TOPIC: WILKINSON'S GRAMMAR OF GRAPHICS
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.
How to centre titles, remove gridlines and write reusable functions in {ggplot2}
20th March 2026
{ggplot2} is widely used for data visualisation in R because it offers a flexible, layered grammar for constructing charts. A plot can begin with a straightforward mapping of data to axes and then be refined with titles, themes and annotations until it better serves the message being communicated. That flexibility is one of the greatest strengths of {ggplot2}, though it also means that many useful adjustments are small, specific techniques that are easy to overlook when first learning the package.
Three of those techniques fit together particularly well. The first is centring a plot title, a common formatting need because {ggplot2} titles are left-aligned by default. The second is removing grid lines and background elements to produce a cleaner, less cluttered appearance. The third is wrapping familiar {ggplot2} code into a reusable function so that the same visual style can be applied across different datasets without rewriting everything each time. Together, these approaches show how a basic plot can move from a default graphic to something more polished and more efficient to reproduce.
Centring the Plot Title
A clear starting point comes from a short tutorial by Luis Serra at Ubiqum Code Academy, published on RPubs, which focuses on one specific goal: centring the title of a {ggplot2} output. The example uses the well-known Iris dataset, which is included with R and contains 150 observations across five variables. Those variables are Sepal.Length, Sepal.Width, Petal.Length, Petal.Width and Species, with Species stored as a factor containing three levels (setosa, versicolor and virginica), each represented by 50 samples.
The first step is to load {ggplot2} and inspect the structure of the data using library(ggplot2), followed by data("iris") and str(iris). The structure output confirms that the first four columns are numeric, and the fifth is categorical. That distinction matters because it makes the dataset well suited to a scatter plot with a colour grouping, allowing two continuous variables to be compared while species differences are shown visually.
The initial chart plots petal length against petal width, with points coloured by species:
ggplot() + geom_point(data = iris, aes(x = Petal.Width, y = Petal.Length, color = Species))This produces a simple scatter plot and serves as the base for later refinements. Even in this minimal form, the grammar is clear: the data are supplied to geom_point(), the x and y aesthetics are mapped to Petal.Width and Petal.Length, and colour is mapped to Species.
Once the scatter plot is in place, a title is added using ggtitle("My dope plot"), appended to the existing plotting code. This creates a title above the graphic, but it remains left-justified by default. That alignment is not necessarily wrong, as left-aligned titles work well in many visual contexts, yet there are situations where a centred title gives a more balanced appearance, particularly for standalone blog images, presentation slides or teaching examples.
The adjustment required is small and direct. {ggplot2} allows title styling through its theme system, and horizontal justification for the title is controlled through plot.title = element_text(hjust = 0.5). Setting hjust to 0.5 centres the title within the plot area, whilst 0 aligns it to the left and 1 to the right. The revised code becomes:
ggplot() +
geom_point(data = iris, aes(x = Petal.Width, y = Petal.Length, color = Species)) +
ggtitle("My dope plot") +
theme(plot.title = element_text(hjust = 0.5))That small example also opens the door to a broader understanding of {ggplot2} themes. Titles, text size, panel borders, grid lines and background fills are all managed through the same theming system, which means that once one element is adjusted, others can be modified in a similar way.
Removing Grids and Background Elements
A second set of techniques, demonstrated by Felix Fan in a concise tutorial on his personal site, begins by generating simple data rather than using a built-in dataset. The code creates a sequence from 1 to 20 with a <- seq(1, 20), calculates the fourth root with b <- a^0.25 and combines both into a data frame using df <- as.data.frame(cbind(a, b)). The plot is then created as a reusable object:
myplot = ggplot(df, aes(x = a, y = b)) + geom_point()From there, several styling approaches become available. One of the quickest is theme_bw(), which removes the default grey background and replaces it with a cleaner black-and-white theme. This does not strip the graphic down completely, but it does provide a more neutral base and is often a practical shortcut when the standard {ggplot2} appearance feels too heavy.
More selective adjustments can also be made independently. Grid lines can be removed with the following:
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())This suppresses both major and minor grid lines, whilst leaving other parts of the panel unchanged. Borderlines can be removed separately with theme(panel.border = element_blank()), though that does not affect the background colour or the grid. Likewise, the panel background can be cleared with theme(panel.background = element_blank()), which removes the panel fill and borderlines but leaves grid lines in place. Each of these commands targets a different component, so they can be combined depending on the desired result.
If the background and border are removed, axis lines can be added back for clarity using theme(axis.line = element_line(colour = "black")). This is an important finishing step in a stripped-back plot because removing too many panel elements can leave the chart without enough visual structure. The explicit axis line restores a frame of reference without reintroducing the full border box.
Two combined approaches are worth knowing. The first uses a single custom theme call:
myplot + theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black")
)The second starts from theme_bw() and then removes the border and grids whilst adding axis lines:
myplot + theme_bw() + theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)Both approaches produce a cleaner chart, though they begin from slightly different defaults. The practical lesson is that {ggplot2} styling is modular, so there is often more than one route to a similar visual result.
This matters because chart design is rarely only about appearance. Cleaner formatting can make a chart easier to read by reducing distractions and placing more emphasis on the data itself. A centred title, a restrained background and the selective use of borders all influence how quickly the eye settles on what is important.
Building Reusable Custom Plot Functions
A third area extends these ideas further by showing how to build custom {ggplot2} functions in R, a topic covered in depth by Sharon Machlis in a tutorial published on Infoworld. The central problem discussed is the mismatch that used to make this awkward: tidyverse functions typically use unquoted column names, whilst base R functions generally expect quoted names. This tension became especially noticeable when users wanted to write their own plotting functions that accepted a data frame and column names as arguments.
The example in that article uses Zillow data containing estimated median home values. After loading {dplyr} and {ggplot2}, a horizontal bar chart is created to show home values by neighbourhood in Boston, with bars ordered from highest to lowest values, outlined in black and filled in blue:
ggplot(data = bos_values, aes(x = reorder(RegionName, Zhvi), y = Zhvi)) +
geom_col(color = "black", fill = "#0072B2") +
xlab("") + ylab("") +
ggtitle("Zillow Home Value Index by Boston Neighborhood") +
theme_classic() +
theme(plot.title = element_text(size = 24)) +
coord_flip()The next step is to turn that pattern into a function. An initial attempt passes unquoted column names but does not work as intended because of the underlying tension between standard R evaluation and the non-standard evaluation of {ggplot2}. The solution came with the introduction of the tidy evaluation {{ operator, commonly known as "curly-curly", in {rlang} version 0.4.0. As noted in the official tidyverse announcement, this operator abstracts the previous two-step quote-and-unquote process into a single interpolation step. Once library(rlang) is loaded, column references inside the plotting code are wrapped in double curly braces:
library(rlang)
mybarplot <- function(mydf, myxcol, myycol, mytitle) {
ggplot2::ggplot(data = mydf, aes(x = reorder({{ myxcol }}, {{ myycol }}), y = {{ myycol }})) +
geom_col(color = "black", fill = "#0072B2") +
xlab("") + ylab("") +
coord_flip() +
ggtitle(mytitle) +
theme_classic() +
theme(plot.title = element_text(size = 24))
}With that change in place, the function can be called with unquoted column names, just as they would appear in many tidyverse functions:
mybarplot(bos_values, RegionName, Zhvi, "Zillow Home Value Index by Boston Neighborhood")That final point is particularly useful in practice. The resulting plot object can be stored and extended further, for example by adding data labels on the bars with geom_text() and the scales::comma() function. A custom plotting function does not lock the user into a fixed result; it provides a well-designed starting point that can still be extended with additional {ggplot} layers.
Putting the Three Techniques Together in {ggplot2}
Seen as a progression, these examples build on one another in a logical way. The first shows how to centre a title with theme(plot.title = element_text(hjust = 0.5)). The second shows how to simplify a chart by removing grids, borders and background elements whilst restoring axis lines where needed. The third scales those preferences up by packaging them inside a reusable function. What begins as a one-off styling adjustment can therefore become part of a repeatable workflow.
These techniques also reflect a wider culture around R graphics. Resources such as the R Graph Gallery, created by Yan Holtz, have helped make this style of incremental learning more accessible by offering reproducible examples across a wide range of chart types. The gallery presents over 400 R-based graphics, with a strong emphasis on {ggplot2} and the tidyverse, and organises them into nearly 50 chart families and use cases. Its broader message is that effective visualisation is often the result of small, deliberate decisions rather than dramatic reinvention.
For anyone working with {ggplot2}, that is a helpful principle to keep in mind. A centred title may seem minor, just as removing a panel grid may seem cosmetic, yet these changes can improve clarity and consistency across a body of work. When those preferences are wrapped into a function, they also save time and reduce repetition, connecting plot styling directly to good code design.