Book Image

Practical Business Intelligence

By : Ahmed Sherif
Book Image

Practical Business Intelligence

By: Ahmed Sherif

Overview of this book

Business Intelligence (BI) is at the crux of revolutionizing enterprise. Everyone wants to minimize losses and maximize profits. Thanks to Big Data and improved methodologies to analyze data, Data Analysts and Data Scientists are increasingly using data to make informed decisions. Just knowing how to analyze data is not enough, you need to start thinking how to use data as a business asset and then perform the right analysis to build an insightful BI solution. Efficient BI strives to achieve the automation of data for ease of reporting and analysis. Through this book, you will develop the ability to think along the right lines and use more than one tool to perform analysis depending on the needs of your business. We start off by preparing you for data analytics. We then move on to teach you a range of techniques to fetch important information from various databases, which can be used to optimize your business. The book aims to provide a full end-to-end solution for an environment setup that can help you make informed business decisions and deliver efficient and automated BI solutions to any company. It is a complete guide for implementing Business intelligence with the help of the most powerful tools like D3.js, R, Tableau, Qlikview and Python that are available on the market.
Table of Contents (16 chapters)
Practical Business Intelligence
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Creating graphs in R


Once a dataframe has been set up with the necessary column, we can begin to graph the data and explore visualization options.

Creating simple charts with plot() in R

The most basic graph can be generated with the plot() function using the following script:

plot(SQL_Query_1, main = 'Discount Code by Week') 

The output of the script can be seen here:

While the plot displays the discount by week, it is difficult to identify the relationship week-to-week without being able to connect the dots.

The following script will connect the dots between each sequential point:

plot(SQL_Query_1, main = 'Discount Code by Week', type="o") 

The output of the script can be seen in the following screenshot:

R has the ability to allow developers to combine multiple charts into a larger overall graph by using the par() function. If we choose to display two charts one above the other, we would create a matrix of two rows and one column by running the following script:

par(mfrow=c(2,1...