Q: What is/are your specialist tech area(s)?
Gustavo: Data Scientist working with Python, R and Databricks for Big Data.
Q: How did you become an author for Packt? Tell us about your journey. What was your motivation for writing this book?
Gustavo: I maintain a blog at Medium and try to be as active as possible writing about Data Science content. The blog helped me to get connected with some people from Packt and write some book reviews. Early in 2022, I was invited to work on this project of a book about Data Wrangling with R language. I thought it would be a good addition to the R community, so I accepeted the challenge.
Q: What kind of research did you do, and how long did you spend researching before beginning the book?
Gustavo: I have researched the best books about Data Wrangling with R while I was creating the outline to my book. I have spent a couple of weeks reading books, blogs and R libraries documentation before and during the book writing process.
Q: Did you face any challenges during the writing process? How did you overcome them?
Gustavo: Writing a book is a challenge itself. I had to create all the coding for each chapter, and that’s a lot of work. Many times, they did not work as expected, so I had to go back to research and fix whatever was not working.
Q: What’s your take on the technologies discussed in the book? Where do you see these technologies heading in the future?
Gustavo: R Language is one of the best in the market. As it was conceived as a statistical tool, it has a good advantage over other programming languages in that area. Other than that, R has been growing in use by Data Scientists outside the academy, so I expect more people adopting it as a resource for their daily jobs.
Q: Why should readers choose this book over others already on the market? How would you differentiate your book from its competition?
Gustavo: My book brings the best practices for wrangling data with R. It covers the most used libraries in the market. Additionally, I have added much of my experience as data analyst and data scientist, including modeling and deploying models with Shiny, which is not something found easily in other books.
Q: What are the key takeaways you want readers to come away with from the book?
Gustavo: Know how is the current state of your data and where/ how you want it to become. The mid part is the transformation and there are many tools to make that happen. Those tools are in this book.
Q. What advice would you give to readers learning tech? Do you have any top tips?
Gustavo: Technology is more about being curious and hands-on than anything else. Study hard, understand the concepts and practice a lot. Make that consistently enough and the results will come.
Q. Do you have a blog that readers can follow?
Q. Can you share any blogs, websites, and forums to help readers gain a holistic view of the tech they are learning?
Q. How would you describe your author journey with Packt? Would you recommend Packt to aspiring authors?
Gustavo: Writing a book is a great accomplishment to anyone. I certainly recommend people stepping out and creating good content. Packt is a great ally in that journey and have good professionals to support our writing. It’s a long journey, but a worthy one.
Q. Do you belong to any tech community groups?
Gustavo: I write to Towards Data Science and contribute with Stack Overflow answering questions from time to time.
Q. What are your favorite tech journals? How do you keep yourself up to date on tech?
Gustavo: Medium blogs and Youtube.
Q. How did you organize, plan, and prioritize your work and write the book?
Gustavo: I created a personal schedule with due dates and kept aligned with it until the end.
Q: What is that one writing tip that you found most crucial and would like to share with aspiring authors?
Gustavo: Know your writing style and keep aligned with it.
Explain your coding in details.
Connect the sections, so your book has a better flow.
Q. Would you like to share your social handles? If so, please share.
You can find Gustavo‘s book on Amazon by following this link: Please click here