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

Part 4: Mean & standard deviation of Capacity

# Display Mean & standard deviation of Capacity
mean(airline.df$Capacity)

## [1] 176.3574

sd(airline.df$Capacity)

## [1] 32.38868

# Higher amount of standard deviation can be observed in the Capacity indicating greater spread in the data

Part 5: Mean & standard deviation of Flying Minutes

# Display Mean & standard deviation of Flying Minutes
mean(airline.df$FlyingMinutes)

## [1] 136.0328

sd(airline.df$FlyingMinutes)

## [1] 4.705006

# Lower standard deviation indicates relatively lesser spread in the flying minutes of the flights