Prepare and connect data
Joins and cohort assembly, reshaping repeated observations, reusable mapping functions, field cleaning, and categorical recoding.
STATISTICS / ENGINEERING / ANALYTICS
A toolkit with
a purpose.
R is at the center of my research work. I use it to prepare longitudinal data, fit statistical models, build interactive tools, and communicate results. Here is how those skills come together in my projects.
MY PRACTICE
Different questions.
A shared approach.
My projects often cross disciplines. These areas offer different ways to explore the work, with examples of the methods and decisions behind it.
BIOSTATISTICS
Research grounded in the study.
I work with repeated health and behavioral measurements, from defining an outcome to choosing an analysis that respects the study design. My recovery and study-planning projects show how I approach endpoints, follow-up, and uncertainty.
survival · lme4 · lmerTest · TrialSize · effectsize
More of my work in this area →010203
Start with the endpoint
Recovery analysis turns repeated cognitive measurements into event times while retaining incomplete follow-up.
View project →Respect the study design
Power calculations and mixed-model simulations explore pairing, participant variation, and missingness before recruitment.
View project →Explain uncertainty
Rest-scheme comparisons keep subgroup counts, confidence intervals, and observational limits beside the estimates.
View project →STATISTICAL MODELING
Models I can explain.
I use survival models, mixed-effects models, and simulation to investigate research questions. I pay particular attention to assumptions, correlated observations, and the checks that shape how a result should be interpreted.
survival · lme4 · glmmTMB · car · DHARMa · equivalence
More of my work in this area →010203
Choose the right model
Cox, linear mixed-effects, and exploratory Poisson formulations answer different versions of a recovery question.
View project →Evaluate the design
Analytical non-inferiority calculations and Monte Carlo experiments test the consequences of study assumptions.
View project →Make comparisons interpretable
Effect sizes and interval estimates communicate magnitude and precision alongside group differences.
View project →DATA ANALYSIS
An answer behind every figure.
I turn research records into cohort summaries, comparisons, and interactive views. My reporting and dashboard work keeps participant counts, missingness, and the meaning of each metric visible.
dplyr · ggplot2 · Plotly · Shiny · DT · openxlsx
More of my work in this area →010203
Define what is being counted
Reporting distinguishes unique people, repeat trips, and usable outcomes before creating monthly summaries.
View project →Make exploration accessible
Shiny filters connect selected cohorts to interactive charts, tables, and participant counts.
View project →Explain differences visually
Timepoint plots and subgroup comparisons show when patterns change and where the data become sparse.
View project →DATA ENGINEERING
Reliable data comes first.
I build R workflows that bring wearable exports, sleep logs, and flight records into a consistent analytical structure. That includes aligning time, resolving trip keys, validating outputs, and producing repeatable reports.
dplyr · tidyr · purrr · lubridate · stringr · readxl · openxlsx
More of my work in this area →010203
Resolve identity and time
Wearable exports, logs, and flight events are aligned to composite trip keys and shared time windows.
View project →Make transformations checkable
Rule-based screening preserves an audit trail and reconciles saved outputs against source records.
View project →Deliver repeatable outputs
Batch reporting applies completion rules and aggregates route-by-month metrics into structured workbooks.
View project →DATA SCIENCE
From features to understanding.
My work connects temporal feature engineering, statistical modeling, diagnostics, and interactive tools. The projects here show that process in research settings, with careful attention to how the data was collected and what the model can support.
R · survival · lme4 · DHARMa · purrr · ggplot2 · Shiny
More of my work in this area →010203
Engineer a meaningful signal
Sleep-window features encode temporal context while making allocation assumptions explicit.
View project →Model and diagnose
Recovery models connect covariates to an event-time outcome, with proportional-hazards and residual checks.
View project →Stress-test before acting
Synthetic repeated-measures studies examine the behavior of model-based decisions under design assumptions.
View project →APPLIED THROUGHOUT THE RESEARCH WORK
Libraries connected
to real analytical tasks.
These are the R libraries I work with across data preparation, statistical analysis, and research communication. Follow the project links to see their context, including contributions to shared workflows.
Resolve time and deliver files
Timestamp normalization, interval calculations, workbook imports, structured exports, and audit sheets. The xlsx workflow is part of shared/legacy reporting.
Model biological and behavioral outcomes
Cox event-time models; linear and logistic mixed models; exploratory Gamma mixed models. nlme appears in the shared layover workflow.
Challenge the model
Collinearity, simulated residual behavior, dispersion, and participant-level influence diagnostics in modeling and exploratory analyses.
See these tools in context
Design studies and quantify differences
Non-inferiority sample-size calculations, equivalence comparisons, standardized effect sizes, and descriptive summaries.
Build interactive views
Statistical graphics, route panels, sleep timelines, interactive plots, reactive cohort filters, and explorable tables.
Make analysis reproducible and readable
Literate analysis, formatted tables, programmatic reports, and route-wise processing. flextable and parallel also appear in shared layover code.
LOOKING AHEAD
Ideas I’d like
to explore.
These are future project ideas, separate from my completed and ongoing research work. They reflect where I would like to apply familiar methods and develop new skills.
DESIGN CONCEPT / 01
Clinical outcomes & follow-up
Extend the recovery workflow to a public or synthetic longitudinal health dataset.
Explore the proposed project
Proposed approach
Define an endpoint and follow-up window; build a cohort flow; compare survival estimates and adjusted associations; report missingness and model diagnostics.
Proposed deliverables
An analysis plan, reproducible cohort pipeline, survival figures, and a methods report.
Technology direction
R · survival · dplyr · ggplot2 · R Markdown
The connection to existing work
Endpoint definition, censoring, longitudinal features, and diagnostic reasoning.
Explore the foundation →DESIGN CONCEPT / 02
A testable analytics data pipeline
Translate research-file integration into a small, documented analytical data model.
Explore the proposed project
Proposed approach
Ingest public or synthetic source files, define stable keys, separate staging from analysis tables, and test uniqueness, required values, and relationships before publishing metrics.
Proposed deliverables
A documented SQL model, data dictionary, quality checks, and a repeatable metrics output.
Technology direction
Proposed stack: Python · pandas · SQL · DuckDB · dbt
The connection to existing work
Composite keys, temporal alignment, deterministic transformations, and reconciliation.
Explore the foundation →DESIGN CONCEPT / 03
Predictive modeling with honest validation
Extend feature-engineering and diagnostics experience into a prediction-focused project.
Explore the proposed project
Proposed approach
Use a public tabular dataset, define a realistic prediction point, keep preprocessing inside validation folds, compare a baseline with candidate models, and examine calibration and subgroup error.
Proposed deliverables
A reproducible training pipeline, held-out evaluation, error analysis, and a model card.
Technology direction
Proposed stack: Python · pandas · NumPy · scikit-learn · matplotlib
The connection to existing work
Temporal feature definitions, missingness checks, model assumptions, and uncertainty.
Explore the foundation →DESIGN CONCEPT / 04
Experiment & survey decision support
Connect study planning with an explorable analysis of an experiment or survey.
Explore the proposed project
Proposed approach
Specify the comparison and unit of randomization or observation, run sensitivity scenarios, visualize effect sizes and intervals, and publish denominator-aware summaries.
Proposed deliverables
A design notebook, scenario explorer, comparative figures, and a plain-language decision brief.
Technology direction
R · TrialSize · effectsize · Shiny · Plotly; potential Python extension: SciPy · statsmodels
The connection to existing work
Power planning, repeated-measures simulation, survey transformations, and reactive reporting.
Explore the foundation →Technical references for the proposed implementations: scikit-learn pipelines · dbt data tests.