Top 5 books for data science using R

Top 5 books for data science using R

·  “R for Data Science” by Hadley Wickham and Garrett Grolemund: This book is a comprehensive introduction to data science using R. It covers a wide range of topics, including data visualization, data manipulation, and machine learning. It is written for both beginners and experienced data scientists, and includes practical examples and code snippets to help readers understand the concepts.

·  “Data Science with R” by Hadley Wickham and Garrett Grolemund: This book is a comprehensive guide to data science using R. It covers a wide range of topics, including data visualization, data manipulation, and machine learning. It is written for both beginners and experienced data scientists, and includes practical examples and code snippets to help readers understand the concepts.

·  “The Art of Data Science” by Roger D. Peng: This book is a comprehensive guide to data science using R. It covers a wide range of topics, including data visualization, data manipulation, and machine learning. It is written for both beginners and experienced data scientists, and includes practical examples and code snippets to help readers understand the concepts.

·  Machine Learning with R” by Brett Lantz: This book is a comprehensive introduction to machine learning using R. It covers a wide range of topics, including supervised and unsupervised learning, deep learning, and natural language processing. It is written for both beginners and experienced data scientists, and includes practical examples and code snippets to help readers understand the concepts.

·  “Data Wrangling with R” by Bradley Boehmke: This book is a comprehensive guide to data wrangling using R. It covers a wide range of topics, including data cleaning, data exploration, and data visualization. It is written for both beginners and experienced data scientists, and includes practical examples and code snippets to help readers understand the concepts.