Search icon CANCEL
Subscription
0
Cart icon
Close icon
You have no products in your basket yet
Save more on your purchases!
Savings automatically calculated. No voucher code required
Arrow left icon
All Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletters
Free Learning
Arrow right icon
The Statistics and Machine Learning with R Workshop

You're reading from  The Statistics and Machine Learning with R Workshop

Product type Book
Published in Oct 2023
Publisher Packt
ISBN-13 9781803240305
Pages 516 pages
Edition 1st Edition
Languages
Author (1):
Liu Peng Liu Peng
Profile icon Liu Peng

Table of Contents (20) Chapters

Preface 1. Part 1:Statistics Essentials
2. Chapter 1: Getting Started with R 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 linear algebra

This chapter delves into one of the most important branches of mathematics: linear algebra. Linear algebra deals with linear operations of mathematical objects, including vectors, matrices, and tensors (high-dimensional matrices), the most common forms of data. For example, the typical table we use to store data in Excel consists of a series of columns. Each column is called a vector, which stores a specific number of elements and takes the form of a column instead of a row by default. A collection of these column vectors forms a matrix, a two-dimensional Excel table, or DataFrame, as we used to call it in the previous chapters. We can also view the same table as a collection of row vectors, where each vector lives in the form of a row.

Let’s put these in context. The following code snippet loads the sleep dataset and prints out the first six rows and three columns. We use A to denote this 6x3 matrix in the following exposition:

>>>...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $15.99/month. Cancel anytime}