Assignment5

airlines <- read.csv("https://raw.githubusercontent.com/Shayaeng/Data607/main/Assignment5/airlines.csv")

print(airlines)

##         X     X.1 Los.Angeles Phoenix San.Diego San.Francisco Seattle
## 1  ALASKA on time         497     221       212           503   1,841
## 2         delayed          62      12        20           102     305
## 3 AM WEST on time         694   4,840       383           320     201
## 4         delayed         117     415        65           129      61

#name the columns without names and add values to the missing fields
airlines <- airlines %>%
  rename('Airline' = X, 'ArrivalStatus' = X.1) %>%
  mutate_all(~if_else(. == "", NA, .)) %>%
  fill(Airline, .direction = "down")

#some columns had commas and are character columns
columns_with_commas <- c('Phoenix', 'Seattle')

for (column in columns_with_commas)
  airlines[[column]] <- as.integer(gsub(",", "", airlines[[column]]))

#lengthen and widen the dataframe to make it easier to calculate the delays
cities <- c('Los.Angeles', 'Phoenix', 'San.Diego',  'San.Francisco', 'Seattle')
airlines_long <- airlines %>%
  pivot_longer(cols = all_of(cities), names_to = "Destination", values_to = "Flights")

airlines_wide <- airlines_long %>%
  pivot_wider(names_from = "ArrivalStatus", values_from = "Flights")

#calculate delay ratios per airline
airlines_wide <- airlines_wide %>%
  mutate(DelayRatio = delayed/(delayed + `on time`))

ggplot(airlines_wide, aes(x = Destination, y = DelayRatio, fill = Airline)) +
  geom_bar(stat = "identity", position = "dodge") +
  labs(title = "Delay Ratio by Airline",
       y = "Delay Ratio",
       x = "City")

As is clear from the above bar plot, Am West has consistently higher ratios of flights being delayed. This held true across all the cities measured.