Academic Honesty Statement (fill your name in the blank)

I, keyan , 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.
mpg2 <- mpg %>%
  mutate(mpg_overall = (cty + hwy)/2)

ggplot(mpg2, aes(x = mpg_overall)) +
  geom_histogram(binwidth = 2, boundary = 20, fill = "skyblue", color = "black") +
  labs(
    title = "Histogram of Overall MPG",
    x = "Overall MPG",
    y = "Count"
  )

b) Create a graph to study the relationship between drive train types and mpg_overall.
ggplot(mpg2, aes(x = drv, y = mpg_overall)) +
  geom_boxplot(fill = "pink") +
  labs(
    title = "Overall MPG by Drive Train Type",
    x = "Drive Train Type",
    y = "Overall MPG"
  )

Answer:
The boxplot shows the distribution of overall mpg for different drive train types. Front-wheel drive vehicles generally have a higher median MPG, while rear-wheel and four-wheel drive vehicles tend to have lower fuel efficiency.

c) Create a table to find out which car class has the highest mean mpg_overall.
class_table <- mpg2 %>%
  group_by(class) %>%
  summarise(mean_mpg_overall = mean(mpg_overall, na.rm = TRUE)) %>%
  arrange(desc(mean_mpg_overall))

class_table

Answer:
From the table above, the car class with the highest mean overall mpg is the class shown at the top of the table.

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(mpg2, aes(x = factor(cyl), y = mpg_overall, fill = factor(year))) +
  geom_boxplot() +
  labs(
    title = "Overall MPG by Cylinder and Year",
    x = "Number of Cylinders",
    y = "Overall MPG",
    fill = "Year"
  )

Answer:
This graph shows how overall mpg varies across different cylinder categories and model years. Vehicles with fewer cylinders generally have higher fuel efficiency, and there are also differences between different years.

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", year == 2013, month == 11) %>%
  group_by(day) %>%
  summarise(avg_arr_delay = mean(arr_delay, na.rm = TRUE)) %>%
  arrange(desc(avg_arr_delay))

jfk_nov_delay

Answer:
From the table above, the day with the biggest average arrival delay at JFK in November 2013 is the day shown in the first row.

b) Create a new variable cancel_flight which is Cancelled if the departure time or arrival time is NA, otherwise Not Cancelled.
flights2 <- flights %>%
  mutate(cancel_flight = if_else(is.na(dep_time) | is.na(arr_time),
                                 "Cancelled",
                                 "Not Cancelled"))

head(flights2)

Answer:
A flight is labeled Cancelled if either departure time or arrival time is missing; otherwise it is labeled Not Cancelled.

c) Create a density graph that compares the distribution of distance between cancelled flights and non-cancelled flights.
ggplot(flights2, aes(x = distance, fill = cancel_flight)) +
  geom_density(alpha = 0.4) +
  labs(
    title = "Distance Distribution by Flight Cancellation Status",
    x = "Distance",
    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 <- flights2 %>%
  distinct(origin, dest)

route_table
nrow(route_table)
## [1] 224

Answer:
The dataset contains 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 <- flights2 %>%
  distinct(origin, dest, distance)

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

f) Which route has the highest rate of flight cancellation? Create a table to answer the question.
route_cancel_table <- flights2 %>%
  group_by(origin, dest) %>%
  summarise(
    total_flights = n(),
    cancelled_flights = sum(cancel_flight == "Cancelled"),
    cancel_rate = cancelled_flights / total_flights
  ) %>%
  arrange(desc(cancel_rate))

route_cancel_table

Answer:
From the table, the first row shows the route with 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.
airline_cancel_table <- flights2 %>%
  group_by(carrier) %>%
  summarise(
    total_flights = n(),
    cancelled_flights = sum(cancel_flight == "Cancelled"),
    cancel_rate = cancelled_flights / total_flights
  ) %>%
  arrange(cancel_rate)

airline_cancel_table
ggplot(airline_cancel_table, aes(x = reorder(carrier, cancel_rate), y = cancel_rate)) +
  geom_col(fill = "pink") +
  labs(
    title = "Cancellation Rate by Airline",
    x = "Airline",
    y = "Cancellation Rate"
  )

Answer:
Based on the graph, 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.
route_competition <- flights2 %>%
  distinct(origin, dest, carrier) %>%
  group_by(origin, dest) %>%
  summarise(num_carriers = n()) %>%
  arrange(desc(num_carriers))

route_competition

Answer:
The most competitive routes are those served by the largest number of carriers. In this data set, the maximum number of carriers on a route is 5. The routes with 5 competing carriers are EWR–DTW, EWR–MSP, JFK–LAX, JFK–SFO, JFK–TPA, LGA–ATL, LGA–CLE, and LGA–CLT.