R statistical consulting for reproducible quantitative research
Use R for transparent data preparation, custom modeling, diagnostics, visualization, validation, and reports that can be rerun as the study changes.
Research problems handled in R
The environment is selected because it fits the workflow and collaborators. The analysis still begins with the question, design, measurement, and data structure.
- Planning and data preparation
- Statistical modeling and diagnostics
- Interpretation and reporting
Methodology before output
Default menus or package calls cannot decide whether a model is suitable. Each workflow records model choice, coding, missingness, exclusions, assumptions, diagnostics, and sensitivity checks.
- Question-to-model alignment
- Documented transformations
- Checks that match the estimand
Reproducibility and handover
Where scope allows, the handover includes readable syntax or scripts, annotated outputs, a decision log, and instructions for rerunning the analysis.
- Reusable analysis file
- Diagnostic summary
- Interpretation and limitations memo
Discuss a R workflow
Bring the research question, design, or existing output. A first review can identify the next statistical decision and the information needed to scope it.