Data Science for Sensory and Consumer Scientists is a comprehensive textbook that provides a practical guide to using data science in the field of sensory and consumer science through real-world applications. It covers key topics including data manipulation, preparation, visualization, and analysis, as well as automated reporting, machine learning, text analysis, and dashboard creation. Written by leading experts in the field, this book is an essential resource for anyone looking to master the tools and techniques of data science and apply them to the study of consumer behavior and sensory-led product development. Whether you are a seasoned professional or a student just starting out, this book is the ideal guide to using data science to drive insights and inform decision-making in the sensory and consumer sciences. Key Features: Elucidation of data scientific workflow. Introduction to reproducible research. In-depth coverage of data-scientific topics germane to sensory and consumer science. Examples based in industrial practice used throughout the book
Author(s): Worch, Thierry; Delarue, Julien; De Souza, Vanessa Rios;Ennis, John; Julien Delarue; Vanessa Rios de Souza; John Ennis
Edition: First Edition
Publisher: CRC Press
Year: 2023
Language: English
Pages: 349
1.
Bienvenue!.........................................................................................................................1
1.1 WhyDataScienceforSensoryandConsumerScience?..................1
1.1.1 CorePrinciplesinSensoryandConsumerScience.............1
1.1.2 ComputationalSensoryScience...............................................7
2.
Getting
Started.............................................................................................................9
2.1 IntroductiontoR.......................................................................................9
2.1.1 WhatIsR?....................................................................................9
2.1.2 WhyLearnR(orAnyProgrammingLanguage)?..............9
2.1.3 WhyR?........................................................................................10
2.1.4 WhyRStudio/Posit?................................................................11
2.1.5 InstallingRandRStudio.........................................................12
2.2 GettingStartedwithR..........................................................................12
2.2.1 Conventions.................................................................................12
2.2.2 InstallandLoadPackages.......................................................13
2.2.3 FirstAnalysisinR....................................................................15
2.2.4 RScripts......................................................................................16
2.2.5 CreateaLocalProject.............................................................17
2.3 FurtherTipsonHowtoReadThisBook?.......................................18
2.3.1 Introduction to {magrittr} and the Notion of Pipes.....19
2.3.2 Tibbles..........................................................................................21
2.3.3 CallingVariables........................................................................26
2.3.4 Printingvs. SavingResults.....................................................27
2.3.5 RunningCodeandHandlingErrors.....................................29
2.4 VersionControl/GitandGitHub........................................................30
2.4.1 Git..................................................................................................30
2.4.2 GitHub..........................................................................................31
3.
Why
Data
Science?..................................................................................................33
3.1 HistoryandDefinition............................................................................33
3.2 BenefitsofDataScience.........................................................................35
3.2.1 ReproducibleResearch.............................................................35
3.2.2 StandardizedReporting...........................................................35
3.3 DataScientificWorkflow........................................................................36
3.3.1 DataCollection..........................................................................36
3.3.2 DataPreparation.......................................................................37
vii
viii Contents
3.3.3 DataAnalysis.............................................................................38
3.3.4 ValueDelivery............................................................................40
3.4 HowtoLearnDataScience...................................................................41
3.5 Cautions:Don’tDoThatEverybodyDoes......................................41
4.
Data
Manipulation...................................................................................................43
4.1 WhyManipulatingData?......................................................................43
4.2 TidyingData.............................................................................................45
4.2.1 SimpleManipulations...............................................................45
4.2.2 ReshapingData..........................................................................59
4.2.3 TransformationThatAlterstheData.................................63
4.2.4 CombiningDatafromDifferentSources.............................67
5.
Data
Visualization....................................................................................................73
5.1 Introduction...............................................................................................73
5.2 DesignPrinciples......................................................................................74
5.3 TableMaking............................................................................................76
5.3.1 Introduction to {flextable}...................................................76
5.3.2 Introdution to {gt}...................................................................80
5.4 ChartMaking............................................................................................85
5.4.1 Philosophy of {ggplot2}.........................................................85
5.4.2 Getting Started with {ggplot2}...........................................85
5.4.3 CommonCharts.........................................................................97
5.4.4 Miscealleneous..........................................................................100
5.4.5 Few Additional Tips and Tricks..........................................107
6.
Automated
Reporting.........................................................................................113
6.1 WhatandWhyAutomatedReporting?..........................................113
6.2 IntegratingReportswithinAnalysisScripts..................................114
6.2.1 Excel............................................................................................115
6.2.2 PowerPoint................................................................................120
6.2.3 Word ...........................................................................................130
6.2.4 NotesonApplyingCorporateBranding ...........................132
6.3 Integrating Analyses Scripts Within Your Reporting Tool........133
6.3.1 What Is {rmarkdown}............................................................133
6.3.2 Starting with {rmarkdown}...................................................134
6.3.3 {rmarkdown} through a Simple Example.........................134
6.3.4 Creating a Document Using {knitr}................................136
6.3.5 Example of Applications........................................................136
6.4 ToGoFurther......................................................................................137
Contents ix
7.
Example
Project:
The
Biscuit
Study.......................................................139
7.1 ObjectiveoftheTest ............................................................................139
7.2 Products...................................................................................................140
7.3 SensoryDescriptiveAnalysis..............................................................140
7.4 Consumer Test........................................................................................141
7.4.1 Participants...............................................................................141
7.4.2 Test Design................................................................................141
7.4.3 Evaluation..................................................................................143
8.
Data
Collection.........................................................................................................145
8.1 DesignsofSensoryExperiments........................................................145
8.1.1 General Approach....................................................................145
8.1.2 Crossover Designs....................................................................147
8.1.3 BalancedIncompleteBlockDesigns(BIBD)...................150
8.1.4 IncompleteDesignsandSensoryInformedDesignsfor
Hedonic Tests............................................................................151
8.2 Product-related Designs.......................................................................155
8.2.1 Factorial Designs......................................................................155
8.2.2 Mixture Designs.......................................................................155
8.2.3 ScreeningDesigns....................................................................159
8.2.4 SensoryInformedDesignsforProductDevelopment....160
8.3 Execute.....................................................................................................161
8.4 Import.......................................................................................................164
8.4.1 ImportingStructuredExcelFile.........................................165
8.4.2 ImportingUnstructuredExcelFile ....................................166
8.4.3 Importing Data Stored in Multiple Sheets.......................168
9.
Data
Preparation....................................................................................................171
9.1 Introduction.............................................................................................171
9.2 Inspect.......................................................................................................172
9.2.1 Data Inspection........................................................................172
9.2.2 Missing Data.............................................................................175
9.2.3 DesignInspection....................................................................185
9.3 Clean.........................................................................................................188
9.3.1 Handling Data Type...............................................................188
9.3.2 Converting between Types....................................................196
10.
Data
Analysis.............................................................................................................199
10.1 Sensory Data...........................................................................................199
10.2 Demographic and Questionnaire Data.............................................207
10.2.1 DemographicData:FrequencyandProportion..............207
10.2.2 EatingBehaviorTraits:TFEQData.................................211
10.3 Consumer Data.......................................................................................217
x Contents
10.4 Combining Sensory and Consumer Data.........................................222
10.4.1 Internal Preference Mapping................................................222
10.4.2 ConsumersClustering ............................................................225
10.4.3 Drivers of Liking......................................................................230
10.4.4 External Preference Mapping...............................................235
11.
Value
Delivery...........................................................................................................239
11.1 HowtoCommunicate?.........................................................................239
11.2 Exploratory, Explanatory, and Predictive Analysis......................241
11.3 Audience Awareness..............................................................................242
11.3.1 TechnicalAudience.................................................................244
11.3.2 Management..............................................................................244
11.3.3 GeneralInterest.......................................................................244
11.4 MethodstoCommunicate...................................................................249
11.4.1 ConsidertheMechanism .......................................................249
11.4.2 Pick the Correct Format........................................................250
11.5 Storytelling..............................................................................................251
11.5.1 TheBeginning(Context)......................................................252
11.5.2 The Middle (Action and Impact)........................................253
11.5.3 The End (Conclusion)............................................................253
11.6 Reformulate.............................................................................................254
12.
Machine
Learning...................................................................................................255
12.1 Introduction.............................................................................................255
12.2 Introduction of the Data......................................................................257
12.3 Machine Learning Methods.................................................................257
12.4 Unsupervised Machine Learning........................................................258
12.4.1 DimensionalityReduction.....................................................259
12.4.2 Clustering..................................................................................261
12.5 Supervised Learning..............................................................................264
12.5.1 Workflow....................................................................................265
12.5.2 Regression..................................................................................265
12.5.3 OtherCommonSupervisedMLAlgorithms....................267
12.6 Practical Guide to Supervised Machine Learning.........................268
12.6.1 Introduction to the {tidymodels} Framework...............268
12.6.2 SamplingtheData..................................................................269
12.6.3 Cross-Validation.......................................................................269
12.6.4 Data Preprocessing {recipes}............................................270
12.6.5 ModelDefinition......................................................................271
12.6.6 SettheWholeProcessintoaWorkflow............................271
12.6.7 Tuning the Parameters...........................................................272
12.6.8 Model Training.........................................................................272
12.6.9 ModelEvaluation....................................................................273
Contents xi
13.
Text
Analysis..............................................................................................................279
13.1 Introduction to Natural Language Processing...............................279
13.2 Application of Text Analysis in Sensory and
Consumer Science..................................................................................280
13.2.1 Text Analysis as Way to Describe Products....................280
13.2.2 Objectives of Text Analysis..................................................281
13.2.3 ClassicalTextAnalysisWorkflow.......................................282
13.2.4 Warnings....................................................................................282
13.3 Illustration Involving Sorting Task Data.........................................283
13.3.1 DataPreprocessing.................................................................283
13.3.2 Introduction to Working with Strings ({stringr}).......284
13.3.3 Tokenization..............................................................................284
13.3.4 SimpleTransformations.........................................................285
13.3.5 SplittingFurthertheTokens................................................286
13.3.6 Stopwords..................................................................................287
13.3.7 Stemming and Lemmatization.............................................289
13.4 TextAnalysis..........................................................................................292
13.4.1 RawFrequenciesandVisualization....................................293
13.4.2 Bigramsandn-grams .............................................................298
13.4.3 Word Embedding.....................................................................299
13.4.4 Sentiment Analysis..................................................................300
13.5 ToGoFurther......................................................................................300
14.
Dashboards...................................................................................................................301
14.1 Objectives ................................................................................................301
14.2 Introduction to Shiny through an Example....................................302
14.2.1 What Is a Shiny Application?..............................................302
14.2.2 Starting with Shiny.................................................................302
14.2.3 Illustration.................................................................................302
14.2.4 DeployingtheApplication....................................................308
14.3 ToGoFurther......................................................................................308
14.3.1 PersonalizingandTuningYourApplication....................309
14.3.2 Upgrading Tables.....................................................................309
14.3.3 BuildingDashboard................................................................310
14.3.4 Interactive Graphics................................................................311
14.3.5 InteractiveDocuments...........................................................311
14.3.6 Documentation and Books....................................................312
15.
Conclusion
and
Next
Steps.............................................................................313
15.1 Other Recommended Resources.........................................................313
15.2 Useful R Packages..................................................................................314
Bibliography..............................................................................................................................317
Index ............................................................................................................................................327
