Work Automation with R provides a solid framework on how to automate tasks and workflows in the workplace with R. This book introduces the most common components used in R automation, one by one, and then shows how to combine them to tackle real-world tasks.

The reader can jump to any chapter to explore solutions or best practices for specific tasks or read the book in chronological order if they are just starting a new automation project. Each chapter begins with a common data task and explores key concepts and multiple solutions using R packages. The author presents a range of solutions as there are often multiple ways to solve the same problem, with the different options have been compared along with key functions discussed in each, and reproducible examples have also been provided.

Aimed at business professionals and data practitioners who want to automate their work, the book assumes a basic working knowledge of R, including writing and running scripts, and performing common data tasks such as importing, transforming, and summarizing data. It also assumes a willingness to write small helper functions to automate repeated steps. Experience with Quarto or R Markdown is helpful but not required. Key tools and patterns are introduced as needed in end-to-end automation examples.

Key Features:

• Offers task-focused resources to smooth automation development.

• Helps identify and minimize common roadblocks that might impact progress.

• Establishes a reproducible workflow of automating tasks with R in a business setting.

Tiger Tang is a data science leader in the automotive industry, where he leads teams building large-scale, production-grade analytics and machine learning systems. His work focuses on automation, natural language processing, and data-driven products that transform complex, real-world data into reliable and reproducible workflows. He has led automation initiatives that have saved tens of thousands of hours of manual effort and developed multiple Shiny applications supporting hundreds of daily analytical decisions. Tiger is also active in the data science community, regularly speaking on applied R, Shiny, and analytics at conferences and professional events.