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Julia 1.0 Programming Complete Reference Guide

You're reading from   Julia 1.0 Programming Complete Reference Guide Discover Julia, a high-performance language for technical computing

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Product type Course
Published in May 2019
Publisher
ISBN-13 9781838822248
Length 466 pages
Edition 1st Edition
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Author (1):
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Ivo Balbaert Ivo Balbaert
Author Profile Icon Ivo Balbaert
Ivo Balbaert
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Table of Contents (18) Chapters Close

Preface 1. Installing the Julia Platform FREE CHAPTER 2. Variables, Types, and Operations 3. Functions 4. Control Flow 5. Collection Types 6. More on Types, Methods, and Modules 7. Metaprogramming in Julia 8. I/O, Networking, and Parallel Computing 9. Running External Programs 10. The Standard Library and Packages 11. Creating Our First Julia App 12. Setting Up the Wiki Game 13. Building the Wiki Game Web Crawler 14. Adding a Web UI for the Wiki Game 15. Implementing Recommender Systems with Julia 16. Machine Learning for Recommender Systems 17. Other Books You May Enjoy

Using Plots on data

Let's apply Plots to show a graph on the famous Iris flower data set, which can be found in the Rdatasets package (don't forget to first add this package). We also need the StatPlots package when visualizing DataFrames. It contains anĀ @df macro, which makes this much easier. Here is the code to draw a scatter plot:

# code in Chapter 10\plots_iris.jl 
  using PyPlots, StatPlots, RDatasets 
  iris = dataset("datasets", "iris") 
  @df iris scatter(:SepalLength, :SepalWidth, group=:Species,m=(0.5,   
[:+ :h :star7], 4), bg=RGB(1.0,1.0,1.0))

We plot the sepalwidth property against the sepallength of the flowers. In the preceding code, iris is the name of our DataFrame, which is passed as the first argument to the @df macro. We then call the scatter function to obtain the following plot:

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