Exploratory Analysis

Data Visualization for Quiz 7, part 1.

1. Load the R package we will use:

library(tidyverse)

2. Quiz questions

• Replace all the ???s. These are answers on your moodle quiz.
• Run all the individual code chunks to make sure the answers in this file correspond with your quiz answers
• After you check all your code chunks run then you can knit it. It won’t knit until the ??? are replaced
• The quiz assumes you have watched the videos had worked through the exercises in exercises_slides-1-49.Rmd
• Pick one of your plots to save as your preview plot. Use the ggsave command at the end of the chunk of the plot that you want to preview.

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 64

ggplot(faithful) + 
   geom_point(aes(x = eruptions, y = waiting, 
                  colour = waiting > 64))  

Question: Modify Slide 35 (Intro)

• 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 darkorange to all the points

ggplot(faithful) + 
   geom_point(aes(x = eruptions, y = waiting),
              colour = "darkorange")

Question: Modify Slide 36 (Intro)

• 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))

Question: 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 diamond
• 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)

Question: 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))

Question: 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))

Question: 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')

Question: 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))))

Question: Modify answer to stat-ex-2

• Use stat_summary() to add a dot at the median of each group
  • color the dot purple
  • 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), geom = "point", 
  fun = "median", color = "purple", 
  shape = "square", size = 9)

Save ggplot to project folder.

ggsave(filename = "preview.png", path = here::here("_posts", "2022-03-08-eploratory-analysis"))