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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

Web scraping with R


When we first open up RStudio, the first thing that we see is four main quadrants. These four quadrants represent our coding input as well as coding output, as shown in the following screenshot:

Let's now set up a new file, which we will use to get our web scraper up and running, by going to File | New File | R Script, as seen in the following screenshot:

We now have a blank R file to begin our coding. The next step is identifying the website data that will be the source of our web scraping exercise.

The following link (https://github.com/asherif844/PracticalBusinessIntelligence/wiki/AdventureWorks---Weekly-Data-by-Discount) within GitHub has a useful table that we can scrape from to incorporate into our database.

WeekInYear

DiscountCode

01

38

02

14

03

4

04

16

05

10

The full data in the table can also be seen in the following screenshot:

In order to scrape this Wikipedia table from R, we will need to install a couple of libraries within our R framework...

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