flight

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1. Create Flight Data

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flights <- data.frame( FlightID = c(“F101”,“F102”,“F103”,“F104”,“F105”), Airline = c(“Indigo”,“AirIndia”,“SpiceJet”,“Vistara”,“Indigo”), Type = c(“Domestic”,“International”,“Domestic”,“International”,“Domestic”), Passengers = c(180,220,150,200,170), Fare = c(5000,12000,4500,15000,4800), Delay = c(10,55,5,120,20) )

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2. Insert Airport column at 3rd position

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flights <- cbind( flights[,1:2], Airport = “Delhi”, flights[,3:6] )

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3. Sort by Fare (descending)

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sorted_flights <- flights[order(-flights$Fare), ]

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4. Count Domestic vs International

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flight_type_count <- table(flights$Type)

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5. Average Passengers

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avg_passengers <- mean(flights$Passengers)

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6. Insert New Flight Row

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new_flight <- data.frame( FlightID=“F106”, Airline=“AirAsia”, Airport=“Delhi”, Type=“Domestic”, Passengers=160, Fare=5200, Delay=25 )

flights <- rbind(flights, new_flight)

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7. Create DelayCategory column

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flights\(DelayCategory <- ifelse( flights\)Delay < 15, “On Time”, ifelse(flights$Delay <= 60, “Moderate Delay”, “High Delay”) )

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8. Top 2 Flights by Fare

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top2_flights <- flights[order(-flights$Fare), ][1:2, ]

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Outputs

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flights sorted_flights flight_type_count avg_passengers top2_flights