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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

Summarizing your data for inspection

We live in an information age. Large and accessible datasets are being widely used in business intelligence and decision making. When you begin the data cleaning process, you will need a way of summarizing your data. You will need to understand its content and structure at the beginning of the process. Large datasets require ways of summarizing the data for inspection. Fortunately, the R language provides them for you! You will learn data cleaning through a use case called the Bike Sharing Analysis Project.

Note

Use case: Bike Sharing Analysis Project

Imagine you are a business analyst on the Bike Sharing Analysis Project. New data has just arrived for analysis. Unlike the dataset you saw in Chapter 1, Extract, Transform, and Load, which was pre-processed for a Kaggle competition, this data you received has arrived in raw form. The book's website at http://jgendron.github.io/com.packtpub.intro.r.bi/ contains the Ch2_raw_bikeshare_data.csv data...

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