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 the ???s. These are answers on your moodle quiz.

• 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 (to your examples folder) and 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 77

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

Question: Modify intro-slide 35

• Create a plot with the faithful dataset

• add points with geom_plottoday

• 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 the shape of the points to cross

• set the point size to 4

• set the point transparency 0.3

  ggplot(faithful)+
    geom_point(aes(x = eruptions, y = waiting),
    shape = "cross", size = 4, alpha =0.3)

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: modift 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 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 = "preview.png",
         path = here::here("_posts", "2022-03-07-exploratory-analysis"))