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

Plotting bivariate distributions and regressions

The easiest way to see how two variables are related is by creating a scatter plot, especially with few samples. We can assign one of the variables to the x axis and the other to the y axis. However, when the number of samples is high, the points overlap, making it hard to know the point density in different plot regions. If the number of points is not too high, adding some transparency can alleviate this problem. You can quickly achieve this in Plots and StatsPlots by setting the alpha keyword argument of the scatter function to a value that’s lower than one (fully opaque) but greater than 0 (fully transparent). Nevertheless, a better way to solve this problem is to create a plot that approximates the joint probability distribution of the two variables. The most common ones are the bi-dimensional versions of histograms and density plots.

We can create a bi-dimensional histogram using the histogram2d function from Plots and...