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

Creating choropleth maps

Choropleth maps are pretty standard in diverse types of media. Usually, they display established geographical entities, such as countries and provinces, using polygons. Following this, we can indicate the value of a variable for a region using the color inside its polygon. Usually, we will use the color or hue to identify the values of a categorical variable. For qualitative and ordinal variables, we use color luminance and saturation instead.

We can read the polygons that are needed to create choropleth maps from a vector file in one Geographic Information System (GIS) file format. The Julia language ecosystem offers the pure Julia Shapefile and GeoJSON packages to read files in the homonymous GIS formats. To read other GIS formats, both vector and raster images, you can use the GDAL or ArchGDAL packages. These last packages are wrappers to the Geospatial Data Abstraction Library (GDAL), which can read and write multiple GIS formats.

The Shapefile and...