library(tidyverse)

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library(nycflights23)
data(flights)

ggplot(
  filter(flights, dep_delay > 0, dep_delay < 120),
  aes(x = dep_delay, fill=origin)
) +
  geom_histogram(bins = 30) +
  labs(
    title = "Departure Delays by NYC Airport",
    x = "Departure Delay (minutes)",
    y = "Number of Flights",
    fill = "Airport",
  )

This histogram visualizes the distribution of departure delays for flights departing from New York City’s three major airports in 2023. I used a fliter() command to focus only on positive delays under 120 minutes, which removes early departures and extreme outliers that would stretch the x-axis and obscure the overall pattern. The plot uses three colors green for JFK, blue for LGA, and red for EWR with a legend identifying each airport. The x-axis shows departure delay in minutes, and the y-axis shows the count of flights. One aspect I’d like to highlight is how the distributions overlap near zero but diverge as delays increase. All three airports have their highest frequency at short delays, which makes sense since most flights depart close to schedule. However, the shape of the right tail reveals differences between airports, some airports appear to have more flights with longer delays.