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

Comparing between groups

Groups and clusters are usually defined using categorical or ordinal variables. For example, Species is a categorical variable grouping observation according to the species name in the Iris dataset. The assignments and cutree functions of Clustering return a vector with integers for encoding a categorical or ordinal variable. We consider that variable to be ordinal if the order of clusters has a meaning, for example, if they are related to the number of elements in the cluster. However, we will always visualize the assignation vector as a categorical variable when discriminating clusters.

We can use the position, color hue, and shape aesthetics to visualize and discriminate groups when using packages based on the Grammar of Graphics. Facets are also an excellent way to compare the same plot between groups. In Chapter 5, Introducing the Grammar of Graphics, we learned how to map categorical variables to those aesthetics and create facet plots using Gadfly...