Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in Oct 2023
Publisher Packt
ISBN-13 9781803240305
Length 516 pages
Edition 1st Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Liu Peng Liu Peng
Author Profile Icon Liu Peng
Liu Peng
Arrow right icon
View More author details
Toc

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 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 $19.99/month. Cancel anytime