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

Creating marginal plots

In the previous sections, we learned how to visualize univariate and bivariate distributions using StatsPlots. In this section, we will learn how to create a plot that simultaneously displays the joint and marginal distributions for two variables. Usually, we can achieve this by showing three plots in the same figure. The central plot shows the bivariate distribution, for example, using a bi-dimensional histogram. Then, two small plots at the top and right-hand sides of the central plot show the univariate distributions for the x and y variables, respectively. We can take advantage of the layout capabilities of the Plots package to create such visualizations. We briefly introduced those capabilities in the Simple layouts section of Chapter 1, An Introduction to Julia for Data Visualization and Analysis, and we will expand on that in Chapter 11, Defining Plot Layouts to Create Figure Panels. However, StatsPlots also offers a series of recipes for marginal plots...