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Practical Business Intelligence

You're reading from   Practical Business Intelligence Optimize Business Intelligence for Efficient Data Analysis

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Product type Paperback
Published in Dec 2016
Publisher Packt
ISBN-13 9781785885433
Length 352 pages
Edition 1st Edition
Languages
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Author (1):
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Ahmed Sherif Ahmed Sherif
Author Profile Icon Ahmed Sherif
Ahmed Sherif
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Table of Contents (16) Chapters Close

Practical Business Intelligence
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Introduction to Practical Business Intelligence FREE CHAPTER 2. Web Scraping 3. Analysis with Excel and Creating Interactive Maps and Charts with Power BI 4. Creating Bar Charts with D3.js 5. Forecasting with R 6. Creating Histograms and Normal Distribution Plots with Python 7. Creating a Sales Dashboard with Tableau 8. Creating an Inventory Dashboard with QlikSense 9. Data Analysis with Microsoft SQL Server

Exporting R to Microsoft Power BI


R is a great standalone tool used to deliver reports to users, but it is also one of the few languages that are incorporated into several other BI tools, such as Microsoft Power BI. When we last explored Power BI in Chapter 3, Analysis with Excel and Creating Interactive Maps and Charts with Power BI, we focused on visualizing data pulled in from a Microsoft SQL Server query. You may then ask why it would be necessary to pull in data through R when it can be done directly through Power BI. As we saw earlier when we were forecasting data, R has the ability to generate data points based on different libraries applied to the original dataset. This generated data can then be easily merged back into the original dataset. It is this merged dataset that brings added value to a visualization inside of Power BI.

Merging new columns to dataframes in R

The original dataframe used in this chapter was called SQL_Query_1 and contained Discount Codes by Week. We could choose...

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