The oilabs package has a data set called pnwflights which contains all of the flights that left from Seattle-Tacoma International Airport and Portland International Airport from June 2016 to May 2017. We will generate simple graphical and numerical summaries of data on these flights and explore mainly delay times. As this is a large data set, you’ll also get to practice the indispensable skills of data processing and subsetting.

library(dplyr)
library(tidyr)
library(ggplot2)
library(oilabs)
data(pnwflights)

To get a sense of what this data frame contains the codebook can be accessed by pulling up the help file:

?pnwflights

One of the variables refers to the carrier (i.e. airline) of the flight, which is coded according to the following system.

  1. Describe the distribution of departure delays for all flights using a histogram and appropriate summary statistics.

  2. Mutate the data frame so that it includes a new variable that contains the average speed, avg_speed traveled by the plane for each flight (in mph). Create a scatterplot of average speed vs. distance and describe the relationship. Hint: Average speed can be calculated as distance divided by number of hours of travel, and note that air_time is given in minutes.

  3. What proportion of the flights left from Portland? What is their most common destination? Most distant destination?

  4. Create a dataframe that contains the median and interquartile range for departure delays, grouped by carrier. Which carrier has the least variable departure delays?

  5. Using the airport nearest one of your hometowns, which day of the week and which airline seems best for flying there from Portland? Be clear on how you’re defining best. (note that there is no explicit weekday column in this data set, but there is sufficient information to piece it together. The following line of code can be added to your pipeline to create that new column. It uses functions in the lubridate package, so be sure to load it in at the start of this exercise).

mutate(day_of_week = wday(ymd(paste(year, month, day, set = "-")), label = T))

  1. Let’s consider any flight which has a departure delay of less than five minutes to be “on time,” and flights that are delayed for more than 5 minutes to be actually “delayed.” Given that, which month has the lowest proportion of delayed flights? Hint: Create a new variable that takes “on_time” and “delayed” as values.

  2. The plot below displays the relationship between the mean arrival delay and the mean distance traveled by every plane in the data set. It also shows the total number of flights made by each plane by the size of the plotted circle. Please form a single chain that will create this plot, starting with the raw data set. You will also want to exclude the edge cases from your analysis, so focus on the planes that have logged more than 20 flights and flown an average distance of less than 2000 miles. The points can be made transparent by adding an argument to geom_point(): alpha = .1.