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BL
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| Record Number
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861160
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| Main Entry
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Chapman, Chris
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| Title & Author
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R for marketing research and analytics /\ Chris Chapman and Elea McDonnell Feit.
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| Edition Statement
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Second edition.
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| Publication Statement
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Cham :: Springer,, 2019.
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| Series Statement
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Use R!,
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| Page. NO
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1 online resource
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| ISBN
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3030143155
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: 3030143163
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: 3030143171
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: 9783030143152
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: 9783030143169
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: 9783030143176
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9783030143152
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| Bibliographies/Indexes
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Includes bibliographical references and index.
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| Contents
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Intro; Preface; What's New in the Second Edition; Acknowledgements; Contents; Part I Basics of R; 1 Welcome to R; 1.1 What is R?; 1.2 Why R?; 1.3 Why Not R?; 1.4 When R?; 1.4.1 R Versus Python, Julia, and Others; 1.5 Which R? Base or Tidy?; 1.6 Using This Book; 1.6.1 About the Text; 1.6.2 About the Data; 1.6.3 Online Material; 1.6.4 When Things Go Wrong; 1.7 Key Points; 2 An Overview of the R Language; 2.1 Getting Started; 2.1.1 Initial Steps; 2.1.2 Starting R; 2.2 A Quick Tour of R's Capabilities; 2.3 Basics of Working with R Commands; 2.4 Basic Objects; 2.4.1 Vectors
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2.4.2 Help! A Brief Detour2.4.3 More on Vectors and Indexing; 2.4.4 aaRgh! A Digression for New Programmers; 2.4.5 Missing and Interesting Values; 2.4.6 Using R for Mathematical Computation; 2.4.7 Lists; 2.5 Data Frames; 2.6 Loading and Saving Data; 2.6.1 Image Files; 2.6.2 CSV Files; 2.7 Writing Your Own Functions*; 2.7.1 Language Structures*; 2.7.2 Anonymous Functions*; 2.8 Clean Up!; 2.9 Key Points; 2.10 Learning More*; 2.11 Exercises; 2.11.1 Preliminary Note on Exercises; 2.11.2 Exercises; Part II Fundamentals of Data Analysis; 3 Describing Data; 3.1 Simulating Data
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3.1.1 Store Data: Setting the Structure3.1.2 Store Data: Simulating Data Points; 3.2 Functions to Summarize a Variable; 3.2.1 Discrete Variables; 3.2.2 Continuous Variables; 3.3 Summarizing Data Frames; 3.3.1 summary(); 3.3.2 describe(); 3.3.3 Recommended Approach to Inspecting Data; 3.3.4 apply()*; 3.4 Single Variable Visualization; 3.4.1 Histograms; 3.4.2 Boxplots; 3.4.3 QQ Plot to Check Normality*; 3.4.4 Cumulative Distribution*; 3.4.5 Language Brief: by() and aggregate(); 3.4.6 Maps; 3.5 Key Points; 3.6 Data Sources; 3.7 Learning More*; 3.8 Exercises; 3.8.1 E-Commerce Data for Exercises
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3.8.2 Exercises4 Relationships Between Continuous Variables; 4.1 Retailer Data; 4.1.1 Simulating the Data; 4.1.2 Simulating Online and In-store Sales Data; 4.1.3 Simulating Satisfaction Survey Responses; 4.1.4 Simulating Non-response Data; 4.2 Exploring Associations Between Variables with Scatterplots; 4.2.1 Creating a Basic Scatterplot with plot(); 4.2.2 Color-Coding Points on a Scatterplot; 4.2.3 Adding a Legend to a Plot; 4.2.4 Plotting on a Log Scale; 4.3 Combining Plots in a Single Graphics Object; 4.4 Scatterplot Matrices; 4.4.1 pairs(); 4.4.2 scatterplotMatrix()
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4.5 Correlation Coefficients4.5.1 Correlation Tests; 4.5.2 Correlation Matrices; 4.5.3 Transforming Variables Before Computing Correlations; 4.5.4 Typical Marketing Data Transformations; 4.5.5 Box-Cox Transformations*; 4.6 Exploring Associations in Survey Responses; 4.6.1 jitter(); 4.6.2 polychoric()*; 4.7 Key Points; 4.8 Data Sources; 4.9 Learning More*; 4.10 Exercises; 5 Comparing Groups: Tables and Visualizations; 5.1 Simulating Consumer Segment Data; 5.1.1 Segment Data Definition; 5.1.2 Language Brief: for() Loops; 5.1.3 Language Brief: if() Blocks; 5.1.4 Final Segment Data Generation
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| Abstract
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The 2nd edition of R for Marketing Research and Analytics continues to be the best place to learn R for marketing research. This book is a complete introduction to the power of R for marketing research practitioners. The text describes statistical models from a conceptual point of view with a minimal amount of mathematics, presuming only an introductory knowledge of statistics. Hands-on chapters accelerate the learning curve by asking readers to interact with R from the beginning. Core topics include the R language, basic statistics, linear modeling, and data visualization, which is presented throughout as an integral part of analysis. Later chapters cover more advanced topics yet are intended to be approachable for all analysts. These sections examine logistic regression, customer segmentation, hierarchical linear modeling, market basket analysis, structural equation modeling, and conjoint analysis in R. The text uniquely presents Bayesian models with a minimally complex approach, demonstrating and explaining Bayesian methods alongside traditional analyses for analysis of variance, linear models, and metric and choice-based conjoint analysis. With its emphasis on data visualization, model assessment, and development of statistical intuition, this book provides guidance for any analyst looking to develop or improve skills in R for marketing applications. The 2nd edition increases the book's utility for students and instructors with the inclusion of exercises and classroom slides. At the same time, it retains all of the features that make it a vital resource for practitioners: non-mathematical exposition, examples modeled on real world marketing problems, intuitive guidance on research methods, and immediately applicable code.
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| Subject
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Marketing research-- Statistical methods.
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| Subject
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R (Computer program language)
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COMPUTERS-- Programming Languages-- General.
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| Subject
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Marketing research-- Statistical methods.
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| Subject
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R (Computer program language)
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| Dewey Classification
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005.133
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| LC Classification
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QA276.45.R3
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| Added Entry
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Feit, Elea McDonnell
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