Data Science for Sensory and Consumer Scientists (Chapman & Hall/CRC Data Science Series)

Cover
Half Title
Series Page
Title Page
Copyright Page
Dedication
Table of Contents
Preface
About the Authors
1. Bienvenue!
	1.1 Why Data Science for Sensory and Consumer Science?
		1.1.1 Core Principles in Sensory and Consumer Science
		1.1.2 Computational Sensory Science
2. Getting Started
	2.1 Introduction to R
		2.1.1 What Is R?
		2.1.2 Why Learn R (or Any Programming Language)?
		2.1.3 Why R?
		2.1.4 Why RStudio/Posit?
		2.1.5 Installing R and RStudio
	2.2 Getting Started with R
		2.2.1 Conventions
		2.2.2 Install and Load Packages
		2.2.3 First Analysis in R
		2.2.4 R Scripts
		2.2.5 Create a Local Project
	2.3 Further Tips on How to Read This Book?
		2.3.1 Introduction to {magrittr} and the Notion of Pipes
		2.3.2 Tibbles
		2.3.3 Calling Variables
		2.3.4 Printing vs. Saving Results
		2.3.5 Running Code and Handling Errors
	2.4 Version Control/Git and GitHub
		2.4.1 Git
		2.4.2 GitHub
3. Why Data Science?
	3.1 History and Definition
	3.2 Benefits of Data Science
		3.2.1 Reproducible Research
		3.2.2 Standardized Reporting
	3.3 Data Scientific Workflow
		3.3.1 Data Collection
		3.3.2 Data Preparation
		3.3.3 Data Analysis
		3.3.4 Value Delivery
	3.4 How to Learn Data Science
	3.5 Cautions: Don’t Do That Everybody Does
4. Data Manipulation
	4.1 Why Manipulating Data?
	4.2 Tidying Data
		4.2.1 Simple Manipulations
		4.2.2 Reshaping Data
		4.2.3 Transformation That Alters the Data
		4.2.4 Combining Data from Different Sources
5. Data Visualization
	5.1 Introduction
	5.2 Design Principles
	5.3 Table Making
		5.3.1 Introduction to {flextable}
		5.3.2 Introdution to {gt}
	5.4 Chart Making
		5.4.1 Philosophy of {ggplot2}
		5.4.2 Getting Started with {ggplot2}
		5.4.3 Common Charts
		5.4.4 Miscealleneous
		5.4.5 Few Additional Tips and Tricks
6. Automated Reporting
	6.1 What and Why Automated Reporting?
	6.2 Integrating Reports within Analysis Scripts
		6.2.1 Excel
		6.2.2 PowerPoint
		6.2.3 Word
		6.2.4 Notes on Applying Corporate Branding
	6.3 Integrating Analyses Scripts Within Your Reporting Tool
		6.3.1 What Is {rmarkdown}
		6.3.2 Starting with {rmarkdown}
		6.3.3 {rmarkdown} through a Simple Example
		6.3.4 Creating a Document Using {knitr}
		6.3.5 Example of Applications
	6.4 To Go Further. . .
7. Example Project: The Biscuit Study
	7.1 Objective of the Test
	7.2 Products
	7.3 Sensory Descriptive Analysis
	7.4 Consumer Test
		7.4.1 Participants
		7.4.2 Test Design
		7.4.3 Evaluation
8. Data Collection
	8.1 Designs of Sensory Experiments
		8.1.1 General Approach
		8.1.2 Crossover Designs
		8.1.3 Balanced Incomplete Block Designs (BIBD)
		8.1.4 Incomplete Designs and Sensory Informed Designs for Hedonic Tests
	8.2 Product-related Designs
		8.2.1 Factorial Designs
		8.2.2 Mixture Designs
		8.2.3 Screening Designs
		8.2.4 Sensory Informed Designs for Product Development
	8.3 Execute
	8.4 Import
		8.4.1 Importing Structured Excel File
		8.4.2 Importing Unstructured Excel File
		8.4.3 Importing Data Stored in Multiple Sheets
9. Data Preparation
	9.1 Introduction
	9.2 Inspect
		9.2.1 Data Inspection
		9.2.2 Missing Data
		9.2.3 Design Inspection
	9.3 Clean
		9.3.1 Handling Data Type
		9.3.2 Converting between Types
10. Data Analysis
	10.1 Sensory Data
	10.2 Demographic and Questionnaire Data
		10.2.1 Demographic Data: Frequency and Proportion
		10.2.2 Eating Behavior Traits: TFEQ Data
	10.3 Consumer Data
	10.4 Combining Sensory and Consumer Data
		10.4.1 Internal Preference Mapping
		10.4.2 Consumers Clustering
		10.4.3 Drivers of Liking
		10.4.4 External Preference Mapping
11. Value Delivery
	11.1 How to Communicate?
	11.2 Exploratory, Explanatory, and Predictive Analysis
	11.3 Audience Awareness
		11.3.1 Technical Audience
		11.3.2 Management
		11.3.3 General Interest
	11.4 Methods to Communicate
		11.4.1 Consider the Mechanism
		11.4.2 Pick the Correct Format
	11.5 Storytelling
		11.5.1 The Beginning (Context)
		11.5.2 The Middle (Action and Impact)
		11.5.3 The End (Conclusion)
	11.6 Reformulate
12. Machine Learning
	12.1 Introduction
	12.2 Introduction of the Data
	12.3 Machine Learning Methods
	12.4 Unsupervised Machine Learning
		12.4.1 Dimensionality Reduction
		12.4.2 Clustering
	12.5 Supervised Learning
		12.5.1 Workflow
		12.5.2 Regression
		12.5.3 Other Common Supervised ML Algorithms
	12.6 Practical Guide to Supervised Machine Learning
		12.6.1 Introduction to the {tidymodels} Framework
		12.6.2 Sampling the Data
		12.6.3 Cross-Validation
		12.6.4 Data Preprocessing {recipes}
		12.6.5 Model Definition
		12.6.6 Set the Whole Process into a Workflow
		12.6.7 Tuning the Parameters
		12.6.8 Model Training
		12.6.9 Model Evaluation
13. Text Analysis
	13.1 Introduction to Natural Language Processing
	13.2 Application of Text Analysis in Sensory and Consumer Science
		13.2.1 Text Analysis as Way to Describe Products
		13.2.2 Objectives of Text Analysis
		13.2.3 Classical Text Analysis Workflow
		13.2.4 Warnings
	13.3 Illustration Involving Sorting Task Data
		13.3.1 Data Preprocessing
		13.3.2 Introduction to Working with Strings ({stringr})
		13.3.3 Tokenization
		13.3.4 Simple Transformations
		13.3.5 Splitting Further the Tokens
		13.3.6 Stopwords
		13.3.7 Stemming and Lemmatization
	13.4 Text Analysis
		13.4.1 Raw Frequencies and Visualization
		13.4.2 Bigrams and n-grams
		13.4.3 Word Embedding
		13.4.4 Sentiment Analysis
	13.5 To Go Further. . .
14. Dashboards
	14.1 Objectives
	14.2 Introduction to Shiny through an Example
		14.2.1 What Is a Shiny Application?
		14.2.2 Starting with Shiny
		14.2.3 Illustration
		14.2.4 Deploying the Application
	14.3 To Go Further. . .
		14.3.1 Personalizing and Tuning Your Application
		14.3.2 Upgrading Tables
		14.3.3 Building Dashboard
		14.3.4 Interactive Graphics
		14.3.5 Interactive Documents
		14.3.6 Documentation and Books
15. Conclusion and Next Steps
	15.1 Other Recommended Resources
	15.2 Useful R Packages
Bibliography
Index