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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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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

Working with multiple variables

A graphic can have more than two variables, not just what is plotted on the x and y axes. We can use colors, marker shapes, or sizes to differentiate data points and create a more complex visual. Look at these basic examples.

Scatterplots are the best fit for multiple variate plots, as the points can be changed to other shapes, sizes, or colors and produce a very rich visual. Knowing that the number of cylinders (cyl) and horsepower (hp) affect directly the fuel efficiency of a car (mpg), a good exploration point is visualizing the effect of increasing cylinders and HP and observing how the fuel efficiency will respond. To perform the task, we plot a scatterplot that shows the relationship between the engine’s HP with the MPG presented by the car. Then, we add the cylinder information as a third variable to control the size of the bubbles, making them larger or smaller, thus bringing more information to this graphic:

# Scatterplot 3 variables...
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