TOPIC: C
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.
Adding visual appeal to bash command line scripts with colour variables on Linux
23rd November 2025
While I was updating some scripts to improve their functionality, I made some unexpected discoveries. One involved adding some colour to the output, and a second will come up later. The colours can be defined as values of variables, as you can see below:
# Colours
RED='\033[0;31m'
GREEN='\033[0;32m'
YELLOW='\033[1;33m'
NC='\033[0m' # no colour
In all cases, \033 is the shell escape sequence while [ is the control sequence initiator and m closes the sequence for colour definitions like we have here. A numeric value of 0 resets things to the default, which is how it is used in the no colour (NC) case that we have above to ensure that the colouration does not overflow beyond the intended text. Otherwise, 31 specifies red, 32 specifies green and 33 specifies yellow, giving options to use later on in the code. All of this is in line with the ANSI standard.
This is how these colour variables get used:
echo -e "\n${YELLOW}$(printf '*' {1..40}) All done $(printf '*' {1..40})${NC}\n"
The above is for an example with yellow text produced using ${YELLOW} segment after the newline sequence (\n) that is activated b y the -e switch passed to the echo command. This is turned off by the ${NC} portion at the end of the text, again before a terminating newline sequence. One extra addition here is the part that outputs forty asterisks: $(printf '*' {1..40}). You could have $(printf '*%.0s' {1..40}) instead, which is clearer to some because of the null output character sequence %.0s. In the earlier example, I opted for the simpler option.
Rendering Markdown in WordPress without plugins by using Parsedown
4th November 2025
Much of what is generated using GenAI as articles is output as Markdown, meaning that you need to convert the content when using it in a WordPress website. Naturally, this kind of thing should be done with care to ensure that you are the creator and that it is not all the work of a machine; orchestration is fine, regurgitation does that add that much. Naturally, fact checking is another need as well.
Writing plain Markdown has secured its own following as well, with WordPress plugins switching over the editor to facilitate such a mode of editing. When I tried Markup Markdown, I found it restrictive when it came to working with images within the text, and it needed a workaround for getting links to open in new browser tabs as well. Thus, I got rid of it to realise that it had not converted any Markdown as I expected, only to provide rendering at post or page display time. Rather than attempting to update the affected text, I decided to see if another solution could be found.
This took me to Parsedown, which proved to be handy for accomplishing what I needed once I had everything set in place. First, that meant cloning its GitHub repo onto the web server. Next, I created a directory called includes under that of my theme. Into there, I copied Parsedown.php to that location. When all was done, I ensured that file and directory ownership were assigned to www-data to avoid execution issues.
Then, I could set to updating the functions.php file. The first line to get added there included the parser file:
require_once get_template_directory() . '/includes/Parsedown.php';
After that, I found that I needed to disable the WordPress rendering machinery because that got in the way of Markdown rendering:
remove_filter('the_content', 'wpautop');
remove_filter('the_content', 'wptexturize');
The last step was to add a filter that parsed the Markdown and passed its output to WordPress rendering to do the rest as usual. This was a simple affair until I needed to deal with code snippets in pre and code tags. Hopefully, the included comments tell you much of what is happening. A possible exception is $matches[0]which itself is an array of entire <pre>...</pre> blocks including the containing tags, with $i => $block doing a $key (not the same variable as in the code, by the way) => $value lookup of the values in the array nesting.
add_filter('the_content', function($content) {
// Prepare a store for placeholders
$placeholders = [];
// 1. Extract pre blocks (including nested code) and replace with safe placeholders
preg_match_all('//si', $content, $pre_matches);
foreach ($pre_matches[0] as $i => $block) {
$key = "§PREBLOCK{$i}§";
$placeholders[$key] = $block;
$content = str_replace($block, $key, $content);
}
// 2. Extract standalone code blocks (not inside pre)
preg_match_all('/).*?<\/code>/si', $content, $code_matches);
foreach ($code_matches[0] as $i => $block) {
$key = "§CODEBLOCK{$i}§";
$placeholders[$key] = $block;
$content = str_replace($block, $key, $content);
}
// 3. Run Parsedown on the remaining content
$Parsedown = new Parsedown();
$content = $Parsedown->text($content);
// 4. Restore both pre and code placeholders
foreach ($placeholders as $key => $block) {
$content = str_replace($key, $block, $content);
}
// 5. Apply paragraph formatting
return wpautop($content);
}, 12); All of this avoided dealing with extra plugins to produce the required result. Handily, I still use the Classic Editor, which makes this work a lot more easily. There still is a Markdown import plugin that I am tempted to remove as well to streamline things. That can wait, though. It best not add any more of them any way, not least avoid clashes between them and what is now in the theme.
Keeping a graphical eye on CPU temperature and power consumption on the Linux command line
20th March 2025
Following my main workstation upgrade in January, some extra monitoring has been needed. This follows on from the experience with building its predecessor more than three years ago.
Being able to do this in a terminal session keeps things lightweight, and I have done that with text displays like what you see below using a combination of sensors and nvidia-smi in the following command:
watch -n 2 "sensors | grep -i 'k10'; sensors | grep -i 'tdie'; sensors | grep -i 'tctl'; echo "" | tee /dev/fd/2; nvidia-smi"
Everything is done within a watch command that refreshes the display every two seconds. Then, the panels are built up by a succession of commands separated with semicolons, one for each portion of the display. The grep command is used to pick out the desired output of the sensors command that is piped to it; doing that twice gets us two lines. The next command, echo "" | tee /dev/fd/2, adds an extra line by sending a space to STDERR output before the output of nvidia-smi is displayed. The result can be seen in the screenshot below.
However, I also came across a more graphical way to do things using commands like turbostat or sensors along with AWK programming and ttyplot. Using the temperature output from the above and converting that needs the following:
while true; do sensors | grep -i 'tctl' | awk '{ printf("%.2f\n", $2); fflush(); }'; sleep 2; done | ttyplot -s 100 -t "CPU Temperature (Tctl)" -u "°C"
This is done in an infinite while loop to keep things refreshing; the watch command does not work for piping output from the sensors command to both the awk and ttyplot commands in sequence and on a repeating, periodic basis. The awk command takes the second field from the input text, formats it to two places of decimals and prints it before flushing the output buffer afterwards. The ttyplot command then plots those numbers on the plot seen below in the screenshot with a y-axis scaled to a maximum of 100 (-s), units of °C (-u) and a title of CPU Temperature (Tctl) (-t).
A similar thing can be done for the CPU wattage, which is how I learned of the graphical display possibilities in the first place. The command follows:
sudo turbostat --Summary --quiet --show PkgWatt --interval 1 | sudo awk '{ printf("%.2f\n", $1); fflush(); }' | sudo ttyplot -s 200 -t "Turbostat - CPU Power (watts)" -u "watts"
Handily, the turbostat can be made to update every so often (every second in the command above), avoiding the need for any infinite while loop. Since only a summary is needed for the wattage, all other output can be suppressed, though everything needs to work using superuser privileges, unlike the sensors command earlier. Then, awk is used like before to process the wattage for plotting; the first field is what is being picked out here. After that, ttyplot displays the plot seen in the screenshot below with appropriate title, units and scaling. All works with output from one command acting as input to another using pipes.
All of this offers a lightweight way to keep an eye on system load, with the top command showing the impact of different processes if required. While there are graphical tools for some things, command line possibilities cannot be overlooked either.
Finding human balance in an age of AI code generation
12th March 2025
Recently, I was asked about how I felt about AI. Given that the other person was not an enthusiast, I picked on something that happened to me, not so long ago. It involved both Perplexity and Google Gemini when I was trying to debug something: both produced too much code. The experience almost inspired a LinkedIn post, only for some of the thinking to go online here for now. A spot of brainstorming using an LLM sounds like a useful exercise.
Going back to the original question, it happened during a meeting about potential freelance work. Thus, I tapped into experiences with code generators over several decades. The first one involved a metadata-driven tool that I developed; users reported that there was too much imperfect code to debug with the added complexity that dealing with clinical study data brings. That challenge resurfaced with another bespoke tool that someone else developed, and I opted to make things simpler: produce some boilerplate code and let users take things from there. Later, someone else again decided to have another go, seemingly with more success.
It is even more challenging when you are insufficiently familiar with the code that is being produced. That happened to me with shell scripting code from Google Gemini that was peppered with some Awk code. There was no alternative but to learn a bit more about the language from Tutorials Point and seek out an online book elsewhere. That did get me up to speed, and I will return to these when I am in need again.
Then, there was the time when I was trying to get a Julia script to deal with Google Drive needing permissions to be set. This started Google Gemini into adding more and more error checking code with try catch blocks. Since I did not have the issue at that point, I opted to halt and wait for its recurrence. When it did, I opted for a simpler approach, especially with the gdrive CLI tool starting up a web server for completing the process of reactivation. While there are times when shell scripting is better than Julia for these things, I added extra robustness and user-friendliness anyway.
During that second task, I was using VS Code with the GitHub Copilot plugin. There is a need to be careful, yet that can save time when it adds suggestions for you to include or reject. The latter may apply when it adds conditional logic that needs more checking, while simple code outputting useful text to the console can be approved. While that certainly is how I approach things for now, it brings up an increasingly relevant question for me.
How do we deal with all this code production? In an environment with myriads of unit tests and a great deal of automation, there may be more capacity for handling the output than mere human inspection and review, which can overwhelm the limitations of a human context window. A quick search revealed that there are automated tools for just this purpose, possibly with their own learning curves; otherwise, manual working could be a better option in some cases.
After all, we need to do our own thinking too. That was brought home to me during the Julia script editing. To come up with a solution, I had to step away from LLM output and think creatively to come up with something simpler. There was a tension between the two needs during the exercise, which highlighted how important it is to learn not to be distracted by all the new technology. Being an introvert in the first place, I need that solo space, only to have to step away from technology to get that when it was a refuge in the first place.
For anyone with a programming hobby, they have to limit all this input to avoid being overwhelmed; learning a programming language could involve stripping out AI extensions from a code editor, for instance, LLM output has its place, yet it has to be at a human scale too. That perhaps is the genius of a chat interface, and we now have Agentic AI too. It is as if the technology curve never slackens, at least not until the current boom ends, possibly when things break because they go too far beyond us. All this acceleration is fine until we need to catch up with what is happening.
Generating custom log messages in R
24th April 2023
While I have been exploring the use of R on a private basis during the last few years, a recent opportunity allowed me to use this exposure at work. This took the form of creating a utility script for use by others. To keep things lightweight, I did not go down the packaging route, but that may come later, possibly for something else.
However, anything used by others needs input checking and comprehensible feedback should anything go wrong. For me, that meant looking at the message, warning and stop functions. The last of these aborts script execution when there is a critical error, something that the other two do not do. The message function is for informative user input, while the warning function suggests things that may need their attention.
Each function takes string input and sends this to the terminal or log. They also can combine different pieces of text in the style of the paste0 function, and can take the text output of other functions as input. Used in combination with conditional logic or error handling, they can help a user track down what went wrong without their needing to ask a script developer. Anything that helps anyone else to help themselves has to be good.
Something to watch with the SYSODSESCAPECHAR automatic SAS macro variable
10th October 2021
Recently, a client of mine updated one of their systems from SAS 9.4 M5 to SAS 9.4 M7. Despite performing due diligence regarding changes between the maintenance release, a change in behaviour of the SYSODSESCAPECHAR automatic macro variable surprised them. The macro variable captures the assignment of the ODS escape character used to prefix RTF codes for page numbering and other things. That setting is made using an ODS ESCAPECHAR statement like the following:
ods escapechar="~";
In the M5 release, the tilde character in this example was output by the automatic macro variable, but that changed in the M7 release to 7E, the hexadecimal code for the same and this tripped up one of their validated macro programs used in output production. The adopted solution was to use the escape sequence (ESC) that gave the same outcome that was there before the change. That was less verbose than alternative code changing the hexadecimal code into the expected ASCII character that follows.
data _null_;
call symput("new",byte(input("&sysodsescapechar.",hex.)));
run;The above supplies a hexadecimal code to the BYTE function for correct rendering, with the SYMPUT routine assigning the resulting value to a macro variable named new. Just using the escape sequence is far more succinct, though there is now an added validation need once user pilot testing has completed. In my line of business, the updating of code is the quickest part of many such changes; documentation and testing always take longer.
Using multi-line commenting in Perl to inactivate blocks of code during testing
26th December 2019
Recently, I needed to inactivate blocks of code in a Perl script while doing some testing. Since this is something that I often do in other computing languages, I sought the same in Perl. To accomplish that, I need to use the POD methodology. This meant enclosing the code as follows.
=start
<< Code to be inactivated by inclusion in a comment >>
=cut
While the =start line could use any word after the equality sign, it seems that =cut is required to close the multi-line comment. If this was actual programming documentation, then the comment block should include some meaningful text for use with perldoc. However, that was not a concern here because the commenting statements would be removed afterwards anyway. It also is good practice not to leave commented code in a production script or program to avoid any later confusion.
In my case, this facility allowed me to isolate the code that I had to alter and test before putting everything back as needed. It also saved time since I did not need to individually comment out every executable line because multiple lines could be inactivated at a time.
Using the IN operator in SAS Macro programming
8th October 2012
This useful addition came in SAS 9.2, and I am amazed that it isn’t enabled by default. To accomplish that, you need to set the MINOPERATOR option, unless someone has done it for you in the SAS AUTOEXEC or another configuration program. Thus, the safety first approach is to have code like the following:
options minoperator;
%macro inop(x);
%if &x in (a b c) %then %do;
%put Value is included;
%end;
%else %do;
%put Value not included;
%end;
%mend inop;%inop(a);
Also, the default delimiter is the space, so if you need to change that, then the MINDELIMITER option needs setting. Adjusting the above code so that the delimiter now is the comma character gives us the following:
options minoperator mindelimiter=",";
%macro inop(x);
%if &x in (a b c) %then %do;
%put Value is included;
%end;
%else %do;
%put Value not included;
%end;
%mend inop;%inop(a);
Without any of the above, the only approach is to have the following, and that is what we had to do for SAS versions before 9.2:
%macro inop(x);
%if &x=a or &x=b or &x=c %then %do;
%put Value is included;
%end;
%else %do;
%put Value not included;
%end;
%mend inop;%inop(a);
While it may be clunky, it does work and remains a fallback in newer versions of SAS. Saying that, having the IN operator available makes writing SAS Macro code that little bit more swish, so it's a good thing to know.
Understanding Perl binding operators for pattern matching
20th May 2009
While this piece is as much an aide de memoire for myself as anything else, putting it here seems worthwhile if it answers questions for others. The binding operators, =~ or !~, come in handy when you are framing conditional statements in Perl using Regular Expressions, for example, testing whether x =~ /\d+/ or not. The =~ variant is also used for changing strings using the s/[pattern1]/[pattern2]/ regular expression construct (here, s stands for "substitute"). What has brought this to mind is that I wanted to ensure that something was done for strings that did not contain a certain pattern, and that's where the !~ binding operator came in useful; ^~ might have come to mind for some reason, but it wasn't what I needed.