Technology Tales

Open Source Tools for Pharmaceutical Clinical Data Reporting, Analysis & Regulatory Submissions

25^th March 2026

There was a time when SAS was the predominant technology for clinical data reporting, analysis and submission work in the pharmaceutical industry. Within the last decade, open-source alternatives have gained a lot of traction, and the {pharmaverse} initiative has arisen from this. The range of packages ranges from dataset creation (SDTM and ADaM) to output production, with utilities for test data and submission activities. All in all, there is quite a range here. The effort also is a marked change from each company working by itself to sharing and collaborating with others. Here then is the outcome of their endeavours.

{admiral}

Designed as an open-source, modular R toolbox, the {admiral} package assists in the creation of ADaM datasets through reusable functions and utilities tailored for pharmaceutical data analysis. Core packages handle general ADaM derivations whilst therapeutic area-specific extensions address more specialised needs, with a structured release schedule divided into two phases. Usability, simplicity and readability are central priorities, supported by comprehensive documentation, vignettes and example scripts. Community contributions and collaboration are actively encouraged, with the aim of fostering a shared, industry-wide approach to ADaM development in R. Related packages for test data and metadata manipulation complement the main toolkit, alongside a commitment to consistent coding practices and accessible code.

{aNCA}

Maintained by contributors from F. Hoffmann-La Roche AG, {aNCA} is an open-source R Shiny application that makes Non-Compartmental Analysis (NCA) accessible to scientists working with clinical and pre-clinical pharmacokinetic datasets. Users can upload their own data, apply pre-processing filters and run NCA with configurable options including half-life calculation rules, manual slope selection and user-defined AUC intervals. Results are explorable through interactive box plots, scatter plots and summary statistics tables, and can be exported in `PP` and `ADPP` dataset domains alongside a reproducible R script. Analysis settings can be saved and reloaded for continuity across sessions. Installation is available from CRAN via a standard install command, from GitHub using the `pak` package manager, or by cloning the repository directly for those wishing to contribute.

{autoslider.core}

The {autoslider.core} package generates standard table templates commonly used in Study Results Endorsement Plans. Its principal purpose is to reduce duplicated effort between statisticians and programmers when creating slides. Available on CRAN, the package can be installed either through the standard installation method or directly from GitHub for the latest development version.

{cards}

Supporting the CDISC Analysis Results Standard, the {cards} package facilitates the creation of analysis results data sets that enhance automation, reproducibility and consistency in clinical research. Structured data sets for statistical summaries are generated to enable tasks such as quality control, pre-calculating statistics for reports and combining results across studies. Tools for creating, modifying and analysing these data sets are provided, with the {cardx} extension offering additional functions for statistical tests and models. Installation is available through CRAN or GitHub, with resources including documentation and community contributions.

{cardx}

Extending the {cards} package, {cardx} facilitates the creation of Analysis Results Data Objects (ARD's) in R by leveraging utility functions from {cards} and statistical methods from packages such as {stats} and {emmeans}. These ARD's enable the generation of tables and visualisations for regulatory submissions, support quality control checks by storing both results and parameters, and allow for reproducible analyses through the inclusion of function inputs. Installation options include CRAN and GitHub, with examples demonstrating its use in t-tests and regression models. External statistical library dependencies are not enforced by the package, requiring explicit references in code for tools like {renv} to track them.

{chevron}

A collection of high-level functions for generating standardised outputs in clinical trials reporting, {chevron} covers a broad range of output types including tables for safety summaries, adverse events, demographics, ECG results, laboratory findings, medical history, response data, time-to-event analyses and vital signs, as well as listings and graphs such as Kaplan-Meier and mean plots. Straightforward implementation with limited parameterisation is a defining characteristic of the package. It is available on CRAN, with a development version accessible via GitHub, and those requiring greater flexibility are directed to the related {tern} package and its associated catalogue.

{clinify}

Built on the {flextable} and {officer} packages, {clinify} streamlines the creation of clinical tables, listings and figures whilst addressing challenges such as adherence to organisational reporting standards, the need for flexibility across different clients and the importance of reusable configurations. Compatibility with existing tools is a key priority, ensuring that its features do not interfere with the core functionalities of {flextable} or {officer}, whilst enabling tasks like dynamic page breaks, grouped headers and customisable formatting. Complex documents such as Word files with consistent layouts and tailored elements like footnotes and titles can be produced with reduced effort by building on these established frameworks.

{connector}

Offering a unified interface for establishing connections to various data sources, the {connector} package covers file systems and databases through a central configuration file that maintains consistent references across project scripts and facilitates switching between data sources. Functions such as connector_fs() for file system access and connector_dbi() for database connections are provided, with additional expansion packages enabling integration with specific platforms like Databricks and SharePoint. Installation is available via CRAN or GitHub, and usage involves defining a YAML configuration file to specify connection details that can then be initialised and utilised to interact with data sources. Operations including reading, writing and listing content are supported, with methods for managing connections and handling data in formats like parquet.

{covtracer}

Linking test traces to package code and documentation using coverage data from {covr}, the {covtracer} package enables the creation of a traceability matrix that maps tests to specific documented functions. Installation is via remotes from GitHub with specific dependencies, and configuration of {covr} is required to record tests alongside coverage traces. Untested behaviours can be identified and the direct testing of functions assessed, providing insights into test coverage and software validation. The example workflow demonstrates generating a matrix to show which tests evaluate code related to documented behaviours, highlighting gaps in test coverage.

{datacutr}

An open-source solution for applying data cuts to SDTM datasets within R, the {datacutr} package is designed to support pharmaceutical data analysis workflows. Available via CRAN or GitHub, it offers options for different types of cuts tailored to specific SDTM domains. Supplemental qualifiers are assumed to be merged with their parent domain before processing, allowing users flexibility in defining cut types such as patient, date, or domain-specific cuts. Documentation, contribution guidelines and community support through platforms like Slack and GitHub provide further assistance.

{datasetjson}

Facilitating the creation and manipulation of CDISC Dataset JSON formatted datasets, the {datasetjson} R package enables users to generate structured data files by applying metadata attributes to data frames. Metadata such as file paths, study identifiers and system details can be incorporated into dataset objects and written to disk or returned as JSON text. Reading JSON files back into data frames is also supported, with metadata preserved as attributes for use in analysis. The package currently supports version 1.1.0 of the Dataset JSON standard and is available via CRAN or GitHub.

{dataviewR}

An interactive data viewer for R, {dataviewR} enhances data exploration through a Shiny-based interface that enables users to examine data frames and tibbles with tools for filtering, column selection and generating reproducible {dplyr} code. Viewing multiple datasets simultaneously is supported, and the tool provides metadata insights alongside features for importing and exporting data, all within a responsive and user-friendly design. By combining intuitive navigation with automated code generation, the package aims to streamline data analysis workflows and improve the efficiency of dataset manipulation and documentation.

{docorator}

Generating formatted documents by adding headers, footers and page numbers to displays such as tables and figures, {docorator} exports outputs as PDF or RTF files. Accepted inputs include tables created with the {gt} package, figures generated using {ggplot2}, or paths to existing PNG files, and users can customise document elements like titles and footers. The package can be installed from CRAN or via GitHub, and its use involves creating a display object with specified formatting options before rendering the output. LaTeX libraries are required for PDF generation.

{envsetup}

Providing a configuration system for managing R project environments, the {envsetup} package enables adaptation to different deployment stages such as development, testing and production without altering code. YAML files are used to define paths for data and output directories, and R scripts are automatically sourced from specified locations to reduce the need for manual configuration changes. This approach supports consistent code usage across environments whilst allowing flexibility in environment-specific settings, streamlining workflows for projects requiring multiple deployment contexts.

{ggsurvfit}

Simplifying the creation of survival analysis visualisations using {ggplot2}, the {ggsurvfit} package offers tools to generate publication-ready figures with features such as confidence intervals, risk tables and quantile markers. Seamless integration with {ggplot2} functions allows for extensive customisation of plot elements whilst maintaining alignment between graphical components and annotations. Competing risks analysis is supported through `ggcuminc()`, and specific functions such as Surv_CNSR() handle CDISC ADaM `ADTTE` data by adjusting event coding conventions to prevent errors. Installation options are available via CRAN or GitHub, with examples and further resources accessible through its documentation and community links.

{gridify}

Addressing challenges in creating consistent and customisable graphical arrangements for figures and tables, the {gridify} package leverages the base {grid} package to facilitate the addition of headers, footers, captions and other contextual elements through predefined or custom layouts. Multiple input types are supported, including {ggplot2}, {flextable} and base R plots, and the workflow involves generating an object, selecting a layout and using functions to populate text elements before rendering the final output. Installation options include CRAN and GitHub, with examples demonstrating its application in enhancing tables with metadata and formatting. Uniformity across different projects is promoted, reducing manual adjustments and aligning visual elements consistently.

{gtsummary}

Offering a streamlined approach to generating publication-quality analytical and summary tables in R, the {gtsummary} package enables users to summarise datasets, regression models and other statistical outputs with minimal code. Variable types are identified automatically, relevant descriptive statistics computed and measures of data incompleteness included, whilst customisation of table formatting such as adjusting labels, adding p-values or merging tables for comparative analysis is also supported. Integration with packages like {broom} and {gt} facilitates the creation of visually appealing tables, and results can be exported to multiple formats including HTML, Word and LaTeX, making the package suitable for reproducible reporting in academic and professional contexts.

{logrx}

Supporting logging in clinical programming environments, the {logrx} package generates detailed logs for R scripts, ensuring code execution is traceable and reproducible. An overview of script execution and the associated environment is provided, enabling users to recreate conditions for verification or further analysis. Available on CRAN, installation is possible via standard methods or from its development repository, offering flexibility for both file-based and scripted usage. Structured logging tailored to the specific requirements of clinical applications is the defining characteristic of the package, with simplicity and minimal intrusion in coding workflows maintained throughout.

{metacore}

Providing a standardised framework for managing metadata within R sessions, the {metacore} package is particularly suited to clinical trial data analysis. Metadata is organised into six interconnected tables covering dataset specifications, variable details, value definitions, derivations, code lists and supplemental information, ensuring consistency and ease of access. By centralising metadata in a structured, immutable format, the package facilitates the development of tools that can leverage this information across different workflows, reducing the need for redundant data structures. Reading metadata from various sources, including Define-XML 2.0, is also supported.

{metatools}

Working with {metacore} objects, {metatools} enables users to build datasets, enhance columns in existing datasets and validate data against metadata specifications. Installation is available from CRAN or via GitHub. Core functionality includes pulling columns from existing datasets, creating new categorical variables, converting columns to factors and running checks to verify that data conforms to control terminology and that all expected variables are present.

{pharmaRTF}

Developed to address gaps in RTF output capabilities within R, {pharmaRTF} is a package for pharmaceutical industry programmers who produce RTF documents for clinical trial data analysis. Whilst the {huxtable} package offers extensive RTF styling and formatting options, it lacks the ability to set document properties such as page size and orientation, repeat column headers across pages, or create multi-level titles and footnotes within document headers and footers. These limitations are resolved by {pharmaRTF}, which wraps around {huxtable} tables to provide document property controls, proper multipage display and title and footnote management within headers and footers. Two core objects form the basis of the package: rtf_doc for document-wide attributes and hf_line for creating individual title and footnote lines, each carrying formatting properties such as alignment, font and bold or italic styling. Default output files use Courier New at 12-point size, Letter page dimensions in landscape orientation with one-inch margins, though all of these can be adjusted through property functions. The package is available on CRAN and supports both a {tidyverse} piping style and a more traditional assignment-based coding approach.

{pharmaverseadam}

Serving as a repository for ADaM test datasets generated by executing templates from related packages such as {admiral} and its extensions, the {pharmaverseadam} package automates dataset creation through a script that installs required packages, runs templates and saves results. Metadata is managed centrally in an XLSX file to ensure consistency in documentation, and updates occur regularly or ad-hoc when templates change. Documentation is generated automatically from metadata and saved as `.R` files, and the package includes contributions from multiple developers with examples provided for each dataset. Preparing metadata, updating configuration files for new therapeutic areas and executing a script to generate datasets and documentation ensures alignment with the latest versions of dependent packages. Installation is available via CRAN or GitHub.

{pharmaverseraw}

Providing raw datasets to support the creation of SDTM datasets, the {pharmaverseraw} package includes examples that are independent of specific electronic data capture systems or data standards such as CDASH. Datasets are named using SDTM domain identifiers with the suffix _raw, and installation options include CRAN or direct GitHub access. Updates involve contributing via GitHub issues, generating new or modified datasets through standalone R scripts stored in the data-raw folder, and ensuring generated files are saved in the data folder as .rda files with consistent naming. Documentation is maintained in R/*.R files, and changes require updating `NAMESPACE` and `.Rd` files using devtools::document.

{pharmaversesdtm}

A collection of test datasets formatted according to the SDTM standard, the {pharmaversesdtm} package is designed for use within the pharmaverse family of packages. Datasets applicable across therapeutic areas, such as `DM` and VS, are included alongside those specific to particular areas, like `RS` and `OE`. Available via CRAN and GitHub, the package provides installation instructions for both stable and development versions, with test data sourced from the CDISC pilot project and ad-hoc datasets generated by the {admiral} team. Naming conventions distinguish between general and therapeutic area-specific categories, with examples such as dm for general use and rs_onco for oncology-specific data. Updates involve creating or modifying R scripts in the data-raw folder, generating `.rda` files and updating metadata in a central JSON file to automate documentation and maintain consistency, including specifying dataset details like labels, descriptions and therapeutic areas.

{pkglite} (R)

Converting R package source code into text files and reconstructing package structures from those files, {pkglite} enables the exchange and management of R packages as plain text. Single or multiple packages can be processed through functions that collate, pack and unpack files, with installation options available via CRAN or GitHub. The tool adheres to a defined format for text files and includes documentation for generating specifications and managing file collections.

{pkglite} (Python)

An open-source framework licensed under the MIT licence, {pkglite} for Python, allows source projects written in any programming language to be packed into portable files and restored to their original directory structure. Installation is available via PyPI or as a development version cloned from GitHub, and the package can also be run without installation using `uvx`. A command line interface is provided in addition to the Python API, which can be installed globally using `pipx`.

{rhino}

Streamlining the development of high-quality, enterprise-grade Shiny applications, {rhino} integrates software engineering best practices, modular code structures and robust testing frameworks. Scalable architecture is supported through modularisation, code quality is enhanced with unit and end-to-end testing, and automation is facilitated via tools for project setup, continuous integration and dependency management. Comprehensive documentation is divided into tutorials, explanations and guides, with examples and resources available for learning.

{risk.assessr}

Evaluating the reliability and security of R packages during validation, the {risk.assessr} package analyses maintenance, documentation and dependencies through metrics such as R CMD check results, unit test coverage and dependency assessments. A traceability matrix linking functions to tests is generated, and risk profiles are based on predefined thresholds including documentation completeness, licence type and code coverage. The tool supports installation from GitHub or CRAN, processes local package files or `renv.lock` dependencies and offers detailed outputs such as risk analysis, dependency lists and reverse dependency information. Advanced features include identifying potential issues in suggested package dependencies and generating HTML reports for risk evaluation, with applications in clinical trial workflows and package validation processes.

{riskassessment}

Built on the {riskmetric} framework, the {riskassessment} application offers a user-friendly interface for evaluating the risk of using R packages within regulated industries, assessing development practices, documentation and sustainability. Non-technical users can review {riskmetric} outputs, add personalised comments, categorise packages into risk levels, generate reports and store assessments securely, with features such as user authentication and role-based access. Alignment with validation principles outlined by the R Validation Hub supports decision-making in regulated settings, though deeper software inspection may be required in some cases. Deployment is possible using tools like Shiny Server or Posit Connect, with installation options including GitHub and local configuration via {renv}.

{riskmetric}

Providing a framework for evaluating the quality of R packages, the {riskmetric} package assesses development practices, documentation, community engagement and sustainability through a series of metrics. Currently operating in a maintenance-only phase, further development is focused on a new tool called {val.metre}. The workflow involves retrieving package information, assessing it against predefined criteria and generating a risk score, with installation available from CRAN or GitHub. An associated application, {riskassessment}, offers a user interface for organisations to review and manage package risk assessments, store metrics and apply organisational rules.

{rlistings}

Designed to create and display formatted listings with a focus on ASCII rendering for tables and regulatory-ready outputs, the {rlistings} R package relies on the {formatters} package for formatting infrastructure. Requirements such as flexible pagination, multiple output formats and repeated key columns informed its development. Available on CRAN and GitHub, the package is under active development and includes features such as adjustable column widths, alignment and support for titles and footnotes.

{rtables}

Tailored for generating submission-ready tables for health authority review, the {rtables} R package creates and displays complex tables with advanced formatting and output options that support regulatory requirements for clinical trial data presentation. Separation of data values from their visualisation is enabled, multiple values can be included within cells, and flexible tabulation and formatting capabilities are provided, including cell spans, rounding and alignment. Output formats include HTML, ASCII, LaTeX, PDF and PowerPoint, with additional formats under development. Also, the package incorporates features such as pagination, distinction between data names and labels for CDISC standards and support for titles and footnotes. Installation is available via CRAN or GitHub, with ongoing community support and training resources.

{rtflite}

A lightweight Python library focused on precise formatting of production-quality tables and figures, {rtflite} is designed for composing RTF documents. Installation is available via PyPI or directly from its GitHub repository, with optional dependencies available to enable DOCX assembly support and RTF-to-PDF or RTF-to-DOCX conversion via LibreOffice.

{sdtm.oak}

Offering a modular, open-source solution for generating CDISC SDTM datasets, the {sdtm.oak} R package is designed to work across different electronic data capture systems and data standards. Industry challenges related to inconsistent raw data structures and varying data collection practices are addressed through reusable algorithms that map raw datasets to SDTM domains, with current capabilities covering Findings, Events and Intervention classes. Future developments aim to expand domain support, introduce metadata-driven code generation and enhance automation potential, though sponsor-specific metadata management tasks are not yet handled by the package. Available on CRAN and GitHub, development is ongoing with refinements based on user feedback and evolving SDTM requirements.

{sdtmchecks}

Providing functions to detect common data issues in SDTM datasets, the {sdtmchecks} package is designed to be broadly applicable and useful for analysis. Installation is available from CRAN or via GitHub, with development versions accessible through specific repositories, and users are not required to specify SDTM versions. A range of data check functions stored as R scripts is included, and contributions are encouraged that maintain flexibility across different data standards.

{siera}

Facilitating the generation of Analysis Results Datasets (ARD's) by processing Analysis Results Standard (ARS) metadata, the {siera} package works with parameters such as analysis sets, groupings, data subsets and methods. Metadata is typically provided in JSON format and used to create R scripts automatically that, when executed with corresponding ADaM datasets, produce ARD's in a structured format. The package can be installed from CRAN or GitHub, and its primary function, `readARS`, requires an ARS file, an output directory and access to relevant ADaM data. The CDISC Analysis Results Standard underpins this process, promoting automation and consistency in analysis outcomes.

{teal}

An open-source, Shiny-based interactive framework for exploratory data analysis, {teal} is developed as part of the pharmaverse ecosystem and maintained by F. Hoffmann-La Roche AG alongside a broad community of contributors. Analytical applications are built by combining supported data types, including CDISC clinical trial data, independent or relational datasets and `MultiAssayExperiment` objects, with modular analytical components known as teal modules. These modules can be drawn from dedicated packages covering general data exploration, clinical reporting and multi-omics analysis and define the specific analyses presented within an application. A suite of companion packages handles logging, reproducibility, data loading, filtering, reporting and transformation. The package is available on CRAN and is under active development, with community support provided through the {pharmaverse} Slack workspace.

{tern}

Supporting clinical trial reporting through a broad range of analysis functions, the {tern} R package offers data visualisation capabilities including line plots, Kaplan-Meier plots, forest plots, waterfall plots and Bland-Altman plots. Statistical model fit summaries for logistic and Cox regression are also provided, along with numerous analysis and summary table functions. Many of these outputs can be integrated into interactive Teal Shiny applications via the {teal.modules.clinical} package.

{tfrmt}

Offering a structured approach to defining and applying formatting rules for data displays in clinical trials, the {tfrmt} package streamlines the creation of mock displays, aligns with industry-standard Analysis Results Data (ARD) formats and integrates formatting tasks into the programming workflow to reduce manual effort and rework. Metadata is leveraged to automate styling and layout, enabling standardised formatting with minimal code, supporting quality control before final output and facilitating the reuse of datasets across different table types. Built on the {gt} package, the tool provides a flexible interface for generating tables and mock-ups, allowing users to focus on data interpretation rather than repetitive formatting tasks.

{tfrmtbuilder}

A tool for defining display-related metadata to streamline the creation and modification of table formats, the {tfrmtbuilder} package supports workflows such as generating tables from scratch, using templates or editing existing ones. Features include a toggle to switch between mock and real data, options to load or create datasets, tools for mapping and formatting data and the ability to export results as JSON, HTML or PNG. Designed for use in study planning and analysis phases, the package allows users to manage table structures efficiently.

{tidyCDISC}

An open-source R Shiny application, {tidyCDISC} is designed to help clinical personnel explore and analyse ADaM-standard data sets without writing any code. Customised clinical tables can be generated through a point-and-click interface, trends across patient populations examined using dynamic figures and individual patient profiles explored in detail. A broad range of users is served, from clinical heads with no programming background to statisticians and statistical programmers, with reported time savings of around 95% for routine trial analysis tasks. The app accepts only `sas7bdat` files conforming to CDISC ADaM standards and includes a feature to export reproducible R scripts from its table generator. A demo version is available without installation using CDISC pilot data, whilst uploading study data requires installing the package from CRAN or via GitHub.

{tidytlg}

Facilitating the creation of tables, listings and graphs using the {tidyverse} framework, the {tidytlg} package offers two approaches: a functional method involving custom scripts for each output and a metadata-driven method that leverages column and table metadata to generate results automatically. Tools for data analysis, including frequency tables and univariate statistics, are included alongside support for exporting outputs to formatted documents.

{Tplyr}

Simplifying the creation of clinical data summaries by breaking down complex tables into reusable layers, {Tplyr} allows users to focus on presentation rather than repetitive data processing. The conceptual approach of {dplyr} is mirrored but applied to common clinical table types, such as counting event-based variables, generating descriptive statistics for continuous data and categorising numerical ranges. Metadata is included with each summary produced to ensure traceability from raw data to final output, and user-acceptance testing documentation is provided to support its use in regulated environments. Installation options are available via CRAN or GitHub, accompanied by detailed vignettes covering features like layer templates, metadata extension and styled table outputs.

{valtools}

Streamlining the validation of R packages used in clinical research and drug development, {valtools} offers templates and functions to support tasks such as setting up validation frameworks, managing requirements and test cases and generating reports. Developed by the R Package Validation Framework PHUSE Working Group, the package integrates with standard development tools and provides functions prefixed with `vt` to facilitate structured validation processes including infrastructure setup, documentation creation and automated checks. Generating validation reports, scraping metadata from validation configurations and executing validation workflows through temporary installations or existing packages are all supported.

{whirl}

Facilitating the execution of scripts in batch mode whilst generating detailed logs that meet regulatory requirements, the {whirl} package produces logs including script status, execution timestamps, environment details, package versions and environmental variables, presented in a structured HTML format. Individual or multiple scripts can be run simultaneously, with parallel processing enabled through specified worker counts. A configuration file allows scripts to be executed in sequential steps, ensuring dependencies are respected, and the package produces individual logs for each script alongside a summary log and a tibble summarising execution outcomes. Installation options include CRAN and GitHub, with documentation available for customisation and advanced usage.

{xportr}

Assisting clinical programmers in preparing CDISC compliant XPT files for clinical data sets, the {xportr} package associates metadata with R data frames, performs validation checks and converts data into transportable SAS v5 XPT format. Tools are included to define variable types, set appropriate lengths, apply labels, format data, reorder variables and assign dataset labels, ensuring adherence to standards such as variable naming conventions, character length limits and the absence of non-ASCII characters. A practical example demonstrates how to use a specification file to apply these transformations to an ADSL dataset, ultimately generating a compliant XPT file.

Some R packages to explore as you find your feet with the language

24^th March 2026

Here are some commonly used R packages and other tools that are pervasive, along with others that I have encountered while getting started with the language, itself becoming pervasive in my line of business. The collection grew organically as my explorations proceeded, and reflects what I was trying out during my acclimatisation.

General

Here are two general packages to get things started, with one of them being unavoidable in the R world. The other is more advanced, possibly offering more to package developers.

{tidyverse}

You cannot use R without knowing about this collection of packages. In many ways, they form a mini-language of their own, drawing some criticism from those who reckon that base R functionality covers a sufficient gamut anyway. Nevertheless, there is so much here that will get you going with data wrangling and visualisation that it is worth knowing what is possible. The complaints may come from your not needing to use anything else for these purposes.

{plumber}

This R package enables developers to convert existing R functions into web API endpoints by adding roxygen2-like comment annotations to their code. Once annotated, functions can handle HTTP GET and POST requests, accept query string or JSON parameters and return outputs such as plain values or rendered plots. The package is available on CRAN as a stable release, with a development version hosted on GitHub. For deployment, it integrates with DigitalOcean through a companion package called {plumberDeploy}, and also supports Posit Connect, PM2 and Docker as hosting options. Related projects in the same space include OpenCPU, which is designed for hosting R APIs in scientific research contexts, and the now-discontinued jug package, which took a more programmatic approach to API construction.

Data Preparation

You simply cannot avoid working with data during any analysis or reporting work. While there is a learning curve if you are used to other languages, there is little doubt that R is well-endowed when it comes to performing these tasks. Here are some packages that extend base R capabilities and might even add some extra user-friendliness along the way.

{forcats}

The {forcats} package in R provides functions to manage categorical variables by reordering factor levels, collapsing infrequent values and adjusting their sequence based on frequency or other variables. It includes tools such as reordering by another variable, grouping rare categories into 'other' and modifying level order manually, which are useful for data analysis and visualisation workflows. Designed as part of the tidyverse, it integrates with other packages to streamline tasks like counting and plotting categorical data, enhancing clarity and efficiency in handling factors within R.

{tidyr}

Around this time last year, I remember completing a LinkedIn course on a set of good practices known as tidy data, where each variable occupies a column, each observation a row and each value a single cell. This package is designed to help users restructure data so it follows those rules. It provides tools for reshaping data between long and wide formats, handling nested lists, splitting or combining columns, managing missing values and layering or flattening grouped data.

Installation options include the {tidyverse} collection, standalone installation, or the development version from GitHub. The package succeeds earlier reshaping tools like {reshape2} and {reshape}, offering a focused approach to tidying data rather than general reshaping or aggregation.

{haven}

Having a long track record of working with SAS, {haven} with its abilities to read and write data files from statistical software such as SAS, SPSS and Stata, leveraging the ReadStat library, arouses my interest. Handily, it supports a range of file formats, including SAS transport and data files, SPSS system and older portable files and Stata data files up to version 15, converting these into tibbles with enhanced printing capabilities. Value labels are preserved as a labelled class, allowing conversion to factors, while dates and times are transformed into standard R classes.

{RMariaDB}

While there are other approaches to working with databases using R, {RMariaDB} provides a database interface and driver for MariaDB, designed to fully comply with the DBI specification and serve as a replacement for the older {RMySQL} package. It supports connecting to databases using configuration files, executing queries, reading and writing data tables and managing results in chunks. Installation options include binary packages from CRAN or development versions from GitHub, with additional dependencies such as MariaDB Connector/C or libmysqlclient required for Linux and macOS systems. Configuration is typically handled through a MariaDB-specific file, and the package includes acknowledgments for contributions from various developers and organisations.

COVID-19 Data Hub

For many people, the pandemic may be a fading memory, yet it offered its chances for learning R, not least because there was a use case with more than a hint of personal interest about it. Here is a library making it easier to get hold of the data, with some added pre-processing too. Memories of how I needed to wrangle what was published by various sources make me appreciate just how vital it is to have harmonised data for analysis work.

Table Production

While many appear to graphical presentation of results to their tabular display, R does have its options here too. In recent times, the options have improved, particularly of the pharmaverse initiative. Here is a selection of what I found during my explorations.

{officer}

Part of the {officeverse} along with {officedown}, {Flextable}, {Rvg} and {mschart}, the {officer} R package enables users to create and modify Word and PowerPoint documents directly from R, allowing the insertion of images, tables and formatted content, as well as the import of document content into data frames. It supports the generation of RTF files and integrates with other packages for advanced features such as vector graphics and native office charts. Installation options include CRAN and GitHub, with community resources available for assistance and contributions. The package facilitates the manipulation of document elements like paragraphs, tables and section breaks and provides tools for exporting and importing content between R and office formats, alongside functions for managing slide layouts and embedded objects in presentations.

{pharmaRTF}

If you work in clinical research like I do, the need to produce data tabulations is a non-negotiable requirement. That is how this package came to be developed and the pharmaverse of which it is part has numerous other options, should you need to look at using one of those. The flavour of RTF produced here is the Microsoft Word variety, which did not look as well in LibreOffice Writer when I last looked at the results with that open-source alternative. Otherwise, the results look well to many eyes.

{formattable}

Here is a package that enhances data presentation by applying customisable formatting to vectors and data frames, supporting formats such as percentages, currency and accounting. Available on GitHub and CRAN, it integrates with dynamic document tools like {knitr} and {rmarkdown} to produce visually distinct tables, with features including gradient colour scales, conditional styling and icon-based representations. It automatically converts to {htmlwidgets} in interactive environments and is licensed under MIT, enabling flexible use in both static and interactive data displays.

{reactable}

The {reactable} package for R provides interactive data tables built on the React Table library, offering features such as sorting, filtering, pagination, grouping with aggregation, virtual scrolling for large datasets and support for custom rendering through R or JavaScript. It integrates seamlessly into R Markdown documents and Shiny applications, enabling the use of HTML widgets and conditional styling. Installation options include CRAN and GitHub, with examples demonstrating its application across various datasets and scenarios. The package supports major web browsers and is licensed under MIT, designed for developers seeking dynamic data presentation tools within the R ecosystem.

{DT}

Particularly useful in dynamic web applications like Shiny, the {DT} package in R provides a means of rendering interactive HTML tables by building on the DataTables JavaScript library. It supports features including sorting, searching, pagination and advanced filtering, with numeric, date and time columns using range-based sliders whilst factor and character columns rely on search boxes or dropdowns. Filtering operates on the client side by default, though server-side processing is also available. JavaScript callbacks can be injected after initialisation to manipulate table behaviour, such as enabling automatic page navigation or adding child rows to display additional detail. HTML content is escaped by default as a safeguard against cross-site scripting attacks, with the option to adjust this on a per-column basis. Whilst the package integrates with Shiny applications, attention is needed around scrolling and slider positioning to prevent layout problems. Overall, the package is well suited to exploratory data analysis and the building of interactive dashboards.

{gt}

The {gt} package in R enables users to create well-structured tables with a variety of formatting options, starting from data frames or tibbles and incorporating elements such as headers, footers and customised column labels. It supports output in HTML, LaTeX and RTF formats and includes example datasets for experimentation. The package prioritises simplicity for common tasks while offering advanced functions for detailed customisation, with installation available via CRAN or GitHub. Users can access resources like documentation, community forums and example projects to explore its capabilities, and it is supported by a range of related packages that extend its functionality.

{gtsummary}

Enabling users to produce publication-ready outputs with minimal code, the {gtsummary} package offers a streamlined approach to generating analytical and summary tables in R. It automates the summarisation of data frames, regression models and other datasets, identifying variable types and calculating relevant statistics, including measures of data incompleteness. Customisation options allow for formatting, merging and styling tables to suit specific needs, while integration with packages such as {broom} and {gt} facilitates seamless incorporation into R Markdown workflows. The package supports the creation of side-by-side regression tables and provides tools for exporting results as images, HTML, Word, or LaTeX files, enhancing flexibility for reporting and sharing findings.

{huxtable}

Here is an R package designed to generate LaTeX and HTML tables with a modern, user-friendly interface, offering extensive control over styling, formatting, alignment and layout. It supports features such as custom borders, padding, background colours and cell spanning across rows or columns, with tables modifiable using standard R subsetting or dplyr functions. Examples demonstrate its use for creating simple tables, applying conditional formatting and producing regression output with statistical details. The package also facilitates quick export to formats like PDF, DOCX, HTML and XLSX. Installation options include CRAN, R-Universe and GitHub, while the name reflects its origins as an enhanced version of the {xtable} package. The logo was generated using the package itself, and the background design draws inspiration from Piet Mondrian’s artwork.

Figure Generation

R has such a reputation for graphical presentations that it is cited as a strong reason to explore what the ecosystem has to offer. While base R itself is not shabby when it comes to creating graphs and charts, these packages will extend things by quite a way. In fact, the first on this list is near enough pervasive.

{ggplot2}

Though its default formatting does not appeal to me, the myriad of options makes this a very flexible tool, albeit at the expense of some code verbosity. Multi-panel plots are not among its strengths, which may send you elsewhere for that need.

{ggforce}

Focusing on features not included in the core library, the {ggforce} package extends {ggplot2} by offering additional tools to enhance data visualisation. Designed to complement the primary role of {ggplot2} in exploratory data analysis, it provides a range of geoms, stats and other components that are well-documented and implemented, aiming to support more complex and custom plot compositions. Available for installation via CRAN or GitHub, the package includes a variety of functionalities described in detail on its associated website, though specific examples are not included here.

{cowplot}

Developed by Claus O. Wilke for internal use in his lab, {cowplot} is an R package designed to help with the creation of publication-quality figures built on top of {ggplot2}. It provides a set of themes, tools for aligning and arranging plots into compound figures and functions for annotating plots or combining them with images. The package can be installed directly from CRAN or as a development version via GitHub, and it has seen widespread use in the book Fundamentals of Data Visualisation.

{sjPlot}

The {sjPlot} package provides a range of tools for visualising data and statistical results commonly used in social science research, including frequency tables, histograms, box plots, regression models, mixed effects models, PCA, correlation matrices and cluster analyses. It supports installation via CRAN for stable releases or through GitHub for development versions, with documentation and examples available online. The package is licensed under GPL-3 and developed by Daniel Lüdecke, offering functions to create visualisations such as scatter plots, Likert scales and interaction effect plots, along with tools for constructing index variables and presenting statistical outputs in tabular formats.

{thematic}

By offering a centralised approach to theming and enabling automatic adaptation of plot styles within Shiny applications, the {thematic} package simplifies the styling of R graphics, including {ggplot2}, {lattice} and base R plots, R Markdown documents and RStudio. It allows users to apply consistent visual themes across different plotting systems, with auto-theming in Shiny and R Markdown relying on CSS and {bslib} themes, respectively. Installation requires specific versions of dependent packages such as {shiny} and {rmarkdown}, while custom fonts benefit from {showtext} or {ragg}. Users can set global defaults for background, foreground and accent colours, as well as fonts, which can be overridden with plot-specific theme adjustments. The package also defines default colour scales for qualitative and sequential data and integrates with tools like bslib to import Google Fonts, enhancing visual consistency across different environments and user interfaces.

Publishing Tools

The R ecosystem goes beyond mere graphical and tabular display production to offer means for taking things much further, often offering platforms for publishing your work. These can be used locally too, so there is no need to entrust everything to a third-party provider. The uses are endless for what is available, and it appears that Posit has used this to help with building documentation and training too.

R Markdown

What you have here is one of those distinguishing facilities of the R ecosystem, particularly for those wanting to share their analysis work with more than a hint of reproducibility. The tool combines narrative text and code to generate various outputs, supporting multiple programming languages and formats such as HTML, PDF and dashboards. It enables users to produce reports, presentations and interactive applications, with options for publishing and scheduling through platforms like RStudio Connect, facilitating collaboration and distribution of results in professional settings.

Distill for R Markdown

Distill for R Markdown is a tool designed to streamline the creation of technical documents, offering features such as code folding, syntax highlighting and theming. It builds on existing frameworks like Pandoc, MathJax and D3, enabling the production of dynamic, interactive content. Users can customise the appearance with CSS and incorporate appendices for supplementary information. The tool acknowledges the contributions of developers who created foundational libraries, ensuring accessibility and functionality for a wide audience. Its design prioritises clarity, allowing authors to focus on presenting results rather than underlying code, while maintaining flexibility for those who wish to include detailed explanations.

{shiny}

For a while, this was one of R's unique selling points, and remains as compelling a reason to use the language even when Python has got its own version of the package. Enabling the creation of interactive web applications for data analysis without requiring web development expertise allows users to build interfaces that let others explore data through dynamic visualisations and filters. Here is a simple example: an app that generates scatter plots with adjustable variables, species filters and marginal plots, hosted either on personal servers or through a dedicated hosting service.

{bslib}

The {bslib} R package offers a modern user interface toolkit for Shiny and R Markdown applications, leveraging Bootstrap to enable the creation of customisable dashboards and interactive theming. It supports the use of updated Bootstrap and Bootswatch versions while maintaining compatibility with existing defaults, and provides tools for real-time visual adjustments. Installation is available through CRAN, with example previews demonstrating its capabilities.

{rhandsontable}

Enabling users to manipulate and validate data within a spreadsheet-like interface, the {rhandsontable} package introduces an interactive data grid for R. It supports features such as custom cell rendering, validation rules and integration with Shiny applications. When used in Shiny, the widget requires explicit conversion of data using the hot_to_r function, as updates may not be immediately reflected in reactive contexts. Examples demonstrate its application in various scenarios, including date editing, financial calculations and dynamic visualisations linked to charts. The package also accommodates bookmarks in Shiny apps with specific handling. Users are encouraged to report issues or contribute improvements, with guidance provided for those seeking to expand its functionality. The development team welcomes feedback to refine the tool further, ensuring it aligns with evolving user needs.

{xaringanExtra}

{xaringanExtra} offers a range of enhancements and extensions for creating and presenting slides with xaringan, enabling features such as adding an overview tile view, making slides editable, broadcasting in real time, incorporating animations, embedding live video feeds and applying custom styles. It allows users to selectively activate individual tools or load multiple features simultaneously through a single function call, supporting tasks like adding banners, enabling code copying, fitting slides to screen dimensions and integrating utility toolkits. The package is available for installation via CRAN or GitHub, providing flexibility for developers and presenters seeking to expand the functionality of their slides.

How to persist R packages across remote Windows server sessions

9^th January 2026

Recently, I was using R to automate some code changes that needed implementation when porting code from a vendor to client systems. While I was doing so, I noticed that packages needed to be reinstalled every time that I logged into their system. This was because they were going into a temporary area by default. The solution was to define another location where the packages could be persisted.

That meant creating a .Renviron file, with Windows Explorer making that manoeuvre an awkward one that could not be completed. Using PowerShell was the solution for this. There, I could use the following command to do what I needed:

New-Item -ItemType File "$env:USERPROFILE\Documents\.Renviron" -Force

That gave me an empty .Renviron file, to which I could add the following text for where the packages should be kept (the path may differ on your system):

R_LIBS_USER=C:/R/packages

Here, the paths are only examples and do not always represent what the real ones were, and that is by design for reasons of client confidentiality. Restarting RStudio to give me a fresh R session meant that I now could install packages using commands like this one:

install.packages("tidyverse")

Version constraints meant for compilation from source in my case, making for a long wait time for completion. Once that was done, though, there was no need for a repeat operation.

One final remark is that file creation and population could be done in the same command in PowerShell:

'R_LIBS_USER=C:/R/packages' | Out-File -Encoding ascii "$env:USERPROFILE\Documents\.Renviron"

It places the text into a new file or completely overwrites an existing, meaning that you really want to do this once should you decide to add any more setting details to .Renviron later on.

Installing PowerShell on Linux Mint for some cross-platform testing

25^th November 2025

Given how well shell scripting works on Linux and my familiarity with it, the need to install PowerShell on a Linux system may seem surprising. However, this was part of some testing that I wanted to do on a machine that I controlled before moving the code to a client's system. The first step was to ensure that any prerequisites were in place:

sudo apt update
sudo apt install -y wget apt-transport-https software-properties-common

After that, the next moves were to download and install the required package for instating Microsoft repository details:

wget -q https://packages.microsoft.com/config/ubuntu/24.04/packages-microsoft-prod.deb
sudo dpkg -i packages-microsoft-prod.deb

Then, I could install PowerShell itself:

sudo apt update
sudo apt install -y powershell

When it was in place, issuing the following command started up the extra shell for what I needed to do:

pwsh

During my investigations, I found that my local version of PowerShell was not the same as on the client's system, meaning that any code was not as portable as I might have expected, Nevertheless, it is good to have this for future reference and proves how interoperable Microsoft has needed to become.

Ansible automation for Linux Mint updates with repository failover handling

7^th November 2025

Recently, I had a Microsoft PPA output disrupt an Ansible playbook mediated upgrade process for my main Linux workstation. Thus, I ended up creating a failover for this situation, and the first step in the playbook was to define the affected repo:

  vars:
    microsoft_repo_url: "https://packages.microsoft.com/repos/code/dists/stable/InRelease"

The next move was to start defining tasks, with the first testing the repo to pick up any lack of responsiveness and flag that for subsequent operations.

  tasks:
  - name: Check Microsoft repository availability
    uri:
      url: "{{ microsoft_repo_url }}"
      method: HEAD
      return_content: no
      timeout: 10
    register: microsoft_repo_check
    failed_when: false

  - name: Set flag to skip Microsoft updates if unreachable
    set_fact:
      skip_microsoft_repos: "{{ microsoft_repo_check.status is not defined or microsoft_repo_check.status != 200 }}"

In the event of a failure, the next task was to disable the repo to allow other processing to take place. This was accomplished by temporarily renaming the relevant files under /etc/apt/sources.list.d/.

    - name: Temporarily disable Microsoft repositories
    become: true
    shell: |
      for file in /etc/apt/sources.list.d/microsoft*.list; do
        [ -f "$file" ] && mv "$file" "${file}.disabled"
      done
      for file in /etc/apt/sources.list.d/vscode*.list; do
        [ -f "$file" ] && mv "$file" "${file}.disabled"
      done
    when: skip_microsoft_repos | default(false)
    changed_when: false

With that completed, the rest of the update actions could be performed near enough as usual.

  - name: Update APT cache (retry up to 5 times)
    apt:
      update_cache: yes
    register: apt_update_result
    retries: 5
    delay: 10
    until: apt_update_result is succeeded

  - name: Perform normal upgrade
    apt:
      upgrade: yes
    register: apt_upgrade_result
    retries: 3
    delay: 10
    until: apt_upgrade_result is succeeded

  - name: Perform dist-upgrade with autoremove and autoclean
    apt:
      upgrade: dist
      autoremove: yes
      autoclean: yes
    register: apt_dist_result
    retries: 3
    delay: 10
    until: apt_dist_result is succeeded

After those, another renaming operation restores the earlier filenames to what they were.

  - name: Re-enable Microsoft repositories
    become: true
    shell: |
      for file in /etc/apt/sources.list.d/*.disabled; do
        base="$(basename "$file" .disabled)"
        if [[ "$base" == microsoft* || "$base" == vscode* || "$base" == edge* ]]; then
          mv "$file" "/etc/apt/sources.list.d/$base"
        fi
      done
    when: skip_microsoft_repos | default(false)
    changed_when: false

Needless to say, this disabling only happens in the event of there being a system failure. Otherwise, the steps are skipped and everything else is completed as it should be. While there is some cause for extended the repository disabling actions to other third repos as well, that is something that I will leave aside for now. Even this shows just how much can be done using Ansible playbooks and how much automation can be achieved. As it happens, I even get Flatpaks updated in much the same way:

    -   name: Ensure Flatpak is installed
      apt:
        name: flatpak
        state: present
        update_cache: yes
        cache_valid_time: 3600

  -   name: Update Flatpak remotes
      command: flatpak update --appstream -y
      register: flatpak_appstream
      changed_when: "'Now at' in flatpak_appstream.stdout"
      failed_when: flatpak_appstream.rc != 0

  -   name: Update all Flatpak applications
      command: flatpak update -y
      register: flatpak_result
      changed_when: "'Now at' in flatpak_result.stdout"
      failed_when: flatpak_result.rc != 0

  -   name: Install unused Flatpak applications
      command: flatpak uninstall --unused
      register: flatpak_cleanup
      changed_when: "'Nothing' not in flatpak_cleanup.stdout"
      failed_when: flatpak_cleanup.rc != 0

  -   name: Repair Flatpak installations
      command: flatpak repair
      register: flatpak_repair
      changed_when: flatpak_repair.stdout is search('Repaired|Fixing')
      failed_when: flatpak_repair.rc != 0

The ability to call system commands as you see in the above sequence is an added bonus, though getting the response detection completely sorted remains an outstanding task. All this has only scratched the surface of what is possible.

Command line installation and upgrading of VSCode and VSCodium on Windows, macOS and Linux

25^th October 2025

Downloading and installing software packages from a website is all very well until you need to update them. Then, a single command streamlines the process significantly. Given that VSCode and VSCodium are updated regularly, this becomes all the more pertinent and explains why I chose them for this piece.

Windows

Now that Windows 10 is more or less behind us, we can focus on Windows 11. That comes with the winget command by default, which is handy because it allows command line installation of anything that is in the Windows store, which includes VSCode and VSCodium. The commands can be as simple as these:

winget install VisualStudioCode
winget install VSCodium.VSCodium

The above is shorthand for this, though:

winget install --id VisualStudioCode
winget install --id VSCodium.VSCodium

If you want exact matches, the above then becomes:

winget install -e --id VisualStudioCode
winget install -e --id VSCodium.VSCodium

For upgrades, this is what is needed:

winget upgrade Microsoft.VisualStudioCode
winget upgrade VSCodium.VSCodium

Even better, you can do an upgrade everything at once operation:

winget upgrade --all

The last part certainly is better than the round trip to a website and back to going through an installation GUI. There is a lot less mouse clicking for one thing.

macOS

On macOS, you need to have Homebrew installed to make things more streamlined. To complete that, you need to run the following command (which may need you to enter your system password to get things to happen):

/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

Then, you can execute one or both of these in the Terminal app, perhaps having to authorise everything with your password when requested to do so:

brew install --cask visual-studio-code
brew install --cask vscodium

The reason for the -cask switch is that these are apps that you want to go into the correct locations on macOS as well as having their icons appear in Launchpad. Omitting it is fine for command line utilities, but not for these.

To update and upgrade everything that you have installed via Homebrew, just issue the following in a terminal session:

brew update && brew upgrade

Debian, Ubuntu & Linux Mint

Like any other Debian or Ubuntu derivative, Linux Mint has its own in-built package management system via apt. Other Linux distributions have their own way of doing things (Fedora and Arch come to mind here), yet the essential idea is similar in many cases. Because there are a number of steps, I have split out VSCode from VSCodium for added clarity. Because of the way that things are set up, one or both apps can be updated using the usual apt commands without individual attention.

VSCode

The first step is to download the repository key using the following command:

wget -qO- https://packages.microsoft.com/keys/microsoft.asc \
| gpg --dearmor > packages.microsoft.gpg
sudo install -D -o root -g root -m 644 packages.microsoft.gpg /etc/apt/keyrings/packages.microsoft.gpg

Then, you can add the repository like this:

echo "deb [arch=amd64 signed-by=/etc/apt/keyrings/packages.microsoft.gpg] \
https://packages.microsoft.com/repos/code stable main" \
| sudo tee /etc/apt/sources.list.d/vscode.list

With that in place, the last thing that you need to do is issue the command for doing the installation from the repository:

sudo apt update; sudo apt install code

Above, I have put two commands together: one to update the repository and another to do the installation.

VSCodium

Since the VSCodium process is similar, here are the three commands together: one for downloading the repository key, another that adds the new repository and one more to perform the repository updates and subsequent installation:

curl -fSsL https://gitlab.com/paulcarroty/vscodium-deb-rpm-repo/raw/master/pub.gpg \
| sudo gpg --dearmor | sudo tee /usr/share/keyrings/vscodium-archive-keyring.gpg >/dev/null

echo "deb [arch=amd64 signed-by=/usr/share/keyrings/vscodium-archive-keyring.gpg] \
https://download.vscodium.com/debs vscodium main" \
| sudo tee /etc/apt/sources.list.d/vscodium.sources

sudo apt update; sudo apt install codium

After the three steps have completed successfully, VSCodium is installed and available to use on your system, and is accessible through the menus too.

Managing Python projects with Poetry

4^th October 2025

Python Poetry has become a popular choice for managing Python projects because it unifies tasks that once required several tools. Instead of juggling pip for installation, virtualenv for isolation and setuptools for packaging, Poetry brings these strands together and aims to make everyday development feel predictable and tidy. It sits in the same family of all-in-one managers as npm for JavaScript and Cargo for Rust, offering a coherent workflow that spans dependency declaration, environment management and package publishing.

At the heart of Poetry is a simple idea: declare what a project needs in one place and let the tool do the orchestration. Projects describe their dependencies, development tools and metadata in a single configuration file, and Poetry ensures that what is installed on one machine can be replicated on another without nasty surprises. That reliability comes from the presence of a lock file. Once dependencies are resolved, their exact versions are recorded, so future installations repeat the same outcome. The intent here is not only convenience but determinism, helping teams avoid the "works on my machine" refrain that haunts software work.

Core Concepts: Configuration and Lock Files

Two files do the heavy lifting. The pyproject.toml file is where a project announces its name, version and description, as well as the dependencies required to run and to develop it. The poetry.lock file captures the concrete resolution of those requirements at a particular moment. Together, they give you an auditable, repeatable picture of your environment. The structure of TOML keeps the configuration readable, and it spares developers from spreading equivalent settings across setup.cfg, setup.py and requirements.txt. A minimal example shows how this looks in practice.

[tool.poetry]
name = "my_project"
version = "0.1.0"
description = "Example project using Poetry"
authors = ["John <john@example.com>"]

[tool.poetry.dependencies]
python = "^3.10"
requests = "^2.31.0"

[tool.poetry.dev-dependencies]
pytest = "^8.0.0"

[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"

Essential Commands

Working with Poetry day to day quickly becomes a matter of a few memorable commands. Initialising a project configuration starts with poetry init, which steps through the creation of pyproject.toml interactively. Adding a dependency is handled by poetry add followed by the package name. Installing everything described in the configuration is done with poetry install, which writes or updates the lock file. When it is time to refresh dependencies within permitted version ranges, poetry update re-resolves and updates what's installed. Removing a dependency is poetry remove, followed by the package name. For environment management, poetry shell opens a shell inside the virtual environment managed by Poetry, and poetry run allows execution of commands within that same environment without entering a shell. Building distributions is as simple as poetry build, which produces a wheel and a source archive, and publishing to the Python Package Index is managed by poetry publish with credentials or an API token.

Advantages and Considerations

There are clear advantages to taking this route. The dependency experience is simplified because you do not need to keep updating a requirements.txt file by hand. With a lock file in place, environments are reproducible across developer machines and continuous integration runners, which stabilises builds and testing. Packaging is integrated rather than an extra chore, so producing and publishing a release becomes a repeatable process that sits naturally alongside development. Virtual environments are created and activated on demand, keeping projects isolated from one another with little ceremony. The configuration in TOML has the benefit of being structured and human-readable, which reduces the likelihood of configuration drift.

There are also points to consider before adopting Poetry. Projects that are deeply invested in setup.py or complex legacy build pipelines may need a clean migration to pyproject.toml for avoiding clashes. Developers who prefer manual venv and pip workflows can find Poetry opinionated at first because it expects to be responsible for the environment and dependency resolution. It is also designed with modern Python versions in mind, with examples here using Python 3.10.

Migration from pip and requirements.txt

For teams arriving from pip and requirements.txt, moving to Poetry can be done in measured steps. The starting point is installation. Poetry provides an installer script that sets up the tool for your user account.

curl -sSL https://install.python-poetry.org | python3 -

If the installer does not add Poetry to your PATH, adding $HOME/.local/bin to PATH resolves that, after which poetry --version confirms the installation. From the root of your existing project, poetry init creates a new pyproject.toml and invites you to provide metadata and dependencies. If you already maintain requirements.txt files for production and development dependencies, Poetry can ingest those in one sweep. A single file can be imported with poetry add $(cat requirements.txt). Where development dependencies live in a separate file, they can be added into Poetry's dev group with poetry add --group dev $(cat dev-requirements.txt). Once added, Poetry resolves and pins exact versions, leaving a lock file behind to capture the resolution. After verifying that everything installs and tests pass, it becomes safe to retire earlier environment artefacts. Many teams remove requirements.txt entirely if they plan to rely solely on Poetry, deleting any remnants of Pipfile and Pipfile.lock that were left by Pipenv and migrate metadata away from setup.py or setup.cfg in favour of pyproject.toml. With that done, using the environment becomes routine. Opening a shell inside the virtual environment with poetry shell makes commands such as python or pytest use the isolated interpreter. If you prefer to avoid entering a shell, poetry run python script.py or poetry run pytest executes the command in the right context.

Package Publishing

Publishing a package is one of the areas where Poetry streamlines the steps. Accurate metadata in pyproject.toml is important, so name, version, description and other fields should be up-to-date. An example configuration shows commonly used fields.

[tool.poetry]
name = "example-package"
version = "1.0.0"
description = "A simple example package"
authors = ["John <john@example.com>"]
license = "MIT"
readme = "README.md"
homepage = "https://github.com/john/example-package"
repository = "https://github.com/john/example-package"
keywords = ["example", "poetry"]

With metadata set, building the distribution is handled by poetry build, which creates a dist directory containing a .tar.gz source archive and a .whl wheel file. Uploading to the official Python Package Index can be done with username and password, though API tokens are the recommended method because they can be scoped and revoked without affecting account credentials. Configuring a token is done once with poetry config pypi-token.pypi, after which poetry publish will use it to upload. When testing a release before publishing for real, TestPyPI provides a safer target. Poetry supports multiple sources and can be directed to use TestPyPI by declaring it as a repository and then publishing to it.

[[tool.poetry.source]]
name = "testpypi"
url = "https://test.pypi.org/legacy/"

poetry publish -r testpypi

Once uploaded, it is sensible to confirm that the package can be installed in a clean environment using pip install example-package, which verifies that dependencies are correctly declared and wheels are intact.

Continuous Integration with GitHub Actions

Beyond local steps, automation closes the loop. Adding a continuous integration workflow that installs dependencies, runs tests and publishes on a tagged release keeps quality checks and distribution consistent. GitHub Actions provides a hosted environment where Poetry can be installed quickly, dependencies cached and tests executed. A straightforward workflow listens for tags that begin with v, such as v1.0.0, then builds and publishes the package once tests pass. The workflow file sits under .github/workflows and looks like this.

name: Publish to PyPI

on:
  push:
    tags:
      - "v*"

jobs:
  build:
    runs-on: ubuntu-latest

    steps:
      - name: Check out repository
        uses: actions/checkout@v4

      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: "3.10"

      - name: Install Poetry
        run: |
          curl -sSL https://install.python-poetry.org | python3 -
          echo "$HOME/.local/bin" >> $GITHUB_PATH

      - name: Install dependencies
        run: poetry install --no-interaction --no-root

      - name: Run tests with pytest
        run: poetry run pytest --maxfail=1 --disable-warnings -q

      - name: Build package
        run: poetry build

      - name: Publish to PyPI
        if: startsWith(github.ref, 'refs/tags/v')
        env:
          POETRY_PYPI_TOKEN_PYPI: ${{ secrets.PYPI_TOKEN }}
        run: poetry publish --no-interaction --username __token__ --password $POETRY_PYPI_TOKEN_PYPI

This arrangement checks out the repository, installs a consistent Python version, brings in Poetry, installs dependencies based on the lock file, runs tests, builds distributions and only publishes when the workflow is triggered by a version tag. The API token used for publishing should be stored as a repository secret named PYPI_TOKEN so it is not exposed in the codebase or logs. Creating the tag is done locally with git tag v1.0.0 followed by git push origin v1.0.0, which triggers the workflow and results in a published package, moments later. It is often useful to extend this with a test matrix, so the suite runs across supported Python versions, as well as caching to speed up repeated runs by re-using Poetry and pip caches keyed on the lock file.

Project Structure

Package structure is another place where Poetry encourages clarity. A simple, consistent layout makes maintenance and onboarding easier. A typical library keeps its importable code in a package directory named to match the project name in pyproject.toml, with hyphens translated to underscores. Tests live in a separate tests directory, documentation in docs and examples in a directory of the same name. The repository root contains README.md, a licence file, the lock file and a .gitignore that excludes environment directories and build artefacts. The following tree illustrates a balanced structure for a data-oriented utility library.

data-utils/
├── data_utils/
│   ├── __init__.py
│   ├── core.py
│   ├── io.py
│   ├── analysis.py
│   └── cli.py
├── tests/
│   ├── __init__.py
│   ├── test_core.py
│   └── test_analysis.py
├── docs/
│   ├── index.md
│   └── usage.md
├── examples/
│   └── demo.ipynb
├── README.md
├── LICENSE
├── pyproject.toml
├── poetry.lock
└── .gitignore

Within the package directory, init.py can define a public interface and hide internal details. This allows users of the library to import the essentials without needing to know the module layout.

from .core import clean_data
from .analysis import summarise_data

__all__ = ["clean_data", "summarise_data"]

If the project offers a command-line interface, Poetry makes it simple to declare an entry point, so users can run a console command after installation. The scripts section in pyproject.toml maps a command name to a callable, in this case the main function in a cli module.

[tool.poetry.scripts]
data-utils = "data_utils.cli:main"

A basic CLI might be implemented using Click, passing arguments to internal functions and relaying progress.

import click
from data_utils import core

@click.command()
@click.argument("path")
def main(path):
    """Simple CLI example."""
    print(f"Processing {path}...")
    core.clean_data(path)
    print("Done!")

if __name__ == "__main__":
    main()

Git ignores should filter out files that do not belong in version control. A sensible default for a Poetry project is as follows.

__pycache__/
*.pyc
*.pyo
*.pyd
.env
.venv
dist/
build/
*.egg-info/
.cache/
.coverage

- Testing and Documentation

Testing sits comfortably alongside this. Many projects adopt pytest because it is straightforward to use and integrates well with Poetry. Running tests through poetry run pytest ensures the virtual environment is used, and a simple unit test demonstrates the pattern.

from data_utils.core import clean_data

def test_clean_data_removes_nulls():
    data = [1, None, 2, None, 3]
    cleaned = clean_data(data)
    assert cleaned == [1, 2, 3]

Documentation can be kept in Markdown or built with tools. MkDocs and Sphinx are common choices for generating websites from your docs, and both can be installed as development dependencies using Poetry. Including notebooks in an examples directory is helpful for illustrating usage in richer contexts, especially for data science libraries. The README should present the essentials succinctly, covering what the project does, how to install it, a short usage example and pointers for development setup. A licence file clarifies terms of use; MIT and Apache 2.0 are widely used options in open source. Advanced CI: Quality Checks and Multi-version Testing

Once structure, tests and documentation are in order, quality checks can be expanded in the continuous integration workflow. Adding automated formatting, import sorting and linting tightens consistency across contributions. An enhanced workflow uses Black, isort and Flake8 before running tests and building, and also includes a matrix to test across multiple Python versions. It runs on pull requests as well as on tagged pushes, which means code quality and compatibility are verified before merging changes and again before publishing a release.

name: Lint, Test and Publish

on:
  push:
    tags:
      - "v*"
  pull_request:

jobs:
  build:
    runs-on: ubuntu-latest

    strategy:
      matrix:
        python-version: ["3.9", "3.10", "3.11"]

    steps:
      - name: Check out repository
        uses: actions/checkout@v4

      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: ${{ matrix.python-version }}

      - name: Install Poetry
        run: |
          curl -sSL https://install.python-poetry.org | python3 -
          echo "$HOME/.local/bin" >> $GITHUB_PATH

      - name: Cache Poetry dependencies
        uses: actions/cache@v4
        with:
          path: |
            ~/.cache/pypoetry
            ~/.cache/pip
          key: poetry-${{ runner.os }}-${{ hashFiles('**/poetry.lock') }}
          restore-keys: |
            poetry-${{ runner.os }}-

      - name: Install dependencies
        run: poetry install --no-interaction --no-root

      - name: Check code formatting with Black
        run: poetry run black --check .

      - name: Check import order with isort
        run: poetry run isort --check-only .

      - name: Run Flake8 linting
        run: poetry run flake8 .

      - name: Run tests with pytest
        run: poetry run pytest --maxfail=1 --disable-warnings -q

      - name: Build package
        run: poetry build

      - name: Publish to PyPI
        if: startsWith(github.ref, 'refs/tags/v')
        env:
          POETRY_PYPI_TOKEN_PYPI: ${{ secrets.PYPI_TOKEN }}
        run: poetry publish --no-interaction --username __token__ --password $POETRY_PYPI_TOKEN_PYPI

This workflow builds on the earlier one by checking style and formatting before tests. If any of those checks fail, the process stops and surfaces the problems in the job logs. Caching based on the lock file reduces the time spent installing dependencies by reusing packages where nothing has changed. The matrix section ensures that the library remains compatible with the declared range of Python versions, which is especially helpful just before a release. It is possible to extend this further with coverage reports using pytest-cov and Codecov, static type checking with mypy, or pre-commit hooks to keep local development consistent with continuous integration. Publishing to TestPyPI in a separate job can help validate packaging without affecting the real index, and once outcomes look good, the main publishing step proceeds when a tag is pushed.

Conclusion

The result of adopting Poetry is a project that states its requirements clearly, installs them reliably and produces distributions without ceremony. For new work, it removes much of the friction that once accompanied Python packaging. For existing projects, the migration path is gentle and reversible, and the gains in determinism often show up quickly in fewer environment-related issues. When paired with a small amount of automation in a continuous integration system, the routine of building, testing and publishing becomes repeatable and visible to everyone on the team. That holds whether the package is destined for internal use on a private index or a public release on PyPI.

Fixing Python path issues after Homebrew updates on Linux Mint

30^th August 2025

With Python available by default, it is worth asking how the version on my main Linux workstation is made available courtesy of Homebrew. All that I suggest is that it either was needed by something else or I fancied having a newer version that was available through the Linux Mint repos. Regardless of the now vague reason for doing so, it meant that I had some work to do after running the following command to update and upgrade all my Homebrew packages:

brew update; brew upgrade

The first result was this message when I tried running a Python script afterwards:

-bash: /home/linuxbrew/.linuxbrew/bin/python3: No such file or directory

The solution was to issue the following command to re-link Python:

brew link --overwrite python@3.13

Since you may have a different version by the time that you read this, just change 3.13 above to whatever you have on your system. All was not quite sorted for me after that, though.

My next task was to make Pylance look in the right place for Python packages because they had been moved too. Initial inquiries were suggesting complex if robust solutions. Instead, I went for a simpler fix. The first step was to navigate to File > Preferences > Settings in the menus. Then, I sought out the Open Settings (JSON) icon in the top right of the interface and clicked on it to open a JSON containing VSCode settings. Once in there, I edited the file to end up with something like this:

    "python.analysis.extraPaths": [
        "/home/[account name]/.local/bin",
        "/home/[account name]/.local/lib/python[python version]/site-packages"
    ]

Clearly, your [account name] and [python version] need to be filled in above. That approach works for me so far, leaving the more complex alternative for later should I come to need that.

SAS Packages: Revolutionising code sharing in the SAS ecosystem

26^th July 2025

In the world of statistical programming, SAS has long been the backbone of data analysis for countless organisations worldwide. Yet, for decades, one of the most significant challenges facing SAS practitioners has been the efficient sharing and reuse of code. Knowledge and expertise have often remained siloed within individual developers or teams, creating inefficiencies and missed opportunities for collaboration. Enter the SAS Packages Framework (SPF), a solution that changes how SAS professionals share, distribute and utilise code across their organisations and the broader community.

The Problem: Fragmented Knowledge and Complex Dependencies

Anyone who has worked extensively with SAS knows the frustration of trying to share complex macros or functions with colleagues. Traditional code sharing in SAS has been plagued by several issues:

• Dependency nightmares: A single macro often relies on dozens of utility macros working behind the scenes, making it nearly impossible to share everything needed for the code to function properly
• Version control chaos: Keeping track of which version of which macro works with which other components becomes an administrative burden
• Platform compatibility issues: Code that works on Windows might fail on Linux systems and vice versa
• Lack of documentation: Without proper documentation and help systems, even the most elegant code becomes unusable to others
• Knowledge concentration: Valuable SAS expertise remains trapped within individuals rather than being shared with the broader community

These challenges have historically meant that SAS developers spend countless hours reinventing the wheel, recreating functionality that already exists elsewhere in their organisation or the wider SAS community.

The Solution: SAS Packages Framework

The SAS Packages Framework, developed by Bartosz Jabłoński, represents a paradigm shift in how SAS code is organised, shared and deployed. At its core, a SAS package is an automatically generated, single, standalone zip file containing organised and ordered code structures, extended with additional metadata and utility files. This solution addresses the fundamental challenges of SAS code sharing by providing:

• Functionality over complexity: Instead of worrying about 73 utility macros working in the background, you simply share one file and tell your colleagues about the main functionality they need to use.
• Complete self-containment: Everything needed for the code to function is bundled into one file, eliminating the "did I remember to include everything?" problem that has plagued SAS developers for years.
• Automatic dependency management: The framework handles the loading order of code components and automatically updates system options like cmplib= and fmtsearch= for functions and formats.
• Cross-platform compatibility: Packages work seamlessly across different operating systems, from Windows to Linux and UNIX environments.

Beyond Macros: A Spectrum of SAS Functionality

One of the most compelling aspects of the SAS Packages Framework is its versatility. While many code-sharing solutions focus solely on macros, SAS packages support a wide range of SAS functionality:

• User-defined functions (both FCMP and CASL)
• IML modules for matrix programming
• PROC PROTO C routines for high-performance computing
• Custom formats and informats
• Libraries and datasets
• PROC DS2 threads and packages
• Data generation code
• Additional content such as documentation PDF files

This comprehensive approach means that virtually any SAS functionality can be packaged and shared, making the framework suitable for everything from simple utility macros to complex analytical frameworks.

Real-World Applications: From Pharmaceutical Research to General Analytics

The adoption of SAS packages has been particularly notable in the pharmaceutical industry, where code quality, validation and sharing are critical concerns. The PharmaForest initiative, led by PHUSE Japan's Open-Source Technology Working Group, exemplifies how the framework is being used to revolutionise pharmaceutical SAS programming. PharmaForest offers a collaborative repository of SAS packages specifically designed for pharmaceutical applications, including:

• OncoPlotter: A comprehensive package for creating figures commonly used in oncology studies
• SAS FAKER: Tools for generating realistic test data while maintaining privacy
• SASLogChecker: Automated log review and validation tools
• rtfCreator: Streamlined RTF output generation

The initiative's philosophy captures perfectly the spirit of the SAS Packages Framework: "Through SAS packages, we want to actively encourage sharing of SAS know-how that has often stayed within individuals. By doing this, we aim to build up collective knowledge, boost productivity, ensure quality through standardisation and energise our community".

The SASPAC Archive: A Growing Ecosystem

The establishment of SASPAC (SAS Packages Archive) represents the maturation of the SAS packages ecosystem. This dedicated repository serves as the official home for SAS packages, with each package maintained as a separate repository complete with version history and documentation. Some notable packages available through SASPAC include:

• BasePlus: Extends BASE SAS with functionality that many developers find themselves wishing was built into SAS itself. With 12 stars on GitHub, it's become one of the most popular packages in the archive.
• MacroArray: Provides macro array functionality that simplifies complex macro programming tasks, addressing a long-standing gap in SAS's macro language capabilities.
• SQLinDS: Enables SQL queries within data steps, bridging the gap between SAS's powerful data step processing and SQL's intuitive query syntax.
• DFA (Dynamic Function Arrays): Offers advanced data structures that extend SAS's analytical capabilities.
• GSM (Generate Secure Macros): Provides tools for protecting proprietary code while still enabling sharing and collaboration.

Getting Started: Surprisingly Simple

Despite the capabilities, getting started with SAS packages is fairly straightforward. The framework can be deployed in multiple ways, depending on your needs. For a quick test or one-time use, you can enable the framework directly from the web:

filename packages "%sysfunc(pathname(work))";
filename SPFinit url "https://raw.githubusercontent.com/yabwon/SAS_PACKAGES/main/SPF/SPFinit.sas";
%include SPFinit;

For permanent installation, you simply create a directory for your packages and install the framework:

filename packages "C:SAS_PACKAGES";
%installPackage(SPFinit)

Once installed, using packages becomes as simple as:

%installPackage(packageName)
%helpPackage(packageName)
%loadPackage(packageName)

Developer Benefits: Quality and Efficiency

For SAS developers, the framework offers numerous advantages that go beyond simple code sharing:

• Enforced organisation: The package development process naturally encourages better code organisation and documentation practices.
• Built-in testing: The framework includes testing capabilities that help ensure code quality and reliability.
• Version management: Packages include metadata such as version numbers and generation timestamps, supporting modern DevOps practices.
• Integrity verification: The framework provides tools to verify package authenticity and integrity, addressing security concerns in enterprise environments.
• Cherry-picking: Users can load only specific components from a package, reducing memory usage and namespace pollution.

The Future of SAS Code Sharing

The growing adoption of SAS packages represents more than just a new tool, it signals a fundamental shift towards a more collaborative and efficient SAS ecosystem. The framework's MIT licensing and 100% open-source nature ensure that it remains accessible to all SAS users, from individual practitioners to large enterprise installations. This democratisation of advanced code-sharing capabilities levels the playing field and enables even small teams to benefit from enterprise-grade development practices.

As the ecosystem continues to grow, with contributions from pharmaceutical companies, academic institutions and individual developers worldwide, the SAS Packages Framework is proving that the future of SAS programming lies not in isolated development, but in collaborative, community-driven innovation.

For SAS practitioners looking to modernise their development practices, improve code quality and tap into the collective knowledge of the global SAS community, exploring SAS packages isn't just an option, it's becoming an essential step towards more efficient and effective statistical programming.

Upgrading a web server from Debian 11 to Debian 12

25^th November 2024

While Debian 12 may be with us since the middle of 2023 and Debian 13 is due in the middle of next year, it has taken me until now to upgrade one of my web servers. The tardiness may have something to do with a mishap on another system that resulted in a rebuild, something to avoid it at all possible.

Nevertheless, I went and had a go with the aforementioned web server after doing some advance research. Thus, I can relate the process that you find here in the knowledge that it worked for me. Also, I will have it on file for everyone's future reference. The first step is to ensure that the system is up-to-date by executing the following commands:

sudo apt update
sudo apt upgrade
sudo apt dist-upgrade

Next, it is best to remove extraneous packages using these commands:

sudo apt --purge autoremove
sudo apt autoclean

Once you have backed up important data and configuration files, you can move to the first step of the upgrade process. This involves changing the repository locations from what is there for bullseye (Debian 11) to those for bookworm (Debian 12). Issuing the following commands will accomplish this:

sudo sed -i 's/bullseye/bookworm/g' /etc/apt/sources.list
sudo sed -i 's/bullseye/bookworm/g' /etc/apt/sources.list.d/*

In my case, I found the second of these to be extraneous since everything was included in the single file. Also, Debian 12 has added a new non-free repository called non-free-firmware. This can be added at this stage by manual editing of the above. In my case, I did it later because the warning message only began to appear at that stage.

Once the repository locations, it is time to update the package information using the following command:

sudo apt update

Then, it is time to first perform a minimal upgrade using the following command, that takes a conservative approach by updating existing packages without installing any new ones:

sudo apt upgrade --without-new-pkgs

Once that has completed, one needs to issue the following command to install new packages if needed for dependencies and even remove incompatible or unnecessary ones, as well as performing kernel upgrades:

sudo apt full-upgrade

Given all the changes, the completion of the foregoing commands' execution necessitates a system restart, which can be the most nerve-wracking part of the process when you are dealing with a remote server accessed using SSH. While, there are a few options for accomplishing this, here is one that is compatible with the upgrade cycle:

sudo systemctl reboot

Once you can log back into the system again, there is one more piece of housekeeping needed. This step not only removes redundant packages that were automatically installed, but also does the same for their configuration files, an act that really cleans up things. The command to execute is as follows:

sudo apt --purge autoremove

For added reassurance that the upgrade has completed, issuing the following command will show details like the operating system's distributor ID, description, release version and codename:

lsb_release -a

If you run the above commands as root, the sudo prefix is not needed, yet it is perhaps safer to execute them under a less privileged account anyway. The process needs the paying of attention to any prompts and questions about configuration files and service restarts if they arise. Nothing like that came up in my case, possibly because this web server serves flat files created using Hugo, avoiding the use of scripting and databases, which would add to the system complexity. Such a simple situation makes the use of scripting more of a possibility. The exercise was speedy enough for me too, though patience is of the essence should a 30–60 minute completion time be your lot, depending on your system and internet speed.