Academic Honesty Statement (fill your name in the blank)

I, Sang Dao, 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.
mpg["mpg_overall"] = (mpg["cty"] + mpg["hwy"]) / 2
ggplot(mpg, aes(x = mpg_overall)) + geom_histogram(binwidth = 2 , boundary = 0)

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()

Answer: “f” (front-wheel drive) definitely has the highest fuel scores, “4” (4wd) gets the worst gas. The fuel scores for “f” are more spread out => has a lot good outline and a bad one

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

Answer: Subcompact 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.
ggplot(mpg, aes(x = as.factor(cyl), y = mpg_overall, fill = as.factor(year))) +
  geom_boxplot()

Answer: A car with a higher number of cylinders will have a lower mpg_overall score and with the same number of cylinders the new cars are more saving fuel but still have a little outline that the some old cars have the highest saving fuel score in this graph.

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) %>%
  summarise(mean_arr_delay = mean(arr_delay, na.rm = TRUE)) %>%
  arrange(desc(mean_arr_delay))

Answer: Day 27 has the biggest average arrival delay.

  1. Create a new variable cancel_flight which is Cancelled if the departure time or arrival time is NA, otherwise Not Cancelled.
flights_new <- flights %>%
  mutate(cancel_flight = ifelse(is.na(dep_time) | is.na(arr_time), "Cancelled", "Not Cancelled"))
c) Create a density graph that compares the distribution of distance between cancelled flights and non-cancelled flights.
ggplot(flights_new, aes(x = distance, fill = cancel_flight)) +
  geom_density(alpha = 0.5)

Answer: More flights in the short trip. And most cancelled flights is the short trip, and it is really hard to see the cancelled flights from more than 3000 distance.

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.
flights %>%
  summarise(total_unique_routes = n_distinct(origin, dest))

Answer: has 224 unique flight

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_dist_table <- flights %>%
  count(origin, dest, distance)
ggplot(route_dist_table, aes(x = distance)) +
  geom_histogram(binwidth = 100)

f) Which route has the highest rate of flight cancellation? Create a table to answer the question.
flights_new %>%
  group_by(origin, dest) %>%
  summarise(
    total_flights = n(), # This counts the flights in the group [1]
    cancel_rate = mean(cancel_flight == "Cancelled") # This calculates the specific rate you need
  ) %>%
  arrange(desc(cancel_rate))

Answer: The route from EWR to LGA has the highest rate of flight cancellation cause it only has 1 flight was cancelled. If not count and choose more objective sample we have 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.
carrier_cancel_rates <- flights %>%
  group_by(carrier) %>%
  summarise(
    cancel_rate = mean(is.na(dep_time) | is.na(arr_time))
  ) %>%
  arrange(cancel_rate)
head(carrier_cancel_rates)
ggplot(carrier_cancel_rates, aes(x = reorder(carrier, cancel_rate), y = cancel_rate)) +
  geom_col()

Answer: HA 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.
competitive_routes <- flights %>%
  group_by(origin, dest) %>%
  summarise(num_carriers = n_distinct(carrier)) %>%
  filter(num_carriers == max(num_carriers))
competitive_routes

Answer: There are the most competitive route with 5 number of carriers run on that.