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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

Binding

Binding data is the last of the main transformations listed at the beginning of this chapter. It is common to find yourself with two or more datasets that you need to put together for analysis. There are a couple of ways to do that, as follows:

Figure 7.16 – Types of data binding

Assume that our Census Income dataset has only 10 rows. After some research, the internal team found another 10 observations and gave them to the data science team. The ten new observations have to be appended to the original dataset since they have the same variables. Let’s see that in action:

# Creating datasets A and B
A <- df[1:10, ]
B <- df[11:20, ]
# Append / bind rows
AB <- rbind(A, B)

To illustrate the other scenario, that is, binding columns, imagine that the original data has only three variables, age, workclass, and fnlwgt. Then, the team was able to collect more information about the taxpayers, adding education grade and occupation....

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