TOPIC: BIOCONDUCTOR
Online R programming books that are worth bookmarking
23rd March 2026
As part of making content more useful following its reorganisation, numerous articles on the R statistical computing language have appeared on here. All of those have taken a more narrative form. With this collation of online books on the R language, I take a different approach. What you find below is a collection of links with associated descriptions. While narrative accounts can be very useful, there is something handy about running one's eye down a compilation as well. Many entries have a corresponding print edition, some of which are not cheap to buy, which makes me wonder about the economics of posting the content online as well, though it can help with getting feedback during book preparation.
We start with this comprehensive collection of over 400 free and affordable resources related to the R programming language, organised into categories such as data science, statistics, machine learning and specific fields like economics and life sciences. In many ways, it is a superset of what you find below and complements this collection with many other finds. The fact that it is a living collection makes it even more useful.
R Programming for Data Science
Here is an introduction to the R programming language, focusing on its application in data science. It covers foundational topics such as installation, data manipulation, function writing, debugging and code optimisation, alongside advanced concepts like parallel computation and data analysis case studies. The text includes practical guidance on handling data structures, using packages such as {dplyr} and {readr} as well as working with dates, times and regular expressions. Additional sections address control structures, scoping rules and profiling techniques, while the author also discusses resources for staying updated through a podcast and accessing e-book versions for ongoing revisions.
Designed for individuals with no prior coding experience, the book provides an introduction to programming in R while using practical examples to teach fundamental concepts such as data manipulation, function creation and the use of R's environment system. It is structured around hands-on projects, including simulations of weighted dice, playing cards and a slot machine, alongside explanations of core programming principles like objects, notation, loops and performance optimisation. Additional sections cover installation, package management, data handling and debugging techniques. While the book is written using RMarkdown and published under a Creative Commons licence, a physical edition is available through O’Reilly.
What you have here is one of several books written by Hadley Wickham. This one is published in its second edition as part of Chapman and Hall's R Series and is aimed primarily at R users who want to deepen their programming skills and understanding of the language, though it is also useful for programmers migrating from other languages. The book covers a broad range of topics organised into sections on foundations, functional programming, object-oriented programming, metaprogramming and techniques, with the latter including debugging, performance measurement and rewriting R code in C++.
Unlike Paul Teetor's separately published R Cookbook, the Cookbook for R was created by Winston Chang. It offers solutions to common tasks and problems in data analysis, covering topics such as basic operations, numbers, strings, formulas, data input and output, data manipulation, statistical analysis, graphs, scripts and functions, and tools for experiments.
The second edition of R for Data Science by Hadley Wickham, Mine Çetinkaya-Rundel and Garrett Grolemund offers a structured approach to learning data science with R, covering essential skills such as data visualisation, transformation, import, programming and communication. Organised into chapters that explore workflows, data manipulation techniques and tools like Quarto for reproducible research, the book emphasises practical applications and best practices for handling data effectively.
The R Graphics Cookbook, 2nd edition, offers a comprehensive guide to creating visualisations in R, structured into chapters that cover foundational skills such as installing and using packages, loading data from various formats and exploring datasets through basic plots. It progresses to detailed techniques for constructing bar graphs, line graphs, scatter plots and histograms, alongside methods for customising axes, annotations, themes and legends.
The book also addresses advanced topics like colour application, faceting data into subplots, generating specialised graphs such as network diagrams and heat maps and preparing data for visualisation through reshaping and summarising. Additional sections focus on refining graphical outputs for presentation, including exporting to different file formats and adjusting visual elements for clarity and aesthetics, while an appendix provides an overview of the {ggplot2} system.
R Markdown: The Definitive Guide
Published by Chapman & Hall/CRC, R Markdown: The Definitive Guide by Yihui Xie, J.J. Allaire and Garrett Grolemund covers the R Markdown document format, which has been in use since 2012 and is built on the knitr and Pandoc tools. The format allows users to embed code within Markdown documents and compile the results into a range of output formats including PDF, HTML and Word. The guide covers a broad scope of practical applications, from creating presentations, dashboards, journal articles and books to building interactive applications and generating blogs, reflecting how the ecosystem has matured since the {rmarkdown} package was first released in 2014.
A key principle running throughout is that Markdown's deliberately limited feature set is a strength rather than a drawback, encouraging authors to focus on content rather than complex typesetting. Despite this simplicity, the format remains highly customisable through tools such as Pandoc templates, LaTeX and CSS. Documents produced in R Markdown are also notably portable, as their straightforward syntax makes conversion between output formats more reliable, and because results are generated dynamically from code rather than entered manually, they are far more reproducible than those produced through conventional copy-and-paste methods.
The R Markdown Cookbook is a practical guide designed to help users enhance their ability to create dynamic documents by combining analysis and reporting. It covers essential topics such as installation, document structure, formatting options and output formats like LaTeX, HTML and Word, while also addressing advanced features such as customisations, chunk options and integration with other programming languages. The book provides step-by-step solutions to common tasks, drawing on examples from online resources and community discussions to offer clear, actionable advice for both new and experienced users seeking to improve their workflow and explore the full potential of R Markdown.
This book provides a practical guide to using R Markdown for scientists, developed from a three-hour workshop and designed to evolve as a living resource. It covers essential topics such as setting up R Markdown documents, integrating with RStudio for efficient workflows, exporting outputs to formats like PDF, HTML and Word, managing figures and tables with dynamic references and captions, incorporating mathematical equations, handling bibliographies with citations and style adjustments, troubleshooting common issues and exploring advanced R Markdown extensions.
bookdown: Authoring Books and Technical Documents with R Markdown
Here is a guide to using the {bookdown} package, which extends R Markdown to facilitate the creation of books and technical documents. It covers Markdown syntax, integration of R code, formatting options for HTML, LaTeX and e-book outputs and features such as cross-referencing, custom blocks and theming. The package supports both multipage and single-document outputs, and its applications extend beyond traditional books to include course materials, manuals and other structured content. The work includes practical examples, publishing workflows and details on customisation, alongside information about licensing and the availability of a printed version.
[blogdown]: Creating Websites with R Markdown
Though the authors note that some information may be outdated due to recent updates to Hugo and the {blogdown} package, and they direct readers to additional resources for the latest features and changes, this book still provides a guide to building static websites using R Markdown and the Hugo static site generator, emphasising the advantages of this approach for creating reproducible, portable content. It covers installation, configuration, deployment options such as Netlify and GitHub Pages, migration from platforms like WordPress and advanced topics including custom layouts and version control as well as practical examples, workflow recommendations and discussions on themes, content management and technical aspects of website development.
[pagedown]: Create Paged HTML Documents for Printing from R Markdown
The R package {pagedown} enables users to create paged HTML documents suitable for printing to PDF, using R Markdown combined with a JavaScript library called paged.js, that later of which implements W3C specifications for paged media. While tools like LaTeX and Microsoft Word have traditionally dominated PDF production, pagedown offers an alternative approach through HTML and CSS, supporting a range of document types including resumes, posters, business cards, letters, theses and journal articles.
Documents can be converted to PDF via Google Chrome, Microsoft Edge or Chromium, either manually or through the chrome_print() function, with additional support for server-based, CI/CD pipeline and Docker-based workflows. The package provides customisable CSS stylesheets, a CSS overriding mechanism for adjusting fonts and page properties, and various formatting features such as lists of tables and figures, abbreviations, footnotes, line numbering, page references, cover images, running headers, chapter prefixes and page breaks. Previewing paged documents requires a local or remote web server, and the layout is sensitive to browser zoom levels, with 100% zoom recommended for the most accurate output.
Dynamic Documents with R and knitr
Developed by Yihui Xie and inspired by the earlier {Sweave} package, {knitr} is an R package designed for dynamic report generation that consolidates the functionality of numerous other add-on packages into a single, cohesive tool. It supports multiple input languages, including R, Python and shell scripts, as well as multiple output markup languages such as LaTeX, HTML, Markdown, AsciiDoc and reStructuredText. The package operates on a principle of transparency, giving users full control over how input and output are handled, and runs R code in a manner consistent with how it would behave in a standard R terminal.
Among its notable features are built-in caching, automatic code formatting via the {formatR} package, support for more than 20 graphics devices and flexible options for managing plots within documents. It also allows advanced users to define custom hooks and regular expressions to extend and tailor its behaviour further. The package is affiliated with the Foundation for Open Access Statistics, a nonprofit organisation promoting free software, open access publishing and reproducible research in statistics.
Mastering Shiny is a comprehensive guide to developing web applications using R, focusing on the Shiny framework designed for data scientists. It introduces core concepts such as user interface design, reactive programming and dynamic content generation, while also exploring advanced topics like performance optimisation, security and modular app development. The book covers practical applications across industries, from academic teaching tools to real-time analytics dashboards, and aims to equip readers with the skills to build scalable, maintainable applications. It includes detailed chapters on workflow, layout, visualisation and user interaction, alongside case studies and technical best practices.
Engineering Production-Grade Shiny Apps
This is aimed at developers and team managers who already possess a working knowledge of the Shiny framework for R and wish to advance beyond the basics toward building robust, production-ready applications. Rather than covering introductory Shiny concepts or post-deployment concerns, the book focuses on the intermediate ground between those two stages, addressing project management, workflow, code structure and optimisation.
It introduces the {golem} package as a central framework and guides readers through a five-step workflow covering design, prototyping, building, strengthening and deployment, with additional chapters on optimisation techniques including R code performance, JavaScript integration and CSS. The book is structured to serve both those with project management responsibilities and those focused on technical development, acknowledging that in many small teams these roles are carried out by the same individual.
Outstanding User Interfaces with Shiny
Written by David Granjon and published in 2022, Outstanding User Interfaces with Shiny is a book aimed at filling the gap between beginner and advanced Shiny developers, covering how to deeply customise and enhance Shiny applications to the point where they become indistinguishable from classic web applications. The book spans a wide range of topics, including working with HTML and CSS, integrating JavaScript, building Bootstrap dashboard templates, mobile development and the use of React, providing a comprehensive resource that consolidates knowledge and experience previously scattered across the Shiny developer community.
Now in its second edition, R Packages by Hadley Wickham and Jennifer Bryan is a freely available online guide that teaches readers how to develop packages in R. A package is the core unit of shareable and reproducible R code, typically comprising reusable functions, documentation explaining how to use them and sample data. The book guides readers through the entire process of package development, covering areas such as package structure, metadata, dependencies, testing, documentation and distribution, including how to release a package to CRAN. The authors encourage a gradual approach, noting that an imperfect first version is perfectly acceptable provided each subsequent version improves on the last.
Written by Javier Luraschi, Kevin Kuo and Edgar Ruiz, Mastering Spark with R is a comprehensive guide designed to take readers from little or no familiarity with Apache Spark or R through to proficiency in large-scale data science. The book covers a broad range of topics, including data analysis, modelling, pipelines, cluster management, connections, data handling, performance tuning, extensions, distributed computing, streaming and contributing to the Spark ecosystem.
Happy Git and GitHub for the useR
Here is a practical guide written by Jenny Bryan and contributors, aimed primarily at R users involved in data analysis or package development. It covers the installation and configuration of Git alongside GitHub, the development of key workflows for common tasks and the integration of these tools into day-to-day work with R and R Markdown. The guide is structured to take readers from initial setup through to more advanced daily workflows, with particular attention paid to how Git and GitHub serve the needs of data science rather than pure software development.
Written by John Coene and intended for release as part of the CRC Press R series, JavaScript for R explore how the R programming language and JavaScript can be used together to enhance data science workflows. Rather than teaching JavaScript as a standalone language, the book demonstrates how a limited working knowledge of it can meaningfully extend what R developers can achieve, particularly through the integration of external JavaScript libraries.
The book covers a broad range of topics, progressing from foundational concepts through to data visualisation using the {htmlwidgets} package, bidirectional communication with Shiny, JavaScript-powered computations via the V8 engine and Node.js and the use of modern JavaScript tools such as Vue, React and webpack alongside R. Practical examples are woven throughout, including the building of interactive visualisations, custom Shiny inputs and outputs, image classification and machine learning operations, with all accompanying code made publicly available on GitHub.
This guide addresses challenges faced by developers of R packages that interact with web resources, offering strategies to create reliable unit tests despite dependencies on internet connectivity, authentication and external service availability. It explores tools such as {vcr}, {webmockr}, {httptest} and {webfakes}, which enable mocking and recording HTTP requests to ensure consistent testing environments, reduce reliance on live data and improve test reliability. The text also covers advanced topics like handling errors, securing tests and ensuring compatibility with CRAN and Bioconductor, while emphasising best practices for maintaining test robustness and contributor-friendly workflows. Funded by rOpenSci and the R Consortium, the resource aims to support developers in building more resilient and maintainable R packages through structured testing approaches.
The Shiny AWS Book is an online resource designed to teach data scientists how to deploy, host and maintain Shiny web applications using cloud infrastructure. Addressing a common gap in data science education, it guides readers through a range of DevOps technologies including AWS, Docker, Git, NGINX and open-source Shiny Server, covering everything from server setup and cost management to networking, security and custom configuration.
{ggplot2}: Elegant Graphics for Data Analysis
The third edition of {ggplot2}: Elegant Graphics for Data Analysis provides an in-depth exploration of the Grammar of Graphics framework, focusing on the theoretical foundations and detailed implementation of the ggplot2 package rather than offering step-by-step instructions for specific visualisations. Written by Hadley Wickham, Danielle Navarro and Thomas Lin Pedersen, the book is presented as an online work-in-progress, with content structured across sections such as layers, scales, coordinate systems and advanced programming topics. It aims to equip readers with the knowledge to customise plots according to their needs, rather than serving as a direct guide for creating predefined graphics.
YaRrr! The Pirate’s Guide to R
Written by Nathaniel D. Phillips, this is a beginner-oriented guide to learning the R programming language from the ground up, covering everything from installation and basic navigation of the RStudio environment through to more advanced topics such as data manipulation, statistical analysis and custom function writing. The guide progresses logically through foundational concepts including scalars, vectors, matrices and dataframes before moving into practical areas such as hypothesis testing, regression, ANOVA and Bayesian statistics. Visualisation is given considerable attention across dedicated chapters on plotting, while later sections address loops, debugging and managing data from a variety of file formats. Each chapter includes practical exercises to reinforce learning, and the book concludes with a solutions section for reference.
Data Visualisation: A Practical Introduction
Data Visualisation: A Practical Introduction is a forthcoming second edition from Princeton University Press, written by Kieran Healy and due for release in March 2026, which teaches readers how to explore, understand and present data using the R programming language and the {ggplot2} library. The book aims to bridge the gap between works that discuss visualisation principles without teaching the underlying tools and those that provide code recipes without explaining the reasoning behind them, instead combining both practical instruction and conceptual grounding.
Revised and updated throughout to reflect developments in R and {ggplot2}, the second edition places greater emphasis on data wrangling, introduces updated and new datasets, and substantially rewrites several chapters, particularly those covering statistical models and map-drawing. Readers are guided through building plots progressively, from basic scatter plots to complex layered graphics, with the expectation that by the end they will be able to reproduce nearly every figure in the book and understand the principles that inform each choice.
The book also addresses the growing role of large language models in coding workflows, arguing that genuine understanding of what one is doing remains essential regardless of the tools available. It is suitable for complete beginners, those with some prior R experience, and instructors looking for a course companion, and requires the installation of R, RStudio and a number of supporting packages before work can begin.
Learning R for Data Analysis: Going from the basics to professional practice
22nd March 2026
R has grown from a specialist statistical language into one of the most widely recognised tools for working with data. Across tutorials, community sites, training platforms and industry resources, it is presented as both a programming language and a software environment for statistical computing, graphics and reporting. It was created by Ross Ihaka and Robert Gentleman at the University of Auckland in New Zealand, and its name draws on the first letter of their first names while also alluding to the Bell Labs language S. It is freely available under the GNU General Public Licence and runs on Linux, Windows and macOS, which has helped it spread across research, education and industry alike.
What Makes R Distinctive
What makes R notable is its combination of programming features with a strong focus on data analysis. Introductory material, such as the tutorials at Tutorialspoint and Datamentor, repeatedly highlights its support for conditionals, loops, user-defined recursive functions and input and output, but these sit alongside effective data handling, a broad set of operators for arrays, lists, vectors and matrices and strong graphical capabilities. That mixture means R can be used for straightforward scripts and for complex analytical workflows. A beginner may start by printing "Hello, World!" with the print() function, while a more experienced user may move on to regression models, interactive dashboards or automated reporting.
The Learning Progression
Learning materials generally present R in a structured progression. A beginner is first introduced to reserved words, variables and constants, operators and the order in which expressions are evaluated. From there, the path usually moves into flow control through if…else, ifelse(), for, while, repeat and the use of break and next, before functions follow naturally, including return values, environments and scope, recursive functions, infix operators and switch(). Most sources agree that confidence with the syntax and fundamentals is the real starting point, and this early sequence matters because it helps learners become comfortable reading and writing R rather than only copying examples.
After the basics, attention tends to turn to the structures that make R so useful for data work. Vectors, matrices, lists, data frames and factors appear in nearly every introductory course because they are central to how information is stored and manipulated. Object-oriented concepts also emerge quite early in some routes through the language, with classes and objects extending into S3, S4 and reference classes. For someone coming from spreadsheets or point-and-click statistical software, this shift can feel significant, but it also opens the way to more reproducible and flexible analysis.
Visualisation
Visualisation is another recurring theme in R education. Basic chart types such as bar plots, histograms, pie charts, box plots and strip charts are common early examples because they show how quickly data can be turned into graphics. More advanced lessons widen the scope through plot functions, multiple plots, saving graphics, colour selection and the production of 3D plots.
Beyond base plotting, there is extensive evidence of the central role of {ggplot2} in contemporary R practice. Data Cornering demonstrates this well, with articles covering how to create funnel charts in R using {ggplot2} and how to diversify stacked column chart data label colours, showing how R is used not only to summarise data but also to tell visual stories more clearly. In the pharmaceutical and clinical research space, the PSI VIS-SIG blog is published by the PSI Visualisation Special Interest Group and summarises its monthly Wonderful Wednesday webinars, presenting real-world datasets and community-contributed chart improvements alongside news from the group.
Data Wrangling and the Tidyverse
Much of modern R work is built around data wrangling, and here the {tidyverse} has become especially prominent. Claudia A. Engel's openly published guide Data Wrangling with R (last updated 3rd November 2023) sets out a preparation phase that assumes some basic R knowledge, a recent installation of R and RStudio and the installation of the {tidyverse} package with install.packages("tidyverse") followed by library(tidyverse). It also recommends creating a dedicated RStudio project and downloading CSV files into a data subdirectory, reinforcing the importance of organised project structure.
That same guide then moves through data manipulation with {dplyr}, covering selecting columns and filtering rows, pipes, adding new columns, split-apply-combine, tallying and joining two tables, before moving on to {tidyr} topics such as long and wide table formats, pivot_wider, pivot_longer and exporting data. These topics reflect a broader pattern in the R ecosystem because data import and export, reshaping, combining tables and counting by group recur across teaching resources as they mirror common analytical tasks.
Applications and Professional Use
The range of applications attached to R is wide, though data science remains the clearest centre of gravity. Educational sources describe R as valuable for data wrangling, visualisation and analysis, often pointing to packages such as {dplyr}, {tidyr}, {ggplot2} and {Shiny}. Statistical modelling is another major strand, with R offering extensible techniques for descriptive and inferential statistics, regression analysis, time series methods and classical tests. Machine learning appears as a further area of growth, supported by a large and expanding package ecosystem. In more advanced contexts, R is also linked with dashboards, web applications, report generation and publishing systems such as Quarto and R Markdown.
R's place in professional settings is underscored by the breadth of organisations and sectors associated with it. Introductory resources mention companies such as Google, Microsoft, Facebook, ANZ Bank, Ford and The New York Times as examples of organisations using R for modelling, forecasting, analysis and visualisation. The NHS-R Community promotes the use of R and open analytics in health and care, building a community of practice for data analysis and data science using open-source software in the NHS and wider UK health and care system. Its resources include reports, blogs, webinars and workshops, books, videos and R packages, with webinar materials archived in a publicly accessible GitHub repository. The R Validation Hub, supported through the pharmaR initiative, is a collaboration to support the adoption of R within a biopharmaceutical regulatory setting and provides tools including the {riskmetric} package, the {riskassessment} app and the {riskscore} package for assessing package quality and risk.
The Wider Ecosystem
The wider ecosystem around R is unusually rich. The R Consortium promotes the growth and development of the R language and its ecosystem by supporting technical and social infrastructure, fostering community engagement and driving industry adoption. It notes that the R language supports over two million users and has been adopted in industries including biotech, finance, research and high technology. Community growth is visible not only through organisations and conferences but through user groups, scholarships, project working groups and local meetups, which matters because learning a language is easier when there is an active support network around it.
Another sign of maturity is the depth of R's package and publication landscape. rdrr.io provides a comprehensive index of over 29,000 CRAN packages alongside more than 2,100 Bioconductor packages, over 2,200 R-Forge packages and more than 76,000 GitHub packages, making it possible to search for packages, functions, documentation and source code in one place. Rdocumentation, powered by DataCamp, covers 32,130 packages across CRAN and Bioconductor and offers a searchable interface for function-level documentation. The Journal of Statistical Software adds a scholarly dimension, publishing open-access articles on statistical computing software together with source code, with full reproducibility mandatory for publication. R-bloggers aggregates R news and tutorials contributed by hundreds of R bloggers, while R Weekly curates a community digest and an accompanying podcast, both helping users keep pace with the steady flow of tutorials, package releases, blog posts and developments across the R world.
Where to Begin
For beginners, one recurring challenge is knowing where to start, and different learning routes reflect different backgrounds. Datamentor points learners towards step-by-step tutorials covering popular topics such as R operators, if...else statements, data frames, lists and histograms, progressing through to more advanced material. R for the Rest of Us offers a staged path through three core courses, Getting Started With R, Fundamentals of R and Going Deeper with R, and extends into nine topics courses covering Git and GitHub, making beautiful tables, mapping, graphics, data cleaning, inferential statistics, package development, reproducibility and interactive dashboards with {Shiny}. The site is explicitly designed for people who may never have coded before and also offers the structured R in 3 Months programme alongside training and consulting. RStudio Education (now part of Posit) outlines six distinct ways to begin learning R, covering installation, a free introductory webinar on tidy statistics, the book R for Data Science, browser-based primers, and further options suited to different learning styles, along with guidance on R Markdown and good project practices.
Despite the variety, the underlying advice is consistent: start by learning the basics well enough to read and write simple code, practise regularly beginning with straightforward exercises and gradually take on more complex tasks, then build projects that matter to you because projects create context and make concepts stick. There is no suggestion that mastery comes from passively reading documentation alone, as practical engagement is treated as essential throughout. The blog Stats and R exemplifies this philosophy well, with the stated aim of making statistics accessible to everyone by sharing, explaining and illustrating statistical concepts and, where appropriate, applying them in R.
That practical engagement can take many forms. Someone interested in data journalism may focus on visualisation and reproducible reporting, while a researcher may prioritise statistical modelling and publishing workflows, and a health analyst may use R for quality assurance, open health data and clinical reporting. Others may work with {Shiny}, package development, machine learning, Git and GitHub or interactive dashboards. The variety shows that R is not confined to a single use case, even if statistics and data science remain the common thread.
Free Learning Resources for R
It is also worth noting that R learning is supported by a great deal of freely available material. Statistics Globe, founded in 2017 by Joachim Schork and now an education and consulting platform, offers more than 3,000 free tutorials and over 1,000 video tutorials on YouTube, spanning R programming, Python and statistical methodology. STHDA (Statistical Tools for High-Throughput Data Analysis) covers basics, data import and export, reshaping, manipulation and visualisation, with material geared towards practical data analysis at every level. Community sites, webinar repositories and newsletters add further layers of accessibility, and even where paid courses exist, the surrounding free ecosystem is substantial.
Taken together, these sources present R as far more than a niche programming language. It is a mature open-source environment with a strong statistical heritage, a practical orientation towards data work and a well-developed community of learners, teachers, developers and organisations. Its core concepts are approachable enough for beginners, yet its package ecosystem and publishing culture support highly specialised and advanced work. For anyone looking to enter data analysis, statistics, visualisation or related areas, R offers a route that begins with simple code and can extend into large-scale analytical workflows.