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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

Exploring and visualizing the data

The exploration phase of an EDA is the main portion of it, naturally. In this section, the idea is to take a thorough look at the variables, understand their distributions, start creating some questions that will lead the exploration, and use the data to answer them.

Univariate analysis

The first step to take concerns univariate analysis—looking at one variable at a time. The best approach is to create some histograms to look at the distribution of the variables. According to Hair Jr. et al. (2019), plotting the variables’ distributions and looking at their shape is a good point to start understanding the nature of those variables.

In the next code snippet, we will loop through all the numeric variables and plot one histogram for each. It starts with a for loop to iterate over each variable in the column names that presents the numeric type (there is the importance of knowing the data types, from previous sections). If it is...

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