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Introduction to R for Business Intelligence
Introduction to R for Business Intelligence

Introduction to R for Business Intelligence: Profit optimization using data mining, data analysis, and Business Intelligence

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Introduction to R for Business Intelligence

Chapter 2. Data Cleaning

Clean data is an essential element of good data analysis. Poor data quality is a primary reason for problems in business intelligence analysis. Data cleaning is the process of transforming raw data into usable data. Cleaning data, checking quality, and standardizing data types accounts for the majority of an analytic project schedule.

Anthony Goldbloom, the CEO of Kaggle, said: Eighty percent of data science is cleaning data and the other twenty percent is complaining about cleaning data (personal communication, February 14, 2016).

This chapter covers four key topics using some of the newer packages available within the R environment:

  • Summarizing your data for inspection
  • Finding and fixing flawed data
  • Converting inputs to data types suitable for analysis
  • Adapting string variables to a standard

Business analysts spend a lot of time cleaning data before moving to the analysis phase. Data cleaning does not have to be a dreaded task. This chapter provides business...

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...

Finding and fixing flawed data

The summarize step revealed some possible flaws in the data. Mind you, the bike sharing data is in good shape compared to many datasets that you will encounter as a business analyst. Your next step in the SFCA approach is to fix any flaws that could impact your analysis. First, you have to find the flaws before you can fix them.

Finding flaws in datasets

There is no single best way to find flaws in data. This activity requires art mixed with some computational methods. The approach presented in this section shares some methods, but they only represent a few of the many possible ways of finding flawed data.

Tip

R tip: Keep an open mind and strive to become a lifelong learner of business analytics. The methods change and adapt continually. The best business analysts have a skillset that goes beyond great coding. Throughout this book, the authors will share their favorite picks from videos, blogs, and books relevant to the chapters.

One tip for business analysts...

Converting inputs to data types suitable for analysis

Another aspect of data cleaning is investigating the input data and shaping it to conform to your analysis design needs. The third step of the SFCA approach is convert. Specifically, converting the data from one data type to another.

Converting between data types

The R environment stores data in one of the several data types. You will experience five different data types in the Bike Sharing Analysis Project:

Data type

Explanation

Example

Numeric

A number having a decimal value

9.84

Integer

A number without decimals

3

Character

A string variable

"www.google.com"

Factor

A categorical variable that has a character and integer representation

"ad campaign", "blog": 1,2

Date

A date or time in various formats

2016-02-16 18:56:57 EST

Each data type has different properties consistent with definitions used in disciplines such as computer science, mathematics, and statistics. Some R...

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...

Summary

Congratulations! You have learned a lot of topics in this chapter. Data cleaning is a very important part of business intelligence analysis. In this chapter, you learned that cleaning data is a four-step process that can be remembered by SFCA (summarize-fix-convert-adapt).

Summarizing the data gives you a big-picture overview and provides a perspective on the data. This shapes your data cleaning strategies. Fixing flawed data can be tedious, but there are common practices to use. Converting data is important to get it in the right data type to support your analysis. Dates and times can be difficult, but tools help with this. Adapting your data to a standard is the key to setting a foundation for a successful data analysis. Standards may be given or you may design one.

Continuing to learn is also important as the packages and methods change frequently. Lastly, the topic of data cleaning is full of interesting ideas that you may find helpful. A recommended resource is An Introduction...

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Key benefits

  • Use this easy-to-follow guide to leverage the power of R analytics and make your business data more insightful.
  • This highly practical guide teaches you how to develop dashboards that help you make informed decisions using R.
  • Learn the A to Z of working with data for Business Intelligence with the help of this comprehensive guide.

Description

Explore the world of Business Intelligence through the eyes of an analyst working in a successful and growing company. Learn R through use cases supporting different functions within that company. This book provides data-driven and analytically focused approaches to help you answer questions in operations, marketing, and finance. In Part 1, you will learn about extracting data from different sources, cleaning that data, and exploring its structure. In Part 2, you will explore predictive models and cluster analysis for Business Intelligence and analyze financial times series. Finally, in Part 3, you will learn to communicate results with sharp visualizations and interactive, web-based dashboards. After completing the use cases, you will be able to work with business data in the R programming environment and realize how data science helps make informed decisions and develops business strategy. Along the way, you will find helpful tips about R and Business Intelligence.

Who is this book for?

This book is for data analysts, business analysts, data science professionals or anyone who wants to learn analytic approaches to business problems. Basic familiarity with R is expected.

What you will learn

  • Extract, clean, and transform data
  • Validate the quality of the data and variables in datasets
  • Learn exploratory data analysis
  • Build regression models
  • Implement popular data-mining algorithms
  • Visualize results using popular graphs
  • Publish the results as a dashboard through Interactive Web Application frameworks
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Table of Contents

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