library("nycflights13")
library("tidyverse")
view(flights)
summarise(flights)
## # A tibble: 1 × 0
flights <- flights%>%select(year, dep_delay, carrier)%>% 
filter(dep_delay>100)
ggplot(flights, aes(x=carrier, y=dep_delay, color=carrier)) + 
  geom_boxplot() +
  coord_flip() +
  labs(title = "Departure Delays by Carrier in 2013",
       x = "Carrier",
       y = "Minutes",)

##I made a box plot. It shows, in minutes, the delays of the different carriers in 2013. There were a lot of information within this data so I only made my boxplot about year, minutes of delay, and the different carriers. An aspect of the plot I would like to highlight is the different ranges of some of the flights. For example, Delta, American Airlines, and MQ all have more delayed flights than the other carriers.