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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

Data visualization

Data visualization is part of every Data Science project, as it supports the ideas and concepts behind the theory. Graphics help us to tell the story in a better way than just numbers or text. This book is a good example if you think it through. How difficult would it be to explain what each kind of visual is if I could not show you a picture, right?

In this chapter, we will work with visualization using functions from what we call Base-R, a neat alias for the functions that come native in R. And before you wonder why we don’t go straight to ggplot2 – possibly the best and most well-known way to plot graphics in R – I would say that we’re learning Base-R plotting because it does not require installation. Ergo, no dependencies, no conflicts, and no errors, making it your go-to option for a quick visual.

To be more specific about data visualization, let’s go over a few good tips to build good graphics, according to Cole Nussbaumer...

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