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Interactive Visualization and Plotting with Julia

You're reading from   Interactive Visualization and Plotting with Julia Create impressive data visualizations through Julia packages such as Plots, Makie, Gadfly, and more

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Product type Paperback
Published in Aug 2022
Publisher Packt
ISBN-13 9781801810517
Length 392 pages
Edition 1st Edition
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Author (1):
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Diego Javier Zea Diego Javier Zea
Author Profile Icon Diego Javier Zea
Diego Javier Zea
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Table of Contents (19) Chapters Close

Preface 1. Section 1 – Getting Started
2. Chapter 1: An Introduction to Julia for Data Visualization and Analysis FREE CHAPTER 3. Chapter 2: The Julia Plotting Ecosystem 4. Chapter 3: Getting Interactive Plots with Julia 5. Chapter 4: Creating Animations 6. Section 2 – Advanced Plot Types
7. Chapter 5: Introducing the Grammar of Graphics 8. Chapter 6: Creating Statistical Plots 9. Chapter 7: Visualizing Graphs 10. Chapter 8: Visualizing Geographically Distributed Data 11. Chapter 9: Plotting Biological Data 12. Section 3 – Mastering Plot Customization
13. Chapter 10: The Anatomy of a Plot 14. Chapter 11: Defining Plot Layouts to Create Figure Panels 15. Chapter 12: Customizing Plot Attributes – Axes, Legends, and Colors 16. Chapter 13: Designing Plot Themes 17. Chapter 14: Designing Your Own Plots – Plot Recipes 18. Other Books You May Enjoy

Drawing regression lines

Drawing regression lines is an excellent way to visualize the association between two non-independent variables. Gadfly and AlgebraOfGraphics offer easy ways to create such plots. There are two kinds of regression lines we can make with these packages. The first is a classical linear regression to visualize the linear association between two variables. The second is a local regression, usually using a locally estimated scatterplot smoothing (LOESS) method. The LOESS method performs polynomial regressions on subsets of the data points. Therefore, they have two important parameters: the bandwidth or smoothing parameter and the degree of the polynomials. The bandwidth determines the sizes of the data subsets. Therefore, small bandwidth values create regressions that are sensitive to the local variations.

We can create these plots in Gadfly using Stat.smooth. This function takes a model keyword argument to select between a linear model, passing the :lm symbol...

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