R Consulting, Code Review & Production Engineering
Architecture, code quality, performance and production readiness for teams that rely on R beyond notebooks and one-off analyses.
R code becomes infrastructure long before most teams treat it like infrastructure.
As analytical code grows, hidden dependencies, duplicated logic, fragile scripts and undocumented business rules create delivery and operational risk. TransformR helps turn that codebase into a structured, testable and maintainable engineering asset.
A defined scope. Tangible deliverables.
Architecture and codebase assessment
Refactoring and performance optimisation
Reusable functions and internal R packages
Testing strategy and quality controls
Documentation and deployment guidance
Hands-on support for complex R issues
Make the technical decisions explicit.
- Understand the business workflow before touching the code
- Prioritise high-risk and high-value areas first
- Refactor incrementally to avoid disrupting delivery
- Build tests around critical logic
- Transfer knowledge to the internal team
Outcomes to define and assess together
- More reliable R workflows with fewer hidden failure points
- Faster delivery through reusable, structured components
- A codebase your team can maintain after handover
A few useful starting points.
Can you work on an existing project?
Yes. We begin by reviewing its purpose, dependencies, documentation and current constraints. This assessment determines what can be retained and what needs to change.
What should we prepare for the first discussion?
The expected outcome, current tools, main difficulties and any deadline. An architecture sketch or a description of the workflow is enough to start; sensitive data is not needed at this stage.
How are scope, timing and price set?
After reviewing the context, we define the intervention, deliverables, dependencies and acceptance criteria. Timing and pricing follow from this scope and are confirmed in the proposal.
Taking ownership of a critical workflow
Several teams rely on legacy scripts. First identify shared business rules, separate data access and agree on reference results before refactoring the code.
A testable R module, a comparison report and an execution guide.
Illustrative scenario, not a claim about a completed client engagement. Final deliverables depend on the agreed scope.
A useful first conversation.
Who runs the code? Which outputs are the reference? Where does execution fail or slow down?
Share a description of the workflow and its constraints first. Confidential files, personal data and system access can be discussed within an appropriate framework later.