13.7 From Spec to Implementation

With requirements, data specifications, and validation criteria in hand, the analyst is ready to build. The spec serves as both a roadmap and a checklist — every deliverable, every key question, every validation check maps to specific code that needs to be written.

13.7.1 Organizing the Project

The project organization principles from Chapter 5 apply directly:

project/
|-- spec/              # Requirements, data spec, validation criteria
|-- data/
|   |-- raw/           # Original, untouched data
|   +-- processed/     # Cleaned, analysis-ready data
|-- scripts/           # R scripts for cleaning, analysis, modeling
|-- output/            # Reports, plots, exported results
+-- README.md          # Project overview and instructions

The spec/ directory is new — it holds the written specifications that define the project. These documents are as much a part of the deliverable as the code and the output.

13.7.2 Iterating Between Spec and Code

In practice, implementation is not a one-way process from spec to code. Building the analysis often reveals issues that require revisiting the spec:

• A required variable turns out to be unavailable or unreliable
• The data does not support a key question at the level of granularity assumed
• A model cannot meet the performance threshold specified in the requirements

When this happens, the analyst updates the spec to reflect the new reality and communicates the change to the stakeholder. The spec remains the single source of truth for what the project delivers — even as it evolves. Changes should be documented: what changed, why, and who approved the change.

13.7.3 AI as a Pair Programmer

At the implementation stage, AI becomes a coding partner. The analyst can feed the spec to an AI assistant and ask it to generate the R code that implements each requirement. Because the spec is explicit, the AI’s output is more focused and accurate than it would be from a vague prompt — demonstrating how clear specifications improve both human and AI productivity.

13.7.4 The Spec as Documentation

When the project is complete, the spec serves a final purpose: documentation. A future analyst who inherits the project can read the requirements spec to understand what was built and why, the data spec to understand the data, and the validation criteria to verify that the analysis is still correct. This is far more valuable than code comments alone — it provides the business context that code cannot capture. Reproducibility — the ability for someone else to re-run your analysis and get the same results — depends on this kind of documentation.