#### Overview of this book

Jupyter Notebook is a web-based environment that enables interactive computing in notebook documents. It allows you to create and share documents that contain live code, equations, visualizations, and explanatory text. The Jupyter Notebook system is extensively used in domains such as data cleaning and transformation, numerical simulation, statistical modeling, machine learning, and much more. This book starts with a detailed overview of the Jupyter Notebook system and its installation in different environments. Next we’ll help you will learn to integrate Jupyter system with different programming languages such as R, Python, JavaScript, and Julia and explore the various versions and packages that are compatible with the Notebook system. Moving ahead, you master interactive widgets, namespaces, and working with Jupyter in a multiuser mode. Towards the end, you will use Jupyter with a big data set and will apply all the functionalities learned throughout the book.
Learning Jupyter
Credits
www.PacktPub.com
Preface
Free Chapter
Introduction to Jupyter
Jupyter Python Scripting
Jupyter R Scripting
Jupyter Julia Scripting
Jupyter JavaScript Coding
Interactive Widgets
Sharing and Converting Jupyter Notebooks
Multiuser Jupyter Notebooks
Jupyter Scala
Jupyter and Big Data

## R cluster analysis

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

The R script we want to use in Jupyter is the following:

```# load the wheat data set from uci.edu
# 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
fit <- kmeans(wheat, 5)
fit
```

Once entered into a notebook, we have something like this:

The resulting generated cluster information is K-means clustering with five clusters of sizes 29, 57, 65, 15, and 32. (Note that, since I had not set the seed value for random number to use, your results may vary.)

Cluster means are:

`      area perimeter compactness   length    width asymmetry  ...`