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Introduction to R for Business Intelligence

You're reading from  Introduction to R for Business Intelligence

Product type Book
Published in Aug 2016
Publisher Packt
ISBN-13 9781785280252
Pages 228 pages
Edition 1st Edition
Languages
Author (1):
Jay Gendron Jay Gendron
Profile icon Jay Gendron
Toc

Table of Contents (19) Chapters close

Introduction to R for Business Intelligence
Credits
About the Author
Acknowledgement
About the Reviewers
www.PacktPub.com
Preface
1. Extract, Transform, and Load 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 References
Other Helpful R Functions R Packages Used in the Book
R Code for Supporting Market Segment Business Case Calculations

Analyzing two variables together


If your dataset has two variables, you have bivariate data. When examining bivariate data, you want to explore possible relationships. This is where good questioning pays off. You can use the following four questions to guide your exploratory data analysis:

  • What does the data look like?

  • Is there any relationship between two variables?

  • Is there any correlation between the two?

  • Is the correlation significant?

Four words summarize these questions: Look-Relationships-Correlation-Significance. You will explore pairs of variables from the marketing dataset to investigate these questions using both tabular and graphical exploration methods.

What does the data look like?

This is something you know very well at this point. Summarize the data in your console as shown in the following:

summary(marketing) 

The following is the output:

google_adwords      facebook        twitter       marketing_total 
 Min.   : 23.65   Min.   : 8.00   Min.   :  5.89   Min.   : 53.65  
 1st...
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