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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

Selecting the best variables

At this point, selecting the best variables should be smooth since exploring the data gives us the answer we’re looking for. When we checked the boxplots and tested the words and characters that impact the classification the most, as well as the impact of the uppercase letters, we were already making a variable selection. We should use those variables that have the highest difference between both groups so that it’s easier for the algorithm to find a clearer separation between the two groups. As we have seen, 23 words maximize the difference, the number of uppercase letters, and the presence of too many symbols.

In this section, we will take the top_words vector, which gathers the top 23 words that have the most impact on the spam classification, as well as the exclamation, parenthesis, dollar sign, and hashtag characters and the uppercase variables and transform the dataset into a seven-variable Tibble, with six explanatory variables and...

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