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

Basic R in Jupyter

Start a new R notebook and call it R Basics. We can enter a small script just so we can see how the steps progress for an R script. Enter the following into separate cells of your notebook:

myString <- "Hello, World!"
print (myString)

You will end up with a starting screen that looks like this:

We should note the aspects of the R notebook view:

  • We have the R logo in the upper-right corner. You will see this logo running in other R installations.

  • There is also the peculiar R O just below the R icon. The unfilled circle indicates that the kernel is at rest, and the filled circle indicates the kernel is working.

  • The rest of the menu items are the same as we have seen before.

This is a very simple script-set a variable in one cell then print out its value in another cell. Once executed (Cell | Run All), you will see your results:

So, just as if you ran the script in an R interpreter, you get your output (with the numerical prefix). Jupyter has counted the statements so we...