Part 1: Read the data..

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

# loading necessary libraries
library(psych)

## Warning: package 'psych' was built under R version 3.5.3

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: Determining the mean and SD

# getting the mean and standard deviation of Capacity and Flyng Minutes
mean(airline.df$Capacity)

## [1] 176.3574

mean(airline.df$FlyingMinutes)

## [1] 136.0328

sd(airline.df$Capacity)

## [1] 32.38868

sd(airline.df$FlyingMinutes)

## [1] 4.705006

# describing the data
describe(airline.df$Capacity)

##    vars   n   mean    sd median trimmed   mad min max range skew kurtosis
## X1    1 305 176.36 32.39    180  172.19 14.83 138 303   165 2.11     5.91
##      se
## X1 1.85

describe(airline.df$FlyingMinutes)

##    vars   n   mean   sd median trimmed  mad min max range skew kurtosis
## X1    1 305 136.03 4.71    135   135.8 7.41 125 145    20 0.28    -0.33
##      se
## X1 0.27