#### 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.
Title Page
Packt Upsell
Contributors
Preface
Free Chapter
Installation and Setting up the Environment
Accessing and Retrieving Data
Working with Widgets
Jupyter Dashboards
Multiuser Jupyter
Jupyter Security
Jupyter Labs
Index

## 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.