Exploratory analysis

Data visualization, part 1. Code for quiz 7.

Introduction

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 that you have watched the videos, downloaded (to your examples folder) and worked through the exercises in exercises_slides-1-49.Rmd

Question: Modify slide 34

• Create a plot with the faithful dataset
• Add points with geom_point
  • Assign the variable eruption 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,
                 color = waiting > 76))

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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 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 ofwaiting` 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

• 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 to 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 the eruptions aregreater 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 here

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

• Use the color 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)

ggsave(filename = "preview.png",
       path= here::here("_posts", "2022-03-17-exploratory-analysis"))