Solutions

Package Loading


As mentioned within the session setup, load the following packages using the library() function.

  library(tidyverse)
  library(RColorBrewer)
  library(ghibli)
  library(palettetown)

Exercise 1: Using the data provided (ex1.dat), generated by the code below, plot the data onto a scatterplot using ggplot(). Plotting the variable sine from the data onto the x variable, and the index (1:1001) onto the y variable


Setup Code

# Generate the sequence 1 to 100, in steps of 0.1
  ex1.dat <- as.data.frame(
    seq(from = 0, 
        to = 100, 
        by = 0.1))

# Apply the sine function
  ex1.dat <- sin(ex1.dat)

# Rename the columns
  colnames(ex1.dat) <- "sine"

Solution

  ggplot(data = ex1.dat,                  
         mapping = aes(x = 1:1001,       
                       y = sine)) +    
    geom_point()

Exercise 1, Bonus Question: Rather than using geom_point(), use geom_line() or another geom_ function to plot this same data in another way.

  ggplot(data = ex1.dat,                  
         mapping = aes(x = 1:1001,       
                       y = sine)) +    
    geom_line()

Exercise 2: Add one of the two following themes to clean up your code!

  ggplot(data = ex1.dat,                  
         mapping = aes(x = 1:1001,       
                       y = sine)) +    
    geom_point() + 
  theme_void()

Exercise 3/Bonus: Combined Mathematical Sequences, Using the same data as before (ex1.dat) transform the data and layer it onto the previous plots. Use the geom_line() function to more easily observe the impact of this transformation.

  ggplot() +    
    geom_line(data = (ex1.dat + 1),
            mapping = aes(x = 1:1001, y = sine)) + 
    geom_line(data = (ex1.dat - 1),
            mapping = aes(x = 1:1001, y = sine)) + 
      
    geom_line(data = (ex1.dat * 2),
            mapping = aes(x = 1:1001, y = sine)) + 
      
    geom_line(data = (ex1.dat / 2),
            mapping = aes(x = 1:1001, y = sine)) + 
  
    theme_void()

Exercise 4: Changing Coordinate System

ex4.dat <- as.data.frame(seq(from = 1,
                                 to = 51.3, 
                                 by = 0.1))
    ex4.dat <- sin(ex4.dat)
    colnames(ex4.dat) <- "sine" 

ggplot(data = ex4.dat,                  
         mapping = aes(x = 1:504,       
                       y = sine)) +    
    geom_point() + 
  theme_void() + 
  coord_polar()

Exercise 4, Bonus

ex4.dat <- as.data.frame(seq(from = 1,
                                 to = 51.3, 
                                 by = 0.1))
    colnames(ex4.dat) <- "seq"
    ex4.dat$sine <- sin(ex4.dat$seq)
    ex4.dat$cos <- cos(ex4.dat$seq)
    ex4.dat$tan <- tan(ex4.dat$seq)

ggplot(data = ex4.dat) +    
    geom_point(mapping = aes(x = 1:504, y = sine)) + 
    geom_point(mapping = aes(x = 1:504, y = cos)) + 
    geom_point(mapping = aes(x = 1:504, y = tan)) + 
  theme_void() + 
  coord_polar() + 
  ylim(-1, 1)
## Warning: Removed 251 rows containing missing values (geom_point).

Exercise 5: Layering Colours, using the code created in Exercise 4, replace geom_point() with geom_polygon() and apply a colour within using fill = or colour =.

ggplot(data = ex4.dat) +    
    geom_polygon(mapping = aes(x = 1:504, y = sine), fill = "blue") + 
  theme_void() + 
  coord_polar() + 
  ylim(-1, 1)

Exercise 6: See inspiration for ideas about what you could do!