Exploratory analysis

Pratice for Exploratory analysis / data visualization for Quiz 7

library(tidyverse)

Question: 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 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))

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 colour purple to all the points

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

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

• See how shapes and sizes of points can be specified here: https://ggplot2.tidyverse.org/articles/ggplot2-specs.html#sec:shape-spec

• 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 7
  • set the point transparency 0.6

ggplot(faithful) + 
  geom_point(aes(x = eruptions, y = waiting), shape = "cross", size = 7, alpha = 0.6)

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

• Create 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 answer to stat-ex-2

• for reference see: https://ggplot2.tidyverse.org/reference/stat_summary.html?q=stat%20_%20summary#examples

• Use stat_summary() to add a dot at the median of each group

  • color the dot orange
  • make the shape of the dot square
  • make the dot size 9

ggplot(mpg) + 
  geom_jitter(aes(x = class, y = hwy), width = 0.2) + stat_summary(aes(x = class, y = hwy),fun = "median",  geom = 'point', colour = 'orange', shape = "square", size = 9) 

ggsave(filename = "preview.png", 
       path = here::here("_posts", "2022-03-14-exploratory-analysis"))