Detaild Lecture Outline and Additional Resources

Resources associated with each section of the lecture.

Detailed outline and resources

Here is the outline of the lecture, along with links to additional resources on each topic.

Introduction

Why data viz matters in economics?

• What do we use data viz for in economics?
  • Allows to synthesize large amount of data
  • Explore, explain and make inference
    • Why looking at data?
    • Existing tools for exploratory data analysis in R
• Why good data viz matters?
  • Visualizations can be deceptive
    • Defense against dishonest charts
  • Credibility
    • Credibility and typography
    • Reasons to make beautiful graphs on credibility and aesthetics
    • In defense of simple charts

What are the characteristics of a good data viz?

• Tells a story
  • Storytelling and data visualisation
• Limits the cognitive load
  • Cognitive Load as a Guide
  • More on cognitive load for data viz
• Leverages principles from data viz and perception theory
  • More on Gestalt principles (plus a research article)
  • How to find and create good color palettes on
  • Data-to-ink ratio
    • A critique of the data-to-ink ratio

How to make an ok data viz?

• Non-negotiable rules
• Good practices
  • Concrete recommendations
    • For color
    • For text
  • Do’s and don’ts
    • Statistics oriented
    • A collection of dataviz caveats
    • Sometimes it’s ok to break the rules
• Choose a plot type
  • Know what exists: graph galeries
    • Data Viz Project
    • Flowing Data - Chart type
    • Dataviz Inspiration
    • R graph gallery
  • What questions to ask when creating charts
  • Decision trees for a graph type
    • Financial Times tree
    • Data viz catalogue
    • Datawrapper guide
    • Data to viz graph

Main take-away messages

• Overall messages
• Concrete points
• Economics specificities

Slides

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