R in Action: Data Analysis and Graphics with R 2nd Edition

This book emerges as a critical resource for data scientists and analysts, providing a deep dive into the capabilities and applications of the R language. R, renowned for its proficiency in analyzing large datasets, boasts compatibility with nearly every popular architectural style currently available. The book demystifies R—a programming environment that interprets the R language to execute commands and process data—highlighting its significance in the data science realm.Furthermore, the book introduces R Studio, a software package celebrated for its user-friendly graphical interface. This feature significantly simplifies the process of inputting R language commands for execution, making it accessible to a wide range of users.The author(s)’s engaging writing style effectively conveys the intricacies of R and its practical usage, making this book an invaluable asset not just for scientists and data analysts, but also for business executives, IT managers, and analysts. It offers insights that are beneficial even to those who may not directly work with R, providing a competitive edge in the fast-paced business world by fostering an understanding of R’s powerful capabilities.Highlighting the syntactical similarities between R and Python, yet emphasizing R's unique identity as a programming language, the book serves as a comprehensive guide. It is highly recommended for anyone seeking to enhance their analytical skills, understand data processing more deeply, or gain a competitive advantage by familiarizing themselves with the functionalities of R and R Studio.

I have read quite a few books on R, and will rate this as perhaps one of the best if not the best. The author has explained the material with excellent clarity, the examples are relevant to real-world applications, and the code executes well using either base installation of R (with added packages) or R studio. Also, the author has managed to concurrently develop both data analysis and data visualization (graphing), which helps the reader, and exposes the capabilities of R very well. The range of topics covered in this book is very broad and gives a good overview of the field. As a clinician with interest in epidemiology, I found almost everything needed for beginner or intermediate level applications. Also, if one works through this book diligently, s/he will be in a very good position to follow more advanced topics (like multilevel models) from other resources.I have only one issue with the book (which is a generic issue with most statistics or data science books centered around a software). That is, it contains very little, if any, mathematical basis or details of the methods being discussed. These days, statistics is taught "thorough" a software. Hence, it is artificial to separate mathematical statistics from computational aspects. I am waiting for a book which will start with the mathematical aspects including derivations of the theorems/equations, give examples of real world applications, and illustrate the application in R.One minor issue is related to very poor quality of the binding of the book. In the copy that I received, perhaps the pasting was defective, and as soon as I first opened it, the first page tore off. Soon, the binding opened up and pages/sections started falling off. I am not sure if this is just limited to the copy that I got or is a systemic issue.

R in Action does an excellent job showing the power of the R language. The book also serves a way to learn statistics and data science through using the R language.At the beginning the examples are practical, showing how to make graphs based off data sets that come packaged with R. Sometimes packages are required to install to get the examples running, and the author is clear about how to do this.In chapter eight there was an excellent description about Regression analysis. I knew what regressions were, but this explanation helped clear up some confusion I had. The author says (p.167): "It's [regression analysis] a broad term for a set of methodologies used to predict a response variable (also called a dependent, criterion, or outcome variable) from one or more predictor variables (also called independent or explanatory variables).Following this explanation is a concrete example given showing how to predict the number of calories a person burns on a treadmill based off different factors. I liked that the author defined all the terms, and then gave a concrete example. It is a pattern throughout the book that worked very well.The later parts of the book briefly dive in machine learning and more advanced data science concepts.The main focus of the book is on statistics, graphing, and plotting. This worked well, because it shows the areas where R shines compared with other languages. Luckily for the reader the author knows which techniques work well, and states when certain data analysis techniques work better than others. The author remains humble throughout, stating when he finds a topic complex.R in Action is a great resource for learning R and learning statistics. Explanations transition from easy to hard in a logical manner and this book will be useful for data scientists of all experience levels.

Excellent book . If you are new to Data Analysis it will more than meet your needs. The first few chapters copy basic R , input , output , data types. If you already know R , you will find that a quick review will show you ways to upload most any type of data including Excel spreadsheets.Chapters 7 on actually cover college level statistics including one-way and two-way tables, t-tests, regression . For statistical reference and data modeling the graphics that are included use the latest library's so you won't be wasting time trying to write your own functions. Chapters 12 on are pretty advanced statistics with real world examples.