2 min read

01 Data Visualisation Introduction Lecture

class: center, inverse

Course Contents


  1. System Configuration - installing software
  2. Using RStudio
  3. Introduction to R
  4. Getting and Cleaning Data
  5. Exploratory Analysis - making rough plots
  6. Different Types of Plots
  7. Playing with Aesthetics
  8. Using Plotting Themes
  9. Advanced Topics - Maps, Networks

Why We’re Here

  • Alternative to Excel
  • Enables Reproducible Research
  • Can Make Lots of Plots Quickly
    • Good for Exploratory Analysis
  • Publication Ready Figures

And…. a gateway to so much more

  • data capture
  • statistical analysis
  • machine learning
  • artificial intelligence
  • writing your thesis
  • writing a blog

Not Why We’re Here

  • Won’t discuss choices for data presentation
  • Nor good practices in visualisations
    • but these are sort of in the background
  • This isn’t a machine learning course
    • but lots of the techniques we’ll use are relevant
  • So, this course it about skills development, how you use these is up to you.

We said we wouldn’t discuss this….but

  • Graphics are important, overlooked, and inconsistent
  • Need to tell a story
  • Can be misleading, almost always by accident
  • Choice of colours - we’ll spend some time on this
  • Choice of fonts
  • Keep it simple - reduce amount of ink
  • Increasing number of options for showcasing your data