Package: hvtiRutilities 1.5.11

The worked example | What Does Registering a Dataset Do? | Why Is It Named That Way? | Which File Does My Job Read? | Which Version Did a Report Use? | What If the SAS File Changes and Nobody Registers It? | Where Do the Older Versions Go? | In Short

The Problem: Datasets Drift | In a Study: Rebuild, Then Update | Setup | Registering Datasets | CSV — automatic row count | Inspecting the manifest | SAS datasets — automatic row count | Excel workbooks — automatic row count | Other formats — supply n_rows explicitly | Verifying Before Every Analysis | Typical script header | What Happens When Data Changes | Collecting all failures at once | Correcting a file | Looking at a Rebuild Before Registering It | Running a Job on the Working File | How a Version Was Produced | A New Pull for a Different Project | What Changed Inside the File? | Policy Recommendations | Session Information

Four Procedures You Already Run | What Arrived? proc_contents() | Reading the Extra Columns | Where the SAS Original Has More | Type Is Two-Valued, and That Hides Things | Do the Numbers Look Right? proc_means() | Stratifying with class | The Quartile Trap | Logical Columns | Do the Categories Look Right? proc_freq() | Missing Values | The Denominator Trap | Weighted Percentiles and Tests: proc_univariate() | Weighted Quantiles Follow a Different Rule | VARDEF Changes More Than the Variance | Tests for Location and Normality | Did the Extract Change? compare_datasets() | Where the R Version Differs | Checklist | See Also

Overview | Generating the Dataset | Data Structure | Outcome Summary | Integration with r_data_types() and label_map() | Extracting Variable Labels | Preparing Data for Survival Analysis | Reproducibility | Session Information

Introduction | Main Functions | Installation | Basic Usage | Automatic Type Conversion with r_data_types() | Transformation Rules | Working with Real Data | Example: mtcars Dataset | Controlling Factor Conversion | Skipping Specific Variables | Binary Variables as Factors | Working with Variable Labels | Extracting and Looking Up Labels | Labeling Derived Variables | Labels Persist Through Transformations | Complete Workflow Example | Handling Missing Data | Advanced Usage | Custom Workflows for Specific Data Types | Integration with Data Import | Best Practices | When to Use r_data_types() | Recommended Settings by Use Case | Checking Results | Summary | Session Information

Why Labels Matter | The Label Lifecycle | Phase 1: Extracting Labels at Ingestion | Detecting Missing Labels | Phase 2: Labeling Derived Variables | Method A: Label the data directly (preferred) | Method B: Update the label map (for reporting) | Which method should I use? | Phase 3: Study-Specific Overrides | Example labels_overrides.yml | Phase 4: Using Labels in Output | Safe single-variable lookup with get_label() | Building a data dictionary | Labels in summary tables | Labels in plots | Complete Workflow | Labels and r_data_types() | Anti-Patterns to Avoid | 1. Hard-coded label replacements in shared code | 2. Creating derived variables without labels | 3. Using match() without error checking | 4. Labeling the map instead of the data | Function Reference | Session Information

The Problem: Scattered Seeds | The Fix: One Seed, One Place | Create a config.yml | Read it in R | Using the Seed with randomForestSRC | Using the Seed with varPro | Read it in SAS | Keeping R and SAS in Sync | How Seeds Work | R | SAS | randomForestSRC / varPro | Implications for Multi-Script Projects | Checklist