Exploratory analysis

Data visualization, part 1. Code for Quiz 7.

1. Load the R package we will use.

library(tidyverse)

2. Quiz questions

• Replace all of the ???

• Run all 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 that you have watched the videos, downloaded, and worked through the exercises in exercises_slides-1-49.Rmd

3. Plot preview

• 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

  • Color the points according to whether waiting is smaller or greater than 76

ggplot(faithful) + 
  geom_point(aes(x = eruptions,
                 y = waiting,
                 colour = waiting > 76))

ggsave(filename = "preview.png",
       path = here::here("_posts", "2022-03-11-exploratory-analysis"))

Question: 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 purple to all the points

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

Question: 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))

Question: 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 asterisk
  • Set the point size to 8
  • Set the point transparency 0.7

ggplot(faithful) + 
  geom_point(aes(x = eruptions,
                 y = waiting),
             shape = "asterisk", size = 8, alpha = 0.7)

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

• 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), geom = "point", 
               fun = "median", color = "orange", shape = "square", size = 9)