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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

Numbers in vectors, matrices, and data frames

A number represents a point in space. You may also have heard of a number being referred to as a scalar when it is followed by a unit of measure. In other words, it is a variable with a single number. When we have more than one number, it is possible to create a line in space, which is referred to as a vector. A collection of vectors put together gives new dimensions to data, which becomes matrices or data frames. These last two are similar structures, but data frames have some more enhanced features, such as headers and indexes, that help us to work with the information held by them.

We can quickly go over scalar, vector, matrix, and data frame creation in R, which is a simple process. You can understand what is being done by reading the comments:

# Creating a scalar
scalar <- 42
print(scalar)
[1] 42
# Creating a vector
vec <- c(1, 2, 3, 4, 5, 6, 7, 8, 9)
print(vec)
[1] 1 2 3 4 5 6 7 8 9
# Creating a Matrix
mtrx <- matrix...
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