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Data Wrangling with R: Load, explore, transform and visualize data for modeling with tidyverse libraries
Take your data wrangling skills to the next level by gaining a deep understanding of tidyverse libraries and effectively prepare your data for impressive analysis Purchase of the print or Kindle book includes a free PDF eBook In this information era, where large volumes of data are being generated every day, companies want to get a better grip on it to perform more efficiently than before. This is where skillful data analysts and data scientists come into play, wrangling and exploring data to generate valuable business insights. In order to do that, you'll need plenty of tools that enable you to extract the most useful knowledge from data. Data Wrangling with R will help you to gain a deep understanding of ways to wrangle and prepare datasets for exploration, analysis, and modeling. This data book enables you to get your data ready for more optimized analyses, develop your first data model, and perform effective data visualization. The book begins by teaching you how to load and explore datasets. Then, you'll get to grips with the modern concepts and tools of data wrangling. As data wrangling and visualization are intrinsically connected, you'll go over best practices to plot data and extract insights from it. The chapters are designed in a way to help you learn all about modeling, as you will go through the construction of a data science project from end to end, and become familiar with the built-in RStudio, including an application built with Shiny dashboards. By the end of this book, you'll have learned how to create your first data model and build an application with Shiny in R. If you are a professional data analyst, data scientist, or beginner who wants to learn more about data wrangling, this book is for you. Familiarity with the basic concepts of R programming or any other object-oriented programming language will help you to grasp the concepts taught in this book. Data analysts looking to improve their data manipulation and visualization skills will also benefit immensely from this book.
384 pages, Paperback
Published February 23, 2023
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May 18, 2023
First, some of my thoughts before I started reading the book. The process of data wrangling is a crucial aspect of data analysis and science. Yet it remains underrepresented in the R literature. I also found myself wondering what the balance is between base R, tidyverse, and other packages would be. I was also curious about the scope of the topics.
Gustavo R Santos, the author, is a known figure in the R community. While our paths have not crossed, we share a network of mutual connections on LinkedIn.
His book shows how to do data wrangling using R. And it includes some next steps. Like visualization and modeling. The book seems intended for beginners. Although the actual content might challenge this audience. There are assumptions of more advanced prior knowledge than a beginner is likely to have. But having read a lot of technical books, there's nothing new there.
The book provides a broad overview of the specific topic of data wrangling. This has inherent advantages and disadvantages. The structure is clear. It starts with data loading and exploration. Moves through data wrangling and visualization, and finishes with modeling and publishing. Yet, the choice to include three different syntaxes - base R, data.table, and tidyverse - might introduce some unnecessary detours.
The book's structure is otherwise clear. But it would have been more accessible to beginners had it focused on the tidyverse (using base R only when needed). Sparing the novice reader the initial confusion of choosing a library/syntax. They can always opt to learn new syntaxes later on as necessary.
The book does not dive into data wrangling as much as I would have preferred. Some concepts are left to the hands of the 'Further reading' section. Still, the use case is a redeeming feature. It makes most of the content practical and usable in real-world situations. Running from data loading all the way to creating a simple Shiny app for the model created.
The quality and relevance of the code examples are generally good. There were some minor errors that I have already provided feedback on (or am about to, depending on the case). I also offer an alternative version of the code on my GitHub page. It demonstrates different coding styles and library choices.
The visual aids used throughout the book are generally effective, neither exceptionally good nor notably bad. When comparing "Data Wrangling with R" to other books in the field, such as R for Data Science: Import, Tidy, Transform, Visualize, and Model Data it stands as a good primer. It might be easier for a beginner to approach, yet it doesn't displace the need for the latter, more thorough work. Also, if you're interested in specific books about visualization or Shiny, there are other books by Hadley Wickham and others.
Despite its limitations, the book successfully bridges the gap between beginner and intermediate users. It would have benefited from providing model answers to the exercises. A feature often overlooked in technical books.
In conclusion, "Data Wrangling with R" is a worthwhile read. Particularly for those new to R and/or data wrangling. It doesn't aim to be the definitive guide. Rather a stepping stone on a long journey. Its accessibility and practical focus provide an excellent introduction to the subject. Despite some detours and oversights, it's a welcome addition to the data science literature.
Disclaimer: I received an eBook version of the book from Packt for this review.
Gustavo R Santos, the author, is a known figure in the R community. While our paths have not crossed, we share a network of mutual connections on LinkedIn.
His book shows how to do data wrangling using R. And it includes some next steps. Like visualization and modeling. The book seems intended for beginners. Although the actual content might challenge this audience. There are assumptions of more advanced prior knowledge than a beginner is likely to have. But having read a lot of technical books, there's nothing new there.
The book provides a broad overview of the specific topic of data wrangling. This has inherent advantages and disadvantages. The structure is clear. It starts with data loading and exploration. Moves through data wrangling and visualization, and finishes with modeling and publishing. Yet, the choice to include three different syntaxes - base R, data.table, and tidyverse - might introduce some unnecessary detours.
The book's structure is otherwise clear. But it would have been more accessible to beginners had it focused on the tidyverse (using base R only when needed). Sparing the novice reader the initial confusion of choosing a library/syntax. They can always opt to learn new syntaxes later on as necessary.
The book does not dive into data wrangling as much as I would have preferred. Some concepts are left to the hands of the 'Further reading' section. Still, the use case is a redeeming feature. It makes most of the content practical and usable in real-world situations. Running from data loading all the way to creating a simple Shiny app for the model created.
The quality and relevance of the code examples are generally good. There were some minor errors that I have already provided feedback on (or am about to, depending on the case). I also offer an alternative version of the code on my GitHub page. It demonstrates different coding styles and library choices.
The visual aids used throughout the book are generally effective, neither exceptionally good nor notably bad. When comparing "Data Wrangling with R" to other books in the field, such as R for Data Science: Import, Tidy, Transform, Visualize, and Model Data it stands as a good primer. It might be easier for a beginner to approach, yet it doesn't displace the need for the latter, more thorough work. Also, if you're interested in specific books about visualization or Shiny, there are other books by Hadley Wickham and others.
Despite its limitations, the book successfully bridges the gap between beginner and intermediate users. It would have benefited from providing model answers to the exercises. A feature often overlooked in technical books.
In conclusion, "Data Wrangling with R" is a worthwhile read. Particularly for those new to R and/or data wrangling. It doesn't aim to be the definitive guide. Rather a stepping stone on a long journey. Its accessibility and practical focus provide an excellent introduction to the subject. Despite some detours and oversights, it's a welcome addition to the data science literature.
Disclaimer: I received an eBook version of the book from Packt for this review.
Displaying 1 of 1 review