Exercises

1. How many different planes are there in the flights data? How many airports?

2. Did every plane visit every airport?

3. Create a data frame of the most delayed flight (departure) from each airport.

Categorize continuous data with cut()

cut divides the range of x into intervals and codes the values in x according to which interval they fall.

Use cut()

library(tidyverse)
atus <- read_csv("https://euclid.nmu.edu/~joshthom/dat309/data/ATUS/atusrost_2025.dat") |>
  janitor::clean_names()

df <- mutate(atus, work_level = cut(act_work,
  breaks = c(0,60,60*9,60*24),
  labels = c("low","medium","high")),
  .before = 1)

Exercise with cut()

4. Re-create a nice-looking plot of the ATUS data that uses cut() to create a category of based on the numerical data of act_work:

Which is the better plot?