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Interactive Visualization and Plotting with Julia

Interactive Visualization and Plotting with Julia

By : Diego Javier Zea
3.9 (10)
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Interactive Visualization and Plotting with Julia

Interactive Visualization and Plotting with Julia

3.9 (10)
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)
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1
Section 1 – Getting Started
6
Section 2 – Advanced Plot Types
12
Section 3 – Mastering Plot Customization

Analyzing graphs

In the previous sections, we learned how to work with Graphs and visualize them using Julia. In this section, we will briefly mention a few analysis tools that are available in the Graphs package and discuss some visualization opportunities.

Graphs offer functions for assessing graph connectivity. Among those functions, we can find the connected_components function for undirected graphs and the strongly_connected_components and weakly_connected_components functions for directed graphs. These functions return a vector of vectors containing the indices of the vertices that belong to a given component. If you want to visualize one of the connected components, you can use the induced_subgraph function to get the induced subgraph by the vertices in the corresponding component.

The Graphs package also offers an articulation function, which returns a vector with all the cut vertices in a connected graph. As we have a list of vertex indices as output, it is easy to...

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