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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
Tools
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Toc

Table of Contents (18) Chapters Close

Title Page
Packt Upsell
Contributors
Preface
1. Introduction to Jupyter 2. Jupyter Python Scripting FREE CHAPTER 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? 1. Other Books You May Enjoy Index

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:

data(iris) 
summary(iris) 
plot(iris) 

 

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 that 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 page:

Note

A feature of Jupyter is to place larger plots, such as this, into a viewport that only shows a part of the image. I was able to drag the image out of the viewport window in its...

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