MATH2349 Semester 2, 2018

Required packages

library(readr)
library(tidyr)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(deductive)
library(validate)

## 
## Attaching package: 'validate'

## The following object is masked from 'package:dplyr':
## 
##     expr

library(outliers)
library(forecast)
library(ggplot2)

## 
## Attaching package: 'ggplot2'

## The following object is masked from 'package:validate':
## 
##     expr

Executive Summary

I have two datasets obtained from the data.gov.au website. They look at passenger and freight movements in and out of Australian airports. They’re two International Airline datasets and have a unique identifier for month that can be used for a join. I will look at the data to see the difference between passenger movements and maximum seat capacity.

Data

Using the International Airlines - Airline by country of port dataset … (data.gov.au 2018).

Month - Identification number for month
Airline - Airline Carrier
Port_Country - Based on the international uplift or discharge port within a single flight number
Passengers_In - Passengers inbound to Australia
Freight_In_(tonnes) - Freight inbound to Australia in tonnes
Mail_In_(tonnes) - Mail inbound to Australia in tonnes
Passengers_Out - Passengers outbound from Australia
Freight_Out_(tonnes) - Freight outbound from Australia in tonnes
Mail_Out_(tonnes) - Mail outbound from Australia in tonnes
Year = Year of measurement
Month_num = Months recorded by calendar number

Using International Airlines - Operated Flights and Seats dataset … (data.gov.au 2018).

Month - Identification number for month
In_Out - Factor for in or out movements. I = in, O = out
Australian_City - Australian city where airport is located
International_City - International city where airport is located
Airline - Airline carrier
Route - Route from and to using abbreviated city code
Port_Country - Country where airport is located
Port_Region - Region where point is located
Service_Country - Country where aircraft was serviced
Service_Region - Region where aircraft was serviced
Stops - Stops to reach destination
All_Flights - All flights from that port in the corresponding month
Max_Seats - Max available seats
Year - Year of observation
Month_num - Months recorded by calendar number

#read in airline port country dataset
aus_international_airline_port <- read_csv("airline_portcountry.csv")

## Parsed with column specification:
## cols(
##   Month = col_integer(),
##   Airline = col_character(),
##   Port_Country = col_character(),
##   Passengers_In = col_integer(),
##   `Freight_In_(tonnes)` = col_double(),
##   `Mail_In_(tonnes)` = col_double(),
##   Passengers_Out = col_integer(),
##   `Freight_Out_(tonnes)` = col_double(),
##   `Mail_Out_(tonnes)` = col_double(),
##   Year = col_integer(),
##   Month_num = col_integer()
## )

#show head of data
head(aus_international_airline_port)

#read in airline movement activity 
aus_international_flights_seats <- read_csv("international_airline_activity_opfltsseats.csv")

## Parsed with column specification:
## cols(
##   Month = col_integer(),
##   In_Out = col_character(),
##   Australian_City = col_character(),
##   International_City = col_character(),
##   Airline = col_character(),
##   Route = col_character(),
##   Port_Country = col_character(),
##   Port_Region = col_character(),
##   Service_Country = col_character(),
##   Service_Region = col_character(),
##   Stops = col_integer(),
##   All_Flights = col_integer(),
##   Max_Seats = col_integer(),
##   Year = col_integer(),
##   Month_num = col_integer()
## )

#show head of data
head(aus_international_flights_seats)

Understand

Assessing the data structure and making sure it it is tidy.

#check the variable class types
sapply(aus_international_airline_port, class)

##                Month              Airline         Port_Country 
##            "integer"          "character"          "character" 
##        Passengers_In  Freight_In_(tonnes)     Mail_In_(tonnes) 
##            "integer"            "numeric"            "numeric" 
##       Passengers_Out Freight_Out_(tonnes)    Mail_Out_(tonnes) 
##            "integer"            "numeric"            "numeric" 
##                 Year            Month_num 
##            "integer"            "integer"

sapply(aus_international_flights_seats, class)

##              Month             In_Out    Australian_City 
##          "integer"        "character"        "character" 
## International_City            Airline              Route 
##        "character"        "character"        "character" 
##       Port_Country        Port_Region    Service_Country 
##        "character"        "character"        "character" 
##     Service_Region              Stops        All_Flights 
##        "character"          "integer"          "integer" 
##          Max_Seats               Year          Month_num 
##          "integer"          "integer"          "integer"

#total variable class types
table(sapply(aus_international_airline_port, class))

## 
## character   integer   numeric 
##         2         5         4

table(sapply(aus_international_flights_seats, class))

## 
## character   integer 
##         9         6

#dataset structure
str(aus_international_airline_port)

## Classes 'tbl_df', 'tbl' and 'data.frame':    40156 obs. of  11 variables:
##  $ Month               : int  31048 31048 31048 31048 31048 31048 31048 31048 31048 31048 ...
##  $ Airline             : chr  "Air Caledonie" "Air China" "Air India" "Air India" ...
##  $ Port_Country        : chr  "New Caledonia" "China" "India" "Singapore" ...
##  $ Passengers_In       : int  725 1311 1423 501 161 24588 2954 3350 574 280 ...
##  $ Freight_In_(tonnes) : num  0.368 9.158 44.823 10.583 0.212 ...
##  $ Mail_In_(tonnes)    : num  0 0 0 0 0 0.374 0 0 0 0 ...
##  $ Passengers_Out      : int  474 1232 970 306 96 20692 2906 2470 578 205 ...
##  $ Freight_Out_(tonnes): num  0.167 2.587 6.708 124.966 23.996 ...
##  $ Mail_Out_(tonnes)   : num  0 0 0 0 0 0.122 0 0 0 0 ...
##  $ Year                : int  1985 1985 1985 1985 1985 1985 1985 1985 1985 1985 ...
##  $ Month_num           : int  1 1 1 1 1 1 1 1 1 1 ...
##  - attr(*, "spec")=List of 2
##   ..$ cols   :List of 11
##   .. ..$ Month               : list()
##   .. .. ..- attr(*, "class")= chr  "collector_integer" "collector"
##   .. ..$ Airline             : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ Port_Country        : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ Passengers_In       : list()
##   .. .. ..- attr(*, "class")= chr  "collector_integer" "collector"
##   .. ..$ Freight_In_(tonnes) : list()
##   .. .. ..- attr(*, "class")= chr  "collector_double" "collector"
##   .. ..$ Mail_In_(tonnes)    : list()
##   .. .. ..- attr(*, "class")= chr  "collector_double" "collector"
##   .. ..$ Passengers_Out      : list()
##   .. .. ..- attr(*, "class")= chr  "collector_integer" "collector"
##   .. ..$ Freight_Out_(tonnes): list()
##   .. .. ..- attr(*, "class")= chr  "collector_double" "collector"
##   .. ..$ Mail_Out_(tonnes)   : list()
##   .. .. ..- attr(*, "class")= chr  "collector_double" "collector"
##   .. ..$ Year                : list()
##   .. .. ..- attr(*, "class")= chr  "collector_integer" "collector"
##   .. ..$ Month_num           : list()
##   .. .. ..- attr(*, "class")= chr  "collector_integer" "collector"
##   ..$ default: list()
##   .. ..- attr(*, "class")= chr  "collector_guess" "collector"
##   ..- attr(*, "class")= chr "col_spec"

str(aus_international_flights_seats)

## Classes 'tbl_df', 'tbl' and 'data.frame':    92386 obs. of  15 variables:
##  $ Month             : int  37865 37865 37865 37865 37865 37865 37865 37865 37865 37865 ...
##  $ In_Out            : chr  "I" "I" "I" "I" ...
##  $ Australian_City   : chr  "Adelaide" "Adelaide" "Adelaide" "Adelaide" ...
##  $ International_City: chr  "Denpasar" "Hong Kong" "Kuala Lumpur" "Singapore" ...
##  $ Airline           : chr  "Garuda Indonesia" "Cathay Pacific Airways" "Malaysia Airlines" "Qantas Airways" ...
##  $ Route             : chr  "DPS-ADL-MEL" "HKG-ADL-MEL" "KUL-ADL" "SIN-DRW-ADL-MEL" ...
##  $ Port_Country      : chr  "Indonesia" "Hong Kong (SAR)" "Malaysia" "Singapore" ...
##  $ Port_Region       : chr  "SE Asia" "NE Asia" "SE Asia" "SE Asia" ...
##  $ Service_Country   : chr  "Indonesia" "Hong Kong (SAR)" "Malaysia" "Singapore" ...
##  $ Service_Region    : chr  "SE Asia" "NE Asia" "SE Asia" "SE Asia" ...
##  $ Stops             : int  0 0 0 1 1 0 0 0 0 0 ...
##  $ All_Flights       : int  13 8 17 4 9 12 36 18 8 14 ...
##  $ Max_Seats         : int  3809 2008 4726 908 2038 3876 12624 2556 2296 5404 ...
##  $ Year              : int  2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 ...
##  $ Month_num         : int  9 9 9 9 9 9 9 9 9 9 ...
##  - attr(*, "spec")=List of 2
##   ..$ cols   :List of 15
##   .. ..$ Month             : list()
##   .. .. ..- attr(*, "class")= chr  "collector_integer" "collector"
##   .. ..$ In_Out            : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ Australian_City   : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ International_City: list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ Airline           : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ Route             : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ Port_Country      : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ Port_Region       : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ Service_Country   : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ Service_Region    : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ Stops             : list()
##   .. .. ..- attr(*, "class")= chr  "collector_integer" "collector"
##   .. ..$ All_Flights       : list()
##   .. .. ..- attr(*, "class")= chr  "collector_integer" "collector"
##   .. ..$ Max_Seats         : list()
##   .. .. ..- attr(*, "class")= chr  "collector_integer" "collector"
##   .. ..$ Year              : list()
##   .. .. ..- attr(*, "class")= chr  "collector_integer" "collector"
##   .. ..$ Month_num         : list()
##   .. .. ..- attr(*, "class")= chr  "collector_integer" "collector"
##   ..$ default: list()
##   .. ..- attr(*, "class")= chr  "collector_guess" "collector"
##   ..- attr(*, "class")= chr "col_spec"

#a better look at the attributes
attributes(aus_international_airline_port[1:10, ])

## $names
##  [1] "Month"                "Airline"              "Port_Country"        
##  [4] "Passengers_In"        "Freight_In_(tonnes)"  "Mail_In_(tonnes)"    
##  [7] "Passengers_Out"       "Freight_Out_(tonnes)" "Mail_Out_(tonnes)"   
## [10] "Year"                 "Month_num"           
## 
## $row.names
##  [1]  1  2  3  4  5  6  7  8  9 10
## 
## $class
## [1] "tbl_df"     "tbl"        "data.frame"

attributes(aus_international_flights_seats[1:10, ])

## $names
##  [1] "Month"              "In_Out"             "Australian_City"   
##  [4] "International_City" "Airline"            "Route"             
##  [7] "Port_Country"       "Port_Region"        "Service_Country"   
## [10] "Service_Region"     "Stops"              "All_Flights"       
## [13] "Max_Seats"          "Year"               "Month_num"         
## 
## $row.names
##  [1]  1  2  3  4  5  6  7  8  9 10
## 
## $class
## [1] "tbl_df"     "tbl"        "data.frame"

Tidy & Manipulate Data I

Group aus_international_flight_seats dataset by Month, Airline, Port_Country, Year and Month_num, with sums of Max_Seats and All_Flights to get ready to join to the aus_international_airline_port dataset
Full join on Month, Airline, Port_Country, Year and Month_num
Factor variables

#group observations and drop varibles 
aus_international_flights_seats <- aus_international_flights_seats %>% group_by(Month, Airline, Port_Country, Year, Month_num) %>% summarise(Max_Seats=sum(Max_Seats,na.rm = TRUE), All_Flights=sum(All_Flights, na.rm = TRUE))

#join ailine movements and airline ports on Month, Airline, Port_Country, Year and Month_num
international_air_traval <- full_join(aus_international_flights_seats, aus_international_airline_port, by = c("Month" = "Month", "Airline" = "Airline", "Port_Country" = "Port_Country", "Year" = "Year", "Month_num" = "Month_num"))
#show head of data
head(international_air_traval)

#factor variables
international_air_traval$Month <- as.factor(international_air_traval$Month)
international_air_traval$Year <- as.factor(international_air_traval$Year)
international_air_traval$Airline <- as.factor(international_air_traval$Airline)
international_air_traval$Port_Country <- as.factor(international_air_traval$Port_Country)

#show head of joined data
head(international_air_traval)

Tidy & Manipulate Data II

The following rules apply for tidy data: 1. Each variable must have its own column. 2. Each observation must have its own row. 3. Each value must have its own cell. - all rules have been met except if you consider the two month variables where on is an identification number. So there some things that should be changed

Rename the variables Month to Month_Id and Month_num to Month
Change month number to corresponding month name and factor it
Create a new dataset total_passengers for the numeric variables
Create two new variables from existing variables - Total_Movement from In/Out movements and Empty_seats by subtracting Total_movents from Max_Seates

#ungrouping
#ungroup(international_air_traval)

#rename the variables
names(international_air_traval)[1]<-"Month_ID"
names(international_air_traval)[5]<-"Month"

#factor and change values to quarters
international_air_traval <- ungroup(international_air_traval)  %>% mutate(Month = case_when (Month == 1 ~ "January", Month == 2 ~ "February", Month  == 3 ~ "March", Month == 4 ~ "April", Month == 5 ~ "May", Month == 6 ~ "June", Month == 7 ~ "July", Month == 8 ~ "August", Month == 9 ~ "September", Month == 10 ~ "October", Month == 11 ~ "November", Month == 12 ~ "December"))

#factor Quarter variable
international_air_traval$Month = as.factor(international_air_traval$Month)

#create new dataframe for totals
total_passenger <- international_air_traval %>% select(Year, Month, Max_Seats, Passengers_In, Passengers_Out)

#creating new variable from passengers in and passengers out
total_passenger <- mutate(total_passenger,
       Total_Movement = Passengers_In + Passengers_Out)

#creating new variable, empty_seats by subtracting the new variable total movement from max seats 
total_passenger <- mutate(total_passenger, Empty_Seats = Max_Seats - Total_Movement)

#group by year and month and create a new dataset
totals_by_month <- total_passenger %>% group_by(Year, Month) %>% summarise(Total_Movement=sum(Total_Movement,na.rm = TRUE), Empty_Seats=sum(Empty_Seats, na.rm = TRUE), Max_Seats=sum(Max_Seats, na.rm = TRUE))

#head data
head(total_passenger)

Scan I

Checking the number of NA values for each variable by using the is.na function with the colSums() function to summarise. Checking for infinites and not a number values with applicable function
Using a custom rule from estimates with the validator function to fill out missing data using the impute_lr function
Filter out years where data doesn’t overlap
Filter out Empty_Seat less than or equal to zero

#checking for NAs
colSums(is.na(total_passenger))

##           Year          Month      Max_Seats  Passengers_In Passengers_Out 
##              0              0          26694            874            874 
## Total_Movement    Empty_Seats 
##            874          27568

#we have a lot of NAs due to the differece in dates of observation
#filtering the 15 year period froom 2003 - 2017, purposely dropping 2018 as the data is incomplete 
fifteen_years_movement <- total_passenger %>% filter (Year == 2003 | Year == 2004 | Year == 2005 | Year == 2006 | Year == 2007 
                                           | Year == 2008 | Year == 2009 | Year == 2010 | Year == 2011 | Year == 2012
                                           | Year == 2013 | Year == 2014 | Year == 2015 | Year == 2016 | Year == 2017)

#checking for NAs again
colSums(is.na(fifteen_years_movement))

##           Year          Month      Max_Seats  Passengers_In Passengers_Out 
##              0              0           5885            866            866 
## Total_Movement    Empty_Seats 
##            866           6751

# Define the rules as an validator expression
Rules <- validator( Passengers_In == Max_Seats * 0.25, Passengers_Out == Max_Seats * 0.25, Empty_Seats == Max_Seats - Total_Movement, Passengers_In + Passengers_Out == Total_Movement)

# Use impute_lr function
fifteen_years_movement <- impute_lr(fifteen_years_movement, Rules)

fifteen_years_movement <- fifteen_years_movement %>% mutate_at(vars(Passengers_In, Passengers_Out, Total_Movement, Empty_Seats), funs(round(., 0)))

#checking for NAs again
colSums(is.na(fifteen_years_movement))

##           Year          Month      Max_Seats  Passengers_In Passengers_Out 
##              0              0              0              0              0 
## Total_Movement    Empty_Seats 
##              0              0

#checking to see if there are any infinite values 
sum(is.infinite(fifteen_years_movement$Max_Seats)|
is.infinite(fifteen_years_movement$Passengers_In) |
is.infinite(fifteen_years_movement$Passengers_Out)|
is.infinite(fifteen_years_movement$Total_Movement) |
is.infinite(fifteen_years_movement$Empty_Seats))

## [1] 0

#checking to see if they're all numeric
sum(is.nan(fifteen_years_movement$Max_Seats)|
is.nan(fifteen_years_movement$Passengers_In) |
is.nan(fifteen_years_movement$Passengers_Out)|
is.nan(fifteen_years_movement$Total_Movement) |
is.nan(fifteen_years_movement$Empty_Seats))

## [1] 0

#removing zero data from Empty_seat variable to avoid skewed results
fifteen_years_movement = filter(fifteen_years_movement, Empty_Seats > 0)

Scan II

Scanning for outliers using the outliers package
Calculate the number of numeric variables values with a z-score absolute value of more than 3
Cap the value of outliers below the lower fence (Q1 - 1.5 x IQR) - the 5th percentile, and the the upper fence (Q3 + 1.5 x IQR) - the 95th percentile

#Calculating the z-scores for the Total_Movement movement variable and showing
#the total number with an absolute value greater than 3.
z.scores_mv <- fifteen_years_movement$Total_Movement %>% scores(type = "z")
z.scores_mv %>% summary()

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
## -0.65441 -0.54116 -0.39204  0.00000  0.06796  6.89021

sum(abs(z.scores_mv) > 3)

## [1] 452

#Calculating the z-scores for the Max_seats variable and showing
#the total number with an absolute value greater than 3.
z.scores_ms <- fifteen_years_movement$Max_Seats %>% scores(type = "z")
z.scores_ms %>% summary()

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## -0.7374 -0.5879 -0.4066  0.0000  0.1238  6.4705

sum(abs(z.scores_ms) > 3)

## [1] 438

#Calculating the z-scores for the Passengers_In variable and showing
#the total number with an absolute value greater than 3.
z.scores_pi <- fifteen_years_movement$Passengers_In %>% scores(type = "z")
z.scores_pi %>% summary()

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
## -0.65044 -0.53986 -0.39149  0.00000  0.07094  7.30039

sum(abs(z.scores_pi) > 3)

## [1] 434

#Calculating the z-scores for the Passengers_Out variable and showing
#the total number with an absolute value greater than 3.
z.scores_po <- fifteen_years_movement$Passengers_Out %>% scores(type = "z")
z.scores_po %>% summary()

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
## -0.65282 -0.54091 -0.39091  0.00000  0.07655  7.65262

sum(abs(z.scores_po) > 3)

## [1] 452

#Calculating the z-scores for the Empty_Seats variable and showing
#the total number with an absolute value greater than 3.
z.scores_es <- fifteen_years_movement$Empty_Seats %>% scores(type = "z")
z.scores_es %>% summary()

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## -0.7478 -0.6032 -0.3976  0.0000  0.1588  8.8727

sum(abs(z.scores_es) > 3)

## [1] 351

#function to cap the values
cap <- function(x){
quantiles <- quantile( x, c(.05, 0.25, 0.75, .95 ) )
x[ x < quantiles[2] - 1.5*IQR(x) ] <- quantiles[1]
x[ x > quantiles[3] + 1.5*IQR(x) ] <- quantiles[4]
x
}

# Capping the numeric variables
fifteen_years_movement$Total_Movement <- fifteen_years_movement$Total_Movement %>% cap()
fifteen_years_movement$Max_Seats <- fifteen_years_movement$Max_Seats %>% cap()
fifteen_years_movement$Passengers_In <- fifteen_years_movement$Passengers_In %>% cap()
fifteen_years_movement$Passengers_Out <- fifteen_years_movement$Passengers_Out %>% cap()
fifteen_years_movement$Empty_seats <- fifteen_years_movement$Empty_Seats %>% cap()

#summary of variables before capping
summary(fifteen_years_movement[, c(3:7)])

##    Max_Seats      Passengers_In   Passengers_Out  Total_Movement  
##  Min.   :     8   Min.   :    0   Min.   :    0   Min.   :     0  
##  1st Qu.:  8398   1st Qu.: 2196   1st Qu.: 2187   1st Qu.:  4443  
##  Median : 18576   Median : 5142   Median : 5118   Median : 10294  
##  Mean   : 39815   Mean   :12121   Mean   :11981   Mean   : 24224  
##  3rd Qu.: 48349   3rd Qu.:14324   3rd Qu.:14254   3rd Qu.: 28341  
##  Max.   :160013   Max.   :51973   Max.   :51527   Max.   :103629  
##   Empty_Seats    
##  Min.   :     2  
##  1st Qu.:  3042  
##  Median :  7366  
##  Mean   : 15727  
##  3rd Qu.: 19066  
##  Max.   :202305

#summary of variables after capping
summary(fifteen_years_movement[, c(3:7)])

##    Max_Seats      Passengers_In   Passengers_Out  Total_Movement  
##  Min.   :     8   Min.   :    0   Min.   :    0   Min.   :     0  
##  1st Qu.:  8398   1st Qu.: 2196   1st Qu.: 2187   1st Qu.:  4443  
##  Median : 18576   Median : 5142   Median : 5118   Median : 10294  
##  Mean   : 39815   Mean   :12121   Mean   :11981   Mean   : 24224  
##  3rd Qu.: 48349   3rd Qu.:14324   3rd Qu.:14254   3rd Qu.: 28341  
##  Max.   :160013   Max.   :51973   Max.   :51527   Max.   :103629  
##   Empty_Seats    
##  Min.   :     2  
##  1st Qu.:  3042  
##  Median :  7366  
##  Mean   : 15727  
##  3rd Qu.: 19066  
##  Max.   :202305

Transform

A histograms of the Empty_seats variables which shows a strong positive skew
Using the BoxCox function on this variable from the forecasts package it should transform the data to an approximately normal distribution

#a histogram showing the Empty_Seats variable before the transformation.
ggplot(fifteen_years_movement, aes(x = Empty_Seats)) +
geom_histogram(bins = 20, colour = "darkblue", fill = "lightblue") +
labs(title = "Histogram of Empty_seats variable from complete_games dataset",
y = "Frequency",
x = "Empty Seats by Month")

#a Box-Cox tranformation on the Empty_Seats variable
fifteen_years_movement$Empty_Seats_BoxCox <- BoxCox(fifteen_years_movement$Empty_Seats, lambda = "auto")
#histogram of the Empty_Seats variable after the transformation
ggplot(fifteen_years_movement, aes(x = Empty_Seats_BoxCox)) +
geom_histogram(bins = 20, colour = "darkblue", fill = "lightblue") +
labs(title = "Histogram of the Empty_seats variable after BoxCox transformation",
y = "Frequency",
x = "Empty Seats by Month")

data.gov.au 2018, International Airlines - Airline by country of port, Airline datasets, data file, Australian Government, Bureau of Infrastructure, Melbourne, viewed 06 November 2018, < https://data.gov.au/dataset/international-airlines-airline-by-country-of-port-data>

data.gov.au 2018, International Airlines - Operated Flights and Seats, Airline datasets, data file, Australian Government, Bureau of Infrastructure, Melbourne, viewed 06 November 2018, https://data.gov.au/dataset/airport-traffic-data