Book Image

Jupyter Cookbook

By : Dan Toomey
Book Image

Jupyter Cookbook

By: Dan Toomey

Overview of this book

Jupyter has garnered a strong interest in the data science community of late, as it makes common data processing and analysis tasks much simpler. This book is for data science professionals who want to master various tasks related to Jupyter to create efficient, easy-to-share, scientific applications. The book starts with recipes on installing and running the Jupyter Notebook system on various platforms and configuring the various packages that can be used with it. You will then see how you can implement different programming languages and frameworks, such as Python, R, Julia, JavaScript, Scala, and Spark on your Jupyter Notebook. This book contains intuitive recipes on building interactive widgets to manipulate and visualize data in real time, sharing your code, creating a multi-user environment, and organizing your notebook. You will then get hands-on experience with Jupyter Labs, microservices, and deploying them on the web. By the end of this book, you will have taken your knowledge of Jupyter to the next level to perform all key tasks associated with it.
Table of Contents (17 chapters)
Title Page
Copyright and Credits
Packt Upsell

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("")

#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

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...