Exploratory Analysis

A short description of the post.

1. Load the R packages 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 to the x-axis assign the variable waiting to the y-axis colour the points according to whether waiting is smaller or greater than 76

ggplot(faithful) + 
   geom_point(aes(x = eruptions, y = waiting, 
                  colour = waiting > 76)) 

#Question: Modify Intro-Slides 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-Slides 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))   

#Questions: 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 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 = 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: 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 blueviolet make the shape of the dot cross 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 = "blueviolet", 
  shape = "cross", size = 9 )