Exploratory analysis

Data visualization, part 1. Code for Quiz 7.

Contents

1.Load the R package we will use.

library(tidyverse)
  1. 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’

3.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 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 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

-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 plus

-set the point size to 1

-set the point transparency 0.4

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 asterisk

-make the dot size 7

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 = "asterisk", size = 7 )

  ggsave(filename = "preview7.png", 
       path = here::here("_posts","2021-03-29-exploratory-analysis"))