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Julia for Data Science

You're reading from  Julia for Data Science

Product type Book
Published in Sep 2016
Publisher Packt
ISBN-13 9781785289699
Pages 346 pages
Edition 1st Edition
Languages
Author (1):
Anshul Joshi Anshul Joshi
Profile icon Anshul Joshi
Toc

Table of Contents (17) Chapters close

Julia for Data Science
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
1. The Groundwork – Julia's Environment 2. Data Munging 3. Data Exploration 4. Deep Dive into Inferential Statistics 5. Making Sense of Data Using Visualization 6. Supervised Machine Learning 7. Unsupervised Machine Learning 8. Creating Ensemble Models 9. Time Series 10. Collaborative Filtering and Recommendation System 11. Introduction to Deep Learning

Visualizing using Vega


Vega is a beautiful visualization library provided by John Myles White. It is available as a registered Julia package, so it can be installed easily.

It is built on top of D3.js and uses JSON to create beautiful visualizations. It requires an Internet connection whenever we need to generate graphs as it doesn't store local copies of the JavaScript libraries needed.

Installation

To install Vega, use the following commands:

Pkg.add("Vega")
using Vega

Examples

Let's walk through various visualizations using Vega.

Scatterplot

Following are the arguments of a scatterplot:

  •  x and y: AbstractVector

  • Group: AbstractVector

Scatterplots are used to determine the correlation between two variables, that is, how one is affected by the other:

scatterplot(x=rand(100), y=rand(100))

We can now move on to building a complex scatterplot:

This will generate the following scatterplot. We can clearly see two clusters generated by Vega. These are d1 and d2:

In this particular example, we grouped...

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