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https://github.com/maxto/R-Books

A non-exhaustive list of R books. Full list here bookdown.org
https://github.com/maxto/R-Books

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A non-exhaustive list of R books. Full list here bookdown.org

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# R-Books



A non-exhaustive list of R books. Full list here [bookdown.org](https://bookdown.org/)



### R



- [R Packages](https://r-pkgs.org/) Packages are the fundamental units of reproducible R code. They include reusable R functions, the documentation that describes how to use them, and sample data. In this book you’ll learn how to turn your code into packages that others can easily download and use.



- [Advanced R](https://adv-r.hadley.nz/) This is the website for work-in-progress 2nd edition of “Advanced R”, a book in Chapman & Hall’s R Series. The book is designed primarily for R users who want to improve their programming skills and understanding of the language. It should also be useful for programmers coming to R from other languages, as it explains some of R’s quirks and shows how some parts that seem horrible do have a positive side.



- [Efficient R programming](https://csgillespie.github.io/efficientR/) Drawing on years of experience teaching R courses, authors Colin Gillespie and Robin Lovelace provide practical advice on a range of topics—from optimizing the set-up of RStudio to leveraging C++—that make this book a useful addition to any R user’s bookshelf. Academics, business users, and programmers from a wide range of backgrounds stand to benefit from the guidance in Efficient R Programming.



- [R, Databases, and Docker](https://smithjd.github.io/sql-pet/) This book will help you create a hybrid environment on your machine that can mimic some of the uncharted territory in your organization. It goes far beyond the basic connection issues and covers issues that you face when you are finding your way around or writing queries to your organization’s databases, not just when maintaining inherited scripts.



- [Mastering Software Development in R](https://bookdown.org/rdpeng/RProgDA/) This book is designed to be used in conjunction with the course sequence Mastering Software Development in R, available on Coursera. The book covers R software development for building data science tools. As the field of data science evolves, it has become clear that software development skills are essential for producing useful data science results and products. You will obtain rigorous training in the R language, including the skills for handling complex data, building R packages and developing custom data visualizations. You will learn modern software development practices to build tools that are highly reusable, modular, and suitable for use in a team-based environment or a community of developers.



- [The tidyverse style guide](http://style.tidyverse.org/index.html) Good coding style is like correct punctuation: you can manage without it, butitsuremakesthingseasiertoread. This site describes the style used throughout the tidyverse. It was originally derived from Google’s R style guide, but has evolved and expanded considerably over the years.



### R Shiny



- [JavaScript for R](https://book.javascript-for-r.com/) This book aims to remedy that by revealing how much JavaScript can greatly enhance various stages of data science pipelines from the analysis to the communication of results.



- [Mastering Shiny](https://mastering-shiny.org/) Shiny is a framework for creating web applications using R code. It is designed primarily with data scientists in mind, and to that end, you can create pretty complicated Shiny apps with no knowledge of HTML, CSS, or JavaScript. 



- [Outstanding User Interfaces with Shiny](https://divadnojnarg.github.io/outstanding-shiny-ui/) This book is not an HTML/Javascript/CSS course! Instead, it provides a survival kit to be able to customize Shiny. I am sure however that readers will want to explore more about these topics.



- [Engineering Production-Grade Shiny Apps](https://engineering-shiny.org/) This book will not get you started with Shiny, nor talk how to work with Shiny once it is sent to production. What we’ll see is the process of building an application that will later be sent to production.



### Data Science 



- [R for Data Science](http://r4ds.had.co.nz/) This is the website for “R for Data Science”. This book will teach you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you’ll learn how to clean data and draw plots—and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You’ll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You’ll also learn how to manage cognitive resources to facilitate discoveries when wrangling, visualising, and exploring data.



- [Functional programming and unit testing for data munging with R](https://b-rodrigues.github.io/fput/) This book serves to show how functional programming and unit testing can be useful for the task of data munging. This book is not an in-depth guide to functional programming, nor unit testing with R. If you want to have an in-depth understanding of the concepts presented in these books, I can’t but recommend Wickham (2014a), Wickham (2015) and Wickham and Grolemund (2016) enough. Here, I will only briefly present functional programming, unit testing and building your own R packages. Just enough to get you (hopefully) interested and going.



- [Exploratory Data Analysis with R](https://bookdown.org/rdpeng/exdata/) This book covers the essential exploratory techniques for summarizing data with R. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data you have. We will cover in detail the plotting systems in R as well as some of the basic principles of constructing informative data graphics. We will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data.



- [Text Mining with R](https://www.tidytextmining.com/) This book serves as an introduction of text mining using the tidytext package and other tidy tools in R. The functions provided by the tidytext package are relatively simple; what is important are the possible applications. Thus, this book provides compelling examples of real text mining problems.



- [Feature Engineering and Selection: A Practical Approach for Predictive Models](https://bookdown.org/max/FES/) The goals of Feature Engineering and Selection are to provide tools for re-representing predictors, to place these tools in the context of a good predictive modeling framework, and to convey our experience of utilizing these tools in practice.



- [Statistical Inference via Data Science](https://moderndive.com/) This book will help you develop your “data science toolbox”, including tools such as data visualization, data formatting, data wrangling, and data modeling using regression. With these tools, you’ll be able to perform the entirety of the “data/science pipeline” while building data communication skills



- [Hands-on Machine Learning with R](https://bradleyboehmke.github.io/HOML/) You will learn how to build and tune these various models with R packages that have been tested and approved due to their ability to scale well. However, our motivation in almost every case is to describe the techniques in a way that helps develop intuition for its strengths and weaknesses. 



- [Introduction to Data Exploration and Analysis with R](https://bookdown.org/mikemahoney218/IDEAR/) This book is designed as a crash course in coding with R and data analysis, built for people trying to teach themselves the skills needed for most analyst jobs today. You won’t need any past experience with R or data analytics - the aim of the book is to work as a primer for people of all backgrounds.



- [Explanatory Model Analysis](https://ema.drwhy.ai/) Explore, Explain, and Examine Predictive Models. With examples in R and Python



### Data Visualization



- [Fundamentals of Data Visualization](https://serialmentor.com/dataviz/) The book is meant as a guide to making visualizations that accurately reflect the data, tell a story, and look professional. It has grown out of my experience of working with students and postdocs in my laboratory on thousands of data visualizations. Over the years, I have noticed that the same issues arise over and over. I have attempted to collect my accumulated knowledge from these interactions in the form of this book. The book’s source code is hosted on GitHub, at https://github.com/clauswilke/dataviz.



- [R Graphics Cookbook, 2nd edition](https://r-graphics.org/) A practical guide that provides more than 150 recipes to help you generate high-quality graphs quickly, without having to comb through all the details of R’s graphing systems. 



- [Data Visualization with R](https://rkabacoff.github.io/datavis/) R is an amazing platform for data analysis, capable of creating almost any type of graph. This book helps you create the most popular visualizations - from quick and dirty plots to publication-ready graphs. The text relies heavily on the ggplot2 package for graphics, but other approaches are covered as well.



- [Interactive web-based data visualization with R, plotly, and shiny](https://plotly-r.com/index.html) In this book, you’ll gain insight and practical skills for creating interactive and dynamic web graphics for data analysis from R. It makes heavy use of plotly for rendering graphics, but you’ll also learn about other R packages that augment a data science workflow, such as the tidyverse and shiny.



- [Data Visualization with ggplot2](https://viz-ggplot2.rsquaredacademy.com/) Data Visualization with ggplot2



### Database 



- [Mongolite User Manual](https://jeroen.github.io/mongolite/) This book provides a high level introduction to using MongoDB with the mongolite client in R.



### Financial



- [Techincal Analysis with R](https://bookdown.org/kochiuyu/Technical-Analysis-with-R/) This short book is a short introduction on how to use R and RStudio to do financial data analysis from the beginning. No prior knowledge of R is required. While you will learn various skills to work on R programming but the main goal is to learn how to use R to backtest a trading strategy and evaluate its performance.



- [Principles of Econometrics with  R](https://bookdown.org/ccolonescu/RPoE4/) Resource for the “Principles of Econometrics” textbook by Carter Hill, William Griffiths and Guay Lim, 4-th edition (Hill, Griffiths, and Lim 2011).



- [Processing and Analyzing Financial Data with R](https://www.msperlin.com/pafdR/) This book introduces the reader to the use of R and RStudio as a platform for processing and analyzing financial data.



- [Forecasting: Principles and Practice](https://otexts.com/fpp2/) This textbook is intended to provide a comprehensive introduction to forecasting methods and to present enough information about each method for readers to be able to use them sensibly. We don’t attempt to give a thorough discussion of the theoretical details behind each method, although the references at the end of each chapter will fill in many of those details.



### Utils



- [Happy Git and GitHub for the useR](https://happygitwithr.com/) Happy Git provides opinionated instructions on how to: Install Git and get it working smoothly with GitHub, in the shell and in the RStudio IDE. Develop a few key workflows that cover your most common tasks. Integrate Git and GitHub into your daily work with R and R Markdown.



- [R Markdown: The Definitive Guide](https://bookdown.org/yihui/rmarkdown/) The document format “R Markdown” was first introduced in the knitr package (Xie 2015, 2018d) in early 2012. The idea was to embed code chunks (of R or other languages) in Markdown documents. In fact, knitr supported several authoring languages from the beginning in addition to Markdown, including LaTeX, HTML, AsciiDoc, reStructuredText, and Textile. Looking back over the five years, it seems to be fair to say that Markdown has become the most popular document format, which is what we expected. The simplicity of Markdown clearly stands out among these document formats.



- [bookdown](https://bookdown.org/yihui/bookdown/) bookdown: Authoring Books and Technical Documents with R Markdown.

This short book introduces an R package, bookdown, to change your workflow of writing books. It should be technically easy to write a book, visually pleasant to view the book, fun to interact with the book, convenient to navigate through the book, straightforward for readers to contribute or leave feedback to the book author(s), and more importantly, authors should not always be distracted by typesetting details.



### Books



- [Big Book of R](https://www.bigbookofr.com/index.html) Your last-ever bookmark.The collection now stands at about 250 books. Most of these are free. Some are paid but usually quite affordable.



Misc books [here](https://bookdown.org/home/archive/)