Correct grammatical typos in transform.Rmd (#153)

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Patrick Kennedy 2016-07-16 08:59:32 -07:00 committed by Hadley Wickham
parent 0e64c64b1a
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1 changed files with 9 additions and 9 deletions

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@ -226,15 +226,15 @@ filter(df, is.na(x) | x > 1)
### Exercises
1. Find all the flights that:
1. Find all flights that
1. That were delayed by more two hours.
1. That flew to Houston (`IAH` or `HOU`).
1. There were operated by United, American, or Delta.
1. Departed in summer (July, August, and September).
1. That arrived more than two hours late, but didn't leave late.
1. Were delayed by at least an hour, but made up over 30 minutes in flight.
1. Departed between midnight and 6am (inclusive).
1. Were delayed by more two hours
1. Flew to Houston (`IAH` or `HOU`)
1. Were operated by United, American, or Delta
1. Departed in summer (July, August, and September)
1. Arrived more than two hours late, but didn't leave late
1. Were delayed by at least an hour, but made up over 30 minutes in flight
1. Departed between midnight and 6am (inclusive)
1. Another useful dplyr filtering helper is `between()`. What does it do?
Can you use it to simplify the code needed to answer the previous
@ -861,7 +861,7 @@ daily %>%
Which is more important: arrival delay or departure delay?
1. Look at the number of cancelled flights per day. Is there are pattern?
1. Look at the number of cancelled flights per day. Is there a pattern?
Is the proportion of cancelled flights related to the average delay?
1. Which carrier has the worst delays? Challenge: can you disentangle the