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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

Visualizing with R


There are severalvisualization mechanisms available in R.

  • Produce a R Scatter plot

In this example, we produce a scatter plot using the standard R plot() function. Built into the plot function, we can chart the relationship between the x and y values as well.

How to do it...

We can use this script:

# load the iris dataset
data <- read.csv("http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data")

#Let us also clean up the data so as to be more readable
colnames(data) <- c("sepal_length", "sepal_width", "petal_length", "petal_width", "species")

# make sure the data is as expected
summary(data)

Produce the Scatter plot:

plot(data$sepal_length, data$petal_length)

And produce the following visualizations of the relationships between the values. This plot is a stepwise look at how changing one value appears, to see the effect on the other:

plot(data$sepal_length, data$petal_length, type="s")

The output is as follows:

And we look at a histogram of the same data...

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