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# Career & Community
## Build Your Career in Data Science
Not R-specific but a great read!
You are going to need more than technical knowledge to succeed as a data scientist. Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager.
https://www.manning.com/books/build-a-career-in-data-science
## Twitter for R Programmers
[Oscar Baruffa](https://twitter.com/OscarBaruffa), [Veerle van Son](https://twitter.com/veerlevanson)
The R community is very active on Twitter. You can learn a lot about the language, about new approaches to problems, make friends and even land a job or next contract. It’s a real-time pulse of the R community.What can you gain from becoming active on Twitter? This book will talk about the benefits and it will show you how to use Twitter.
https://www.t4rstats.com
## Twitter for Scientists
Not R-specific, but as many R users are also Scientists this book can really help.
https://t4scientists.com/
## Conversations On Data Science
Roger Peng and Hilary Parker
This book collects many of their discussions from the podcast
[__Not So Standard Deviations__](https://soundcloud.com/nssd-podcast)
and distills them into a readable format.
Pay what you want for the ebook, minimum $0.00
https://leanpub.com/conversationsondatascience
## Executive Data Science
Brian Caffo, [Roger D. Peng](https://twitter.com/rdpeng), and [Jeffrey Leek](https://twitter.com/jtleek)
A Guide to Training and Managing the Best Data Scientists. Learn what you need
to know to begin assembling and leading a data science enterprise.
Pay what you want for the PDF, minimum $0.00
https://leanpub.com/eds
## Essays on Data Analysis
Roger Peng
This book draws a complete picture of the data analysis process, filling out
many details that are missing from previous presentations. It presents a new
perspective on what makes for a successful data analysis and how the quality
of data analyses can be judged.
Pay what you want for the ebook, minimum $0.00
https://leanpub.com/dataanalysisessays
## Data Science in a Box
[Mine Çetinkaya-Rundel](https://twitter.com/minebocek)
This book focuses on how to efficiently teach data science to students with little to no background in computing and statistical thinking. The core content of the course focuses on data acquisition and wrangling, exploratory data analysis, data visualization, inference, modelling, and effective communication of results.
This book is free online.
https://datasciencebox.org/
# Blogdown
## blogdown: Creating Websites with R Markdown
We introduce an R package, blogdown, in this short book, to teach you how to create websites using R Markdown and Hugo.
https://bookdown.org/yihui/blogdown/
# 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.
https://bookdown.org/yihui/bookdown/
## A Minimal Book Example
This is a sample book written in Markdown.
https://benmarwick.github.io/bookdown-ort/
# Data Science
## R for Data Science
[Hadley Whickham](https://twitter.com/hadleywickham) [Garret Grolemund](https://twitter.com/StatGarrett)
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.
https://r4ds.had.co.nz/
## R for Data Science Solutions
Solutions for the hadley and Grolemund R4Ds book
https://jrnold.github.io/r4ds-exercise-solutions/
*Yet another ‘R for Data Science’ study guide*
An alternative set of solutions for R4Ds.
https://brshallo.github.io/r4ds_solutions/
## Introduction to Data Science
Rafael A Irizarry
The demand for skilled data science practitioners in industry, academia, and government is rapidly growing. This book introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling with dplyr, data visualization with ggplot2, algorithm building with caret, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation with knitr and R markdown.
https://rafalab.github.io/dsbook/
Pay what you want for PDF, minimum $0.00
https://leanpub.com/datasciencebook
## R Programming for Data Science
Roger Peng
This book is about the fundamentals of R programming. You will get started with the basics of the language, learn how to manipulate datasets, how to write functions, and how to debug and optimize code. With the fundamentals provided in this book, you will have a solid foundation on which to build your data science toolbox.
https://bookdown.org/rdpeng/rprogdatascience/
## Exploratory Data Analysis… by Roger D. Peng
Roger Peng
This book teaches you to use R to effectively visualize and explore complex datasets. Exploratory data analysis is a key part of the data science process because it allows you to sharpen your question and refine your modeling strategies. This book is based on the industry-leading Johns Hopkins Data Science Specialization
Pay what you want, minimum $0.00
https://leanpub.com/exdata
## edav.info/
Zach Bogart, Joyce Robbins
With this resource, we try to give you a curated collection of tools and references that will make it easier to learn how to work with data in R.
In addition, we include sections on basic chart types/tools so you can learn by doing.
There are also several walkthroughs where we work with data and discuss problems as well as some tips/tricks that will help you.
https://edav.info/
## APS 135: Introduction to Exploratory Data Analysis with R
Dylan Z. Childs
This is the online course book for the Introduction to Exploratory Data Analysis with R component of APS 135, a module taught by the Department and Animal and Plant Sciences at the University of Sheffield. You will be introduced to the R ecosystem.You will learn how to use R to carry out data manipulation and visualisation.This book provides a foundation for learning statistics later on.
https://dzchilds.github.io/eda-for-bio/
## The Art of Data Science
[Roger D. Peng](https://twitter.com/rdpeng) and Elizabeth Matsui
A Guide for Anyone Who Works with Data
This book describes the process of analyzing data. The authors have extensive
experience both managing data analysts and conducting their own data analyses,
and this book is a distillation of their experience in a format that is applicable
to both practitioners and managers in data science. Printed copies are available through [Lulu](https://www.lulu.com/content/paperback-book/the-art-of-data-science/18733039).
Pay what you want for the ebook, minimum $0.00
https://leanpub.com/artofdatascience
## The Elements of Data Analytic Style
[Jeffrey Leek](https://twitter.com/jtleek)
Data analysis is at least as much art as it is science. This book is focused
on the details of data analysis that sometimes fall through the cracks in
traditional statistics classes and textbooks. It is based in part on the authors
blog posts, lecture materials, and tutorials.
Pay what you want for the ebook, minimum $0.00
https://leanpub.com/datastyle
## Beginning Data Science in R
[Thomas Mailund](https://twitter.com/ThomasMailund)
Beginning Data Science in R details how data science is a combination of statistics, computational science, and machine learning. You’ll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. Those with some data science or analytics background, but not necessarily experience with the R programming language
Paid, ~$40
https://amzn.to/2Ns1HHi
## R Data Science Quick Reference
[Thomas Mailund](https://twitter.com/ThomasMailund)
In this book, you’ll learn about the following APIs and packages that deal specifically with data science applications: readr, dibble, forecasts, lubridate, stringr, tidyr, magnittr, dplyr, purrr, ggplot2, modelr, and more.
Paid, ~$30
https://amzn.to/2WN1mQy
# Data Visualization
## ggplot2: Elegant Graphics for Data Analysis
Hadley Wickham
ggplot2 is an R package for producing statistical, or data, graphics. Unlike most other graphics packages, ggplot2 has an underlying grammar, based on the Grammar of Graphics (Wilkinson 2005), that allows you to compose graphs by combining independent components. This makes ggplot2 powerful. Rather than being limited to sets of pre-defined graphics, you can create novel graphics that are tailored to your specific problem.
https://ggplot2-book.org/
## ggplot2 in 2
Lucy D'Agostino McGowan
Pay what you want, minimum $4.99
Really good overview of ggplot2. The premise is that you'll cover the fundamentals in 2 hours. Oscar Baruffa made a sped-up [screencast](https://youtu.be/_G7_J8M9588) while working through it. It did take 2 hours :).
https://leanpub.com/ggplot2in2
## Data Visualization - A practical introduction
[Kieran Healy](https://twitter.com/kjhealy)
This book is a hands-on introduction to the principles and practice of looking at and presenting data using R and ggplot.
The book is free online.
https://socviz.co/
## Data Processing & Visualization
This document provides some tools, demonstrations, and more to make data processing, programming, modeling, visualization, and presentation easier.While the programming language focus is on R, where applicable (which is most of the time), Python notebooks are also available,.
https://m-clark.github.io/data-processing-and-visualization/
## Data Visualization in R
Brooke Anderson
Workshop for the 2019 Navy and Marine Corps Public Health Conference. I have based this workshop on examples for you to try yourself, because you won’t be able to learn how to program unless you try it out. I’ve picked example data that I hope will be interesting to Navy and Marine Corp public health researchers and practitioners.
https://geanders.github.io/navy_public_health/index.html#prerequisites
## Data Visualization with R
Rob Kabakoff
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.
https://rkabacoff.github.io/datavis/
## R Graphics Cookbook, 2nd edition
The goal of the cookbook is to provide solutions to common tasks and problems in analyzing data.
The book is free online.
https://r-graphics.org/
## plotly Interactive web-based data visualization with R, plotly, and shiny
Carson Sievert
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. Along the way, you’ll gain insight into best practices for visualization of high-dimensional data, statistical graphics, and graphical perception.
https://plotly-r.com/
## BBC Visual and Data Journalism cookbook for R graphics
At the BBC data team, we have developed an R package and an R cookbook to make the process of creating publication-ready graphics in our in-house style using R’s ggplot2 library a more reproducible process, as well as making it easier for people new to R to create graphics.
https://bbc.github.io/rcookbook/
## Fundamentals of Data Visualization
[Claus Wilke](https://twitter.com/ClausWilke)
The book is meant as a guide to making visualizations that accurately reflect the data, tell a story, and look professional.
The book is free online.
https://clauswilke.com/dataviz/
## Graphical Data Analysis with R
[Antony Unwin](http://www.gradaanwr.net/author/)
The main aim of the book is to show, using real datasets, what information graphical displays can reveal in data. The target readership includes anyone carrying out data analyses who wants to understand their data using graphics.
The book is published by CRC Press and [available to purchase](https://www.routledge.com/Graphical-Data-Analysis-with-R/Unwin/p/book/9781498715232), but all the examples and code are freely available on a comprehensive website accompanying the text at http://www.gradaanwr.net/
## JavaScript for R
[John Coene](https://john-coene.com)
Learn how to build your own data visualsiation packages, improve shiny with
JavaScript, and use JavaScript for computations.
Freely avaiable online, paid print.
https://javascript-for-r.com
# Distributed computing
## Mastering Spark with R
Javier Luraschi, Kevin Kuo, Edgar Ruiz
In this book you will learn how to use Apache Spark with R. The book intends to take someone unfamiliar with Spark or R and help you become proficient by teaching you a set of tools, skills and practices applicable to large-scale data science.
PS the first chapter has a Jon Snow quote ;)
https://therinspark.com/
# Getting, cleaning and wrangling data
## A Beginner's Guide to Clean Data - beginners-guide-to-clean-data
Benjamin Greve
This book will help you to become a better data scientist by showing you the things that can go wrong when working with data - particularly low-quality data. A key difference between a junior and a senior data scientist is the awareness of potential pitfalls. The experienced data scientist will expect them, navigate around them and avoid costly iteration cycles. After reading this book, you will be able to spot data quality problems and deal with them before they can break your work, saving yourself a lot of time.
https://b-greve.gitbook.io/beginners-guide-to-clean-data/
## 21 Recipes for Mining Twitter Data with rtweet
Bob Rudis
The recipes contained in this book use the rtweet package by Michael W. Kearney.
https://rud.is/books/21-recipes/
## Text Mining with R
[Julia Silge](https://twitter.com/juliasilge) and [David Robinson](https://twitter.com/drob)
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.
https://www.tidytextmining.com/
## Spreadsheet Munging Strategies
Duncan Garmonsway
This is a work-in-progress book about getting data out of spreadsheets, no matter how peculiar. The book is designed primarily for R users who have to extract data from spreadsheets and who are already familiar with the tidyverse. It has a cookbook structure, and can be used as a reference, but readers who begin in the middle might have to work backwards from time to time.
https://nacnudus.github.io/spreadsheet-munging-strategies/
# Geospatial
## Geocomputation with R
Robin Lovelace, Jakub Nowosad, Jannes Muenchow
This is the online home of Geocomputation with R, a book on geographic data analysis, visualization and modeling.
https://geocompr.robinlovelace.net/
## Spatial Data Science
Edzer Pebesma, Roger Bivand
This book introduces and explains the concepts underlying spatial data: points, lines, polygons, rasters, coverages, geometry attributes, data cubes, reference systems, as well as higher-level concepts including how attributes relate to geometries and how this affects analysis.
https://keen-swartz-3146c4.netlify.app/
## Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny
Paula Moraga
This book describes spatial and spatio-temporal statistical methods and visualization techniques to analyze georeferenced health data in R. After a detailed introduction of geospatial data, the book shows how to develop Bayesian hierarchical models for disease mapping and apply computational approaches such as the integrated nested Laplace approximation (INLA) and the stochastic partial differential equation (SPDE) to analyze areal and geostatistical data.
https://www.paulamoraga.com/book-geospatial/
## Introduction to Spatial Data Programming with R
[Michael Dorman](https://twitter.com/MichaelDorman84)
This book introduces processing and analysis methods for working with spatial data in R. The book is composed of two parts. The *first* part gives an overview of the basic syntax and usage of the R language, required before we can start working with spatial data. The *second* part then covers spatial data workflows, including how to process rasters, vector layers, and both of them together, as well as two selected advanced topics: spatio-temporal data and spatial interpolation.
https://geobgu.xyz/r
# Life Sciences
## Data Analysis for the Life Sciences
Rafael A Irizarry and Michael I Love
Data analysis is now part of practically every research project in the life sciences. In this book we use data and computer code to teach the necessary statistical concepts and programming skills to become a data analyst. Instead of showing theory first and then applying it to toy examples, we start with actual applications.
Pay what you want for the ebook, minimum $0.00
https://leanpub.com/dataanalysisforthelifesciences
[Accompanying website](http://genomicsclass.github.io/book/)
## Modern Statistics for Modern Biology
Susan Holmes, Wolfgang Huber
The aim of this book is to enable scientists working in biological research to quickly learn many of the important ideas and methods that they need to make the best of their experiments and of other available data.
https://www.huber.embl.de/msmb/
## Orchestrating Single-Cell Analysis with Bioconductor
[Aaron Lun, Robert Amezquita, Stephanie Hicks, Raphael Gottardo](https://osca.bioconductor.org/contributors.html)
This is the website for “Orchestrating Single-Cell Analysis with Bioconductor”, a book that teaches users some common workflows for the analysis of single-cell RNA-seq data (scRNA-seq).
https://osca.bioconductor.org/
## Assigning cell types with SingleR
[Aaron Lun]((https://osca.bioconductor.org/contributors.html)
This book covers the use of SingleR, one implementation of an automated annotation method for cell type annotation.
https://bioconductor.org/books/3.12/SingleRBook/
## Statistics in R for Biodiversity Conservation Paperback
by Carl Smith , [Antonio Uzal](https://twitter.com/antonio_uzal) , Mark Warren
A practical handbook to introduce data analysis and model fitting using R to ecologists and conservation biologists. The book is aimed at undergraduate and post-graduate students and provides access to datasets and RScript.
Paid product ~$10
https://www.amazon.co.uk/dp/B08HBLYHQL/ref=cm_sw_r_cp_apa_i_g0luFb86PXJ9Z
## Computational Genomics with R
Altuna Akalin
The aim of this book is to provide the fundamentals for data analysis for genomics. We developed this book based on the computational genomics courses we are giving every year.
http://compgenomr.github.io/book/
# Machine Learning
## Hands-On Machine Learning with R
Bradley Boehmke & Brandon Greenwell
This book provides hands-on modules for many of the most common machine learning methods to include:
Generalized low rank models, Clustering algorithms, Autoencoders, Regularized models, Random forests, Gradient boosting machines, Deep neural networks, Stacking / super learners and more!
https://bradleyboehmke.github.io/HOML/
## Feature Engineering and Selection: A Practical Approach for Predictive Models
Max Kuhn and Kjell Johnson
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.
http://www.feat.engineering/index.html
## Interpretable Machine Learning
[Christoph Molnar](https://twitter.com/ChristophMolnar)
A Guide for Making Black Box Models Explainable
[Online book](https://christophm.github.io/interpretable-ml-book/)
Pay what you want for the ebook, minimum $0.00
[Leanpub PDF](https://leanpub.com/interpretable-machine-learning)
## Explanatory Model Analysis
Responsible, Fair and Explainable Predictive Modeling
with examples in R and Python
[Przemyslaw Biecek](https://github.com/pbiecek), Tomasz Burzykowski
Free online: https://pbiecek.github.io/ema/
Or paid printed: https://www.routledge.com/Explanatory-Model-Analysis-Explore-Explain-and-Examine-Predictive-Models/Biecek-Burzykowski/p/book/9780367135591
## Supervised Machine Learning for Text Analysis in R
[Emil Hvitfeldt](https://twitter.com/Emil_Hvitfeldt), [Julia Silge](https://twitter.com/juliasilge)
Modeling as a statistical practice can encompass a wide variety of activities. This book focuses on supervised or predictive modeling for text, using text data to make predictions about the world around us. We use the tidymodels framework for modeling, a consistent and flexible collection of R packages developed to encourage good statistical practice.
https://smltar.com/
## Machine Learning for Factor Investing
[Guillaume Coqueret](https://twitter.com/g_coqueret) and [Tony Guida](https://twitter.com/TonyGUIDA_Quant)
This book is intended to cover some advanced modelling techniques applied to equity investment strategies that are built on firm characteristics.
http://www.mlfactor.com/
## The caret Package
[Max Kuhn]([email protected])
The caret package (short for Classification And REgression Training) is a set of functions that attempt to streamline the process for creating predictive models.
This book is free online.
https://topepo.github.io/caret/index.html
## A Minimal rTorch Book
Alfonso R. Reyes
Practically, you can do everything you could with PyTorch within the R ecosystem.
https://f0nzie.github.io/rtorch-minimal-book/
## Tidy Modeling with R
[Max Kuhn](https://twitter.com/topepos) and [Julia Silge](https://twitter.com/juliasilge)
This book provides an introduction to how to use the tidymodels suite of packages to create models using a tidyverse approach and encourages good methodology and statistical practice throughout demonstrated using series of applied examples.
This book is free online (currently a work in progress).
https://www.tmwr.org/
## mlr3 book
[Michel Lang](https://twitter.com/michellangts?lang=en)
The mlr3 package and [ecosystem](https://github.com/mlr-org/mlr3/wiki/Extension-Packages) provide a generic, object-oriented, and extensible framework for classification, regression, survival analysis, and other machine learning tasks for the R language. They do not implement any learners, but provide a unified interface to many existing learners in R.
The book is free and work in progress.
https://mlr3book.mlr-org.com/
# Network analysis
## Network Analysis in R Cookbook
Sacha Epskamp
http://sachaepskamp.com/files/Cookbook.html
# Reports: R Markdown and knitr
## R Markdown: The Definitive Guide
[Yihui Xie](https://yihui.org/), [J. J. Allaire](https://github.com/jjallaire), [Garrett Grolemund](https://twitter.com/StatGarrett)
The first official book authored by the core R Markdown developers that provides a comprehensive and accurate reference to the R Markdown ecosystem. With R Markdown, you can easily create reproducible data analysis reports, presentations, dashboards, interactive applications, books, dissertations, websites, and journal articles, while enjoying the simplicity of Markdown and the great power of R and other languages.
https://bookdown.org/yihui/rmarkdown/
## R Markdown Cookbook
[Yihui Xie](https://yihui.org/), [Christophe Dervieux](https://twitter.com/chrisderv), [Emily Riederer](https://twitter.com/emilyriederer)
This book showcases short, practical examples of lesser-known tips and tricks to helps users get the most out of these tools. After reading this book, you will understand how R Markdown documents are transformed from plain text and how you may customize nearly every step of this processing. For example, you will learn how to dynamically create content from R code, reference code in other documents or chunks, control the formatting with customer templates, fine-tune how your code is processed, and incorporate multiple languages into your analysis.
https://bookdown.org/yihui/rmarkdown-cookbook/
## Getting used to R, RStudio, and R Markdown
Chester Ismay
This resource is designed to provide new users to R, RStudio, and R Markdown with the introductory steps needed to begin their own reproducible research. A review of many of the common R errors encountered (and what they mean in layman’s terms) will also provided be provided.
https://bookdown.org/chesterismay/rbasics/
## Report Writing for Data Science in R
[Roger D. Peng](https://twitter.com/rdpeng)
This book teaches the fundamental concepts and tools behind reporting modern
data analyses in a reproducible manner. As data analyses become increasingly
complex, the need for clear and reproducible report writing is greater than ever.
Pay what you want for the ebook, minimum $0.00
https://leanpub.com/reportwriting
## Introduction to R Markdown
Michael Clark
The goal is for you to be able to get quickly started with your own document, and understand the possibilities available to you. You will get a feel for the basic mechanics at play, as well as have ideas on how to customize the result to your own tastes.
https://m-clark.github.io/Introduction-to-Rmarkdown/
## RMarkdown for Scientists
Nicholas Tierney
This is a book on rmarkdown, aimed for scientists. It was initially developed as a 3 hour workshop, but is now developed into a resource that will grow and change over time as a living book.
https://rmd4sci.njtierney.com/
## Pimp my RMD: a few tips for R Markdown
Yan Holtz
R markdown creates interactive reports from R code. This post provides a few tips I use on a daily basis to improve the appearance of output documents.
https://holtzy.github.io/Pimp-my-rmd/
## knitr
[Yihui Xie](https://yihui.org/)
Dynamic documents with R and knitr!
The knitr package was designed to be a transparent engine for dynamic report generation with R, solve some long-standing problems in Sweave, and combine features in other add-on packages into one package.
The book is free online.
https://yihui.org/knitr/
## Reproducible Research with R and RStudio
By Christopher Gandrud
This book present all the Tools for Gathering and Analyzing Data and Presenting Results Reproducible Research with R and RStudio through practical examples.
The book can be reproduced by using the R package bookdown
https://github.com/christophergandrud/Rep-Res-Book
Also, you can buy the copy.
https://www.routledge.com/Reproducible-Research-with-R-and-RStudio/Gandrud/p/book/9780367143985
# R package development
## R packages
[Hadley Whickham](https://twitter.com/hadleywickham)
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 section you’ll learn how to turn your code into packages that others can easily download and use. Writing a package can seem overwhelming at first. So start with the basics and improve it over time. It doesn’t matter if your first version isn’t perfect as long as the next version is better.
http://r-pkgs.had.co.nz/
## rOpenSci Packages: Development, Maintenance, and Peer Review
This book is a package development guide for authors, maintainers, reviewers and editors of rOpenSci.
https://devguide.ropensci.org/index.html
## HTTP mocking in R
Scott Chamberlain, Maëlle Salmon
This book is meant to be a free, central reference for developers of R packages accessing web resources, to help them have a faster and more robust development. Our aim is to develop an useful guidance to go with the great recent tools that vcr, webmockr, httptest and presser are.
https://books.ropensci.org/http-testing/
# R programming
## Modern R with the tidyverse
Bruno Rodrigues
This book can be useful to different audiences. If you have never used R in your life, and want to start, start with Chapter 1 of this book. Chapter 1 to 3 are the very basics, and should be easy to follow up to Chapter 9. Starting with Chapter 9, it gets more technical, and will be harder to follow. But I suggest you keep on going, and do not hesitate to contact me for help if you struggle! Chapter 9 is also where you can start if you are already familiar with R and the {tidyverse}, but not functional programming. If you are familiar with R but not the {tidyverse} (or have no clue what the {tidyverse} is), then you can start with Chapter 4. If you are familiar with R, the {tidyverse} and functional programming, you might still be interested in this book, especially Chapter 9 and 10, which deal with package development and further advanced topics respectively.
https://b-rodrigues.github.io/modern_R/
## stats545 Data wrangling, exploration, and analysis with R
Jenny Bryan
Learn how to:
Explore, groom, visualize, and analyze data, make all of that reproducible, reusable, and shareable, using R.
This site is about everything that comes up during data analysis except for statistical modelling and inference.
https://stat545.com/
## What They Forgot to Teach You About R
Jennifer Bryan and Jim Hester
The initial impetus for creating these materials is a two-day hands-on workshop. The target learner:
Has a moderate amount of R and RStudio experience.Is largely self-taught.Suspects they have drifted into some idiosyncratic habits that may slow them down or make their work products more brittle.Is interested in (re)designing their R lifestyle, to be more effective and more self-sufficient.
https://rstats.wtf/
## Field Guide to the R Ecosystem
Mark Sellors
This field guide aims to introduce the reader to the main components of the R ecosystem that may be encountered in “the field”.Whatever the reason, whilst there is a wealth of in-depth information for people actually using the language, I could find precious little information that provided the sort of overview of the ecosystem that I know I’d have appreciated when I first came to the language. And with that thought, a field guide is born…
https://fg2re.sellorm.com/
## YaRrr! The Pirate’s Guide to R
Nathaniel D. Phillips
Learn R from the ground up.
Let me make something very, very clear…
I did not write this book.
This whole story started in the Summer of 2015. I was taking a late night swim on the Bodensee in Konstanz and saw a rusty object sticking out of the water. Upon digging it out, I realized it was an ancient usb-stick with the word YaRrr inscribed on the side. Intrigued, I brought it home and plugged it into my laptop. Inside the stick, I found a single pdf file written entirely in pirate-speak. After watching several pirate movies, I learned enough pirate-speak to begin translating the text to English. Sure enough, the book turned out to be an introduction to R called The Pirate’s Guide to R.
https://bookdown.org/ndphillips/YaRrr/
## Advanced R.
[Hadley Whickham](https://twitter.com/hadleywickham)
This is the companion website for “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.
The book is free online.
http://adv-r.had.co.nz/
## Efficient R programming
[Colin Gillespie](http://www.mas.ncl.ac.uk/~ncsg3/) and [Robin Lovelace](https://twitter.com/robinlovelace)
This book is for anyone who wants to make their R code faster to type, faster to run and more scalable. These considerations generally come after learning the very basics of R for data analysis.
The book is free online.
https://csgillespie.github.io/efficientR/
## The Tidyverse Cookbook
Edited by Garrett Grolemund
This book collects code recipes for doing data science with R’s tidyverse. Each recipe solves a single common task, with a minimum of discussion.
https://rstudio-education.github.io/tidyverse-cookbook/
## The tidyverse style guide
[Hadley Whickham](https://twitter.com/hadleywickham)
Good coding style is like correct punctuation: you can manage without it, butitsuremakesthingseasiertoread. This site describes the style used throughout the tidyverse. It was derived from Google’s original R Style Guide - but Google’s current guide is derived from the tidyverse style guide.
https://style.tidyverse.org/
## Tidyverse design guide
Tidyverse team
The goal of this book is to help you write better R code. It has four main components:
1. Design problems which lead to suboptimal outcomes.
1. Useful patterns that help solve common problems.
1. Key principles that help you balance conflicting patterns.
1. Selected case studies that help you see how all the pieces fit together with real code.
It is used by the tidyverse team to promote consistency across packages in the core tidyverse.
https://design.tidyverse.org/
## Hands-On Programming with R
Garrett Grolemund
This book will teach you how to program in R, with hands-on examples. I wrote it for non-programmers to provide a friendly introduction to the R language. You’ll learn how to load data, assemble and disassemble data objects, navigate R’s environment system, write your own functions, and use all of R’s programming tools. Throughout the book, you’ll use your newfound skills to solve practical data science problems.
https://rstudio-education.github.io/hopr/
## The R Language
[R Core team](https://stat.ethz.ch/R-manual/R-patched/doc/AUTHORS)
A collection of manuals:
1. An Introduction to R
1. The R Language Definition
1. Writing R Extensions
1. R Installation and Administration
1. R Data Import/Export
1. R Internals
https://stat.ethz.ch/R-manual/R-patched/doc/html/
## R language for programmers
[John D Cook](https://www.johndcook.com/blog/services-2/)
I have written software professionally in perhaps a dozen programming languages, and the hardest language for me to learn has been R. The language is actually fairly simple, but it is unconventional. These notes are intended to make the language easier to learn for someone used to more commonly used languages such as C++, Java, Perl, etc.
https://www.johndcook.com/blog/r_language_for_programmers/
## R Cookbook - 2nd edition
JD Long, Paul Teetor
I have written software professionally in perhaps a dozen programming languages, and the hardest language for me to learn has been R. The language is actually fairly simple, but it is unconventional. These notes are intended to make the language easier to learn for someone used to more commonly used languages such as C++, Java, Perl, etc.
Not to be confused with Cookbook for R
https://rc2e.com/index.html
## Cookbook for R
Winston Chang
The goal of the cookbook is to provide solutions to common tasks and problems in analyzing data.
Not to be confused with R Cookbook
http://www.cookbook-r.com/
## Tidy evaluation
Lionel Henry and Hadley Wickham
This guide is now superseded by more recent efforts at documenting tidy evaluation in a user-friendly way. We now recommend reading:
The new Programming with dplyr vignette.
The Using ggplot2 in packages vignette.
(Oscar's note: I'm keeping this in for my own reference)
https://tidyeval.tidyverse.org/
## The R Inferno
Patrick Burns
If R's behaviour has ever suprised you, then this book is a guide for many more surprises, written in the style of Dante. It's a concise report on number of common-errors and unexpected behaviours in R. This book would make more sense, if you have been programming and are familiar with such behaviours (not all though), as there is little time spent on explaining why part of behaviour. As mentioned, it's a concise book, 126 books only.
https://www.burns-stat.com/pages/Tutor/R_inferno.pdf
## A sufficient Introduction to R
Derek l. Sonderegger
This book is intended to guide people that are completely new to programming along a path towards a useful skill level using R. I belive that while people can get by with just copying code chunks, that doesn’t give them the background information to modify the code in non-trivial ways. Therefore we will spend more time on foundational details than a “crash-course” would.
https://dereksonderegger.github.io/570L/
## Introduction to Programming with R
Reto Stauffer, Joanna Chimiak-Opoka, Thorsten Simon, Achim Zeileis
a learning resource for programming novices who want to learn programming using the statistical programming language R. While one of the major strengths of R is the broad variety of packages for statistics and data science, this resource focuses on learning and understanding basic programming concepts using base R. Only a couple of additional packages are used and/or briefly discussed for special tasks.
This online book is specifically written for participants of the course “Introduction to Programming: Programming in R” offered by the Digital Science Center at Universität Innsbruck.
https://eeecon.uibk.ac.at/~discdown/rprogramming/index.html
## Mastering Software Development in R
[Roger D. Peng](https://twitter.com/rdpeng), [Sean Kross](https://twitter.com/seankross),
and [Brooke Anderson](https://twitter.com/gbwanderson)
This book covers R software development for building data science tools. This book provides rigorous training in the R language and covers modern software development practices for building tools that are highly reusable, modular, and suitable for use in a team-based environment or a community of
developers.
Pay what you want for the ebook, minimum $0.00
https://leanpub.com/msdr
## Introduction to R - R spatial
R Spatial
This document provides a concise introduction to R. It emphasizes what you need to know to be able to use the language in any context. There is no fancy statistical analysis here. We just present the basics of the R language itself. We do not assume that you have done any computer programming before (but we do assume that you think it is about time you did). Experienced R users obviously need not read this. But the material may be useful if you want to refresh your memory, if you have not used R much, or if you feel confused.
https://rspatial.org/intr/index.html
## Another Book on Data Science : Learn R and Python in Parallel
Nailong Zhang
There has been considerable debate over choosing R vs. Python for Data Science. Based on my limited knowledge/experience, both R and Python are great languages and are worth learning; so why not learn them together?
Besides the side-by-side comparison of the two popular languages used in Data Science, this book also focuses on the translation from mathematical models to codes. In the book, the audience could find the applications/implementations of some important algorithms from scratch, such as maximum likelihood estimation, inversion sampling, copula simulation, simulated annealing, bootstrapping, linear regression (lasso/ridge regression), logistic regression, gradient boosting trees, etc.
https://www.anotherbookondatascience.com/
## Functional Programming in R
[Thomas Mailund](https://twitter.com/ThomasMailund)
Master functions and discover how to write functional programs in R. In this concise book, you’ll make your functions pure by avoiding side-effects; you’ll write functions that manipulate other functions, and you’ll construct complex functions using simpler functions as building blocks.
https://amzn.to/2wY4m11
## Advanced Object-Oriented Programming in R
[Thomas Mailund](https://twitter.com/ThomasMailund)
Learn how to write object-oriented programs in R and how to construct classes and class hierarchies in the three object-oriented systems available in R. This book gives an introduction to object-oriented programming in the R programming language and shows you how to use and apply R in an object-oriented manner. You will then be able to use this powerful programming style in your own statistical programming projects to write flexible and extendable software.
Paid ~$20
https://amzn.to/2wZnBbp
## Metaprogramming in R
[Thomas Mailund](https://twitter.com/ThomasMailund)
Learn how to manipulate functions and expressions to modify how the R language interprets itself. This book is an introduction to metaprogramming in the R language, so you will write programs to manipulate other programs. Metaprogramming in R shows you how to treat code as data that you can generate, analyze, or modify.
Paid ~$20
https://amzn.to/2x1cYUR
## Functional Data Structures in R
[Thomas Mailund](https://twitter.com/ThomasMailund)
Get an introduction to functional data structures using R and write more effective code and gain performance for your programs. This book teaches you workarounds because data in functional languages is not mutable: for example you’ll learn how to change variable-value bindings by modifying environments, which can be exploited to emulate pointers and implement traditional data structures. You’ll also see how, by abandoning traditional data structures, you can manipulate structures by building new versions rather than modifying them. You’ll discover how these so-called functional data structures are different from the traditional data structures you might know, but are worth understanding to do serious algorithmic programming in a functional language such as R.
Paid~ $20
https://amzn.to/2oUG2cP
## Domain-Specific Languages in R
[Thomas Mailund](https://twitter.com/ThomasMailund)
Gain an accelerated introduction to domain-specific languages in R, including coverage of regular expressions. This compact, in-depth book shows you how DSLs are programming languages specialized for a particular purpose, as opposed to general purpose programming languages. Along the way, you’ll learn to specify tasks you want to do in a precise way and achieve programming goals within a domain-specific context.
Domain-Specific Languages in R includes examples of DSLs including large data sets or matrix multiplication; pattern matching DSLs for application in computer vision; and DSLs for continuous time Markov chains and their applications in data science. After reading and using this book, you’ll understand how to write DSLs in R and have skills you can extrapolate to other programming languages.
Paid ~$25
https://amzn.to/2CDqhAU
# Shiny
## A gRadual intRoduction to Shiny
Ted Laderas and Jessica Minnier
By the end of this workshop, you should be able to:
Browse examples in the shiny gallery and understand how they work.Understand the components of a Shiny app and how they communicate.Learn three basic design patterns to the shiny apps.
https://laderast.github.io/gradual_shiny/
## Mastering Shiny
Hadley Wickham
This book complements Shiny’s online documentation and is intended to help app authors develop a deeper understanding of Shiny. After reading this book, you’ll be able to write apps that have more customized UI, more maintainable code, and better performance and scalability.
https://mastering-shiny.org/
## Shiny Production with AWS Book
Business Science
A big problem exists… No one teaches Data Scientists how to deploy web applications. You spend all of this time building Shiny web applications. And then… [silence].
This book alongside the Shiny Developer with AWS Course (DS4B 202A-R) solves this problem - teaching Data Scientists how to deploy, host, and maintain web applications.
https://business-science.github.io/shiny-production-with-aws-book/
## Engineering Production-Grade Shiny Apps
Colin Fay, Sébastien Rochette, Vincent Guyader, Cervan Girard
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.
https://engineering-shiny.org/
## Supplement to Shiny in Production
This document is full of supplemental resources and content from the [Shiny in Production Workshop](https://rstudio.com/resources/rstudioconf-2019/shiny-in-production-principles-practices-and-tools/) delievered at rstudio::conf 2019.
https://kellobri.github.io/shiny-prod-book/
## Outstanding User Interfaces with Shiny
David Granjon
This book will help you to:
Manipulate Shiny tags from R to create custom layouts. Harness the power of CSS and JavaScript to quickly design apps standing out from the pack. Discover the steps to import and convert existing web frameworks like Bootstrap 4, framework7 and more
Learn how Shiny internally deals with inputs. Learn more about less documented Shiny mechanisms (websockets, sessions, …)
https://divadnojnarg.github.io/outstanding-shiny-ui/
## JavaScript for R
[John Coene](https://john-coene.com)
Learn how to build your own data visualsiation packages, improve shiny with
JavaScript, and use JavaScript for computations.
Freely avaiable online, paid print.
https://javascript-for-r.com
# Sport analytics