In this article, I trace the evolution of the tidyverse, a cohesive ecosystem of R packages for data science. Beginning with early packages I created during my PhD at Iowa State University, I explain how they coalesced into a unified collection with consistent principles. I detail key innovations including tidy data, tibbles, the pipe operator, tidy evaluation and the role of hex stickers in community building. I describe the transition from my individual efforts to a collaborative enterprise supported by a team at Posit and a vibrant global community. I highlight the importance of human-centred design, consistency, composability and inclusivity as our guiding principles. I discuss how our recent priorities have shifted from innovation to maintenance as the ecosystem has matured. Looking to the future, I discuss our current areas of focus including Positron (a new data science IDE), R in production environments and integrating large language models into data science workflows.

This book offers solutions to all 284 exercises in Advanced R, Second Edition. All the solutions have been carefully documented and made to be as clear and accessible as possible. Working through the exercises and their solutions will give you a deeper understanding of a variety of programming challenges, many of which are relevant to everyday work. This will expand your set of tools on a technical and conceptual level. You will be able to transfer many of the specific programming schemes directly and will discover far more elegant solutions to everyday problems. Features: When R creates copies, and how it affects memory usage and code performance Everything you could ever want to know about functions The differences between calling and exiting handlers How to employ functional programming to solve modular tasks The motivation, mechanics, usage, and limitations of R's highly pragmatic S3 OO system The R6 OO system, which is more like OO programming in other languages The rules that R uses to parse and evaluate expressions How to use metaprogramming to generate HTML or LaTeX with elegant R code How to identify and resolve performance bottlenecks