Open Research & Resources

Open and reproducible research is part of how I work, particularly where methods, code, and teaching materials can be reused beyond a single study. Selected resources are listed below.

Routine data analysis across R, Python, and Stata

Risk of Suicide After Cancer Diagnosis in England is a worked teaching resource demonstrating approaches to analysing routinely collected health data. It covers survival-analysis workflows, person-time, standardised mortality ratios, and absolute excess risks, with examples in R, Python, and Stata.

The material uses synthetic and published data for methodological and educational purposes. Source code and documentation are available on GitHub.

Mapping and spatial analysis in R

Mapping and Spatial Analysis in R is an introductory tutorial developed for the UCL R User Group. It covers spatial data structures, coordinate reference systems, vector and raster analysis, mapping, spatial joins, proximity and adjacency, spatial autocorrelation, hotspot analysis, and introductory spatial interpolation.

The tutorial is designed for researchers and analysts with basic R experience who are new to spatial analysis. Source materials are available on GitHub.

Reproducible website infrastructure

The MINDSET website source is public. The site uses Hugo, R, ORCID, OpenAlex, Crossref, GitHub Actions, and small curated data files to maintain publications, funding, talks, teaching, and other academic content with limited manual duplication.

The repository documents the build, dependency-management, content-refresh, and validation workflow.

More code, talks, and materials

Additional public repositories are available through my GitHub profile, and selected academic and methods presentations are listed under Talks & Presentations.

Project-specific code and materials are also linked from the relevant research, publication, and teaching pages where available.