Lab 7

We’ll work more with the nycflights13 dataset here.

Here’s a plot of how the data is related to each other:

1. Load the nycflights13 and all five data frames into R.

2. How many different plane “brands” (i.e. manufacturers) fly from “LGA”?

3. Calculate the average age of each plane for each flight; How does the average age of each plane differ by origin airport?

4. How many airports are in the airports data frame that are not a direct destination from any of the NYC airports (i.e. the flights dataframe)?

5. Calculate the number of tailnumbers (observations) per carrier. Arrange in alphabetical order by the full carrier name from the airlines dataframe.

6. How many flights were made to an “America/Los_Angeles” timezone from “JFK”? Which destination within that timezone received the most flights from “JFK”?

7. For each flight, find if it departed with decent weather. Drop observations that we don’t have weather information or dep_delay information. See if decent weather is associated with the delay of the plane.

   • Define “fair weather” to mean no precipitation, wind-speeds under 20 mph, and visibility of at least 10 miles.

flights %>%
  mutate(hour = dep_time %/% 100) %>%
  YOUDECIDE_join() %>%
  mutate() ... and so on

8. Working with sets
   • Create a table called “high_altitude” that contains the faa and full name of the aiport only altitudes above 5000.
   • Create a second table called “low_arr_delay” and display the faa and full name of airports with an average arrival delay under 3.
   • Which airlines are both high_altitude and low_arr_delay?
   • Which airports are in low_arr_delay but not in high_altitude?
   • Combine these two dataframes into one using a function we learned this week.