Problem 1 (50 pts). This problem will involve the nycflights13dataset (including tables airlines, airports, planes and weather),which we saw in class. It is available in both R and Python,however R is recommended for at least the visualization portion ofthe question. Start by installing and importing the dataset to yourchosen platform. We will first use joins to search and manipulatethe dataset, then we will produce a flightpath visualization.
Question e) Produce a map that colors each destination airportby the average air time of its incoming flights. Here is a codesnippet to draw a map of all flight destinations, which you can useas a starting point. You may need to install the maps packages ifyou have not already. Adjust the title, axis labels and aestheticsto make this visualization as clear as possible. Hint: You may findit useful to use a different type of join in your solution than theone in the snippet.
airports %>% semi_join(flights, c(\"faa\" = \"dest\")) %>%ggplot(aes(lon, lat)) + borders(\"state\") + geom_point() +coord_quickmap()
