Assignment_5
Nwe Oo Mon (Nina)
2024-09-28
Loading the libraries and the data
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsfile_path <- "C:\\ProgramData\\MySQL\\MySQL Server 8.0\\Uploads\\airlines.csv"
airlines_data <- read_csv(file_path)## Rows: 4 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Airline, Status
## dbl (5): Los Angeles, Phoenix, San Diego, San Francisco, Seattle
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.head (airlines_data)## # A tibble: 4 × 7
## Airline Status `Los Angeles` Phoenix `San Diego` `San Francisco` Seattle
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 ALASKA on time 497 221 212 503 1841
## 2 ALASKA delayed 62 12 20 102 305
## 3 AM WEST on time 694 4840 383 320 201
## 4 AM WEST delayed 117 415 65 129 61from wide data form to long form
airlines_long <- airlines_data %>%
pivot_longer(cols = c("Los Angeles", "Phoenix", "San Diego", "San Francisco", "Seattle"),
names_to = "Destination",
values_to = "Result")
head (airlines_long)## # A tibble: 6 × 4
## Airline Status Destination Result
## <chr> <chr> <chr> <dbl>
## 1 ALASKA on time Los Angeles 497
## 2 ALASKA on time Phoenix 221
## 3 ALASKA on time San Diego 212
## 4 ALASKA on time San Francisco 503
## 5 ALASKA on time Seattle 1841
## 6 ALASKA delayed Los Angeles 62Total Flights by Airlines
# Summarize total flights for each destination by airline
destination_summary <- airlines_long %>%
group_by(Destination, Airline) %>%
summarise(total_flights = sum(Result))## `summarise()` has grouped output by 'Destination'. You can override using the
## `.groups` argument.print(destination_summary)## # A tibble: 10 × 3
## # Groups: Destination [5]
## Destination Airline total_flights
## <chr> <chr> <dbl>
## 1 Los Angeles ALASKA 559
## 2 Los Angeles AM WEST 811
## 3 Phoenix ALASKA 233
## 4 Phoenix AM WEST 5255
## 5 San Diego ALASKA 232
## 6 San Diego AM WEST 448
## 7 San Francisco ALASKA 605
## 8 San Francisco AM WEST 449
## 9 Seattle ALASKA 2146
## 10 Seattle AM WEST 262# Grouped bar chart to show total flights for each destination by airline
ggplot(data = destination_summary, aes(x = Destination, y = total_flights, fill = Airline)) +
geom_bar(stat = "identity", position = "dodge") +
theme_minimal() +
labs(title = "Total Flights to Each Destination by Airline",
x = "Destination",
y = "Total Number of Flights",
fill = "Airline") +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
scale_fill_manual(values = c("ALASKA" = "#0077C8", "AM WEST" = "#C8102E"))
The graph shows that AM WEST has significantly more flights to Phoenix compared to ALASKA and also has a higher number of flights to Los Angeles and San Diego. On the other hand, ALASKA has the most flights to Seattle, and relatively higher flights to San Francisco.
Flight Delays
airlines_long <- airlines_long %>%
group_by(Destination, Airline) %>%
mutate(percentage = ifelse(Status == "delayed", (Result / sum(Result)) * 100, NA))
airlines_long <- airlines_long %>%
group_by(Destination, Airline) %>%
mutate(label_y = ifelse(Status == "delayed",
lag(cumsum(Result), default = 0) + (Result / 2),
NA))
print(airlines_long)## # A tibble: 20 × 6
## # Groups: Destination, Airline [10]
## Airline Status Destination Result percentage label_y
## <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 ALASKA on time Los Angeles 497 NA NA
## 2 ALASKA on time Phoenix 221 NA NA
## 3 ALASKA on time San Diego 212 NA NA
## 4 ALASKA on time San Francisco 503 NA NA
## 5 ALASKA on time Seattle 1841 NA NA
## 6 ALASKA delayed Los Angeles 62 11.1 528
## 7 ALASKA delayed Phoenix 12 5.15 227
## 8 ALASKA delayed San Diego 20 8.62 222
## 9 ALASKA delayed San Francisco 102 16.9 554
## 10 ALASKA delayed Seattle 305 14.2 1994.
## 11 AM WEST on time Los Angeles 694 NA NA
## 12 AM WEST on time Phoenix 4840 NA NA
## 13 AM WEST on time San Diego 383 NA NA
## 14 AM WEST on time San Francisco 320 NA NA
## 15 AM WEST on time Seattle 201 NA NA
## 16 AM WEST delayed Los Angeles 117 14.4 752.
## 17 AM WEST delayed Phoenix 415 7.90 5048.
## 18 AM WEST delayed San Diego 65 14.5 416.
## 19 AM WEST delayed San Francisco 129 28.7 384.
## 20 AM WEST delayed Seattle 61 23.3 232.ggplot(data = airlines_long, aes(x = Destination, y = Result, fill = Status)) +
geom_bar(stat = "identity", width = 0.7) +
geom_text(data = airlines_long %>% filter(Status == "delayed"),
aes(label = paste0(round(percentage, 1), "%"), y = label_y),
color = "black", size = 4) +
facet_grid(~ Airline) +
theme_minimal() +
labs(title = "Comparison of Delays and On-Time Flights by Airline and Destination",
x = "Destination",
y = "Total Flights",
fill = "Status") +
theme(axis.text.x = element_text(angle = 60, hjust = 1, vjust = 1)) 
Alaska Airline experiences the highest percentage of delays for flights to San Francisco at 16.9%, followed by Seattle at 14.2%. Delays for Phoenix and San Diego are relatively low. AM West Airline shows the most significant percentage of delays for flights to San Francisco (28.7%), followed by Seattle (23.3%) and Los Angeles (8.5%). Phoenix has low delay percentages.
# Summarize flight status by airline
status_summary <- airlines_long %>%
group_by(Status, Airline) %>%
summarise(total_flights = sum(Result))## `summarise()` has grouped output by 'Status'. You can override using the
## `.groups` argument.status_summary <- status_summary %>%
group_by(Airline) %>%
mutate(percentage = ifelse(Status == "delayed", (total_flights / sum(total_flights)) * 100, NA))
print(status_summary)## # A tibble: 4 × 4
## # Groups: Airline [2]
## Status Airline total_flights percentage
## <chr> <chr> <dbl> <dbl>
## 1 delayed ALASKA 501 13.3
## 2 delayed AM WEST 787 10.9
## 3 on time ALASKA 3274 NA
## 4 on time AM WEST 6438 NA# Create a stacked bar chart_airlines
ggplot(data = status_summary, aes(x = Airline, y = total_flights, fill = Status)) +
geom_bar(stat = "identity", width = 0.5) +
geom_text(data = status_summary %>% filter(Status == "delayed"),
aes(label = paste0(round(percentage, 1), "%")),
position = position_stack(vjust = 9.5),
color = "black", size = 4) +
theme_minimal() +
labs(title = "Comparison of Delays and On-Time Flights for Airlines",
x = "Airline",
y = "Number of Flights",
fill = "Flight Status") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ALASKA Airline has 501 delays and 3274 on time flights, and delays accounted for 13.3% of their total flights. AM WEST Airline has 787 delayed flights and 6438 on time flights, and delayed flights represented 10.9% of their total flights.
# Summarize flights for each destination by flight status
destination_status <- airlines_long %>%
group_by(Status, Destination) %>%
summarise(total_flights = sum(Result))## `summarise()` has grouped output by 'Status'. You can override using the
## `.groups` argument.destination_status <- destination_status %>%
group_by(Destination) %>%
mutate(percentage = ifelse(Status == "delayed", (total_flights / sum(total_flights)) * 100, NA))
print(destination_status)## # A tibble: 10 × 4
## # Groups: Destination [5]
## Status Destination total_flights percentage
## <chr> <chr> <dbl> <dbl>
## 1 delayed Los Angeles 179 13.1
## 2 delayed Phoenix 427 7.78
## 3 delayed San Diego 85 12.5
## 4 delayed San Francisco 231 21.9
## 5 delayed Seattle 366 15.2
## 6 on time Los Angeles 1191 NA
## 7 on time Phoenix 5061 NA
## 8 on time San Diego 595 NA
## 9 on time San Francisco 823 NA
## 10 on time Seattle 2042 NA# Create a stacked bar chart_destination
ggplot(data = destination_status, aes(x = Destination, y = total_flights, fill = Status)) +
geom_bar(stat = "identity", width = 0.5) +
theme_minimal() +
labs(title = "Comparison of Delays and On-Time Flights by destination",
x = "Destination",
y = "Number of Flights",
fill = "Flight Status") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
San Francisco has the highest percentage of delayed flights at 21.9%, followed by Seattle at 15.2%. Phoenix has the lowest percentage of delayed flights, with only 7.8% of flights being delayed.