The focus of this book is to perform the statistical implementation of various methodologies in R. In order to gain a comprehensive insight into the contents of this book, there is a MOOC provided by Stanford Lagunita that comprises of series of lectures that will help you along the way.

With the help of this book, you will not only gain a theoretical understanding of how various statistical methodologies work but also learn to implement them with R.

If you want to make RStudio your ideal IDE for performing statistical computing in R, then this is the best book for you. You will learn how to use various functionalities with RStudio, perform reporting and optimise the development process.

With the various functionalities of R, you can create efficient statistical models without any hassle. You can also manage various projects, easily import the data and plot robust visualisations.

The approach of this book is very pragmatic as it teaches R through its various applications and use-cases in Data Science.

The various authors of the book have an in-depth understanding of the different packages of R that are used in Data Science. In this way, they have combined the useful packages together to provide comprehensive implementation of R in data science.

For aspiring data scientists, who wish to learn all the essential tools and packages of R in one single place, this book will prove to be the best guide for them.

Another important feature of this book is that in order to increase the readability, it does away with all the unnecessary. This book has laid out only the important facts and figures that are necessary while learning R programming.

This book covers the diverse topics of Data Science in R such as data visualisation, data manipulation, predictive modeling as well as clustering and building of R packages.

One downside of this book is its limitation in covering only the breadth of R but not its depth, due to which this book is much briefer in its approach.

Furthermore, the emphasis of this book is on the implementation of various algorithms in R and their various examples. People who are much more interested in getting a hands-on approach in R without dwelling much over the details will find this book appealing.

With the help of this book, you will gain the knowledge of the various important techniques that are essential for performing data analysis in R.

Introduces a clear structure with numbered section headings to help readers locate information more efficiently.

The breadth of topics covered is unsurpassed when it comes to texts on data analysis in R. And now anybody can get to grips with it thanks to The R Book Today, software engineers need to know not only how to program effectively but also how to ….

Deitel, Paul Deitel, Harvey Deitel. R is a flexible and powerful programming language. Readers of this book will benefit from learning the basics of programming in R; however, descriptions of R programming will be kept to a minimum here.

There are books and online resources available to learn R programming. Non-commercial reproduction of this content, with attribution, is permitted.

For-profit reproduction without permission is prohibited. If you use the code or information in this site in a published work, please cite it as a source.

Also, if you are an instructor and use this book in your course, please let me know. My contact information is on the About the Author of this Book page.

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