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Statistical analysis and data science in R
Service description
I work in R as my primary language for statistics and data analysis, and I have spent years turning messy spreadsheets and raw exports into results you can actually defend. My focus is genuine statistical work, not generic scripting: I fit and validate regression, mixed-effects, GLM, survival and time-series models, run hypothesis tests and power calculations, design sampling schemes, and check assumptions properly so the conclusions hold up under scrutiny. If you have a research question, a dataset, and a deadline, I can take it from raw file to a clear, honest answer.
Day to day I live inside the tidyverse. I clean, reshape and join data with dplyr and tidyr, automate repetitive pipelines, and document every transformation so the whole analysis is reproducible from a single command. For communication I build publication-quality graphics in ggplot2, from exploratory plots to polished figures for papers and dashboards, and I package findings as R Markdown or Quarto reports that knit straight to HTML, PDF or Word. This keeps me clearly distinct from Python-first data scientists, Julia developers and drag-and-drop BI-dashboard specialists: my whole toolchain is the R statistical ecosystem.
When the result needs to be interactive rather than a static report, I develop Shiny applications so your team can explore the data themselves, filter, and re-run models without touching code. I care about correctness, clear methodology and readable code, and I explain what the numbers mean in plain language, including the caveats. Whether it is a one-off analysis, an academic study, an A/B test, or an ongoing reporting pipeline, I deliver work you can trust and reuse.
— Statistical modeling: regression, GLM, mixed-effects, survival, time series
— Hypothesis testing, power analysis and experiment design
— Tidyverse data wrangling: dplyr, tidyr, purrr
— Data visualization with ggplot2 for papers and dashboards
— Interactive Shiny web applications
— Reproducible reports in R Markdown and Quarto
Day to day I live inside the tidyverse. I clean, reshape and join data with dplyr and tidyr, automate repetitive pipelines, and document every transformation so the whole analysis is reproducible from a single command. For communication I build publication-quality graphics in ggplot2, from exploratory plots to polished figures for papers and dashboards, and I package findings as R Markdown or Quarto reports that knit straight to HTML, PDF or Word. This keeps me clearly distinct from Python-first data scientists, Julia developers and drag-and-drop BI-dashboard specialists: my whole toolchain is the R statistical ecosystem.
When the result needs to be interactive rather than a static report, I develop Shiny applications so your team can explore the data themselves, filter, and re-run models without touching code. I care about correctness, clear methodology and readable code, and I explain what the numbers mean in plain language, including the caveats. Whether it is a one-off analysis, an academic study, an A/B test, or an ongoing reporting pipeline, I deliver work you can trust and reuse.
— Statistical modeling: regression, GLM, mixed-effects, survival, time series
— Hypothesis testing, power analysis and experiment design
— Tidyverse data wrangling: dplyr, tidyr, purrr
— Data visualization with ggplot2 for papers and dashboards
— Interactive Shiny web applications
— Reproducible reports in R Markdown and Quarto
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