Exploratory analysis

Data visualization, part 1. Code for Quiz 7.

1. Load the R package we will use.

library(tidyverse)

Question: modify slide 34

-create a plot with the faithful dataset

-add points with geom_point

-assign the variable eruptions the the x-axis

-assign the variable waiting to the y-axis

-colour the points according to whether waiting is smaller or greater than 77

data("faithful")

ggplot(data = faithful,
       mapping = aes(x = eruptions, y = waiting)) + 
  geom_point()

ggplot() + 
  geom_point(mapping = aes(x = eruptions, y = waiting),
             data = faithful)

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

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 purple to all the points

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

ggsave(filename = "preview.png",
       path = here::here("_posts", "2021-03-30-exploratory-analysis"))

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

-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

data("mpg") + 
  geom_bar(aes(x = manufacturer))

NULL

Question: modify stat-slide-41

-change code to count and to plot the variable manufacturer instead of class

mpg_counted <- mpg %>% 
  count(class, name = 'count')
ggplot(mpg_counted) + 
  geom_bar(aes(x = class, 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 examples.

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

-color the dot dodgerblue

-make the shape of the dot plus

-make the dot size 2

ggplot(mpg) + 
  geom_jitter(aes(x = class, y = hwy), width = 0.2) + 
  stat_summary(aes(x = class, y = hwy), geom = "point", 
               fun = "median", color = "dodgerblue", size = 2)