Data visualization, part 1. Code for Quiz 7.
ggsave
command at the end of the chunk of the plot that you want to preview.faithful
datasetgeao_point
eruptions
to the x-axiswaiting
to the y-axiswaiting
is smaller or greater than 76ggplot(faithful) +
geom_point(aes(x = eruptions, y = waiting, colour = waiting > 76))
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), colour = "purple")
Create a plot with the faithful
dataset
use geom_histogram()
to plot the distribution of waiting
time
waiting
to the x-axisggplot(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 classmpg_counted <- mpg %>%
count(manufacturer, name = 'count')
ggplot(mpg_counted) +
geom_bar(aes(x = manufacturer, y = count), stat = 'identity')
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: example
Use stat_summary()
to add a dot at the median
of each group
ggplot(mpg) +
geom_jitter(aes(x = class, y = hwy), width = 0.2) +
stat_summary(aes(x = class, y = hwy), geom = "point",
fun = "median", color = "purple3",
shape = "diamond", size = 4 )
ggsave(filename = "preview.png", path = here::here("_posts", "2021-03-29-exploratory-analysis"))