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

Chapter 6: Creating Statistical Plots

Creating statistical plots is a standard data analysis task, especially during data exploration. It is an essential part of data visualization, helping make meaningful visual representations for our data. It is crucial, as in many cases, that we learn more from our data by looking at it than by exclusively analyzing its summary statistics. Anscombe’s quartet is an example of this as its four datasets show similar descriptive statistics but different distributions we can see after plotting them. Figure 6.1 shows these datasets with a Pearson correlation coefficient, r, of 0.82, but various joint distributions.

Also, we can rely on statistical plots to effectively communicate our findings to the world – a common data visualization task. Some visualizations, such as histograms, are easily understood by people from many backgrounds. Others, such as boxplots, are better suited for a statistically versed audience:

...