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Data Wrangling with R

You're reading from  Data Wrangling with R

Product type Book
Published in Feb 2023
Publisher Packt
ISBN-13 9781803235400
Pages 384 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Gustavo R Santos Gustavo R Santos
Profile icon Gustavo R Santos

Table of Contents (21) Chapters

Preface 1. Part 1: Load and Explore Data
2. Chapter 1: Fundamentals of Data Wrangling 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

Understanding the data

Once the data is up in RStudio, we should look at it. The first checkup points are to confirm that the data was fully loaded and without errors. For that, we can use the software’s built-in viewer by typing View(df), or we can use the head() function to look at just a couple of lines, remembering that the default is to show the first six observations:

df %>% head()

It displays the following result:

Figure 9.1 – Head of the College Majors dataset

After a first look, the data looks good. In general, what we are looking for here are the following:

  • Whether the data is rectangular—in other words, divided into rows and columns.
  • Whether we see any problems with language encoding, which, when it occurs, shows some symbols amidst the words.
  • Whether the CSV reading was successful for all columns because if the separator for the file is a semicolon or tab, for example, the columns can appear all merged...
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