p1_data <- mpg %>%
filter(class == "suv") %>%
select(manufacturer, hwy, cty) %>%
mutate(avg_mpg = (hwy+cty)/2)
ggplot(p1_data) +
geom_boxplot(aes(manufacturer, avg_mpg)) +
labs(title = "SUV Fuel Economy Data by Vehicle Manufacturer",
x = "Manufacturer of SUVs",
y = "Average Mile per Gallon") +
theme(plot.title = element_text(hjust = 0.5))
Comments: Subaru produced the most fuel economy SUVs.
p2_data <- mpg %>%
filter(class == "suv") %>%
select(manufacturer, hwy, cty, year) %>%
mutate(avg_mpg = (hwy+cty)/2)
ggplot(p2_data) +
geom_boxplot(aes(as.factor(year), avg_mpg)) +
facet_wrap(~ manufacturer) +
labs(title = "SUV manufacturers fuel econmy improvment between 1999 and 2008",
x = "Year",
y = "Average MPG of SUVs") +
theme(plot.title = element_text(hjust = 0.5, size = rel(1.3)))
Comment:The land rover has the most fuel economy improvment.
p3_delay <- flights %>%
select(dep_delay, arr_delay, distance) %>%
filter(!is.na(distance) & !is.na(dep_delay) & !is.na(arr_delay))
ggplot(p3_delay) +
geom_bin2d(aes(dep_delay, distance), bins = 40) +
labs(title = "Plot Of Departure Deplay Time And Flight Distance",
x = "Departure Delay time",
y = "Flight Distance (miles)") +
theme(plot.title = element_text(hjust = 0.5, size = 15),
axis.title = element_text(size = 12))
ggplot(p3_delay) +
geom_bin2d(aes(arr_delay, distance), bins = 40) +
labs(title = "Plot Of Departure Deplay Time And Flight Distance",
x = "Arrive Delay time",
y = "Flight Distance (miles)") +
theme(plot.title = element_text(hjust = 0.5, size = 15),
axis.title = element_text(size = 12))
Comment: Flight distance have no clear relationship with the departure delay time.