Academic Honesty Statement (fill your name in the blank)

I, Yi Tao Wang, hereby state that I have not gained information in any way not allowed by the exam rules during this exam, and that all work is my own.

Load packages

library(tidyverse)
library(openintro)
library(nycflights13)

1. The mpg data set

After loading tidyverse library, a data set named mpg should be ready to explore. The following questions are based on this data set.

a) Create a new variable mpg_overall which is the average of city and highway fuel consumption in miles per gallon. Then create a histogram of this new variable with each group covering values of 20-22, 22-24 etc.
mpg <- mpg %>% 
  mutate(mpg_overall = (cty + hwy) / 2)

ggplot(mpg, aes(x = mpg_overall)) +
  geom_histogram(binwidth = 2, boundary = 20, fill = "steelblue", color = "white")

b) Create a graph to study the relationship between drive train types and mpg_overall.
ggplot(mpg, aes(x = drv, y = mpg_overall, fill = drv)) +
  geom_boxplot(show.legend = FALSE) +
  labs(title = "Relationship: Drive Train vs. Overall MPG", x = "Drive Train", y = "Overall MPG")

Answer: Front-wheel drive (f) vehicles have the highest average combined fuel consumption, followed by rear-wheel drive (r). Four-wheel drive (4) vehicles have significantly lower fuel efficiency.

c) Create a table to find out which car class has the highest mean mpg_overall.
mpg %>%
  group_by(class) %>%
  summarize(mean_mpg = mean(mpg_overall)) %>%
  arrange(desc(mean_mpg))

Answer: The subcompact car class has the highest average overall MPG (around 24.5), closely followed by compact cars.

d) Create a proper graph to study the composite effect of year and cyl to mpg_overall. You shall treat year and cyl as categorical variables in your graph.
ggplot(mpg, aes(x = factor(cyl), y = mpg_overall, fill = factor(year))) +
  geom_boxplot(position = position_dodge(width = 0.8)) +
  labs(title = "Effect of Engine Cylinders and Year on Overall MPG", x = "Number of Cylinders", y = "Overall MPG", fill = "Year")

Answer: Overall MPG decreases as the number of cylinders increases.

2. The flights data set

For the following tasks, use data set flights of the nycflights13 package.

a) For JFK airport, which day in November 2013 has the biggest average arrival delay? Create a table to answer the question.
flights %>%
  filter(origin == "JFK", year == 2013, month == 11) %>%
  group_by(day) %>%
  summarize(avg_arr_delay = mean(arr_delay, na.rm = TRUE)) %>%
  arrange(desc(avg_arr_delay)) %>%
  head(1)

Answer: On 27th, it had the biggest average arrival delay for flights originating from JFK, with an average delay of approximately 21.33 minutes.

b) Create a new variable cancel_flight which is Cancelled if the departure time or arrival time is NA, otherwise Not Cancelled.
cancel_flight <- flights %>%
  mutate(cancel_flight = ifelse(is.na(dep_time) | is.na(arr_time), "Cancelled", "Not Cancelled"))

cancel_flight %>% 
  count(cancel_flight)

Answer: 8713 cancelled flights.

c) Create a density graph that compares the distribution of distance between cancelled flights and non-cancelled flights.
ggplot(cancel_flight, aes(x = distance, fill = cancel_flight)) +
  geom_density(alpha = 0.5) +
  labs(
    title = "Distance Distribution: Cancelled vs. Not Cancelled Flights",
    x = "Flight Distance (miles)",
    y = "Density",
    fill = "Flight Status"
  )

d) How many unique flight routes are there in the data set? That is, each unique combination of an origin airport and a destination airport (such as from EWR to ORD) is considered as a route. Create a table to answer the question.
route_table <- flights %>%
  distinct(origin, dest)

nrow(route_table)
## [1] 224

Answer: 224 unique flight routes.

e) Add distance as a column to the table you created in d).

Hint: You should go back to the original flights data set and reconstruct the table with distance included. Create a histogram of distance for the route table.

route_distance_table <- flights %>%
  distinct(origin, dest, distance)

ggplot(route_distance_table, aes(x = distance)) +
  geom_histogram(fill = "darkgreen", color = "black") +
  labs(
    title = "Distribution of Distances for Unique Flight Routes",
    x = "Distance (miles)",
    y = "Number of Unique Routes"
  )

f) Which route has the highest rate of flight cancellation? Create a table to answer the question.
flights %>%
  mutate(cancel_flight = ifelse(is.na(dep_time) | is.na(arr_time), "Cancelled", "Not Cancelled")) %>%
  group_by(origin, dest) %>%
  summarize(
    total_flights = n(),
    cancelled_flights = sum(cancel_flight == "Cancelled"),
    cancellation_rate = cancelled_flights / total_flights,
    .groups = 'drop'
  ) %>%
  arrange(desc(cancellation_rate)) %>%
  head(5)

Answer: Based on the table, the route from EWR to LGA has the highest cancellation rate.

Bonus Question for flights data set

The following questions are also from flights data set. Each question is worth 5% bonus points if answered correctly.


a) Create a proper graph to show the rate of cancellation flights for each airline. Answer which airline has the lowest rate of cancellation.
carrier_cancellations <- flights %>%
  group_by(carrier) %>%
  summarize(cancel_rate = mean(is.na(dep_time) | is.na(arr_time))) %>%
  arrange(cancel_rate)

ggplot(carrier_cancellations, aes(x = reorder(carrier, cancel_rate), y = cancel_rate)) +
  geom_col(fill = "coral") +
  coord_flip() + 
  labs(
    title = "Cancellation Rate by Airline",
    x = "Airline (Carrier Code)",
    y = "Cancellation Rate"
  )

Answer: HA has the lowest cancellation rate.

b) If multiple airlines run the same route, they can be considered as competitors. Which route is most competitive (has the most number of carriers)? List all of them in a table.
# Enter code here.

Answer: