Book Of R

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Book Of R

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Book of r

The bookdown package is built on top of R Markdown. It inherits the simplicity of Markdown syntax and adds features like multi-page HTML output, numbering, and cross-referencing.

An introduction to the grammar of graphics, how to use the grammar to build graphs, and how ggplot2 implements the grammar in R.

Some of the code details may be out of date. An explanation of R for advanced users. The book explains R as a programming language, covering topics such as S3 and S4 methods, scoping rules, performance and much more.

Turn your R code into packages that others can easily download and use. An introduction to programming in R. The book uses three hands-on projects to teach every aspect of R programming, from loading data to writing fast, vectorized functions.

Archives: Book 1 Book 2 More. Home Top Content Editorial Guidelines. Top Content Archives. Books about the R programming language fall in different categories: Learning R Reference books for the professional R programmer Books about data science or visualization, using R to illustrate the concepts Books are a great way to learn a new programming language.

Author Joseph Adler illustrates each process with a wealth of examples from medicine, business, and sports. This comprehensive video course shows you how to explore and understand data, as well as how to build linear and non-linear models in the R language and environment.

The R language provides everything you need to do statistical work, but its structure can be difficult to master. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression.

Each recipe tackles a specific problem with a solution you can apply to your own project, and includes a discussion of how and why the recipe works.

Most of the recipes use the ggplot2 package, a powerful and flexible way to make graphs in R. 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.

The focus of this book is mainly on the practical implementation of the fundamentals of data analysis in R. Another feature of this book is that it covers several topics like data pre-processing, statistics, probability, time-series analysis etc.

You will also learn the various techniques that are applied to the datasets in R. There are practical recipes using which you will be able to perform data analysis in R without any hassle.

For R programmers, who want to write their own packages, this book provides all the necessary skills required for the job. Using the documentation of the reusable R functions, the author provides a detailed description of how to use them.

This book makes it easy to write your own customised packages which may seem daunting to the beginners. You will also gain knowledge of several components of R packages such as unit tests and vignettes.

As the name suggests, this book is not suitable for R programmers who wish to append additional knowledge to their arsenal of R skills.

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Powered by. Archives: Book 1 Book 2 More. Home Top Content Editorial Guidelines. Top Content Archives. Books about the R programming language fall in different categories: Learning R Reference books for the professional R programmer Books about data science or visualization, using R to illustrate the concepts Books are a great way to learn a new programming language.

Author Joseph Adler illustrates each process with a wealth of examples from medicine, business, and sports. This comprehensive video course shows you how to explore and understand data, as well as how to build linear and non-linear models in the R language and environment.

The R language provides everything you need to do statistical work, but its structure can be difficult to master. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression.

Each recipe tackles a specific problem with a solution you can apply to your own project, and includes a discussion of how and why the recipe works.

Impractical Python Projects picks up where the complete beginner books leave off, expanding on existing concepts …. Automate the Boring Stuff with Python teaches simple programming skills to automate everyday computer tasks.

Deitel, Paul Deitel, Harvey Deitel. Skip to main content. Start your free trial. The Book of R by Tilman M. You will master skills like data cleaning, wrangling, visualisation, etc.

This book also provides an introduction to RStudio. Furthermore, the trending packages like tidyverse are part of this book, helping you to gain recent tools that are used in data science.

With this book, you will learn the everyday tasks of a data scientist. The sole focus of this book is to teach programming in R.

This book is ideal for people who want to expand their programming knowledge of R. This book also contains extended examples along with black-box packages to help you understand how R makes use of various programming constructs.

Another important and rare feature that this book provides is the debugging principles in R. The prerequisite knowledge of statistics is not mandatory and you can be a hobbyist or a pro-programmer.

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.

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