A short description of the post.
Replace all the ???s. These are answers on your moodle quiz.
Run all the individual code chunks to make sure the answers in this file correspond with your quiz answers
After you check all your code chunks run then you can knit it. It won’t knit until the ??? are replaced
The quiz assumes you have watched the videos had worked through the exercises in exercises_slides-1-49.Rmd
Create a plot with the faithful
dataset
add points with geom_point
assign the variable eruptions
to the x-axis
assign the variable waiting
to the y-axis
colour the points according to whether waiting
is smaller or greater than 76
ggplot(faithful) +
geom_point(aes(x = eruptions, y = waiting,
colour = waiting > 76))
faithful
datasetgeom_point
eruptions
to the x-axiswaiting
to the y-axisggplot(faithful) +
geom_point(aes(x = eruptions, y = waiting),
colour = "purple")
ggplot(faithful) +
geom_histogram(aes(x = waiting))
See how shapes and sizes of points can be specified here
Create a plot with the faithful dataset
add points with geom_point
eruptions
to the x-axiswaiting
to the y-axisggplot(faithful) +
geom_point(aes(x = eruptions, y = waiting),
shape = "triangle", size = 7, alpha =0.5)
faithful
datasetgeom_histogram()
to plot the distribution of the eruptions
(time)ggplot(faithful) +
geom_histogram(aes(x = eruptions, fill = eruptions > 3.2))
mpg
datasetgeom_bar()
to create a bar chart of the variable manufacturer
ggplot(mpg) +
geom_bar(aes(x = manufacturer))
manufacturer
instead of class
mpg_counted <- mpg %>%
count(manufacturer, name = 'count')
ggplot(mpg_counted) +
geom_bar(aes(x = manufacturer, y = count), stat = 'identity')
ggsave(here::here("_posts/2021-03-28-exploratory-analysis/preview.png"))
manufacturer
as a percent of totalclass
to manufacturer
ggplot(mpg) +
geom_bar(aes(x = manufacturer, y = after_stat(100 * count / sum(count))))
For reference see examples.
Use stat_summary()
to add a dot SEE QUIZ at the median of each group
color the dot purple
make the shape of the dot asterisk
make the dot size 7
ggplot(mpg) +
geom_jitter(aes(x = class, y = hwy), width = 0.2) +
stat_summary(aes(x = class, y = hwy), geom = "point",
fun = "median", color = "purple",
shape = "asterisk", size = 7 )