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Data Wrangling with R

You're reading from   Data Wrangling with R Load, explore, transform and visualize data for modeling with tidyverse libraries

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Product type Paperback
Published in Feb 2023
Publisher Packt
ISBN-13 9781803235400
Length 384 pages
Edition 1st Edition
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Concepts
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Author (1):
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Gustavo Santos Gustavo Santos
Author Profile Icon Gustavo Santos
Gustavo Santos
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Table of Contents (21) Chapters Close

Preface 1. Part 1: Load and Explore Data
2. Chapter 1: Fundamentals of Data Wrangling FREE CHAPTER 3. Chapter 2: Loading and Exploring Datasets 4. Chapter 3: Basic Data Visualization 5. Part 2: Data Wrangling
6. Chapter 4: Working with Strings 7. Chapter 5: Working with Numbers 8. Chapter 6: Working with Date and Time Objects 9. Chapter 7: Transformations with Base R 10. Chapter 8: Transformations with Tidyverse Libraries 11. Chapter 9: Exploratory Data Analysis 12. Part 3: Data Visualization
13. Chapter 10: Introduction to ggplot2 14. Chapter 11: Enhanced Visualizations with ggplot2 15. Chapter 12: Other Data Visualization Options 16. Part 4: Modeling
17. Chapter 13: Building a Model with R 18. Chapter 14: Build an Application with Shiny in R 19. Conclusion 20. Other Books You May Enjoy

Transformations with Tidyverse Libraries

The journey through data wrangling is still at its core. We have just finished studying the major transformations from the perspective of the built-in functions of base R and counting on the support of data.table library.

We saw how easy it was to reach the solution for some of those transformations, without needing to load extra libraries. However, as the problems get more complicated, the basic functions will not be able to provide a sufficiently clean and fast solution. The code will get busier and will probably underperform as the size of the dataset increases. For complex cases, there are several libraries built for R language that can help us to get through most problems with better performance and clean code. If you are interested in comparison times between base R, data.table, and tidyverse, refer to this page (https://tinyurl.com/2udfcvx2), where the author compares the most common tasks using the three libraries.

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