library(tidyverse)

Modify Slide 34

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 -color the points according to whether waiting is smaller or greater than 58

ggplot(faithful) + 
  geom_point(aes(x = eruptions, y = waiting, colour = waiting > 58))

Modify intro Slide 35

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 assign the color blueviolet to all the points

ggplot(faithful) + 
  geom_point(aes(x = eruptions, y = waiting),
             colour = 'blueviolet')

Modify intro Slide 36

Create a plot with the faithful dataset use geom_histogram() to plot the distribution of waiting time assign the variable waiting to the x-axis

ggplot(faithful) + 
  geom_histogram(aes(x = waiting))

Modify geom ex 1

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 cross set the point size to 5 set the point transparency 0.9

ggplot(faithful) + 
  geom_point(aes(x = eruptions, y = waiting),
shape = "diamond", size = 5, alpha = 0.9)

Modify geom ex 2

Create a plot with the faithful dataset use geom_histogram() to plot the distribution of the eruptions (time) fill in the histogram based on whether eruptions are greater than or less than 3.2 minutes

ggplot(faithful) + 
  geom_histogram(aes(x = eruptions, fill = eruptions > 3.2))

Modify stat Slide 40

reate a plot with the mpg dataset add geom_bar() to create a bar chart of the variable manufacturer

ggplot(mpg) + 
  geom_bar(aes(x = manufacturer))

Modify stat Slide 41

change code to count and to plot the variable manufacturer instead of class

mpg_counted <- mpg %>% 
  count(manufacturer, name = 'count')
ggplot(mpg_counted) + 
  geom_bar(aes(x = manufacturer, y = count), stat = 'identity')

Modify stat Slide 43

change code to plot bar chart of each manufacturer as a percent of total change class to manufacturer

ggplot(mpg) + 
  geom_bar(aes(x = manufacturer, y = after_stat(100 * count / sum(count))))

Modify stat ex 2

Use stat_summary() to add a dot at the median of each group color the dot blueviolet make the shape of the dot cross make the dot size 9

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)

ggsave(filename = "preview.png", 
       path = here::here("_posts", "2021-03-29-exploratory-analysis"))

Exploratory analysis