assigment 2 part 2

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library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.2.1     ✔ readr     2.2.0
✔ forcats   1.0.1     ✔ stringr   1.6.0
✔ ggplot2   4.0.3     ✔ tibble    3.3.1
✔ lubridate 1.9.5     ✔ tidyr     1.3.2
✔ purrr     1.2.2     
── 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 errors

library(nycflights23)

data(flights)
data(airlines)

flights_nona <- flights |>
  filter(!is.na(dep_delay))

by_airline <- flights_nona |>
  group_by(carrier) |>
  summarise(
    avg_dep_delay = mean(dep_delay),
    count = n()
  )

print(by_airline) 

# A tibble: 14 × 3
   carrier avg_dep_delay count
   <chr>           <dbl> <int>
 1 9E               7.44 52380
 2 AA              14.2  39940
 3 AS              12.0   7774
 4 B6              23.8  64622
 5 DL              15.1  60616
 6 F9              35.7   1218
 7 G4               3.98   667
 8 HA              22.9    364
 9 MQ              10.5    354
10 NK              18.2  14827
11 OO              19.8   6214
12 UA              17.6  77810
13 WN              16.1  12105
14 YX               4.21 85723

by_airline <- by_airline |>
  left_join(airlines, by = "carrier")

print(by_airline)

# A tibble: 14 × 4
   carrier avg_dep_delay count name                  
   <chr>           <dbl> <int> <chr>                 
 1 9E               7.44 52380 Endeavor Air Inc.     
 2 AA              14.2  39940 American Airlines Inc.
 3 AS              12.0   7774 Alaska Airlines Inc.  
 4 B6              23.8  64622 JetBlue Airways       
 5 DL              15.1  60616 Delta Air Lines Inc.  
 6 F9              35.7   1218 Frontier Airlines Inc.
 7 G4               3.98   667 Allegiant Air         
 8 HA              22.9    364 Hawaiian Airlines Inc.
 9 MQ              10.5    354 Envoy Air             
10 NK              18.2  14827 Spirit Air Lines      
11 OO              19.8   6214 SkyWest Airlines Inc. 
12 UA              17.6  77810 United Air Lines Inc. 
13 WN              16.1  12105 Southwest Airlines Co.
14 YX               4.21 85723 Republic Airline      

ggplot(by_airline, aes(x = name, y = avg_dep_delay, fill = name)) +
  geom_bar(stat = "identity") +
  labs(
    title = "Average Departure Delay by Airline",
    x = "Airline",
    y = "Average Departure Delay (Minutes)",
    fill = "Airline",
    caption = "Source: NYCFlights23"
  ) +
  theme_minimal() +
  coord_flip()

I decided to use a bar graph even though we used the box chart in class because personally it is easier for me to understand this information in this format. Because of that, we can immediately see that Frontier Airlines has by far the highest average delay out of all 14 airlines at around 36 minutes. Then we have JetBlue and Hawaiian Airlines with pretty high average delays at around 23 minutes, but nothing as crazy as Frontier. But if we take a look at Allegiant Air and Republic Airline, they have the lowest averages at just around 4 minutes, which I think is very impressive. Republic Airline especially stands out because it also has a huge roster of flights, with over 85,000 flights in the dataset, while still having one of the lowest average delays. If you ask me, I think this graph does an incredible job at showing this information in a nice and easy to understand way.