Book Image

Learning Jupyter

By : Dan Toomey
Book Image

Learning Jupyter

By: Dan Toomey

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.
Table of Contents (16 chapters)
Learning Jupyter
About the Author
About the Reviewer

R dataset access

For this example, we will use the Iris dataset. Iris is built into R installations and is available directly. Let's just pull in the data, gather some simple statistics, and plot the data. This will show R accessing a dataset in Jupyter, using an R built-in package, as well as some available statistics (since we have R), and the interaction with R graphics.

The script we will use is as follows:


If we enter this small script into a new R notebook, we get an initial display that looks like the following:

I would expect the standard R statistical summary as output, and I know the Iris plot is pretty interesting. We can see exactly what happened in the following screenshot:

The plot continues in the following screenshot as it wouldn't fit into a single screenshot: