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

Product type Paperback

Published in Aug 2022

Publisher Packt

ISBN-13 9781801810517

Length 392 pages

Edition 1st Edition

Languages

Julia

Tools

Jupyter

Concepts

Data Visualization

Author (1):

Diego Javier Zea

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Table of Contents (19) Chapters

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 libraries

Unlike languages such as R, Julia doesn't have a built-in plotting solution. This has motivated the Julia community to create various packages for plotting and data visualization. Some of these packages are wrappers around plotting engines from other languages, while others are pure Julia solutions. This section will briefly describe some of the multiple plotting solutions that the Julia package ecosystem offers. It will center on plotting packages with high-level interfaces that allow for data visualization. Therefore, the section will not describe packages defining plotting primitives. Also, it will not list packages derived from the listed ones.

For each package, we will show you the syntax to build a simple line plot with default attributes so that you can get a feeling for it. Also, we will show the created output for some packages. For that, we are going to use the following input data: