From 5af2edeed141726bff89570fc4de822e1b6deea7 Mon Sep 17 00:00:00 2001 From: Patrick Kennedy Date: Sat, 16 Jul 2016 08:59:32 -0700 Subject: [PATCH] Correct grammatical typos in transform.Rmd (#153) --- transform.Rmd | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/transform.Rmd b/transform.Rmd index 88af009..70efc96 100644 --- a/transform.Rmd +++ b/transform.Rmd @@ -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