NYCFlights23

library(tidyverse)

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library(nycflights23)

Warning: package 'nycflights23' was built under R version 4.3.3

data("flights")

flights23 <- flights |>
      select(dep_time, dep_delay) |>
      group_by(carrier = "UA", origin = "EWR")|>
      filter(dep_time > 2200)|>
      head(10)
flights23

# A tibble: 10 × 4
# Groups:   carrier, origin [1]
   dep_time dep_delay carrier origin
      <int>     <dbl> <chr>   <chr> 
 1     2201        31 UA      EWR   
 2     2202         3 UA      EWR   
 3     2205         6 UA      EWR   
 4     2206        10 UA      EWR   
 5     2207        38 UA      EWR   
 6     2207         8 UA      EWR   
 7     2209        70 UA      EWR   
 8     2210        40 UA      EWR   
 9     2211        18 UA      EWR   
10     2212        37 UA      EWR   

library(viridis)

Warning: package 'viridis' was built under R version 4.3.3

Loading required package: viridisLite

ggplot(data = flights23, aes(x = dep_time, fill = dep_delay)) +
  geom_bar(alpha = .8, position = "dodge")+
  scale_fill_brewer()+
       labs(x = "Departure Time", y = "Departure Delay", 
       title = "Departure Delays for United Airlines at Newark Airport - Late Night",
       caption = "From FAA Airport Registry")

Warning: The following aesthetics were dropped during statistical transformation: fill
ℹ This can happen when ggplot fails to infer the correct grouping structure in
  the data.
ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
  variable into a factor?

This barplot shows how late flights were late at night departing Newark airport (New Jersey, very near New York City) with United Airlines. The delay is shown on the Y axis per departure time on the x axis. Each departure time has it’s own delay on a scale, not in minutes or parts of a hour. Interestingly, Most of the delays are around One on this scale, with only the one time frame being around 2 units of a delay.