Part 1: Read the data

# reading external data and storing into a dataframe called "airline.df"
airline.df <- read.csv("BOMDELBOM.csv")

Part 2: Column names

# Display the column names
colnames(airline.df)

##  [1] "FlightNumber"        "Airline"             "DepartureCityCode"  
##  [4] "ArrivalCityCode"     "DepartureTime"       "ArrivalTime"        
##  [7] "Departure"           "FlyingMinutes"       "Aircraft"           
## [10] "PlaneModel"          "Capacity"            "SeatPitch"          
## [13] "SeatWidth"           "DataCollectionDate"  "DateDeparture"      
## [16] "IsWeekend"           "Price"               "AdvancedBookingDays"
## [19] "IsDiwali"            "DayBeforeDiwali"     "DayAfterDiwali"     
## [22] "MarketShare"         "LoadFactor"

Part 3: Data Dimensions

# Display the Data Dimensions
dim(airline.df)

## [1] 305  23

str(airline.df)

## 'data.frame':    305 obs. of  23 variables:
##  $ FlightNumber       : Factor w/ 63 levels "6E 129","6E 155",..: 25 32 62 4 61 45 57 16 59 17 ...
##  $ Airline            : Factor w/ 4 levels "Air India","IndiGo",..: 3 3 4 2 4 3 4 2 4 3 ...
##  $ DepartureCityCode  : Factor w/ 2 levels "BOM","DEL": 2 1 2 2 1 1 2 2 1 1 ...
##  $ ArrivalCityCode    : Factor w/ 2 levels "BOM","DEL": 1 2 1 1 2 2 1 1 2 2 ...
##  $ DepartureTime      : int  225 300 350 455 555 605 635 640 645 700 ...
##  $ ArrivalTime        : int  435 505 605 710 805 815 850 855 855 915 ...
##  $ Departure          : Factor w/ 2 levels "AM","PM": 1 1 1 1 1 1 1 1 1 1 ...
##  $ FlyingMinutes      : int  130 125 135 135 130 130 135 135 130 135 ...
##  $ Aircraft           : Factor w/ 2 levels "Airbus","Boeing": 2 2 2 1 2 2 2 1 2 2 ...
##  $ PlaneModel         : Factor w/ 9 levels "738","739","77W",..: 1 1 1 6 1 1 1 6 1 2 ...
##  $ Capacity           : int  156 156 189 180 189 156 189 180 189 138 ...
##  $ SeatPitch          : int  30 30 29 30 29 30 29 30 29 30 ...
##  $ SeatWidth          : num  17 17 17 18 17 17 17 18 17 17 ...
##  $ DataCollectionDate : Factor w/ 7 levels "Sep 10 2018",..: 2 4 6 7 6 4 6 7 6 4 ...
##  $ DateDeparture      : Factor w/ 20 levels "Nov 6 2018","Nov 8 2018",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ IsWeekend          : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Price              : int  4051 11587 3977 4234 6837 6518 3189 4234 8623 6833 ...
##  $ AdvancedBookingDays: int  54 52 48 59 48 52 48 59 48 52 ...
##  $ IsDiwali           : Factor w/ 2 levels "No","Yes": 2 2 2 2 2 2 2 2 2 2 ...
##  $ DayBeforeDiwali    : Factor w/ 2 levels "No","Yes": 2 2 2 2 2 2 2 2 2 2 ...
##  $ DayAfterDiwali     : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
##  $ MarketShare        : num  15.4 15.4 13.2 39.6 13.2 15.4 13.2 39.6 13.2 15.4 ...
##  $ LoadFactor         : num  83.3 83.3 94.1 87.2 94.1 ...