Read the data into a dataframe called airline.df

airline.df <- read_csv("../datasets/BOMDELBOM.csv")

## Parsed with column specification:
## cols(
##   .default = col_character(),
##   DepartureTime = col_double(),
##   ArrivalTime = col_double(),
##   FlyingMinutes = col_double(),
##   Capacity = col_double(),
##   SeatPitch = col_double(),
##   SeatWidth = col_double(),
##   Price = col_double(),
##   AdvancedBookingDays = col_double(),
##   MarketShare = col_double(),
##   LoadFactor = col_double()
## )

## See spec(...) for full column specifications.

Write R code to list 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"

Write R code to output the dimensions of the data frame.

dim(airline.df)

## [1] 305  23

Write R code to find the mean and standard deviation of Capacity and FlyingMinutes.

#Capacity Mean & SD
mean(airline.df$Capacity);sd(airline.df$Capacity)

## [1] 176.3574

## [1] 32.38868

#FlyingMinutes Mean & SD
mean(airline.df$FlyingMinutes);sd(airline.df$FlyingMinutes)

## [1] 136.0328

## [1] 4.705006

Write some simple comments based on your interpretation of the answers.

There’s more variance in capacity as compared to flying minutes.