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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
Languages
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

We have been through a lot in this chapter. After all, the core of data wrangling is about the transformations that we covered here. Most of the work of analysts, data scientists, and developers involves filtering, reshaping, joining, and doing all kinds of data wrangling to get the data into the shape needed for the project.

We started by studying slicing and filtering, allowing us to zoom in on parts of a dataset and revisited the difference between both. Then, we moved on to show you how to group and summarize data, which turns out to be an important task, possibly one of the most used functions when munging data.

Replacing and filtering were the next two subjects. That section covered how to replace values and how to fill in missing data in our dataset. Once the data was cleaned of NAs, the subsequent step was ordering the data, making it easier to read and understand the information.

It is worth repeating that datasets have more information than what it may initially...

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