Book Image

Interactive Visualization and Plotting with Julia

By : Diego Javier Zea
Book Image

Interactive Visualization and Plotting with Julia

By: Diego Javier Zea

Overview of this book

The Julia programming language offers a fresh perspective into the data visualization field. Interactive Visualization and Plotting with Julia begins by introducing the Julia language and the Plots package. The book then gives a quick overview of the Julia plotting ecosystem to help you choose the best library for your task. In particular, you will discover the many ways to create interactive visualizations with its packages. You’ll also leverage Pluto notebooks to gain interactivity and use them intensively through this book. You’ll find out how to create animations, a handy skill for communication and teaching. Then, the book shows how to solve data analysis problems using DataFrames and various plotting packages based on the grammar of graphics. Furthermore, you’ll discover how to create the most common statistical plots for data exploration. Also, you’ll learn to visualize geographically distributed data, graphs and networks, and biological data. Lastly, this book will go deeper into plot customizations with Plots, Makie, and Gadfly—focusing on the former—teaching you to create plot themes, arrange multiple plots into a single figure, and build new plot types. By the end of this Julia book, you’ll be able to create interactive and publication-quality static plots for data analysis and exploration tasks using Julia.
Table of Contents (19 chapters)
1
Section 1 – Getting Started
6
Section 2 – Advanced Plot Types
12
Section 3 – Mastering Plot Customization

Summary

In this chapter, we learned how to work with graphs in Julia using the Graphs package. We also explored the MetaGraphs package for storing graph, edge, and vertex metadata. Then, we learned how to visualize those graphs using the Julia ecosystem’s three main packages: GraphPlot, GraphRecipes, and GraphMakie. The three packages, while similar, have different strengths. We learned about the main attributes available to customize their produced visualizations, and we saw examples of their strong points. Then, we saw how to change the graph layout of our visualization, and we briefly mentioned the analysis tools available in the Graphs package. With the knowledge you’ve acquired in this chapter, you can start visualizing and analyzing your graphs and networks using the Julia language.

In the next chapter, we will briefly introduce the tools in the Julia ecosystem for visualizing geographically distributed data.