the dataset used is:

library(nycflights13) and library(tidyverse)

the actual data set is called: flights

5.5.2 1. Currently dep_time and sched_dep_time are convenient to look at, but hard to compute with because they're not really continuous numbers. Convert them to a more convenient representation of number of minutes since midnight. Then, compare the difference between these new variables to each flight's dep_delay value. Does anything surprise you? Can you explain what accounts for the "anomaly"? How many cases do you observe? Include as part of your report a listing that shows the first five observations of a tibble with the following columns: carrier, flight, your two new variables, their difference, and dep_delay. Hint: Make sure the data you're working with has a dep_delay value for each observation. 5.5.2 1. Currently dep_time and sched_dep_time are convenient to look at, but hard to compute with because they're not really continuous numbers. Convert them to a more convenient representation of number of minutes since midnight. Then, compare the difference between these new variables to each flight's dep_delay value. Does anything surprise you? Can you explain what accounts for the "anomaly"? How many cases do you observe? Include as part of your report a listing that shows the first five observations of a tibble with the following columns: carrier, flight, your two new variables, their difference, and dep_delay. Hint: Make sure the data you're working with has a dep_delay value for each observation