Academic Honesty Statement (fill your name in the blank)

I, Linh Le, 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(airports)
 library(cherryblossom)
 library(usdata)
 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.
new_mpg <- mpg %>%
  mutate(mpg_overall = (cty+hwy)/2)

new_mpg
ggplot(new_mpg) +
  geom_histogram(aes(x=mpg_overall), binwidth = 2, boundary = 10)

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

ggplot(data = new_mpg, mapping = aes(x = drv, y = mpg_overall)) +
    stat_boxplot(geom = "errorbar", width = 0.5) +
    geom_boxplot()

Answer: Based on the graph, front wheel drive is the one has the highest mile per gallon while 4 wheel drive and rear wheel drive has the same range. For 4 wheel drive, in average, it can no go far with just 1 gallon which make sense because it use more energy to control 4 wheel differently. for ther front wheel drive it has a lot of outlier data, some of them can even run 40 mile per gallon, but overall it can run alot more which 1 gallon compare to the others.


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

highst<- new_mpg %>%
  group_by(class)%>%
  summarise(mean= mean(mpg_overall))%>%
  arrange(desc(mean))

print(highst)
## # A tibble: 7 × 2
##   class       mean
##   <chr>      <dbl>
## 1 subcompact  24.3
## 2 compact     24.2
## 3 midsize     23.0
## 4 2seater     20.1
## 5 minivan     19.1
## 6 suv         15.8
## 7 pickup      14.9

Answer: Subcompact class has the highest mean of overall miles per gallon with 24.3 mpg


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(data = new_mpg) + 
  geom_point(mapping = aes(x = cyl, y = mpg_overall)) + 
  facet_wrap(~ factor(year)) +
  labs(title = "Vehicle Fuel Economy Data by Year and Number of Cylinders", 
       x = "Number of Cylinders", 
       y = "Overall Mile per Gallon") + 
  theme(plot.title = element_text(hjust = 0.5, size = rel(1.5), margin = margin(15,15,15,15)), 
        axis.title = element_text(size = rel(1.2)), 
        axis.title.x = element_text(margin = margin(10,5,5,5)), 
        axis.title.y = element_text(margin = margin(5,10,5,5)), 
        axis.text = element_text(size = rel(1.2)))

# Enter code here.
ggplot(data = new_mpg, aes(x= factor(cyl), y = mpg_overall)) + 
  stat_boxplot(geom = "errorbar", width = 0.5) +
    geom_boxplot() + 
  facet_wrap(~ factor(year)) +
  labs(title = "Vehicle Fuel Economy Data by Year and Number of Cylinders", 
       x = "Number of Cylinders", 
       y = "Overall Mile per Gallon") + 
  theme(plot.title = element_text(hjust = 0.5, size = rel(1.5), margin = margin(15,15,15,15)), 
        axis.title = element_text(size = rel(1.2)), 
        axis.title.x = element_text(margin = margin(10,5,5,5)), 
        axis.title.y = element_text(margin = margin(5,10,5,5)), 
        axis.text = element_text(size = rel(1.2)))

# Enter code here.
ggplot(data = new_mpg) + 
  stat_summary(mapping = aes(x= factor(cyl), y = mpg_overall, fill = factor(year)), fun = "mean",  geom = "bar", position = "dodge")+
  labs(title = "Vehicle Fuel Economy Data by Year and Number of Cylinders", 
       x = "Number of Cylinders", 
       y = "Mean of Overall Mile per Gallon") + 
  theme(plot.title = element_text(hjust = 0.5, size = rel(1.5), margin = margin(15,15,15,15)), 
        axis.title = element_text(size = rel(1.2)), 
        axis.title.x = element_text(margin = margin(10,5,5,5)), 
        axis.title.y = element_text(margin = margin(5,10,5,5)), 
        axis.text = element_text(size = rel(1.2)))

Answer:

Based on the three graph above, We can tell that the Mile per Gallon for car market that was manufacture in 2008 is more stable with less outlier and in general can run further compare to the car that was manufacture in 1999. A special thing is that there are cars in 2008 that use 5 cylinders and it’s stably can run for almost 25 miles per gallon. In 1999 they didn’t have this 5 cylinders option.


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.

nov23<- flights %>%
  filter(origin == "JFK", month == 11, year == 2013, !is.na(arr_delay))%>%
  dplyr :: select(year,month,day, arr_delay)%>%
  arrange(desc(arr_delay))
  
print(nov23)
## # A tibble: 8,645 × 4
##     year month   day arr_delay
##    <int> <int> <int>     <dbl>
##  1  2013    11    24       614
##  2  2013    11    27       396
##  3  2013    11    11       344
##  4  2013    11    27       334
##  5  2013    11    26       324
##  6  2013    11    24       304
##  7  2013    11     2       299
##  8  2013    11    24       290
##  9  2013    11    27       285
## 10  2013    11    27       281
## # ℹ 8,635 more rows

Answer:

For JFK as the origin, The 24th of November 2013 has the biggest average arrival delay with 614 mins 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.

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

filter(cancel_data, cancel_flight == "Cancelled") 
print(cancel_data)
## # A tibble: 336,776 × 20
##     year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
##    <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
##  1  2013     1     1      517            515         2      830            819
##  2  2013     1     1      533            529         4      850            830
##  3  2013     1     1      542            540         2      923            850
##  4  2013     1     1      544            545        -1     1004           1022
##  5  2013     1     1      554            600        -6      812            837
##  6  2013     1     1      554            558        -4      740            728
##  7  2013     1     1      555            600        -5      913            854
##  8  2013     1     1      557            600        -3      709            723
##  9  2013     1     1      557            600        -3      838            846
## 10  2013     1     1      558            600        -2      753            745
## # ℹ 336,766 more rows
## # ℹ 12 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
## #   tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
## #   hour <dbl>, minute <dbl>, time_hour <dttm>, cancel_flight <chr>

Answer:

cancel_data <- 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.
# Enter code here.

ggplot(cancel_data)+
  geom_density(aes(x = distance, fill = cancel_flight))+
  labs(title = "Distance vs Cancelation", x = "Distance (mile)", y = "")  +
  scale_y_continuous(labels = scales::comma)

ggplot(cancel_data) +
  stat_summary(mapping = aes(x = cancel_flight, y = distance, fill = cancel_flight), geom = "bar", fun = "mean") +
  labs(title = "Mean Distance vs Cancelation", x = "", y = "Mean Distance (mile)")

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.
flight_route <- flights%>%
  group_by(origin, dest)%>%
  summarise(count= n()) %>%
  dplyr :: select(origin, dest)

flight_route
print(nrow(flight_route))
## [1] 224

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.

# Enter code here.

flight_route_d <- flights%>%
  group_by(origin, dest, distance)%>%
  summarise(count= n(), )%>%
  dplyr :: select(origin, dest, distance)

flight_route_d
ggplot(flight_route_d)+
  geom_histogram(aes(distance))

# This is just for fun
ggplot(flight_route_d)+
  geom_histogram(aes(distance, fill = dest, color = origin))
## `stat_bin()` using `bins = 30`. Pick better value `binwidth`.

f) Which route has the highest rate of flight cancellation? Create a table to answer the question.
# Enter code here.
flight_route_c <- cancel_data%>%
  group_by(origin, dest, cancel_flight)%>%
  summarise(count = n())

flight_route_c_t <- flight_route_c %>%
  pivot_wider(names_from = cancel_flight, values_from = count) 

flight_route_c_f <- flight_route_c_t %>%
  mutate ( Cancel_rate = Cancelled/(`Cancelled` + `Not Cancelled`))%>%
  arrange(desc(Cancel_rate))
flight_route_c_f

Answer:

Route from LGA to MHT has the highest rate of flight cancellation with 23.94%


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.

flight_route_b <- cancel_data%>%
  group_by(carrier, cancel_flight)%>%
  summarise(count = n())%>%
  pivot_wider(names_from = cancel_flight, values_from = count) %>%
  mutate ( Cancel_rate = Cancelled/(`Cancelled` + `Not Cancelled`))%>%
  arrange(Cancel_rate)



  flight_route_b
ggplot(flight_route_b)+
  stat_summary(aes(x = carrier, y =Cancel_rate, fill = carrier), geom = "bar", fun = "mean")+
  labs(title = "Mean cancel rate by Airline", 
       x = "Airline", 
       y = "Mean Cancel Rate")

Answer: AS airline has the smallest cancel 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.

Answer: