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

Plots' backends

The Plots package interfaces many of the plotting packages we described at the beginning of this chapter. Let's list Plots' backends while highlighting their strengths and weaknesses:

  • GR: It is fast and supports most of the Plots features.
  • Plotly: It creates interactive plots. It is always available.
  • PlotlyJS: Like Plotly, but you need to install it. Also, it offers more output formats than the Plotly backend. You can update IJulia inline plots from any cell.
  • PyPlot: It uses Python, which can lead to set-up and speed issues. It is a mature library that supports most of the Plots features.
  • PGFPlotsX: Its dependency on LaTeX makes it hard to install, but it produces nice publication-quality plots. It supports most of the Plots features.
  • UnicodePlots: It supports only a few Plots features. It is fast and allows for plotting in the REPL. You get better-looking bar and box plots when you use UnicodePlots outside Plots.
  • InspectDR...