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Learning Jupyter 5

You're reading from   Learning Jupyter 5 Explore interactive computing using Python, Java, JavaScript, R, Julia, and JupyterLab

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Product type Paperback
Published in Aug 2018
Publisher
ISBN-13 9781789137408
Length 282 pages
Edition 2nd 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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Table of Contents (14) Chapters Close

Preface 1. Introduction to Jupyter FREE CHAPTER 2. Jupyter Python Scripting 3. Jupyter R Scripting 4. Jupyter Julia Scripting 5. Jupyter Java Coding 6. Jupyter JavaScript Coding 7. Jupyter Scala 8. Jupyter and Big Data 9. Interactive Widgets 10. Sharing and Converting Jupyter Notebooks 11. Multiuser Jupyter Notebooks 12. What's Next? 13. Other Books You May Enjoy

R cluster analysis

In this example, we will use R's cluster analysis functions to determine the clustering in the wheat dataset from https://uci.edu/.

The R script we want to use in Jupyter is as follows:

# load the wheat data set from uci.edu 
wheat <- read.csv("http://archive.ics.uci.edu/ml/machine-learning-databases/00236/seeds_dataset.txt", sep="\t") 
 
# define useful column names 
colnames(wheat) <-c("area", "perimeter", "compactness", "length", "width", "asymmetry", "groove", "undefined") 
 
# exclude incomplete cases from the data 
wheat <- wheat[complete.cases(wheat),] 
 
# calculate the clusters 
set.seed(117) #to make reproducible results 
fit <- kmeans(wheat, 5) 
fit 

Once entered into a Notebook, we will have something such as this:

The resulting,...

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