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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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Toc

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

After reading this chapter, you should be able to make enhanced plots, such as facet grids, maps, and 3D plots.

We started by learning about facet grids, which are one of the grammatical elements of the grammar of graphics. With facet grids, a graphic can be divided into subplots, making the interpretation easier for the reader. The next topics were how to plot maps and time series in R using ggplot2. These are vast subjects that lie within geospatial data analysis and time series analysis in data science, so we just covered the basics, but that should be enough for you to create great visualizations.

3-dimensional plots are beautiful and impactful, no doubt. However, they are not well suited for big data or for visualizations where precision is a requirement. They are good, though, for plotting surfaces or viewing the separation of data points that is only visible with the addition of a third dimension.

Finally, we closed the chapter with a function that combines...

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