe
## The following objects are masked from 'package:base':
##
## format.pval, units
colairlines <- c("PricePremium","PriceEconomy","PitchDifference","WidthDifference")
corMatrix <- rcorr(as.matrix(airline.df[,colairlines]))
corMatrix
## PricePremium PriceEconomy PitchDifference WidthDifference
## PricePremium 1.00 0.90 -0.02 -0.01
## PriceEconomy 0.90 1.00 -0.10 -0.08
## PitchDifference -0.02 -0.10 1.00 0.76
## WidthDifference -0.01 -0.08 0.76 1.00
##
## n= 458
##
##
## P
## PricePremium PriceEconomy PitchDifference WidthDifference
## PricePremium 0.0000 0.6998 0.8059
## PriceEconomy 0.0000 0.0332 0.0708
## PitchDifference 0.6998 0.0332 0.0000
## WidthDifference 0.8059 0.0708 0.0000