Technology Tales

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's

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 perfectly captures 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.

Broadening data science horizons: Useful Python packages for working with data

14^th October 2021

My response to changes in the technology stack used in clinical research is to develop some familiarity with programming and scripting platforms that complement and compete with SAS, a system with which I have been programming since 2000. While one of these has been R, Python is another that has taken up my attention, and I now also have Julia in my sights as well. There may be others to assess in the fullness of time.

While I first started to explore the Data Science world in the autumn of 2017, it was in the autumn of 2019 that I began to complete LinkedIn training courses on the subject. Good though they were, I find that I need to actually use a tool to better understand it. At that time, I did get to hear about Python packages like Pandas, NumPy, SciPy, Scikit-learn, Matplotlib, Seaborn and Beautiful Soup though it took until of spring of this year for me to start gaining some hands-on experience with using any of these.

During the summer of 2020, I attended a BCS webinar on the CodeGrades initiative, a programming mentoring scheme inspired by the way classical musicianship is assessed. In fact, one of the main progenitors is a trained classical musician and teacher of classical music who turned to Python programming when starting a family to have a more stable income. The approach is that a student selects a project and works their way through it, with mentoring and periodic assessments carried out in a gentle and discursive manner. Of course, the project has to be engaging for the learning experience to stay the course, and that point came through in the webinar.

That is one lesson that resonates with me with subjects as diverse as web server performance and the ongoing pandemic supplying data, and there are other sources of public data to examine as well before looking through my own personal archive gathered over the decades. Though some subjects are uplifting while others are more foreboding, the key thing is that they sustain interest and offer opportunities for new learning. Without being able to dream up new things to try, my knowledge of R and Python would not be as extensive as it is, and I hope that it will help with learning Julia too.

In the main, my own learning has been a solo effort with consultation of documentation along with web searches that have brought me to the likes of Real Python, Stack Abuse, Data Viz with Python and R and others for longer tutorials as well as threads on Stack Overflow. Usually, the web searching begins when I need a steer on a particular or a way to resolve a particular error or warning message, but books are always worth reading even if that is the slower route. While those from the Dummies series or from O'Reilly have proved must useful so far, I do need to read them more completely than I already have; it is all too tempting to go with the try the "programming and search for solutions as you go" approach instead.

To get going, many choose the Anaconda distribution to get Jupyter notebook functionality, but I prefer a more traditional editor, so Spyder has been my tool of choice for Python programming and there are others like PyCharm as well. Because Spyder itself is written in Python, it can be installed using pip from PyPi like other Python packages. It has other dependencies like Pylint for code management activities, but these get installed behind the scenes.

The packages that I first met in 2019 may be the mainstays for doing data science, but I have discovered others since then. It also seems that there is porosity between the worlds of R and Python, so you get some Python packages aping R packages and R has the Reticulate package for executing Python code. There are Python counterparts to such Tidyverse stables as dplyr and ggplot2 in the form of Siuba and Plotnine, respectively. Though the syntax of these packages are not direct copies of what is executed in R, they are close enough for there to be enough familiarity for added user-friendliness compared to Pandas or Matplotlib. The interoperability does not stop there, for there is SQLAlchemy for connecting to MySQL and other databases (PyMySQL is needed as well) and there also is SASPy for interacting with SAS Viya.

While Python may not have the speed of Julia, there are plenty of packages for working with larger workloads. Of these, Dask, Modin and RAPIDS all have their uses for dealing with data volumes that make Pandas code crawl. As if to prove that there are plenty of libraries for various forms of data analytics, data science, artificial intelligence and machine learning, there also are the likes of Keras, TensorFlow and NetworkX. These are just a selection of what is available, and there is always the possibility of checking out others. It may be tempting to stick with the most popular packages all the time, especially when they do so much, but it never hurts to keep an open mind either.