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

Working with DataFrames and tidy data

To work within the grammar of graphics, we need data. But we do not need any data; we need tidy data. Tidy data is data that’s been arranged in a tabular way, in which each row of the table represents an observation, and each column represents a variable. This layout is essential as we usually want to map data variables, and therefore columns, to geometry aesthetics. Usually, we use the DataFrame data structure to represent and store this kind of data. The DataFrames package defines this structure for the Julia language and exports many valuable functions to work with it. We are going to explore this package in this section.

DataFrames are usually stored using text files in Comma-Separated Values (CSV) format; therefore, we typically need the CSV package to load them. There is also a series of helpful datasets stored in the RDatasets and VegaDatasets packages that we will use for demonstration purposes throughout this book. Now, let&...