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Data Into Insights (Spring 2021)/R4DS Chapter 5 Exercises
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=== 5.6 === 1. Brainstorm at least 5 different ways to assess the typical delay characteristics of a group of flights. Consider the following scenarios: * A flight is 15 minutes early 50% of the time, and 15 minutes late 50% of the time. * A flight is always 10 minutes late. * A flight is 30 minutes early 50% of the time, and 30 minutes late 50% of the time. * 99% of the time a flight is on time. 1% of the time itβs 2 hours late. * Which is more important: arrival delay or departure delay? 2. Come up with another approach that will give you the same output as not_cancelled %>% count(dest) and not_cancelled %>% count(tailnum, wt = distance) (without using count()). 4. Look at the number of cancelled flights per day. Is there a pattern? Is the proportion of cancelled flights related to the average delay? 5. Which carrier has the worst delays? Challenge: can you disentangle the effects of bad airports vs. bad carriers? Why/why not? (Hint: think about flights %>% group_by(carrier, dest) %>% summarise(n())) 6. What does the sort argument to count() do. When might you use it?
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