Airlines Analysis
zrseah
2024-06-30
Introduction
The dataset encompasses variables pertaining to various aircraft and includes detailed records on the landing count and landed weight of 107 distinct operating airlines, each associated with 97 unique published airlines. This comprehensive data offers valuable insights into flight statistics and provides extensive information about the diverse range of aircraft involved.
Key Findings
- SkyWest Airlines leads in landing counts among all operating airlines.
- United Airlines tops in landed weight among all operating airlines.
- United Airlines - Pre 07/01/2013 holds the highest landing count among published airlines.
- United Airlines leads in landed weight among published airlines.
- Domestic flights exceed international flights in both landing count and landed weight.
- US-based airlines dominate domestic flight operations.
- Asia, Canada, and Europe are key regions serving international flights.
- Passenger flights constitute the majority of air traffic, both domestically and internationally.
- Boeing and Airbus dominate aircraft manufacturing for both domestic and international fleets.
- Wide-bodied aircraft are predominant in freight operations, while narrow-bodied and wide-bodied aircraft are common in passenger flights.
- Boeing is the leading manufacturer for both freighter and passenger aircraft.
- McDonnell Douglas ranks second in freighters, while Airbus follows closely in passenger aircraft manufacturing.
Analysis
Import Data
airlinedf <- read.csv("~/Case Studies/Airlines/cleaned_air.csv")Load Library
library('dplyr')
library('magrittr')
library('ggplot2')
library('tidyr')
library('patchwork')Data Dleaning
## add underscores into column names to separate words
colnames(airlinedf) <- gsub("\\.", "_", colnames(airlinedf))## convert all column names to lower case
colnames(airlinedf) <- tolower(colnames(airlinedf))## check for duplicates
sum(duplicated(airlinedf))## [1] 9206## see where the duplicates are
dupes <- duplicated(airlinedf)
head(dupes)## [1] FALSE FALSE FALSE FALSE FALSE FALSE## remove duplicates
airlinedf <- airlinedf %>%
distinct()## check for NAs
sum(is.na(airlinedf))## [1] 0## trim words
airlinedf <- airlinedf %>%
mutate(across(where(is.character), trimws))# see structure of data
str(airlinedf)## 'data.frame': 12839 obs. of 12 variables:
## $ operating_airline : chr "SkyWest Airlines" "Air Canada" "Japan Airlines" "COPA Airlines, Inc." ...
## $ operating_airline_iata_code: chr "OO" "AC" "JL" "CM" ...
## $ published_airline : chr "United Airlines" "Air Canada" "Japan Airlines" "COPA Airlines, Inc." ...
## $ published_airline_iata_code: chr "UA" "AC" "JL" "CM" ...
## $ geo_summary : chr "International" "International" "International" "International" ...
## $ geo_region : chr "Canada" "Canada" "Asia" "Central America" ...
## $ landing_aircraft_type : chr "Passenger" "Passenger" "Passenger" "Passenger" ...
## $ aircraft_body_type : chr "Regional Jet" "Narrow Body" "Wide Body" "Narrow Body" ...
## $ aircraft_manufacturer : chr "Bombardier" "Airbus" "Boeing" "Boeing" ...
## $ aircraft_model : chr "CRJ2" "A320" "B773" "B739" ...
## $ landing_count : int 30 125 30 3 16 9 30 60 283 29 ...
## $ total_landed_weight : int 1410000 17787978 16620000 491700 7301712 3710376 12235650 8778000 40901600 16066000 ...Data Visualization and Summary Tables
summary(airlinedf)## operating_airline operating_airline_iata_code published_airline
## Length:12839 Length:12839 Length:12839
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
## published_airline_iata_code geo_summary geo_region
## Length:12839 Length:12839 Length:12839
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
## landing_aircraft_type aircraft_body_type aircraft_manufacturer
## Length:12839 Length:12839 Length:12839
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
## aircraft_model landing_count total_landed_weight
## Length:12839 Min. : 1.0 Min. : 6850
## Class :character 1st Qu.: 18.0 1st Qu.: 3720277
## Mode :character Median : 44.0 Median : 10410400
## Mean : 165.6 Mean : 23821308
## 3rd Qu.: 147.0 3rd Qu.: 26255000
## Max. :2245.0 Max. :273042000Airline
# distinct number of operating airlines
length(unique(airlinedf$operating_airline))## [1] 107# distinct number of published airlines
length(unique(airlinedf$published_airline))## [1] 97Number of Distinct Operating Airline under Each Published Airline
airline <- airlinedf %>%
group_by(published_airline) %>%
summarise(airline_count = n_distinct(operating_airline)) %>%
arrange(desc(airline_count))
airline ## # A tibble: 97 × 2
## published_airline airline_count
## <chr> <int>
## 1 Delta Air Lines 6
## 2 American Airlines 5
## 3 Air Canada 3
## 4 Alaska Airlines 3
## 5 United Airlines 3
## 6 Frontier Airlines 2
## 7 US Airways 2
## 8 United Airlines - Pre 07/01/2013 2
## 9 ABC Aerolineas S.A. de C.V. dba Interjet 1
## 10 ABX Air 1
## # ℹ 87 more rowsTop 10 Operating Airlines by Landing Count and Landed Weight
top10_O_airlines <- airlinedf %>%
group_by(operating_airline) %>%
summarise(total_landing_count = sum(landing_count), .groups = 'drop') %>%
slice_max(total_landing_count, n = 10) %>%
arrange(desc(total_landing_count))
top10_O_airlines2 <- airlinedf %>%
group_by(operating_airline) %>%
summarise(total_landed_weight = sum(total_landed_weight), .groups = 'drop') %>%
slice_max(total_landed_weight, n = 10) %>%
arrange(desc(total_landed_weight))
OAcolours <- c("tan4", "wheat2", "skyblue4", "indianred4", "paleturquoise4",
"salmon4", "lightsteelblue3", "lightyellow2", "honeydew3", "burlywood3")
p1 <- ggplot(top10_O_airlines, aes(x = operating_airline, y = total_landing_count, fill = operating_airline)) +
geom_bar(stat = "identity", colour = "grey35") +
scale_fill_manual(values = OAcolours) +
labs(x = "Operating Airlines", y = "Total Landing Count", fill = "Operating Airlines",
title = "Bar Plot of the Top 10 Operating Airlines (Total Landing Count)") +
theme(plot.background = element_rect(fill = "grey90"),
panel.background = element_rect(fill = "seashell2"),
panel.grid = element_line(colour = "grey90"),
legend.background = element_rect(fill = "grey90"),
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1))
p2 <- ggplot(top10_O_airlines2, aes(x = operating_airline, y = total_landed_weight, fill = operating_airline)) +
geom_bar(stat = "identity", colour = "grey35") +
scale_fill_manual(values = OAcolours) +
labs(x = "Operating Airlines", y = "Total Landed Weight", fill = "Operating Airlines",
title = "Bar Plot of the Top 10 Operating Airlines (Total Landed Weight)") +
theme(plot.background = element_rect(fill = "grey90"),
panel.background = element_rect(fill = "seashell2"),
panel.grid = element_line(colour = "grey90"),
legend.background = element_rect(fill = "grey90"),
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1))
combined1 <- p1 + p2 + plot_layout(ncol = 2)
combined1
Top 10 Published Airlines by Landing Count and Landed Weight
top10_P_airlines <- airlinedf %>%
group_by(published_airline) %>%
summarise(total_landing_count = sum(landing_count), .groups = 'drop') %>%
slice_max(total_landing_count, n = 10) %>%
arrange(desc(total_landing_count))
top10_P_airlines2 <- airlinedf %>%
group_by(published_airline) %>%
summarise(total_landed_weight = sum(total_landed_weight), .groups = 'drop') %>%
slice_max(total_landed_weight, n = 10) %>%
arrange(desc(total_landed_weight))
PAcolours <- c("bisque3", "lemonchiffon", "dodgerblue4" , "indianred4", "lightcyan3",
"lightcoral", "slategray3", "wheat2", "lightblue", "tan4")
p3 <- ggplot(top10_P_airlines, aes(x = published_airline, y = total_landing_count, fill = published_airline)) +
geom_bar(stat = "identity", colour = "grey35") +
scale_fill_manual(values = PAcolours) +
labs(x = "Published Airlines", y = "Total Landing Count", fill = "Published Airlines",
title = "Bar Plot of the Top 10 Published Airlines (Total Landing Count)") +
theme(plot.background = element_rect(fill = "grey90"),
panel.background = element_rect(fill = "seashell2"),
panel.grid = element_line(colour = "grey90"),
legend.background = element_rect(fill = "grey90"),
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1))
p4 <- ggplot(top10_P_airlines2, aes(x = published_airline, y = total_landed_weight, fill = published_airline)) +
geom_bar(stat = "identity", colour = "grey35") +
scale_fill_manual(values = PAcolours) +
labs(x = "Published Airlines", y = "Total Landed Weight", fill = "Published Airlines",
title = "Bar Plot of the Top 10 Published Airlines (Total Landed Weight)")+
theme(plot.background = element_rect(fill = "grey90"),
panel.background = element_rect(fill = "seashell2"),
panel.grid = element_line(colour = "grey90"),
legend.background = element_rect(fill = "grey90"),
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1))
combined2 <- p3 + p4 + plot_layout(ncol = 2)
combined2 
GEO Summary
Landing Counts and Landed Weight for Domestic and International Flights
airlinedf <- airlinedf %>%
mutate(geo_summary = factor(geo_summary, levels = c("Domestic", "International")))
geosummarycolours <- c("salmon4", "wheat3")
p5 <- ggplot(airlinedf, aes(x = geo_summary, y = landing_count, fill = geo_summary)) +
geom_bar(stat = "identity") +
scale_fill_manual(values = geosummarycolours) +
labs(x = "GEO Summary", y = "Landing Count", fill = "GEO Summary",
title = "Bar Plot of Total Landing Count for Domestic & International Flights") +
theme(plot.background = element_rect(fill = "grey90"),
panel.background = element_rect(fill = "seashell2"),
panel.grid = element_line(colour = "grey90"),
legend.background = element_rect(fill = "grey90"),
text = element_text(size = 10))
p6 <- ggplot(airlinedf, aes(x = geo_summary, y = total_landed_weight, fill = geo_summary)) +
geom_bar(stat = "identity") +
scale_fill_manual(values = geosummarycolours) +
labs(x = "GEO Summary", y = "Total Landed Weight", fill = "GEO Summary",
title = "Bar Plot of Total Landed Weight for Domestic & International Flights") +
theme(plot.background = element_rect(fill = "grey90"),
panel.background = element_rect(fill = "seashell2"),
panel.grid = element_line(colour = "grey90"),
legend.background = element_rect(fill = "grey90"),
text = element_text(size = 10))
combined3 <- p5 + p6 + plot_layout(ncol = 2)
combined3
Geographical Regions, Landing Aircraft Types & Aircraft Manufacturers Across Domestic and International Flights
regioncolours <- c("burlywood4", "lemonchiffon2", "lightsteelblue4", "lightcoral", "aquamarine4",
"antiquewhite3", "lightblue3", "tan", "honeydew", "indianred4")
p7<- ggplot(airlinedf, aes(x = geo_summary, fill = geo_region)) +
geom_bar(colour = "grey35") +
scale_fill_manual(values = regioncolours) +
labs(x = "GEO Summary", y = "Count", fill = "Region",
title = "Distribution of Geographical Regions across Domestic & International Flights") +
theme(plot.background = element_rect(fill = "grey90"),
panel.background = element_rect(fill = "seashell2"),
panel.grid = element_line(colour = "grey90"),
legend.background = element_rect(fill = "grey90"),
text = element_text(size = 9))
landingaircrafttypecolours <- c("wheat2", "salmon4", "paleturquoise4")
p8 <- ggplot(airlinedf, aes(x = geo_summary, fill = landing_aircraft_type)) +
geom_bar(colour = "grey35") +
scale_fill_manual(values = landingaircrafttypecolours) +
labs(x = "GEO Summary", y = "Count", fill = "Landing Aircraft Type",
title = "Distribution of Landing Aircraft Type across Domestic & International Flights") +
theme(plot.background = element_rect(fill = "grey90"),
panel.background = element_rect(fill = "seashell2"),
panel.grid = element_line(colour = "grey90"),
legend.background = element_rect(fill = "grey90"),
text = element_text(size = 9))
aircraftmanufacturercolours <- c("tan4", "wheat2", "cadetblue4", "indianred4", "paleturquoise4",
"salmon4", "lightblue3", "antiquewhite3", "lightcyan3", "bisque",
"burlywood4", "lemonchiffon2", "honeydew", "lightcoral", "honeydew4")
p9 <- ggplot(airlinedf, aes(x = geo_summary, fill = aircraft_manufacturer)) +
geom_bar(colour = "grey35", linewidth = 0.5) +
scale_fill_manual(values = aircraftmanufacturercolours) +
labs(x = "GEO Summary", y = "Count", fill = "Aircraft Manufacturer",
title = "Distribution of Aircraft Manufacturer across Domestic & International Flights") +
theme(plot.background = element_rect(fill = "grey90"),
panel.background = element_rect(fill = "seashell2"),
panel.grid = element_line(colour = "grey90"),
legend.background = element_rect(fill = "grey90"),
text = element_text(size = 9)) +
guides(fill = guide_legend(ncol = 2))
combined4 <- p7 + p8 + p9 +plot_layout(ncol = 2)
combined4
Region
Total Landing Count and Landed Weight of Each Region
regiondf1 <- airlinedf %>%
group_by(geo_region) %>%
summarise(landing_count = sum(landing_count), total_landed_weight = sum(total_landed_weight))
p10 <- ggplot(regiondf1, aes(x = geo_region, y = landing_count, fill = geo_region)) +
geom_bar(stat = "identity", color = "grey35") +
scale_fill_manual(values = regioncolours) +
labs(x = "Regions", y = "Landing Count", fill = "Regions",
title = "Bar Plot of Total Landing Counts of Each Region") +
theme(plot.background = element_rect(fill = "grey90"),
panel.background = element_rect(fill = "seashell2"),
panel.grid = element_line(colour = "grey90"),
legend.background = element_rect(fill = "grey90"),
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1))
p11 <- ggplot(regiondf1, aes(x = geo_region, y = total_landed_weight, fill = geo_region)) +
geom_bar(stat = "identity", color = "grey35") +
scale_fill_manual(values = regioncolours) +
labs(x = "Region", y = "Total Landed Weight", fill = "Region",
title = "Bar Plot of Total Landed Weight of Each Region") +
theme(plot.background = element_rect(fill = "grey90"),
panel.background = element_rect(fill = "seashell2"),
panel.grid = element_line(colour = "grey90"),
legend.background = element_rect(fill = "grey90"),
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1))
combined5 <- p10 + p11 + plot_layout(ncol = 2)
combined5
Number of Distinct Operating Airlines for Each Region
region_O_airline <- airlinedf %>%
group_by(geo_region) %>%
summarise(unique_operating_airline_count = n_distinct(operating_airline))
region_O_airline ## # A tibble: 10 × 2
## geo_region unique_operating_airline_count
## <chr> <int>
## 1 Asia 34
## 2 Australia / Oceania 6
## 3 Canada 13
## 4 Caribbean 3
## 5 Central America 4
## 6 Europe 30
## 7 Mexico 12
## 8 Middle East 3
## 9 South America 5
## 10 US 53Number of Distinct Number of Published Airlines for Each Region
region_P_airline <- airlinedf %>%
group_by(geo_region) %>%
summarise(unique_published_airline_count = n_distinct(published_airline))
region_P_airline## # A tibble: 10 × 2
## geo_region unique_published_airline_count
## <chr> <int>
## 1 Asia 34
## 2 Australia / Oceania 6
## 3 Canada 10
## 4 Caribbean 3
## 5 Central America 4
## 6 Europe 30
## 7 Mexico 12
## 8 Middle East 3
## 9 South America 5
## 10 US 45Landing Count of Different Aircraft Body Types Across Each Region
region_aircraft_type_landing_count <- airlinedf %>%
group_by(geo_region, aircraft_body_type) %>%
summarise(total_landing_count = sum(landing_count)) %>%
ungroup()
region_aircraft_type_landing_count_wide <- region_aircraft_type_landing_count %>%
pivot_wider(names_from = geo_region,
values_from = total_landing_count,
values_fill = list(total_landing_count = 0))
region_aircraft_type_landing_count_wide## # A tibble: 4 × 11
## aircraft_body_type Asia `Australia / Oceania` Canada Caribbean
## <chr> <int> <int> <int> <int>
## 1 Narrow Body 18 0 40845 0
## 2 Wide Body 47549 4116 1523 3
## 3 Regional Jet 0 0 28845 0
## 4 Turbo Prop 0 0 0 0
## # ℹ 6 more variables: `Central America` <int>, Europe <int>, Mexico <int>,
## # `Middle East` <int>, `South America` <int>, US <int>Total Landed Weight of Different Aircraft Body Types Across Each Region
region_aircraft_type_landing_weight <- airlinedf %>%
group_by(geo_region, aircraft_body_type) %>%
summarise(total_landed_weight = sum(total_landed_weight)) %>%
ungroup()
region_aircraft_type_landing_weight_wide <- region_aircraft_type_landing_weight %>%
pivot_wider(names_from = geo_region,
values_from = total_landed_weight,
values_fill = list(total_landed_weight = 0))
region_aircraft_type_landing_weight_wide## # A tibble: 4 × 11
## aircraft_body_type Asia `Australia / Oceania` Canada Caribbean
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Narrow Body 2831400 0 5723798718 0
## 2 Wide Body 26561953943 2335740691 536214638 1508878
## 3 Regional Jet 0 0 2092961976 0
## 4 Turbo Prop 0 0 0 0
## # ℹ 6 more variables: `Central America` <dbl>, Europe <dbl>, Mexico <dbl>,
## # `Middle East` <dbl>, `South America` <dbl>, US <dbl>Landing Count and Total Landed Weight of Aircraft Body Types acorss Each Region
aircraftbodytypecolours <- c("bisque3", "indianred4", "honeydew", "lightblue4")
p12 <- ggplot(region_aircraft_type_landing_count, aes(x = geo_region, y = total_landing_count, fill = aircraft_body_type)) +
geom_bar(stat = "identity", colour = "grey35") +
scale_fill_manual(values = aircraftbodytypecolours) +
labs(x = "Region", y = "Landing Count", fill = "Aircraft Body Type",
title = "Bar Plot of Aircraft Body Type across of Each Region (Landing Count)") +
theme(plot.background = element_rect(fill = "grey90"),
panel.background = element_rect(fill = "seashell2"),
panel.grid = element_line(colour = "grey90"),
legend.background = element_rect(fill = "grey90"),
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
text = element_text(size = 9))
p13 <- ggplot(region_aircraft_type_landing_weight, aes(x = geo_region, y = total_landed_weight, fill = aircraft_body_type)) +
geom_bar(stat = "identity", colour = "grey35") +
scale_fill_manual(values = aircraftbodytypecolours) +
labs(x = "Regions", y = "Total Landed Weight", fill = "Aircraft Body Type",
title = "Bar Plot of Aircraft Body Type across of Each Region (Total Landed Weight)") +
theme(plot.background = element_rect(fill = "grey90"),
panel.background = element_rect(fill = "seashell2"),
panel.grid = element_line(colour = "grey90"),
legend.background = element_rect(fill = "grey90"),
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
text = element_text(size = 9))
combined6 <- p12 + p13 + plot_layout(ncol = 2)
combined6
Landing Aircraft Type
Landing Count and Total Landed Weight across Each Landing Aircraft Type
landingaircrafttypedf <- airlinedf %>%
group_by(landing_aircraft_type) %>%
summarise(landing_count = sum(landing_count), total_landed_weight = sum(total_landed_weight))
p14 <- ggplot(landingaircrafttypedf, aes(x = landing_aircraft_type, y = landing_count, fill = landing_aircraft_type)) +
geom_bar(stat = "identity", colour = "grey35") +
scale_fill_manual(values = aircraftbodytypecolours) +
labs(x = "Aircraft Body Types", y = "Landing Count", fill = "Aircraft Body Types",
title = "Bar Plot of Landing Count across each Landing Aircraft Type") +
theme(plot.background = element_rect(fill = "grey90"),
panel.background = element_rect(fill = "seashell2"),
panel.grid = element_line(colour = "grey90"),
legend.background = element_rect(fill = "grey90"),
text = element_text(size = 10))
p15 <- ggplot(landingaircrafttypedf, aes(x = landing_aircraft_type, y = total_landed_weight, fill = landing_aircraft_type)) +
geom_bar(stat = "identity", colour = "grey35") +
scale_fill_manual(values = aircraftbodytypecolours) +
labs(x = "Aircraft Body Types", y = "Total Landed Weight", fill = "Aircraft Body Types",
title = "Bar Plot of Total Landed Weight across each Landing Aircraft Type") +
theme(plot.background = element_rect(fill = "grey90"),
panel.background = element_rect(fill = "seashell2"),
panel.grid = element_line(colour = "grey90"),
legend.background = element_rect(fill = "grey90"),
text = element_text(size = 10))
combined7 <- p14 + p15 + plot_layout(ncol = 2)
combined7
Aircraft Body Types and Aircraft Manufacturers acorss each Landing Aircraft Type
p16<- ggplot(airlinedf, aes(x = landing_aircraft_type, fill = aircraft_body_type)) +
geom_bar(colour = "grey35") +
scale_fill_manual(values = aircraftbodytypecolours) +
labs(x = "Landing Aircraft Types", y = "Count", fill = "Aircraft Body Types",
title = "Bar Plot of Aircraft Body Types across each Landing Aircraft Type") +
theme(plot.background = element_rect(fill = "grey90"),
panel.background = element_rect(fill = "seashell2"),
panel.grid = element_line(colour = "grey90"),
legend.background = element_rect(fill = "grey90"),
text = element_text(size = 10))
p17 <- ggplot(airlinedf, aes(x = landing_aircraft_type, fill = aircraft_manufacturer)) +
geom_bar(colour = "grey35") +
scale_fill_manual(values = aircraftmanufacturercolours) +
labs(x = "Landing Aircraft Types", y = "Count", fill = "Aircraft Manufacturer",
title = "Bar Plot of Aircraft Manufacturers across each Landing Aircraft Type") +
theme(plot.background = element_rect(fill = "grey90"),
panel.background = element_rect(fill = "seashell2"),
panel.grid = element_line(colour = "grey90"),
legend.background = element_rect(fill = "grey90"),
text = element_text(size = 10))
combined8 <- p16 + p17 + plot_layout(ncol = 2)
combined8
Box Plots
Top 10 Highest Landing Count of Operating and Published Airlines
top10_O_airlines_boxplotdf1 <- airlinedf %>%
select(operating_airline, published_airline, landing_count) %>%
filter(operating_airline %in% top10_O_airlines$operating_airline)
top10_P_airlines_boxplotdf1 <- airlinedf %>%
select(published_airline, landing_count) %>%
filter(published_airline %in% top10_P_airlines$published_airline)
p18 <- ggplot(top10_O_airlines_boxplotdf1, aes(x = operating_airline, y = landing_count, fill = operating_airline)) +
geom_boxplot() +
scale_fill_manual(values = OAcolours) +
geom_point() +
labs(x = "Operating Airlines", y = "Landing Count", fill = "Operating Airlines",
title = "Box Plots of Top 10 Highest Landing Count Operating Airlines") +
theme(plot.background = element_rect(fill = "grey90"),
panel.background = element_rect(fill = "seashell2"),
panel.grid = element_line(colour = "grey90"),
legend.background = element_rect(fill = "grey90"),
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
text = element_text(size = 10))
p19 <- ggplot(top10_P_airlines_boxplotdf1, aes(x = published_airline, y = landing_count, fill = published_airline)) +
geom_boxplot() +
geom_point() +
scale_fill_manual(values = PAcolours) +
labs(x = "Published Airlines", y = "Landing Count", fill = "Published Airlines",
title = "Box Plots of Top 10 Highest Total Landing Count Published Airlines") +
theme(plot.background = element_rect(fill = "grey90"),
panel.background = element_rect(fill = "seashell2"),
panel.grid = element_line(colour = "grey90"),
legend.background = element_rect(fill = "grey90"),
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
text = element_text(size = 10))
combined9 <- p18 + p19 + plot_layout(ncol = 2)
combined9
Top 10 Highest Total Landed Weight of Operating and Published Airlines
top10_O_airlines_boxplotdf2 <- airlinedf %>%
select(operating_airline, published_airline, total_landed_weight) %>%
filter(operating_airline %in% top10_O_airlines2$operating_airline)
top10_P_airlines_boxplotdf2 <- airlinedf %>%
select(published_airline, total_landed_weight) %>%
filter(published_airline %in% top10_P_airlines2$published_airline)
p20 <- ggplot(top10_O_airlines_boxplotdf2, aes(x = operating_airline, y = total_landed_weight, fill = operating_airline)) +
geom_boxplot() +
geom_point() +
scale_fill_manual(values = OAcolours) +
labs(x = "Operating Airlines", y = "Total Landed Weight", fill = "Operating Airlines",
title = "Box Plots of Top 10 Highest Total Landed Weight Operating Airlines") +
theme(plot.background = element_rect(fill = "grey90"),
panel.background = element_rect(fill = "seashell2"),
panel.grid = element_line(colour = "grey90"),
legend.background = element_rect(fill = "grey90"),
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
text = element_text(size = 10))
p21 <- ggplot(top10_P_airlines_boxplotdf2, aes(x = published_airline, y = total_landed_weight, fill = published_airline)) +
geom_boxplot() +
geom_point() +
scale_fill_manual(values = PAcolours) +
labs(x = "Published Airlines", y = "Total Landed Weight", fill = "Published Airlines",
title = "Box Plots of Top 10 Highest Total Landed Weight Published Airlines") +
theme(plot.background = element_rect(fill = "grey90"),
panel.background = element_rect(fill = "seashell2"),
panel.grid = element_line(colour = "grey90"),
legend.background = element_rect(fill = "grey90"),
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
text = element_text(size = 10))
combined10 <- p20 + p21 + plot_layout(ncol = 2)
combined10
Landing Count and Total Landed Weight for Domestic and International Flights
p22 <- ggplot(airlinedf, aes(x = geo_summary, y = landing_count, fill = geo_summary)) +
geom_boxplot() +
scale_fill_manual(values = geosummarycolours) +
geom_point() +
labs(x = "GEO Summary", y = "Landing Count", fill = "GEO Summary",
title = "Box Plots of Landing Counts for Domestic & International Flights") +
theme(plot.background = element_rect(fill = "grey90"),
panel.background = element_rect(fill = "seashell2"),
panel.grid = element_line(colour = "grey90"),
legend.background = element_rect(fill = "grey90"),
text = element_text(size = 10))
p23 <- ggplot(airlinedf, aes(x = geo_summary, y = total_landed_weight, fill = geo_summary)) +
geom_boxplot() +
scale_fill_manual(values = geosummarycolours) +
geom_point() +
labs(x = "GEO Summary", y = "Total Landed Weight", fill = "GEO Summary",
title = "Box Plots of Total Landed Weight for Domestic & International Flights") +
theme(plot.background = element_rect(fill = "grey90"),
panel.background = element_rect(fill = "seashell2"),
panel.grid = element_line(colour = "grey90"),
legend.background = element_rect(fill = "grey90"),
text = element_text(size = 10))
combined11 <- p22 + p23 + plot_layout(ncol = 2)
combined11
Landing Count and Total Landed Weight across Each Region
p24 <- ggplot(airlinedf, aes(x = geo_region, y = landing_count, fill = geo_region)) +
geom_boxplot() +
scale_fill_manual(values = regioncolours) +
geom_point() +
labs(x = "Regions", y = "Landing Count", fill = "Regions",
title = "Box Plots of Landing Counts across Regions") +
theme(plot.background = element_rect(fill = "grey90"),
panel.background = element_rect(fill = "seashell2"),
panel.grid = element_line(colour = "grey90"),
legend.background = element_rect(fill = "grey90"),
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
text = element_text(size = 10))
p25 <- ggplot(airlinedf, aes(x = geo_region, y = total_landed_weight, fill = geo_region)) +
geom_boxplot() +
scale_fill_manual(values = regioncolours) +
geom_point() +
labs(x = "Regions", y = "Total Landed Weight", fill = "Regions",
title = "Box Plots of Total Landed Weight across Regions") +
theme(plot.background = element_rect(fill = "grey90"),
panel.background = element_rect(fill = "seashell2"),
panel.grid = element_line(colour = "grey90"),
legend.background = element_rect(fill = "grey90"),
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
text = element_text(size = 10))
combined12 <- p24 + p25 + plot_layout(ncol = 2)
combined12
Landing Count and Total Landed Weight across Each Landing Aircraft Type
p26 <- ggplot(airlinedf, aes(x = landing_aircraft_type, y = landing_count, fill = landing_aircraft_type)) +
geom_boxplot() +
scale_fill_manual(values = landingaircrafttypecolours) +
geom_point() +
labs(x = "Landing Aircraft Types", y = "Landing Count", fill = "Landing Aircraft Types",
title = "Box Plots of Landing Counts across Landing Aircraft Types") +
theme(plot.background = element_rect(fill = "grey90"),
panel.background = element_rect(fill = "seashell2"),
panel.grid = element_line(colour = "grey90"),
legend.background = element_rect(fill = "grey90"),
text = element_text(size = 10))
p27 <- ggplot(airlinedf, aes(x = landing_aircraft_type, y = total_landed_weight, fill = landing_aircraft_type)) +
geom_boxplot() +
scale_fill_manual(values = landingaircrafttypecolours) +
geom_point() +
labs(x = "Landing Aircraft Types", y = "Total Landed Weight", fill = "Landing Aircraft Types",
title = "Box Plots of Total Landed Weight across Landing Aircraft Types") +
theme(plot.background = element_rect(fill = "grey90"),
panel.background = element_rect(fill = "seashell2"),
panel.grid = element_line(colour = "grey90"),
legend.background = element_rect(fill = "grey90"),
text = element_text(size = 10))
combined13 <- p26 + p27 + plot_layout(ncol = 2)
combined13
Landing Count and Total Landed Weight across Each Aircraft Body Type
p28 <- ggplot(airlinedf, aes(x = aircraft_body_type, y = landing_count, fill = aircraft_body_type)) +
geom_boxplot() +
scale_fill_manual(values = aircraftbodytypecolours) +
geom_point() +
labs(x = "Aircraft Body Types", y = "Landing Count", fill = "Aircraft Body Types",
title = "Box Plots of Landing Counts across Aircraft Body Types") +
theme(plot.background = element_rect(fill = "grey90"),
panel.background = element_rect(fill = "seashell2"),
panel.grid = element_line(colour = "grey90"),
legend.background = element_rect(fill = "grey90"),
text = element_text(size = 10))
p29 <- ggplot(airlinedf, aes(x = aircraft_body_type, y = total_landed_weight, fill = aircraft_body_type)) +
geom_boxplot() +
scale_fill_manual(values = aircraftbodytypecolours) +
geom_point() +
labs(x = "Aircraft Body Types", y = "Total Landed Weight", fill = "Aircraft Body Types",
title = "Box Plots of Total Landed Weight across Aircraft Body Types") +
theme(plot.background = element_rect(fill = "grey90"),
panel.background = element_rect(fill = "seashell2"),
panel.grid = element_line(colour = "grey90"),
legend.background = element_rect(fill = "grey90"),
text = element_text(size = 10))
combined14 <- p28 + p29 + plot_layout(ncol = 2)
combined14
Landing Count and Total Landed Weight across Each Aircraft Manufacturer
p30 <- ggplot(airlinedf, aes(x = aircraft_manufacturer, y = landing_count, fill = aircraft_manufacturer)) +
geom_boxplot() +
scale_fill_manual(values = aircraftmanufacturercolours) +
geom_point() +
labs(x = "Aircraft Manufacturers", y = "Landing Count", fill = "Aircraft Manufacturers",
title = "Box Plots of Landing Counts across Aircraft Manufacturers") +
theme(plot.background = element_rect(fill = "grey90"),
panel.background = element_rect(fill = "seashell2"),
panel.grid = element_line(colour = "grey90"),
legend.background = element_rect(fill = "grey90"),
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
text = element_text(size = 10))
p31 <- ggplot(airlinedf, aes(x = aircraft_manufacturer, y = total_landed_weight, fill = aircraft_manufacturer)) +
geom_boxplot() +
scale_fill_manual(values = aircraftmanufacturercolours) +
geom_point() +
labs(x = "Aircraft Manufacturers", y = "Total Landed Weight", fill = "Aircraft Manufacturers",
title = "Box Plots of Total Landed Weight across Aircraft Manufacturers") +
theme(plot.background = element_rect(fill = "grey90"),
panel.background = element_rect(fill = "seashell2"),
panel.grid = element_line(colour = "grey90"),
legend.background = element_rect(fill = "grey90"),
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
text = element_text(size = 10))
combined15 <- p30 + p31 + plot_layout(ncol = 2)
combined15
Conclusion
In conclusion, our analysis of the airlines dataset has yielded valuable insights into various airlines and their fleets. It has become evident that examining both landing counts and landed weights is crucial, as a higher landing count does not necessarily correlate with a higher landed weight, as demonstrated in our findings. This underscores the importance of comprehensive data analysis in understanding the dynamics of airline operations and fleet management.