R in Action, 2ed | BS

Got the book in good condition. The author via this book actually gives a captivating learning journey for the aspirant.

I have few books on data science, machine learning etc by well known authors. I found mention of this book in Quora answer by one University professor. I was not sure what I was getting when I bought the book. And I am glad I bought it.This is one solid book on exploratory data analysis which included lots of data visualization. This book covers lot of ground on data import, transformation, analysis and visualization and also delves into machine learning models. I found the writing style very easy to understand and coverage of topics pretty wide. This is very thoughtful book no doubt.Although this is great book there are couple of weaknesses I must point out.1. The author uses base R packages for all his data munging and visualization (and he did splendid job to be honest). important visualization package ggplot2 is covered in one chapter at the end. It is basically introduction to ggplot2. But he has stupendous job even in one chapter. He has covered lot of ground in just one chapter. ggplot2 is defacto package in R for visualization,.2. tidyverse package is defacto today for data processing and transformation in R. This book completely avoids tidyverse. You need to look elsewhere (R for Data science may be) for tidyverse coverage.3. The coverage of ML is limited for obvious reasons. for AI and other advanced ML algos you need to get other books.On ggplot2, the same author has written a complete book on data visualization using ggplot2 package. And it is free. Available on Net if you search. And that book is as good as this book in terms of content quality and thoughtful design of topics.But one thing is clear tidyverse or no tidyvesrse this book can definitely make you competent data analyst in no time.You will learn to slice and dice the data in variety of ways. You will learn to massage and get ready your data in many ways. You will learn to graphically display your insights in many ways. For the price I paid I got significant value from this book.I would not hesitate to recommend this book to all budding data scientists...professional or hobbyist. This book will help you confidently tackle your data.

Nice book for beginners to advanced people who are likely to learn data science with R

No second thoughts. The best book for the beginners and could meet the requirements of the advanced learners also. make sure to buy "second edition" which has data mining techniques also. Delivery was perfect by Amazon.

Very good book for learning programming and statstical Analysis using R

A great book to get started in R

Most usefull book for R language.

The book is ok type. It doesn't explain in details. This is fine for experienced folks but certainly not for bigginers.

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.

Nowadays, if you add “statistics” and “data science” in the book title, it’s trendy. But Dr. Kabacoff was “trendy” a long time ago.Today’s challenge for Data ScientistsIn fact, I would argue that today’s “statistics books for data science” are not as broad and deep as this book. Of course, there’s a reason for it. Today, data scientists “must” have such a broad range of skills that it’s literally impossible to dive deep in many subjects. There is simply not enough time to learn it all, let alone master it.Back to this outstanding book. The title “R in Action” basically means “statistics with code.” It doesn’t teach you statistics but rather shows you how to code it in R. And the range is really wide. As a practicing data scientists, it’s almost impossible to have a deep knowledge of all topics.I would argue, this is a fantastic book if you want to go deeper where your interests lie.Python userI’m a Python user and are not willing to learn R. Some experts recommend learning several programming languages, I don’t. I battle to improve my current skills.I deeply love Python, but for advanced statistics, the libraries, literature and courses are thin. The new book “Statistics for Data Scientists” doesn’t change it. It’s very basic. If you’re like me who wants to stick to Python while doing advanced statistics, here’s my tip: read this book to understand the ideas and try to implement them in Python. Some advanced courses on DataCamp can help (e.g. Statistical Simulation in Python).My personal highlights:Power Analysis (Chapter 10)I have never seen a more lucid explanation. Probably because many don’t understand it (including me). In the real-world, I would argue, we don’t power analysis often. But when we do, it’s extremely important. Power analysis answers the question for parametric tests “how many samples do we need” with a certain confidence interval.Advanced methods for missing data (Chapter 18)An often-neglected topic which can quickly become very difficult. In the real-world, we often take short cuts such as creating a new column and labeling missing data (often seen in Kaggle competitions).Resampling statistics and bootstrapping (Chapter 12)At first, one might think this is overkill, but if you have a non-parametric dataset (i.e. you don’t know the distribution or variance) and need to be 95% sure that you have enough samples, then this technique is super powerful. Bootstrapping can become extremely hard when you have different bins and weights.

It's a great self-learning book and, after two years, it still serves me well as a reference. Its focus is on how to do analyses in R so it's always useful to check before I delve fully into a new project. Unlike other books, it doesn't really cover the tidyverse, which leaves space for the really useful stuff. It's a book for people that want to do statistics in R and not so much aspiring data scientists. I liked it so much that I bought a copy for my girlfriend and it's pretty useful in her research.

El texto de Kabacoff es una excelente introducción al lenguaje R. El enfoque por ejemplo prácticos hace que se comprendan todos los temas sin dificultad . Incluye temas que van desde la estadística descriptiva hasta temas más avanzados como la gráficación y el modelo lineal entre muchos otros temas. En esta segunda edición se incluyen temas adicionales como el análisis de series de tiempo y el análisis de conglomerados. Adicionalmente los libros de la editorial Manning incluyen la descarga de la versión digital del libro, lo que lo hace doblemente valioso. Es un libro que puede ser de utilidad para el novato en R así como para al experto en estadística. Totalmente recomendable.

My daughter (studying economics) had to learn R in a short period from a background of no coding whatsoever. I helped her identify this book which has been immensely helpful to her. The author very kindly lets you read the whole text as a free e-book first, which we did. We found it so helpful that we’ve now done the right thing and bought the book. If you’re faced with learning R I really recommend this book.