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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

Loading and Exploring Datasets

Every data exploration begins with data, quite obviously. Thus, it is important for us to know how to load datasets to RStudio before we get to work. In this chapter, we will learn the different ways to load data to an RStudio session. We will begin by importing some sample datasets that come with some preinstalled libraries from R, then move on to reading data from Comma-Separated Values (CSV) files, which turns out to be one of the most used file types in Data Science, given its compatibility with many other programs and data readers.

We will also learn about the basic differences between a Data Frame and a Tibble, followed by a section where we will learn the basics of Web Scraping, which is another good way to acquire data. Later in the chapter, we will learn how to save our data to our local machine and paint a picture of a good workflow for data exploration.

We will cover the following main topics:

  • How to load files to RStudio
  • ...
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