Exploratory Analysis

Data visualization, part 1. Code for Quiz 7.

1. Load the R packages 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 1: 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 77

ggplot(faithful) +
   geom_point(aes(x = eruptions, y = waiting, 
                  colour = waiting > 77))

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Question 2: 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 dodgerblue to all the points

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

ggsave(filename = "preview.png",
       path = here::here("_posts", "2022-03-13-exploratory-analysis"))

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Question 3: 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))

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Question 4: 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 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)

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Question 5: 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)) 

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Question 6: 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))

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Question 7: 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')

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Question 8: 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))))

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Question 9: 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 plus - make the dot size 3

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 = "plus", size = 3)