https://github.com/mvuorre/dataviz

A data visualization workshop
https://github.com/mvuorre/dataviz

Last synced: 2 days ago
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A data visualization workshop

• Host: GitHub
• URL: https://github.com/mvuorre/dataviz
• Owner: mvuorre
• License: cc0-1.0
• Created: 2023-10-03T08:32:05.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-11-20T11:41:21.000Z (about 1 year ago)
• Last Synced: 2025-01-20T22:57:49.780Z (8 days ago)
• Homepage:
• Size: 4.05 MB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Data visualization workshop

*What*: PhD Data visualization workshop  

*When*: 2023-11-20 @ 13.30 - 17.00  

*Where*: S8  

*Who*: Matti Vuorre (instructor), y'all  

*Why*: What makes a good graph? In this workshop, we try to sketch an answer to this question, and then use what we learned in making our own figures that intrigue, inform, and impress.

## Materials

- Slides: 

- Shared notebook: 

- Grab a copy of the code and play with it in the cloud: https://posit.cloud/content/7071123

- Download slides from [GitHub](https://github.com/mvuorre/dataviz)

## Program

We will talk about theoretical approaches to data communication and how to apply them in practice. 

### Theory

- A round of greetings

  - Tell everyone who you are and why you are here

- Group exercise 1

  - Please form small groups (2-4) and take a few minutes each to tell other group members what kind of quantitative information you would like to display, how, and why. 

  - If possible, share existing or work-in-progress figures with your group.

- Lecture 1

  - Theories / concepts underlying effective data visualization. 

- Group exercise 2 

  - Do you find the concepts discussed useful for your own visualization goals? How? If not, why not? Discuss in your group.

### Practice

- Lecture 2

  - Introduction to the grammar of graphics and its implementation in R

- Group exercise 3

  - Work on your figures and discuss them with others in your group.

- Wrap-up

## Credits & further reading

Image sources and credits are in slide notes.

### Theory

- The Royal Statistical Society's "Best Practices in Data Visualization" guide: [Repository](https://github.com/royal-statistical-society/datavisguide) & [website](https://royal-statistical-society.github.io/datavisguide/).

- European Commission's [data visualization guide](https://data.europa.eu/apps/data-visualisation-guide/). 

- [From data to viz](https://www.data-to-viz.com/), "a classification of chart types based on input data format".

### Practice

- https://ggplot2.tidyverse.org/

- https://r4ds.hadley.nz/data-visualize.html (Chapters 1 & 9-11)

- Cédric Scherer's ggplot2-focused workshop: [Repository](https://github.com/rstudio-conf-2022/ggplot2-graphic-design) & [website](https://rstudio-conf-2022.github.io/ggplot2-graphic-design/).

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