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The Statistics and Machine Learning with R Workshop
The Statistics and Machine Learning with R Workshop

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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The Statistics and Machine Learning with R Workshop

Getting Started with R

In this chapter, we will cover the basics of R, the most widely used open source language for statistical analysis and modeling. We will start with an introduction to RStudio, how to perform simple calculations, the common data structures and control logic, and how to write functions in R.

By the end of the chapter, you will be able to do basic computations in R using common data structures such as vectors, lists and data frames in the RStudio integrated development environment (IDE). You will also be able to wrap these calculations in functions using different methods.

In this chapter, we will cover the following:

  • Introducing R
  • Covering the R and RStudio basics
  • Common data structures in R
  • Control logic in R
  • Exploring functions in R

Technical requirements

To complete the exercises in this chapter, you will need to have the following:

  • The latest version of R, which is 4.1.2 at the time of writing
  • The latest version of RStudio Desktop, which is 2021.09.2+382

All the code for this chapter is available at https://github.com/PacktPublishing/The-Statistics-and-Machine-Learning-with-R-Workshop/blob/main/Chapter_1/Chapter_1.R.

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Key benefits

  • Advance your ML career with the help of detailed explanations, intuitive illustrations, and code examples
  • Gain practical insights into the real-world applications of statistics and machine learning
  • Explore the technicalities of statistics and machine learning for effective data presentation
  • Purchase of the print or Kindle book includes a free PDF eBook

Description

The Statistics and Machine Learning with R Workshop is a comprehensive resource packed with insights into statistics and machine learning, along with a deep dive into R libraries. The learning experience is further enhanced by practical examples and hands-on exercises that provide explanations of key concepts. Starting with the fundamentals, you’ll explore the complete model development process, covering everything from data pre-processing to model development. In addition to machine learning, you’ll also delve into R's statistical capabilities, learning to manipulate various data types and tackle complex mathematical challenges from algebra and calculus to probability and Bayesian statistics. You’ll discover linear regression techniques and more advanced statistical methodologies to hone your skills and advance your career. By the end of this book, you'll have a robust foundational understanding of statistics and machine learning. You’ll also be proficient in using R's extensive libraries for tasks such as data processing and model training and be well-equipped to leverage the full potential of R in your future projects.

Who is this book for?

This book is for beginner to intermediate-level data scientists, undergraduate to masters-level students, and early to mid-senior data scientists or analysts looking to expand their knowledge of machine learning by exploring various R libraries. Basic knowledge of linear algebra and data modeling is a must.

What you will learn

  • Hone your skills in different probability distributions and hypothesis testing
  • Explore the fundamentals of linear algebra and calculus
  • Master crucial statistics and machine learning concepts in theory and practice
  • Discover essential data processing and visualization techniques
  • Engage in interactive data analysis using R
  • Use R to perform statistical modeling, including Bayesian and linear regression

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Oct 25, 2023
Length: 516 pages
Edition : 1st
Language : English
ISBN-13 : 9781803237756
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Product Details

Publication date : Oct 25, 2023
Length: 516 pages
Edition : 1st
Language : English
ISBN-13 : 9781803237756
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Table of Contents

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

Customer reviews

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Sangita Mahala Nov 27, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is a highly recommended resource for anyone seeking a comprehensive and practical introduction to statistics and machine learning using R. Peng Liu's masterful guidance and engaging approach make this book an essential tool for data scientists of all levels.
Amazon Verified review Amazon
Steven Fernandes Dec 05, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The book expertly bridges theory and practice in statistics and machine learning, focusing on R for practical application. It starts with probability distributions and hypothesis testing, building a foundation in linear algebra and calculus. The book excels in making complex statistical and machine learning concepts accessible, emphasizing data processing and visualization techniques. Interactive data analysis using R is a key feature, enhancing engagement and understanding. The detailed coverage of statistical modeling, including Bayesian and linear regression in R, makes it an indispensable resource for those aspiring to master data analysis in a hands-on, applied manner.
Amazon Verified review Amazon
Gustavo Feb 29, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I have received this book from Packt to provide my review. The book has a good R foundation for those who are not familiar with the Language. It also brings a couple of Math/ Algebra/ Calculus chapters that don't have very strong application examples, but it's interesting and well explained.The Stats portion in the last part is very good, with many regression examples.Great book.
Amazon Verified review Amazon
Papu Siameja Feb 06, 2024
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
The combination of a little bit of theory and practical examples makes this book a good introduction to the use of R for statistical analysis and machine learning. The examples are clear and easy to follow as the required R packages are clearly stated at the beginning of each chapter.
Feefo Verified review Feefo
H2N Nov 16, 2023
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
This book is an excellent introduction for early-career data scientists and undergraduate students with a basic grasp of linear algebra and modeling. It starts with the essentials of R programming, focusing on key data structures and logical operations. The journey continues with data processing techniques using dplyr, covering transformations and aggregations. The reader is then guided through more complex data processing and quality enhancement methods. Data visualization is masterfully explained through ggplot2, from elementary to sophisticated techniques. The book also delves into exploratory data analysis, R Markdown for interactive documents, and advanced topics such as linear algebra, calculus in R, probability, statistical estimation, and regression models, finishing with Bayesian statistics. This comprehensive guide is invaluable for practical R applications in data science, though readers may long for a Python version.
Amazon Verified review Amazon
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