Reproducible Quantitative Methods

Instructor Guide, Lesson 9

Making better plots / Visualization for outreach and communication

yeah bar

Topics and Resources

  1. Plotting in R

    There are several ways you can approach plotting lessons with your students, but we suggest taking your target dataset and plotting it with your students in a variety of ways- scatter plots, bar graphs, etc. We encourage you to use ggplot2, but there are some inspiring resources written with base R too. The way you do this will depend somewhat on the form your data takes- be sure to take inspiration from the conventions of data presentation in your field. Live code plots of the same data in a few different ways. Ask your students to interpret the graph at each step, and talk about the advantages and disadvantages of presenting the data each way.


    A helpful hint from those that came before

    Keep it real. Students find it very rewarding to produce "real" journal quality figures right from their R scripts. If time permits, show them how they can create multi-panel figures or add annotations to graphs- these often trip students up when working on projects solo.

  2. Visual communication with data

    This is a great opportunity to talk to students about accessibility, communication and openness in science. Here’s a fascinating blog post about color palettes touching on many of these issues.


  1. Making figures
  2. Work through creating figures with your students, based on their project data. Think about things like color palettes, and what that means for accessibility.


Visualization for communication

For this discussion, there's a lot of material you can potentially present to your students. Conceptually, this is pretty open ended, but we want students to leave with an idea of why they should put a little bit more thought into graphing their data. Two readings we like are:

  • Ten simple rules for better figures

  • Finding the Right Color Palettes for Data Visualizations


TEDxWaterloo - Miriah Meyer - Information Visualization for Scientific Discovery (12:26)


Why is visualization important?

How should we visualize our project data?

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