Data visualization, part 1. Code for Quiz 7.
ggplot(faithful) +
geom_point(aes(x = eruptions, y = waiting, colour = waiting > 60))
ggplot(faithful) +
geom_point(aes(x = eruptions, y = waiting),
colour = 'blue')
ggsave(filename = here::here("_posts/2021-03-26-exploratory-analysis/preview.png"))
ggplot(faithful) +
geom_histogram(aes(x = eruptions))
See how shapes and sizes of points can be specified here
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
set the shape of the points to SEE QUIZ
set the point size to SEE QUIZ
set the point transparency SEE QUIZ
ggplot(faithful) +
geom_point(aes(x = eruptions, y = waiting),
shape ="diamond", size = 5, alpha = 0.9)
ggplot(faithful) +
geom_histogram(aes(x = eruptions, fill = eruptions > 3.2))
data("mpg")
# variable definitions
# ?mpg
# mpg %>% glimpse()
ggplot(mpg) +
geom_bar(aes(x = manufacturer))
mpg_counted <- mpg %>%
count(manufacturer, name = 'count')
ggplot(mpg_counted) +
geom_bar(aes(x = manufacturer, y = count), stat = 'identity')
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 SEE QUIZ
make the shape of the dot SEE QUIZ
make the dot size SEE QUIZ
ggplot(mpg) +
geom_jitter(aes(x = class, y = hwy), width = 0.2) +
stat_summary(aes(x = class, y = hwy), geom = "point",
fun = "median", color = "blueviolet",
shape = "cross", size = 9)