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Interactive Visualization and Plotting with Julia

You're reading from   Interactive Visualization and Plotting with Julia Create impressive data visualizations through Julia packages such as Plots, Makie, Gadfly, and more

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Product type Paperback
Published in Aug 2022
Publisher Packt
ISBN-13 9781801810517
Length 392 pages
Edition 1st Edition
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Author (1):
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Diego Javier Zea Diego Javier Zea
Author Profile Icon Diego Javier Zea
Diego Javier Zea
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Table of Contents (19) Chapters Close

Preface 1. Section 1 – Getting Started
2. Chapter 1: An Introduction to Julia for Data Visualization and Analysis FREE CHAPTER 3. Chapter 2: The Julia Plotting Ecosystem 4. Chapter 3: Getting Interactive Plots with Julia 5. Chapter 4: Creating Animations 6. Section 2 – Advanced Plot Types
7. Chapter 5: Introducing the Grammar of Graphics 8. Chapter 6: Creating Statistical Plots 9. Chapter 7: Visualizing Graphs 10. Chapter 8: Visualizing Geographically Distributed Data 11. Chapter 9: Plotting Biological Data 12. Section 3 – Mastering Plot Customization
13. Chapter 10: The Anatomy of a Plot 14. Chapter 11: Defining Plot Layouts to Create Figure Panels 15. Chapter 12: Customizing Plot Attributes – Axes, Legends, and Colors 16. Chapter 13: Designing Plot Themes 17. Chapter 14: Designing Your Own Plots – Plot Recipes 18. Other Books You May Enjoy

Plotting bivariate distributions and regressions

The easiest way to see how two variables are related is by creating a scatter plot, especially with few samples. We can assign one of the variables to the x axis and the other to the y axis. However, when the number of samples is high, the points overlap, making it hard to know the point density in different plot regions. If the number of points is not too high, adding some transparency can alleviate this problem. You can quickly achieve this in Plots and StatsPlots by setting the alpha keyword argument of the scatter function to a value that’s lower than one (fully opaque) but greater than 0 (fully transparent). Nevertheless, a better way to solve this problem is to create a plot that approximates the joint probability distribution of the two variables. The most common ones are the bi-dimensional versions of histograms and density plots.

We can create a bi-dimensional histogram using the histogram2d function from Plots and...

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