Business Analytics with R A Tidy Approach

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Business Analytics with R

A Tidy Approach

Bowei Chen | Tianyuan Huang

Business & Economics / Management

Machine learning has become an essential component of modern business analytics -- driving insights, optimizing operations, and supporting strategic, data-informed decisions. This textbook offers a hands-on and accessible introduction to applying machine learning in business settings using R, with a strong emphasis on the tidy ecosystem. Key packages such as tidyverse, tidymodels, and tidytext provide a cohesive and efficient framework for data analysis, modeling, and communication.

Balancing theoretical foundations with applied practice, the book introduces mathematical ideas in a clear, intuitive manner, always anchored in realistic business scenarios. Suitable for the classroom and independent research, readers will gain the confidence and tools to apply the tidy ecosystem effectively, from data preparation to modeling to interpretation, enabling reproducible, interpretable, and impactful analytics in dynamic business environments.

Bowei Chen is a Professor of Business Analytics and Artificial Intelligence at the Adam Smith Business School, University of Glasgow, UK. He has also undertaken work and research visits at institutions including Copenhagen Business School, Trinity College Dublin, University College London, Aarhus University, Université de Technologie de Belfort-Montbéliard, University of Bath, National University of Singapore, and Microsoft Research Cambridge. He has published widely in books and journals.

Tianyuan Huang is a Lecturer of Data Science and Big Data Technologies at the School of Data Science, Zhejiang University of Finance & Economics, China. Previously, he worked as a Special Research Assistant at the National Science Library, Chinese Academy of Sciences, where he conducted research in the field of information science. He has authored three R programming textbooks in China. In addition to his publications, he supports the R community by maintaining several popular CRAN packages such as akc for automated topic classification and tidyfst for efficient tidy data manipulation.


Publication Date: 30 November 2026
Publisher: Springer Nature Switzerland
Imprint: Palgrave Macmillan
ISBN-13: 9783032320773
Format: Paperback / softback

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