DAS522/DAS241 MIDTERM EXAM PART2

Academic Honesty Statement (fill your name in the blank)

I, Lisbeth Liu, 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(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.

# Enter code here.
mpg2 <- mpg%>%
  mutate(mpg_overall = (cty + hwy)/2)

ggplot(mpg2, aes(x = mpg_overall)) + 
  geom_histogram(binwidth = 2, boundary = 10, color = 'purple', fill = 'pink') +
  labs(title = "Distribution of Overall MPG",
    x = "Overall MPG",
    y = "Number of Cars") + 
  theme(plot.title = element_text(hjust = 0.5, size = 12))

b) Create a graph to study the relationship between drive train types and `mpg_overall`.

# Enter code here.
ggplot(mpg2, aes(x = drv, y = mpg_overall)) + 
  geom_boxplot(color = 'navy') + 
  labs(title = "MPG by Drive Train Type",
    x = "Drive Train Type",
    y = "Overall MPG")  + 
  theme(plot.title = element_text(hjust = 0.5, size = 16))

Answer: Cars with front-wheel drive (f) tend to have higher overall MPG compared with four-wheel drive (4) and rear-wheel drive (r) vehicles.

c) Create a table to find out which car class has the highest mean `mpg_overall`.

# Enter code here.
mpg2%>%
  group_by(class)%>%
  summarise(mean_mpg = mean(mpg_overall))%>%
  arrange(desc(mean_mpg))

Answer: The subcompact car class has the highest mean overall MPG, indicating that subcompact cars tend to be the most fuel-efficient.

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.

# Enter code here.
ggplot(mpg2, aes(x = factor(cyl), y = mpg_overall)) + 
  geom_point() + 
  facet_wrap(~year) + 
  labs(title = 'Effect of Year and Cylinders on Overall MPG', 
       x = 'Number of Cylinders', 
       y = 'Overall MPG') + 
  theme(plot.title = element_text(hjust = 0.5, size = 16))

Answer: Cars with fewer cylinders generally have higher MPG. This pattern appears in both 1999 and 20008. Additionally, cars in 2008 shows slightly higher MPG overall, suggesting that fuel efficiency may have improved a little bit over time.

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', month == 11)%>%
  group_by(day) %>%
  summarise(avg_arr_delay = mean(arr_delay, na.rm = T))%>%
  arrange(desc(avg_arr_delay))

Answer: 11/27/2013 has the largest average arrival delay.

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.
flights2 <- flights %>%
  mutate(cancel_flight = ifelse(is.na(dep_time)|is.na(arr_time),
                                'Cancelled',
                                'Not Cancelled'))
glimpse(flights2)

## Rows: 336,776
## Columns: 20
## $ year           <int> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2…
## $ month          <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ day            <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ dep_time       <int> 517, 533, 542, 544, 554, 554, 555, 557, 557, 558, 558, …
## $ sched_dep_time <int> 515, 529, 540, 545, 600, 558, 600, 600, 600, 600, 600, …
## $ dep_delay      <dbl> 2, 4, 2, -1, -6, -4, -5, -3, -3, -2, -2, -2, -2, -2, -1…
## $ arr_time       <int> 830, 850, 923, 1004, 812, 740, 913, 709, 838, 753, 849,…
## $ sched_arr_time <int> 819, 830, 850, 1022, 837, 728, 854, 723, 846, 745, 851,…
## $ arr_delay      <dbl> 11, 20, 33, -18, -25, 12, 19, -14, -8, 8, -2, -3, 7, -1…
## $ carrier        <chr> "UA", "UA", "AA", "B6", "DL", "UA", "B6", "EV", "B6", "…
## $ flight         <int> 1545, 1714, 1141, 725, 461, 1696, 507, 5708, 79, 301, 4…
## $ tailnum        <chr> "N14228", "N24211", "N619AA", "N804JB", "N668DN", "N394…
## $ origin         <chr> "EWR", "LGA", "JFK", "JFK", "LGA", "EWR", "EWR", "LGA",…
## $ dest           <chr> "IAH", "IAH", "MIA", "BQN", "ATL", "ORD", "FLL", "IAD",…
## $ air_time       <dbl> 227, 227, 160, 183, 116, 150, 158, 53, 140, 138, 149, 1…
## $ distance       <dbl> 1400, 1416, 1089, 1576, 762, 719, 1065, 229, 944, 733, …
## $ hour           <dbl> 5, 5, 5, 5, 6, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 5, 6, 6, 6…
## $ minute         <dbl> 15, 29, 40, 45, 0, 58, 0, 0, 0, 0, 0, 0, 0, 0, 0, 59, 0…
## $ time_hour      <dttm> 2013-01-01 05:00:00, 2013-01-01 05:00:00, 2013-01-01 0…
## $ cancel_flight  <chr> "Not Cancelled", "Not Cancelled", "Not Cancelled", "Not…

Answer: Flights are labeled “Cancelled” when either the departure time or arrival time is missing (NA). Otherwise, they are labeled “Not Cancelled”.

c) Create a density graph that compares the distribution of `distance` between cancelled flights and non-cancelled flights.

# Enter code here.
ggplot(flights2, aes(x = distance, fill = cancel_flight)) + 
  geom_density(alpha = 0.4) + 
  labs(title = "Distance Distribution of Cancelled vs Non-Cancelled Flights",
    x = "Distance",
    y = "Density") + 
  theme(plot.title = element_text(hjust = 0.5))

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.
routes <- flights %>%
  distinct(origin, dest)
routes

nrow(routes)

## [1] 224

Answer: The table lists all unique routes, and there are 224 unique routes in the dataset.

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.
routes2 <- flights%>%
  distinct(origin, dest, distance)
ggplot(routes2, aes(x = distance)) + 
  geom_histogram(binwidth = 20) +
  labs(title = 'Distribution of Route Distances', 
       x = 'Distance', 
       y = 'Number of Routes') + 
  theme(plot.title = element_text(hjust = 0.5, size = 16))

f) Which route has the highest rate of flight cancellation? Create a table to answer the question.

# Enter code here.
#cancel rate = cancelled flights / total flights
flights2%>%
  group_by(origin, dest) %>%
  summarise(total_flights = n(), 
            cancelled = sum(cancel_flight == 'Cancelled'),
            cancel_rate = cancelled / total_flights) %>%
  arrange(desc(cancel_rate))

Answer: The route from EWR to LGA has the highest flight cancellation rate. Actually, this answer may not be very helpful as a reference, cuz there is just one route from EWR to LGA. In normal cases, the route with highest cancellation rate should be the one from LGA to MHT.

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.
flights2 <- flights %>%
  mutate(cancel_flight = ifelse(is.na(dep_time)|is.na(arr_time),
                                1,
                                0))
cancel_rate_airline <- flights2 %>%
  group_by(carrier) %>%
  summarise(cancel_rate = mean(cancel_flight))
ggplot(cancel_rate_airline, aes(x = carrier, y = cancel_rate)) +
  geom_col(fill = 'pink') +
  labs(title = 'Cancellation Rate by Airline',
       x = 'Airline(Carrier)', 
       y = 'Cancellation Rate')+ 
  theme(plot.title = element_text(hjust = 0.5, size = 12))

Answer: The HA airline 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.
flights %>%
  group_by(origin, dest) %>%
  summarise(num_carries = n_distinct(carrier)) %>%
  arrange(desc(num_carries))

Answer: The most competitive routes are routes from EWR to DTW, from EWR to MSP, from JFK to LAX, from JFK to SFO, from JFK to TPA, from LGA to ATL, from LGA to CLE, and the one from LGA to CLT.

DAS522/DAS241 MIDTERM EXAM PART2 - STUDENT TEMPLATE

[Lisbeth Liu]

Mar 11 2026

Academic Honesty Statement (fill your name in the blank)

Load packages

1. The `mpg` 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.

b) Create a graph to study the relationship between drive train types and `mpg_overall`.

c) Create a table to find out which car class has the highest mean `mpg_overall`.

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.

2. The `flights` data set

a) For JFK airport, which day in November 2013 has the biggest average arrival delay? Create a table to answer the question.

b) Create a new variable `cancel_flight` which is `Cancelled` if the departure time or arrival time is `NA`, otherwise `Not Cancelled`.

c) Create a density graph that compares the distribution of `distance` between cancelled flights and non-cancelled flights.

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.

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

f) Which route has the highest rate of flight cancellation? Create a table to answer the question.

Bonus Question for `flights` data set

a) Create a proper graph to show the rate of cancellation flights for each airline. Answer which airline has the lowest rate of cancellation.

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.

DAS522/DAS241 MIDTERM EXAM PART2 - STUDENT TEMPLATE

[Lisbeth Liu]

Mar 11 2026

Academic Honesty Statement (fill your name in the blank)

Load packages

1. The mpg 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.

b) Create a graph to study the relationship between drive train types and mpg_overall.

c) Create a table to find out which car class has the highest mean mpg_overall.

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.

2. The flights data set

a) For JFK airport, which day in November 2013 has the biggest average arrival delay? Create a table to answer the question.

b) Create a new variable cancel_flight which is Cancelled if the departure time or arrival time is NA, otherwise Not Cancelled.

c) Create a density graph that compares the distribution of distance between cancelled flights and non-cancelled flights.

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.

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

f) Which route has the highest rate of flight cancellation? Create a table to answer the question.

Bonus Question for flights data set

a) Create a proper graph to show the rate of cancellation flights for each airline. Answer which airline has the lowest rate of cancellation.

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.

1. The `mpg` 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.

b) Create a graph to study the relationship between drive train types and `mpg_overall`.

c) Create a table to find out which car class has the highest mean `mpg_overall`.

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.

2. The `flights` data set

b) Create a new variable `cancel_flight` which is `Cancelled` if the departure time or arrival time is `NA`, otherwise `Not Cancelled`.

c) Create a density graph that compares the distribution of `distance` between cancelled flights and non-cancelled flights.

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

Bonus Question for `flights` data set