# Exploratory Analysis

Data visualisation part 1. Code for Quiz 7

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
• 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.

# Questions: Modify Silde 34

• Create a plot with the ‘faithful’ dataset
• assign the variable ‘eruptions’ to the x-axis
• assign the variable ‘waiting’ to the y-axis
• color the points according to whether ‘waiting’ is smaller or greater than 58
``````ggplot(faithful) +
geom_point(aes(x = eruptions, y = waiting,
colour = waiting > 58))
``````

# Question: Modify Intro-Slide 35

• Create a plot with the ‘faithful’ dataset
• assign the variable “eruptions” to the x-axis
• assign the variable “waiting” to the y-axis
• assign the color ‘darkorange’ to all points
``````ggplot(faithful) +
geom_point(aes(x = eruptions, y = waiting),
colour = "darkorange")
``````

# Question: modify intro-slide 36

• Create a plot with the ‘faithful’ dataset
• use ‘geom_histogram()’ to plot the distribution of the ‘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
• 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: 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
• color the dot ’orange
• 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 = "orange",
shape = "square", size = 9 )
``````
``````ggsave(filename = "preview.png",
path = here::here("_posts", "2021-03-29-exploratory-analysis"))
``````