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Introduction to R for Business Intelligence

You're reading from   Introduction to R for Business Intelligence Profit optimization using data mining, data analysis, and Business Intelligence

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Product type Paperback
Published in Aug 2016
Publisher Packt
ISBN-13 9781785280252
Length 228 pages
Edition 1st Edition
Languages
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Author (1):
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Jay Gendron Jay Gendron
Author Profile Icon Jay Gendron
Jay Gendron
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Table of Contents (13) Chapters Close

Preface 1. Extract, Transform, and Load FREE CHAPTER 2. Data Cleaning 3. Exploratory Data Analysis 4. Linear Regression for Business 5. Data Mining with Cluster Analysis 6. Time Series Analysis 7. Visualizing the Datas Story 8. Web Dashboards with Shiny A. References
B. Other Helpful R Functions C. R Packages Used in the Book
D. R Code for Supporting Market Segment Business Case Calculations

Adapting string variables to a standard

You are almost done with the basics of data cleaning. At this point in the process, you have summarized, fixed, and converted your input data. This means that it is time for you to accomplish the fourth SFCA step, adapting your data to a standard.

The term standard has many possible meanings. It may be that an R package will set a standard for you. In other cases, you may wish to establish one. For instance, notice in the previous data view that the sources variable is a character data type. You will see that it contains the advertising source where the customer learned about bike sharing. Leaving this as a character data type seems reasonable, but R cannot group character items to summarize them in analysis.

Your implied standard is that sources should be a categorical variable. What might happen if you use the as.factor(bike$sources) function? This will convert the data, but before you do that, you should consider a couple of questions:

  • How many unique...
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