Data Visualization: Lesson Design

Stage 0 - Assumptions

Stage 1 - Desired Results


  1. Learners will be able to navigate Choosing a Good Chart.
    • The appropriate graphic should be chosen to respond directly to the scientific question/hypothesis that is to be addressed with the data
    • Key decisions
      1. What is the structure of the data/results to communicate?
        • Relationship
        • Comparison
        • Distribution
        • Composition
      2. How many variables or categories need to be conveyed?
        • Is one of those variables time?
      3. How much data is available for each variable or category?
      4. Are absolute or relative values more appropriate to communicate?
  2. Learners will be able to implement the appropriate graphical device using dplyr and ggplot2.
    • Basic graphing layers
      • ggplot()
      • aes()
      • geom_line()
      • geom_bar()
      • geom_smooth()
      • geom_point()
      • geom_boxplot()
      • geom_smooth()
    • Facet subplots
      • facet_grid()
      • facet_wrap()
    • Dataset and aesthetic adjustments (Publication quality figures)
      • scale_manual()
      • lims()
      • labs()
      • guide_legend()
      • theme()

Summative Assessment

Essential Questions

How do I…

Learners Will Be Able To…

Learners Will Know…

Stage 2 - Learning Plan

Exercises Note

Introductions and Learning Objectives

Data Management in R

Data Structures

dplyr Recap

Scientific Questions and Hypotheses

Introduce ‘Choosing a Good Chart’

ggplot: Grammar of Graphics

Line Histogram

Publication Quality Figures

Coffee Break 15 min

Bubble Chart

Tidy Data Structure

Faceted Table of Histogram Bar Charts

Stacked Area Chart