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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

Adding interactivity to graphics

Images are interpreted by our brains faster than words or numbers (https://tinyurl.com/nhtbw9jk). That makes graphics an interesting way to show data, as we have learned throughout this book. But there is still more enhancement to be done when working with data visualization, and one of these enhancements is interactivity.

The ggplot2 library creates static graphics. Hence, the plots will not show values at the tops of bars or names of points on a scatterplot, for example. If that is a requirement for a visualization, it must be added using an annotation or text. However, when you combine the graphic’s code with plotly, some interaction is added to the visualization, such as making values appear just by hovering over a data point or zooming in and out the graphic.

To create an interactive scatterplot out of the same code that generated in Figure 11.1, we only have to add the ggplotly() function around the entire ggplot code. See the following...

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