Chapter 15 Conclusion and Next Steps

Congratulations, you’ve reached the end of this book!

We hope we have motivated you to continue your amazing journey into the emerging computational sensory science field. To give you a little hand, we are listing here list some other resources we would recommend and a summary of the main useful packages for sensory and consumer data analysis/visualization, including the ones we used throughout this book.

15.1 Other Recommended Resources

• R for Data Science by Garrett Grolemund and Hadley Wickham (https://r4ds.had.co.nz/)

• Analyzing Sensory Data with R by Sebastien Le and Thierry Worch

• Practical Guide to Cluster Analysis in R by Alboukadel Kassambara

• Practical Guide to Principal Component Methods in R by Alboukadel Kassambara

• Using the flextable R package by David Gohel (https://ardata-fr.github.io/flextable-book/index.html)

• Hands-On Machine Learning with R by Brad Boehmke and Brandon Greenwell (https://bradleyboehmke.github.io/HOML/)

• Introduction to Statistical and Machine Learning Methods for Data Science by Carlos Andre Reis Pinheiro and Mike Patetta

• R Graphics Cookbook: Practical Recipes for Visualizing Data by Winston Chang

• Text Mining with R: A Tidy Approach by David Robinson and Julia Silge

• Textual Data Science with R by Mónica Bécue-Bertaut

• Supervised Machine Learning for Text Analysis in R by Emil Hvitfeldt and Julia Sigle

Some interesting book related to story telling, graphical design and data visualization:

• Storytelling with Data by Cole Nussbaumer Knaflic

• Beyond Bullet Points by Cliff Atkinson

• Once Upon an Innovation by Jean Storlie and Mimi Sherlock

• Show me the Number: Designing Table and Graphs to Enlighten by Stephen Few

15.2 Useful R Packages

• FactoMineR: package dedicated to multivariate Exploratory Data Analysis including Principal Components Analysis (PCA), Correspondence analysis (CA), Multiple Correspondence Analysis (MCA), clustering.

• FactoExtra: package that makes easy to extract and visualize the output of exploratory multivariate data analyses, including Principal Component Analysis (PCA), Correspondence Analysis (CA), Multiple Correspondence Analysis (MCA), Multiple Factor Analysis (MFA), Hierarchical Clustering (HCKUST) and partioning Clustering (E.g. k-means, PAM,CLARA, etc.)

• SensR: package for Thurstonian Models for sensory discrimination methods, including duotrio, tetrad, triangle, 2-AFC, 3-AFC, A-not A, same-different, 2-AC and degree-of-difference. This package enables the calculation of d-primes, standard errors of d-primes, sample size and power computations, and comparisons of different d-primes.

• SensoMineR: package dedicated to the statistical analysis of sensory data. It tackles the characterization of the products, panel performance assessment, links between sensory and instrumental data, consumer’s preferences, napping evaluation, optimal designs.

• tempR: package for Analysis and visualization of data from temporal sensory methods, including temporal check-all-that-apply (TCATA) and temporal dominance of sensation.

• sensmixed: package to analyze sensory and consumer data within mixed effects model framework.

• corrplot: package that provides a visual exploratory tool on correlation matrix that supports automatic variable reordering to help detect hidden patterns among variables.

• stats: this This package contains functions for statistical calculations and random number generation. The analysis include ANOVA, posthoc tests, Clustering, Correlation, multivariate analysis, among many others.