Academic Honesty Statement (fill your name in the blank)

I, Franklin Li, 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 <- mpg %>%
  mutate(mpg_overall = (cty + hwy) / 2)

ggplot(mpg_mpg, aes(x = mpg_overall)) +
  geom_histogram(binwidth = 2, boundary = 20, color = "black", fill = "skyblue") +
  labs(
    title = "Histogram of Overall MPG",
    x = "Overall MPG",
    y = "Count"
  ) +
  theme_minimal()

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

Answer:
Front wheel drives tend to have higher mpg oveerall, while four wheel drives tends to have the worest fuel economy out of the three types.

c) Create a table to find out which car class has the highest mean mpg_overall.
class_mpg <- mpg_mpg %>%
  group_by(class) %>%
  summarise(mean_mpg_overall = mean(mpg_overall), .groups = "drop") %>%
  arrange(desc(mean_mpg_overall))

class_mpg

Answer:
Car class with the highest overall mpg is the subcompact, followed closely by the compact and the midsized.

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

Answer:
Cars with higher cylinders have worst fuel economy then those that have fewer cylinders.And cars made in the more recent years tend to have higher fuel efficiency.

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.
jfk_nov_delay <- flights %>%
  filter(origin == "JFK", month == 11) %>%
  group_by(day) %>%
  summarise(avg_arr_delay = mean(arr_delay, na.rm = TRUE), .groups = "drop") %>%
  arrange(desc(avg_arr_delay))

jfk_nov_delay

Answer:
The day with the longest delay in November was on the 27th, with a delay of 21 hours.

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

flights_can %>%
  count(cancel_flight)

Answer:
There were 8,713 cancelled flights and 328,063 flights not cancelled.

c) Create a density graph that compares the distribution of distance between cancelled flights and non-cancelled flights.
ggplot(flights_can, aes(x = distance, fill = cancel_flight)) +
  geom_density(alpha = 0.4) +
  labs(
    title = "Distribution of Flight Distance by Cancellation Status",
    x = "Distance",
    y = "Density",
    fill = "Flight Status"
  ) +
  theme_minimal()

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)

route_table %>%
  summarise(num_unique_routes = n())

Answer:
There are 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)

route_distance_table
ggplot(route_distance_table, aes(x = distance)) +
  geom_histogram(binwidth = 100, color = "black", fill = "orange") +
  labs(
    title = "Histogram of Route Distances",
    x = "Distance",
    y = "Count"
  ) +
  theme_minimal()

f) Which route has the highest rate of flight cancellation? Create a table to answer the question.
route_cancel_rate <- flights_can %>%
  group_by(origin, dest) %>%
  summarise(
    total_flights = n(),
    cancelled_flights = sum(cancel_flight == "Cancelled"),
    cancel_rate = cancelled_flights / total_flights,
    .groups = "drop"
  ) %>%
  arrange(desc(cancel_rate), desc(cancelled_flights))

route_cancel_rate

Answer:
Technically, EWR to LGA has the highest cancel rate of 100%, 1 out of 1. The second highest cancel rate route is LGA to MHT, out of 142 flights, there were 34 cancelled flights, giving it a cancel rate of 23.9%.

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.
airline_cancel <- flights_can %>%
  group_by(carrier) %>%
  summarise(
    total_flights = n(),
    cancelled_flights = sum(cancel_flight == "Cancelled"),
    cancel_rate = cancelled_flights / total_flights,
    .groups = "drop"
  ) %>%
  left_join(airlines, by = "carrier") %>%
  arrange(cancel_rate)

airline_cancel
ggplot(airline_cancel, aes(x = reorder(name, cancel_rate), y = cancel_rate)) +
  geom_col(fill = "steelblue") +
  coord_flip() +
  labs(
    title = "Cancellation Rate by Airline",
    x = "Airline",
    y = "Cancellation Rate"
  ) +
  theme_minimal()

Answer:
HA, Hawaiian Airlines had the lowest cancel rate, with a total of 342 flights.

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.
most_competitive_routes <- flights %>%
  distinct(origin, dest, carrier) %>%
  group_by(origin, dest) %>%
  summarise(num_carriers = n(), .groups = "drop") %>%
  filter(num_carriers == max(num_carriers)) %>%
  arrange(origin, dest)

most_competitive_routes

Answer:
There are 8 routes that are tied for having the most carriers, each having 5 carriers.