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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

Preparing data for modeling in R

We must wrangle the data to prepare it for modeling. Since we know where we want to go at the end of this project, the next step is a matter of finding a way to get there.

The first thing we must do is load the libraries to be used for wrangling and modeling the data. We will use tidyverse to perform data wrangling and visualization, skimr to create a descriptive statistics summary, patchwork, a great library to put graphics side by side, randomForest to create the model, caret to create the confusion matrix, and ROCR to plot the ROC curve of model performance.

To load the dataset, the best option is to pull it directly from the internet, without the need to save it locally on our machine. Just use the read_csv() function and point to the web address where the raw dataset is located, as we’ve done previously in this book. Here, we are using the trim_ws=TRUE argument to trim any unwanted white spaces and the col_names=headers argument, where...

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