Environmental Data Analysis and Visualization

Working on a Workflow

Visualization critique


Visualization critique


Visualization critique


Next visualization critiques

  • Carmen

  • Madeleine

  • Gillian

Communicating beyond the notebook



Interactive Visualizations

Working on a workflow

Code notebooks provide a convenient way to document and organize a data science project from beginning to end; in otherwords, they can form the basis of a workflow.

Working on a workflow

Working on a workflow

Working on a workflow

Working on a workflow

Viewer Tab

Rather than render HTML directly to your browser, you can preview the rendered version in the Viewer tab.

What about files?

When we write code that reads from files, these need to be in places where the Quarto document can find them.

# load tidyverse
# read in abalone data
# plot abalone rings by diameter
ggplot(abalone,aes(x=diameter,y=rings)) +

Sharing your file system

The zip function provides a convenient way to turn your entire working directory into a .zip file

#zip up all of my project files


Assignment Due Date
Lab Assignment 5 Tuesday, October 10
Coding Assignment 2 Thursday, October 12
Lab Assignment 6 Tuesday, October 17
Project Proposal Thursday, October 19

Project proposal

  • What is the topic?

  • What is the background to this topic? (2-3 citations)

  • What datasets are you intending to use?

  • How would data analysis/visualization to help you understand the topic?

  • Suggest two visualizations

Activity: Brainstorming with Quarto

Put the Quarto workflow to work in brainstorming ideas for your final project!

  • Create a file system for your final project

  • Start a new Quarto document and save it to your working directory

  • Use headers to outline a data analysis process from a project you’d like to do.

  • Begin filling out the sections with some preliminary ideas about what kinds of data you’d useand

    • Use links to promising datasets in the text

    • Use code chunks with comments to draft code

Next week

  • Working with messy data

  • Exploring the tidyverse

  • Data transformations using dplyr