Academic Honesty Statement (fill your name in the blank)

I Charles 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

# load required packages here
library(tidyverse)
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.
# Enter code here.
mpg <- mpg %>%
  mutate(mpg_overall = (cty + hwy) / 2)

ggplot(mpg, aes(x = mpg_overall)) +
  geom_histogram(binwidth = 2, boundary = 20, color = "black", fill = "steelblue") +
  labs(title = "Histogram of Overall Fuel Consumption",
       x = "Overall MPG",
       y = "Count") +
  scale_x_continuous(breaks = seq(10, 45, by = 2))

b) Create a graph to study the relationship between drive train types and mpg_overall.
# Enter code here.
ggplot(mpg, aes(x = drv, y = mpg_overall, fill = drv)) +
  geom_boxplot() +
  labs(title = "Overall MPG by Drive Train",
       x = "Drive Train (f = front, r = rear, 4 = 4wd)",
       y = "Overall MPG") +
  theme_minimal()

Answer:

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

Answer:

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(year), y = mpg_overall, fill = factor(cyl))) +
  geom_boxplot() +
  labs(title = "Effect of Year and Cylinders on Overall MPG",
       x = "Year",
       y = "Overall MPG",
       fill = "Cylinders") +
  theme_classic()

Answer:

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.
# Enter code here.
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))

Answer: Day 27

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

Answer:

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

# Enter code here.
route_table_with_dist <- flights %>%
  distinct(origin, dest, distance)

ggplot(route_table_with_dist, aes(x = distance)) +
  geom_histogram(binwidth = 200, color = "black", fill = "lightgreen") +
  labs(title = "Histogram of Distances for Unique Flight Routes",
       x = "Distance",
       y = "Count")

f) Which route has the highest rate of flight cancellation? Create a table to answer the question.
# Enter code here.
flights %>%
  group_by(origin, dest) %>%
  summarize(
    total_flights = n(),
    cancel_count = sum(cancel_flight == "Cancelled"),
    cancel_rate = cancel_count / total_flights,
    .groups = "drop"
  ) %>%
  arrange(desc(cancel_rate))

Answer:EWR to LGA

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.
# Enter code here.
airline_cancel_rates <- flights %>%
  group_by(carrier) %>%
  summarize(cancel_rate = mean(cancel_flight == "Cancelled"))

ggplot(airline_cancel_rates, aes(x = reorder(carrier, cancel_rate), y = cancel_rate)) +
  geom_col(fill = "coral") +
  labs(title = "Cancellation Rates by Airline",
       x = "Airline (Carrier)",
       y = "Cancellation Rate") +
  theme_minimal()

Answer: HA with 0%

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.
competitive_routes <- flights %>%
  group_by(origin, dest) %>%
  summarize(num_carriers = n_distinct(carrier), .groups = "drop") %>%
  arrange(desc(num_carriers))

competitive_routes %>%
  filter(num_carriers == max(num_carriers))

Answer: