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Jupyter Cookbook

You're reading from   Jupyter Cookbook Over 75 recipes to perform interactive computing across Python, R, Scala, Spark, JavaScript, and more

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Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788839440
Length 238 pages
Edition 1st 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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Toc

Table of Contents (12) Chapters Close

Preface 1. Installation and Setting up the Environment 2. Adding an Engine FREE CHAPTER 3. Accessing and Retrieving Data 4. Visualizing Your Analytics 5. Working with Widgets 6. Jupyter Dashboards 7. Sharing Your Code 8. Multiuser Jupyter 9. Interacting with Big Data 10. Jupyter Security 11. Jupyter Labs

Producing a bar chart using R


There are several bar chart tools available from R. We will use the barplot function in this example.

How to do it...

We can use this script:

# we are using the haireyecolor data from the MASS library
library(MASS)
summary(HairEyeColor)

Excellent p-value, so we should have good data to work with!

# display the data
HairEyeColor

I hadn't thought about sex being a determinant. We will combine all of the data into one set:

# build a table of the information
counts <- table(HairEyeColor)
# produce the bar chart
barplot(counts)

That produces this result:

Interesting, that there are many cases with high coincidence (the 34s, 50s, and 64s) and there are many with low numbers (most under 10).

How it works...

We are displaying the relationship, if any, between eye color and hair color. The data is organized in that fashion already.

The barplot function takes the R table and maps out the appropriate bar chart.

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