My Blog: Exploratory analysis

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 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.

##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 57.

ggplot(faithful) + 
   geom_point(aes(x = eruptions, y = waiting, 
                  colour = waiting > 57))

##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 ")

##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))

##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 bullet - set the point size to 10
set the point transparency 0.6

ggplot(faithful) + 
   geom_point(aes(x = eruptions, y = waiting), 
   shape = "bullet", size = 10, alpha =0.6)

##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 ))

##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))

##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')

##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))))

##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)

ggsave(filename = "preview.png", 
       path = here::here("_posts", "2022-03-08-exploratory-analysis"))