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

✍ Scribed by Thierry Worch, Julien Delarue, Vanessa Rios De Souza, John Ennis

• Publisher: Chapman and Hall/CRC
• Year: 2023
• Tongue: English
• Leaves: 349
• Edition: 1
• Category: Library

✦ Synopsis

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

✦ Table of Contents

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