"R for Data Science" by Hadley Wickham and Garrett Grolemund: This book is considered a must-read for anyone looking to learn R for data analysis and visualization. It covers key concepts and practical techniques using tidyverse packages.
"Advanced R" by Hadley Wickham: For those looking to deepen their understanding of R programming, this book delves into the inner workings of R and provides insights into advanced topics like object-oriented programming and performance optimization.
"Text Mining with R: A Tidy Approach" by Julia Silge and David Robinson: With the increasing importance of text mining and natural language processing, this book offers a comprehensive guide to analyzing text data using R, focusing on tidy principles.
"Machine Learning with R" by Brett Lantz: As machine learning continues to be a crucial field in data science, this book provides a practical introduction to machine learning techniques using R, covering topics like classification, regression, clustering, and more.
"Time Series Analysis and Its Applications: With R Examples" by Robert H. Shumway and David S. Stoffer: Time series analysis is essential for analyzing data with temporal dependencies. This book offers a rigorous yet accessible treatment of time series methods with R implementations.
"Bayesian Data Analysis" by Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, and Donald B. Rubin: Bayesian methods are increasingly used in statistics and data science. This book provides a comprehensive introduction to Bayesian data analysis with practical examples in R.
"R Graphics Cookbook" by Winston Chang: Visualization is a crucial aspect of data analysis, and this cookbook-style guide offers a plethora of recipes for creating various types of plots and graphics using R, along with tips for customization and best practices.