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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 studied one of the main graphic packages in marketing. The ggplot2 library is capable of so much that it was even translated into other languages, such as Python.

We began the chapter discussing the interesting theory of the grammar of graphics, using an analogy of textual grammatical elements and looking at the elements needed to construct and plot a good visualization. ggplot2 was built on top of that concept, enabling analysts to code a graphic by layers, adding one piece at a time. We then introduced a template of questions to help organize our thinking when creating code: (1) start with a dataset, (2) choose a geometry, (3) provide axes and aesthetics, and (4) add a title, labels, statistics, and themes.

After familiarizing ourselves with the syntax, we studied the code for the most commonly used types of graphics, such as histograms, boxplots, scatterplots, bar plots, and line plots. Then, we introduced smooth geometry, which helps us to create...

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