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The Statistics and Machine Learning with R Workshop

You're reading from   The Statistics and Machine Learning with R Workshop Unlock the power of efficient data science modeling with this hands-on guide

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Product type Paperback
Published in Oct 2023
Publisher Packt
ISBN-13 9781803240305
Length 516 pages
Edition 1st Edition
Languages
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Author (1):
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Liu Peng Liu Peng
Author Profile Icon Liu Peng
Liu Peng
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Table of Contents (20) Chapters Close

Preface 1. Part 1:Statistics Essentials
2. Chapter 1: Getting Started with R FREE CHAPTER 3. Chapter 2: Data Processing with dplyr 4. Chapter 3: Intermediate Data Processing 5. Chapter 4: Data Visualization with ggplot2 6. Chapter 5: Exploratory Data Analysis 7. Chapter 6: Effective Reporting with R Markdown 8. Part 2:Fundamentals of Linear Algebra and Calculus in R
9. Chapter 7: Linear Algebra in R 10. Chapter 8: Intermediate Linear Algebra in R 11. Chapter 9: Calculus in R 12. Part 3:Fundamentals of Mathematical Statistics in R
13. Chapter 10: Probability Basics 14. Chapter 11: Statistical Estimation 15. Chapter 12: Linear Regression in R 16. Chapter 13: Logistic Regression in R 17. Chapter 14: Bayesian Statistics 18. Index 19. Other Books You May Enjoy

Introducing tidyverse and dplyr

One of the most widely used R libraries that contains a set of individual packages is tidyverse; it includes dplyr and ggplot2 (to be covered in Chapter 4). It can support most data processing and visualization needs and comes with an easy and fast implementation compared to base R commands. Therefore, it is recommended to outsource a specific data processing or visualization task to tidyverse instead of implementing it ourselves.

Before we dive into the world of data processing, there is one more data structure that’s used in the ecosystem of tidyverse: tibble. A tibble is an advanced version of a DataFrame and offers much better format control, leading to clean expressions in code. It is the central data structure in tidyverse. A DataFrame can be converted into a tibble object and vice versa. Let’s go through an exercise on this.

Exercise 2.01 – converting between tibble and a DataFrame

First, we will explore the tidyverse...

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