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Jupyter Cookbook

You're reading from   Jupyter Cookbook Over 75 recipes to perform interactive computing across Python, R, Scala, Spark, JavaScript, and more

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Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788839440
Length 238 pages
Edition 1st Edition
Languages
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Author (1):
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Dan Toomey Dan Toomey
Author Profile Icon Dan Toomey
Dan Toomey
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Toc

Table of Contents (12) Chapters Close

Preface 1. Installation and Setting up the Environment 2. Adding an Engine FREE CHAPTER 3. Accessing and Retrieving Data 4. Visualizing Your Analytics 5. Working with Widgets 6. Jupyter Dashboards 7. Sharing Your Code 8. Multiuser Jupyter 9. Interacting with Big Data 10. Jupyter Security 11. Jupyter Labs

Producing a Scatter plot matrix using R


A Scatter plot matrix is a useful device to display a miniature Scatter plot of every variable in your dataset against every other variable. The resulting display gives you a quick scan to determine variables that may be related.

How to do it...

Use this script:

# load the iris dataset
data <- read.csv("http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data")

#Let us also clean up the data so as to be more readable
colnames(data) <- c("sepal_length", "sepal_width", "petal_length", "petal_width", "species")

pairs(data)

This produces this graphic:

The pairs graphic shows petal width and petal length as related (fairly good straight lines of the plot points), and little relationship between sepal length and sepal width.

How it works...

The pairs function draws upon the underlying plot to walk through all pairs of data points in the dataset and produce a Scatter plot. I have used this many times to get a quick handle on which variables...

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