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caalo merged 3 commits into from
Nov 14, 2024
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Update 05-data-visualization.Rmd #37

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3269753
Update 05-data-visualization.Rmd
caalo Nov 13, 2024
150db43
Merge branch 'main' into S4
caalo Nov 14, 2024
037d163
Update config_automation.yml
caalo Nov 14, 2024
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6 changes: 5 additions & 1 deletion 05-data-visualization.Rmd
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Expand Up @@ -32,7 +32,7 @@ Categorical (between 1 categorical and 1 continuous variable)

- Violin plots

[![Image source: Seaborn's overview of plotting functions](https://seaborn.pydata.org/_images/function_overview_8_0.png)](https://seaborn.pydata.org/tutorial/function_overview.html)
[![Image source: Seaborn\'s overview of plotting functions](https://seaborn.pydata.org/_images/function_overview_8_0.png)](https://seaborn.pydata.org/tutorial/function_overview.html)

Why do we focus on these common plots? Our eyes are better at distinguishing certain visual features more than others. All of these plots are focused on their position to depict data, which gives us the most effective visual scale.

Expand Down Expand Up @@ -180,6 +180,10 @@ plot = sns.displot(data=metadata, x="Age", hue="Sex", multiple="dodge", palette=

```

## Other resources

We recommend checking out the workshop [Better Plots](https://hutchdatascience.org/better_plots/), which showcase examples of how to clean up your plots for clearer communication.

## Exercises

Exercise for week 5 can be found [here](https://colab.research.google.com/drive/17iwr8NwLLrmzRj4a6zRZucETXpPkmDNR?usp=sharing).
2 changes: 1 addition & 1 deletion config_automation.yml
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Expand Up @@ -27,4 +27,4 @@ make-book-txt: yes

# What docker image should be used for rendering?
# The default is jhudsl/base_ottr:main
rendering-docker-image: 'jhudsl/base_ottr:main'
rendering-docker-image: 'jhudsl/ottr_python:main'
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