R Tutorials for Biomechanical Analysis¶

Comprehensive R Markdown templates and tutorials for research-grade biomechanical analysis using the LocomotionData package.

Research Templates¶

Clinical and Research Workflows¶

Template	Description	Use Case	Features
Gait Analysis Report	Comprehensive clinical gait analysis	Clinical assessments, patient reports	Automated interpretation, quality metrics
Intervention Study	Pre/post intervention analysis	Treatment efficacy studies	Statistical comparisons, effect sizes
Population Comparison	Multi-group comparative analysis	Cohort studies, demographics	ANOVA, post-hoc tests, visualizations
Longitudinal Study	Time-series and repeated measures	Disease progression, development	Mixed-effects models, trajectories

Advanced Analysis Templates¶

Template	Description	Features
Interactive Research Dashboard	Real-time data exploration	Plotly integration, dynamic filtering
Automated Quality Assessment	Data validation and outlier detection	Automated flagging, validation reports
Publication-Ready Analysis	Journal-ready research analysis	APA formatting, statistical reporting

Learning Tutorials¶

Beginner Level¶

Getting StartedCore Analysis

Statistical Analysis Fundamentals (45 min)
Gait Cycle Analysis (40 min)
Quality Control Methods (30 min)

Advanced Level¶

Research MethodsClinical Applications

Mixed-Effects Modeling (60 min)
Functional Data Analysis (75 min)
Machine Learning Applications (90 min)

Pathological Gait Analysis (50 min)
Intervention Assessment (65 min)
Pediatric Analysis Considerations (45 min)

Interactive Features¶

Plotly Integration¶

All templates include interactive visualizations with:

Hover Information: Biomechanical context and values
Zoom and Pan: Detailed examination of patterns
Dynamic Filtering: Real-time data subset exploration
3D Trajectories: Joint angle patterns in 3D space
Comparative Views: Side-by-side analysis tools

Parameterized Reports¶

Templates support automated generation with:

# Example parameterized report generation
rmarkdown::render("gait_analysis_report.Rmd", 
                  params = list(
                    dataset = "patient_data.parquet",
                    subject_id = "PATIENT_001",
                    control_group = "healthy_controls.parquet",
                    output_dir = "reports/"
                  ))

Setup and Requirements¶

Installation¶

# Install required packages
install.packages(c(
  "rmarkdown", "knitr", "DT", "plotly", 
  "flexdashboard", "crosstalk", "htmlwidgets",
  "lme4", "emmeans", "effectsize", "report"
))

# Load LocomotionData package
devtools::load_all("path/to/locomotion-data-standardization/source/lib/r")

Template Usage¶

Choose Template: Select appropriate research template
Configure Parameters: Set dataset paths and analysis options
Knit Report: Generate HTML or PDF output
Review Results: Automated interpretation and recommendations

Data Requirements¶

Phase-indexed data: 150 points per gait cycle
Standard naming: Follow LocomotionData conventions
Required columns: subject, task, phase
File formats: Parquet (preferred) or CSV

Key Features¶

Automated Analysis Pipeline¶

Quality Assessment: Automatic outlier detection and validation
Statistical Analysis: Appropriate tests based on data structure
Effect Size Calculation: Clinical significance metrics
Result Interpretation: AI-assisted biomechanical insights

Professional Reporting¶

Publication Standards: APA formatting and statistical reporting
Clinical Summaries: Patient-friendly result interpretation
Executive Reports: Research summary for stakeholders
Appendix Generation: Detailed technical information

Research Reproducibility¶

Version Control: Template versioning and change tracking
Parameter Documentation: Complete analysis configuration
Data Provenance: Source data tracking and validation
Code Transparency: Full analysis code inclusion

Quick Start¶

1. Clinical Gait Analysis¶

# Generate a clinical gait report
rmarkdown::render("gait_analysis_report.Rmd", 
                  params = list(
                    patient_data = "patient_001.parquet",
                    reference_data = "normative_database.parquet"
                  ))

2. Research Study Analysis¶

# Analyze intervention study
rmarkdown::render("intervention_study_template.Rmd",
                  params = list(
                    pre_data = "baseline_measurements.parquet",
                    post_data = "followup_measurements.parquet",
                    intervention = "gait_training"
                  ))

3. Interactive Dashboard¶

# Create interactive research dashboard
rmarkdown::render("interactive_dashboard_template.Rmd",
                  output_format = "flexdashboard::flex_dashboard",
                  params = list(
                    datasets = c("study1.parquet", "study2.parquet"),
                    interactive_mode = TRUE
                  ))