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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

Summary

This chapter brought in many tools for working with strings. To refresh our memory, string are textual information. Like any other data, text can carry a lot of information and good insights if we have the right tools to extract it. That was exactly what we did in the last few pages.

We learned about the main functions from the stringr library, which is part of the tidyverse package. The benefit of learning how to use libraries from tidyverse is easy coding and its adherence to the tidy concept, helping you transform your data and prepare it for modeling. We saw that stringr functions start with the str_ prefix followed by a task identifier, making it easier to learn and remember the coding.

Next, we covered the most used regexp patterns, which are strings for creating highly customized textual patterns for many different analysis tasks.

Finally, we learned how to use a combination of stringr functions plus regexps and some base R functions, to create data summaries...

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