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_pointeruptions 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 manufacturerggplot(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 manufacturerggplot(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"))