Package index • swereg

Core functions

Main functions for creating and manipulating longitudinal skeletons

create_skeleton(): Create longitudinal data skeleton
add_onetime(): Add one-time data to skeleton
add_annual(): Add annual data to skeleton

Medical data integration

Functions for adding medical registry data

add_diagnoses(): Add diagnosis data to skeleton
add_operations(): Add surgical operation data to skeleton
add_icdo3s(): Add ICD-O-3 oncology codes to skeleton
add_snomed3s(): Add SNOMED-CT version 3 codes to skeleton
add_snomedo10s(): Add SNOMED-CT version 10 codes to skeleton
add_rx(): Add prescription drug data to skeleton
add_cods(): Add cause of death data to skeleton
add_quality_registry(): Add quality registry data to skeleton

Data transformation

Functions for transforming data structure and creating derived variables

make_rowind_first_occurrence(): Transform a row-dependent variable to a row-independent variable using first occurrence

Survival analysis

Helper functions for time-to-event and survival analysis

steps_to_first(): Calculate steps until first TRUE in a forward window
any_events_prior_to(): Check for any TRUE values in a prior window

Target trial emulation

Functions and classes for causal inference using target trial emulation methodology

TTEDesign: TTEDesign class for target trial emulation
TTEPlan: TTEPlan class for trial generation planning
TTEEnrollment: TTEEnrollment class for target trial emulation
tteplan_apply_derived_confounders(): Compute derived confounder columns from a study spec
tteplan_apply_exclusions(): Apply exclusion criteria from a study spec to a skeleton
tteplan_from_spec_and_registrystudy(): Create a TTEPlan from a study specification
tteplan_load(): Load a TTEPlan from disk with the current class definition
tteplan_locate_and_load(): Locate and load a TTEPlan from candidate directories
tteplan_read_spec(): Read and validate a YAML study specification
tteplan_validate_spec(): Validate spec variables against skeleton data
tteenrollment_combined_combine(): Combine rates + IRR outputs into a single wide publication-ready table
tteenrollment_impute_confounders(): Impute missing confounders by sampling from observed values
tteenrollment_irr_combine(): Combine and format multiple irr outputs into a publication-ready table
tteenrollment_rates_combine(): Combine and format multiple rates outputs into a publication-ready table
tteenrollment_rbind(): Combine multiple enrollment objects
skeleton_eligible_age_range(): Check eligibility based on age range
skeleton_eligible_combine(): Combine multiple eligibility criteria
skeleton_eligible_isoyears(): Check eligibility based on ISO years
skeleton_eligible_no_events_in_window_excluding_wk0(): Check eligibility based on no events in prior window (excluding baseline week)
skeleton_eligible_no_events_lifetime_before_and_after_baseline(): Check eligibility based on no events ever (person-level, before and after baseline)
skeleton_eligible_no_observation_in_window_excluding_wk0(): Check eligibility based on no observation of a specific value (excluding baseline week)

Parallel processing

Parallel batch processing via processx

parallel_pool(): Run a function on each work item in parallel via processx
setup_progress_handlers(): Install a progressr handler that works in interactive R and RStudio jobs

Skeleton pipeline

RegistryStudy + Skeleton R6 classes and functions for batched skeleton processing

RegistryStudy: RegistryStudy: Unified R6 class for skeleton pipeline
Skeleton: Skeleton: per-batch time grid + derived columns with provenance
registrystudy_load(): Locate and load a RegistryStudy from candidate metadata directories
create_skeleton(): Create longitudinal data skeleton
validate_skeleton_structure(): Validate skeleton structure

Multi-host path resolution

CandidatePath R6 class + helpers for resolving directories that live at different locations on different hosts

CandidatePath: CandidatePath: a directory with multiple candidate locations
first_existing_path(): First candidate path that exists
invalidate_candidate_paths(): Invalidate every CandidatePath cache inside an R6 object

Utility functions

Helper functions for data processing

fread_raw(): Read a raw registry file with fread, then lowercase names
qs2_read(): Read a qs2 file (auto-detecting format)
make_lowercase_names(): Convert column names to lowercase and optionally clean date columns
parse_swedish_date(): Parse Swedish registry dates
first_non_na(): Get first non-NA value from vector
last_non_na(): Get last non-NA value from vector
min_with_infinite_as_na(): Calculate minimum while treating infinite values as NA
max_with_infinite_as_na(): Calculate maximum while treating infinite values as NA
as_logical_min_with_infinite_as_na(): Convert minimum to logical while treating infinite values as NA
as_logical_max_with_infinite_as_na(): Convert maximum to logical while treating infinite values as NA

Specialized functions

Functions for specific research applications

x2023_mht_add_lmed(): Add 2023 MHT-specific prescription data to skeleton
x2026_mht_add_lmed(): Add 2023 MHT-specific prescription data to skeleton

Datasets

Synthetic registry data for development and examples

fake_person_ids fake_demographics fake_annual_family fake_diagnoses fake_prescriptions fake_cod: Fake Swedish Registry Datasets