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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

Creating layouts with Plots

We already saw a little introduction to layouts using Plots in the Simple layouts section of Chapter 1, An Introduction to Julia for Data Visualization and Analysis. In particular, we have seen that Plots automatically composes a figure when we pass multiple plots into the plot function. We have learned how to gain more control over the final subplot placement using the layout attribute of the plot function. To help us arrange the subplots, we saw how to create a grid layout using the grid function. We also learned to use the grid function’s widths and heights keyword arguments. These arguments define the relative proportion of the final figure assigned to each cell of the grid layout. Finally, in that section, we saw that we can use the link keyword argument of plot to match the axis across subplots.

Then, we introduced more complex layout topics in The anatomy of a Plots plot section of Chapter 10, The Anatomy of a Plot. Specifically, we introduced...

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