Exploratory analysis

Data visualization, part 1. Code for Quiz 7.

1. Load the R packages we will use

library(tidyverse)

Question: modify slide 34

• create a plot with the faithful dataset

• add point 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 57

ggplot(faithful) + 
  geom_point(aes(x= eruptions, y= waiting, color = waiting > 57))

Question: modify intro-slide 35

• create a plot with 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 blueviolet to all the points

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

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 shape of the points to plus
  • set the point size to 1
  • set the point transparency 0.4

ggplot(faithful) +
  geom_point(aes(x = eruptions, y = waiting), 
             shape = "plus", size = 1, alpha = 0.4)

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 = "time" > 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 the 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 purple3
  • make the shape of the dot diamond
  • make the dot size 4

ggplot(mpg) + 
  geom_jitter(aes(x = class, y = hwy), width = 0.2) +
  stat_summary(aes(x = class, y = hwy), geom = "point", 
               fun = "median", color = "purple3", 
               shape = "diamond", size = 4)