Session Objectives and Transferable Skills

  • To be introduced to the R Universe, in particular the tidyverse and ggplot2
  • To be able to code something artistic in R
  • To begin to think more creatively regarding code and data analysis
  • To consider the beauty of code, mathematics and patterns
  • Basic introduction to the use of R and the tidyverse
  • Basic visualisation techniques using ggplot2
  • Creative problem solving and debugging

Schedule

  • Introduction (15 minutes)
  • Part 1: (30 minutes)
    • Introduction to basic mathematical coding and patterns
    • Introduction to layered coding using the Tidyverse and ggplot2
  • Break (5 minutes)
  • Part 2: (30 minutes)
    • Integration of mathematical patterns into ggplot graphics
    • Experimentation and Exploration
  • Review and Conclusion (10 minutes)

Why creativity

  • Creativity is important for approaching novel and complex problems.

  • It can be applied both in the design and application of black-box procedures to problems.

  • Like many programming languages, there are multiple ways in R to achieve any singular goal.

Examples

From Danielle Navarro (@djnavarro)

Examples

From the Fronkonstin Blog

Generated Mathematical Sequences

Coordinate Systems

Introduction to R, the Tidyverse & ggplot2

Part 1

Grammer of Graphics Example

ggplot(data = diamonds, 
       mapping = aes(x = price, 
                     y = carat, 
                     colour = cut)) + 
  geom_point() + 
  labs(title = "Diamond Price, plotted against Carat", 
       x = "Price",
       y = "Carat")

Grammer of Graphics Example 2

ggplot() + 
  geom_point(data = diamonds, 
       mapping = aes(x = price, 
                     y = carat, 
                     colour = cut)) + 
  labs(title = "Diamond Price, plotted against Carat", 
       x = "Price",
       y = "Carat")

ggplot example

ggplot example

Layered Coding

  • geom functions
    • geom_line() - line chart
    • geom_point() - scatter plot
    • geom_ploygon() - shape diagram
  • Aesthetic Descriptors
    • Fill = - inside/fill colour
    • Colour = - Border colour
    • Alpha = - Transparency Level (0 -> 1)
    • Linetype = - Line Type / Border Type
    • Size = - Border thickness / Point Size
    • Shape = - Point Shape
  • Themes
    • theme() - base theme
    • theme_bw() - black and white theme
    • theme_void() - empty theme (no scales, legends etc)
  • Scales
    • labs(), xlab(), ylab(), ggtitle() - Labels
    • lims(), xlim(), ylim() - Scale Limits
  • Coordinate Systems
    • coord_cartesian() - Cartesian Coordinate System
    • coord_polar() - Polar Coordinate System

Understanding Polar Coordinates

  • Coordinates on the x-axis indicate the circular movement.
  • Coordinates on the y-axis indicate the distance of the points from the origin.
  • The origin of the plot is defined as the centre of the plot
  • The plot begins and ends at the top of the plot (before rotating right)

Incorporating Colours

  • Base R:
    • Blue: “blue”
    • Red: “red”
  • Hex/RGB Code:
    • Blue: #0074D9
    • Red: #FF4136
  • Palettes from R Packages
    • RColorBrewer
    • ghibli (colours inspired by Studio Ghibli Films)
    • palettetown (colour inspired by pokemon)

Putting together the basics

Exercise 1:

  • Using the data provided (ex1.dat), plot the data onto a scatterplot using ggplot().
  • This will map the numerical values of sin(x), where x is the number 0 to 100 (at an interval of 0.1), onto its index (1:1001) as a scatterplot.
  • For those using the beginners worksheet, a template has been provided.

Bonus:

  • Using geom_line() or another geom function within ggplot(), plot this same data in another way.
ex1.dat <- as.data.frame(
    seq(from = 0,
        to = 100,
        by = 0.1))
ex1.dat <- sin(ex1.dat)
colnames(ex1.dat) = “sine”

Putting together the basics: Solution

ex1.dat <- as.data.frame(seq(from = 0,
        to = 100,
        by = 0.1))
ex1.dat <- sin(ex1.dat)
colnames(ex1.dat) <- "sine"
ggplot(data = ex1.dat,
    mapping = aes(x = 1:1001, 
                     y = sine)) + 
    geom_point()

Tidying up the graphics

Questions?

Break (5 minutes)

Advanced Artistic ggplot patterns

Exercise 6: Lets get creative!

  • Ideas:
    • Try different geom_() functions (point, polygon, line)
    • Try different colours, palettes or layers
    • Try different plotting types, polar vs standard
    • Try multiple different layers and effects together
    • Try changing aesthetic features, alpha, colour, fill etc…

Any questions or feedback?