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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

In this chapter, we learned what data visualization is and understood its importance for any project. Our brain is designed to quickly capture images; thus, graphics are more likely to be absorbed by an audience than by a table or text.

We introduced the basic types of single-variable plots – that is, histograms, which are commonly used to view the distribution of variables, and boxplots, which are especially good at detecting outliers.

In the sequence, we learned about graphics with two variables, such as scatterplots, that can show us how x and y are related and whether that relationship is positive or negative. We also learned about bar plots, a good representation of categorical variables because they are one of the simplest types of visual and easily understandable. Finally, we looked at line plots and how they are a great fit for continuous data and time series plots.

The chapter concluded with some examples of plots with many variables, with the scatterplot...

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