Question #1: What is the most popular fuel type in this data
set?
ggplot(mpg) +
geom_bar(aes(x=fl, fill = fl)) +
xlab("Fuel Type") + ggtitle("Distribution of Fuel Types") +
theme(plot.title = element_text(hjust = 0.5))

Answer:
According to the above figure, the most popular fuel type is clearly
“r” (regular petrol)
Question #2: Regarding the fuel type variable, the value “d”
represents diesel, “p” represents premium (petrol) and “r” represents
regular (petrol). Do you think there is an effect of fuel type on how
many miles a vehicle can run on average per gallon of fuel?
ggplot(data = mpg) +
geom_boxplot(mapping = aes(x = fl, y = hwy)) +
xlab("Fuel Type") +
ylab("Miles Per Gallon in Highway") +
ggtitle("Fuel Economy (Highway) vs Fuel Type") +
theme(plot.title = element_text(hjust = 0.5))

ggplot(data = mpg) +
geom_boxplot(mapping = aes(x = fl, y = cty)) +
xlab("Fuel Type") + ylab("Miles Per Gallon in City") +
ggtitle("Fuel Economy (City) vs Fuel Type") +
theme(plot.title = element_text(hjust = 0.5))

Answer:
According to the figure, the fuel type seems to have certain effect
on the miles per gallon a car can drive. In which, “d” diesel and “c”
reveal larger miles per gallon while “e” shows the least fuel efficiency
in miles to drive per gallon, in both city and highway
Question #3:Do you think there is a difference in fuel economy for
vehicles made in 1999 and 2008? (When plotting with “year” variable, use
as.factor(year) to convert it to categorical variables. This will be
explained in future classes.)
ggplot(data = mpg, mapping = aes(x = as.factor(year), y = cty)) +
stat_boxplot(geom = "errorbar", width = 0.5) +
geom_boxplot() +
labs(x = "Year", y = "Miles Per Gallon in City", title = "Fuel Economy (City) Between 1999 and 2008") +
theme(plot.title = element_text(hjust = 0.5))

Answer:
According to the above figure, there is no significant difference in
fuel economy between vehicles made in 1999 and 2008
Question #4: What happens if you make a scatter plot of class vs
drv? Do you think this plot is useful or not?
ggplot(mpg) +
geom_point(mapping = aes(x = class, y = drv)) +
labs(x = "Type of car", y = "Type of drive train") +
theme(plot.title = element_text(hjust = 0.5))

Answer:
Although the plot is scattering without showing a concrete pattern,
it is still useful to identify the according drive train for each car
type. For example, 2seater cars are all rear wheel drive while minivans
are all front-wheel drive.