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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

Modeling

Training

Now that the new dataset has been created, the next step is to replace 1 with is_spam and 0 with not_spam so that the random forest algorithm can understand that the target variable is not numeric and that it is a classification model. We can do this by using the recode() function within a mutate function:

# Replace the binary 1(spam) and 0(not_spam)
spam_for_model <- spam_for_model %>% 
  mutate( spam= recode(spam, '1'='is_spam','0'='not_spam')    )

Now, it is time to separate the data into train and test subsets. The train subset is used to present the model with the patterns and the labels associated with it so that it can study how to classify each observation according to the patterns that occur. The test set is like a school test, where new data is presented to the trained model so that we can measure how accurate it is or how much it has learned.

As we learned during the...

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