Package 'tidybrregpanel'

Annotation infotype codes with English descriptions

Description

Maps brreg påtegning infotype codes to English descriptions. Used by brreg_annotations() when translate = TRUE. Covers the infotype codes documented in the brreg API reference ("NAVN", "FADR") and those observed in live data (role codes used for missing-role annotations); unknown codes pass through unchanged. Each annotation's own Norwegian text is available in the tekst column of brreg_annotations() output.

Usage

annotation_infotypes

Format

A tibble with 7 rows and 2 columns:

code

Annotation infotype code (e.g. "FADR", "NAVN").

name_en

English description.

See Also

brreg_annotations() for the function that uses this table.

Examples

annotation_infotypes

---

Convert tidybrreg output to tsibble

Description

Convert the output of brreg_panel() or brreg_series() to a tsibble for use with the tidyverts ecosystem (fable, feasts, slider). Uses regular = FALSE since brreg snapshots are irregularly spaced.

Usage

as_brreg_tsibble(x, key = NULL, index = NULL)

Arguments

x	A tibble from brreg_panel() or brreg_series().
key	Character vector of key column(s). For panels, typically "org_nr". For series, the grouping variable (e.g. "legal_form"). If NULL, inferred from the brreg_panel_meta attribute.
index	Character. Name of the time index column. Default "period" for series output, "snapshot_date" for panel output.

Value

A tsibble.

See Also

brreg_panel(), brreg_series().

Other tidybrreg panel functions: brreg_change_summary(), brreg_changes(), brreg_events(), brreg_flows(), brreg_panel(), brreg_replay(), brreg_series()

Examples

panel <- brreg_panel(cols = c("employees", "legal_form"))
as_brreg_tsibble(panel)

---

Count entities with active annotations

Description

Quick summary of how many entities currently carry påtegninger, grouped by annotation type.

Usage

brreg_annotation_summary()

Value

A tibble with infotype and n.

See Also

Other tidybrreg data management functions: brreg_annotations(), brreg_historical_names(), brreg_status(), brreg_sync(), brreg_sync_status(), diff_roller_state(), read_changelog()

Examples

brreg_annotation_summary()

---

Retrieve registry annotations (påtegninger) for entities

Description

Påtegninger are public annotations placed on entities by the register keeper to warn third parties of irregularities — missing board members, undelivered accounts, deceased officers. They are the earliest formal signal that an entity is in regulatory trouble, preceding forced dissolution warnings by weeks to months.

Usage

brreg_annotations(
  org_nr = NULL,
  infotype = NULL,
  active_only = TRUE,
  translate = FALSE
)

Arguments

org_nr	Optional character vector of organisation numbers. NULL returns all annotations.
infotype	Optional character vector of annotation type codes to filter by (e.g. "FADR", "NAVN").
active_only	Logical. If TRUE (default), return only annotations currently in force. If FALSE, include cleared annotations from the changelog.
translate	Logical. If TRUE, add an infotype_desc column with English descriptions from annotation_infotypes; unknown codes pass through unchanged. Default FALSE.

Details

Requires brreg_sync() to have been run at least once to populate the local påtegninger state.

Value

A tibble with columns: org_nr, position (array index), infotype, tekst (annotation text), innfoert_dato (date introduced). With translate = TRUE, an infotype_desc column is inserted after infotype.

See Also

brreg_sync() to populate annotation data, brreg_changes() to track annotation events over time, annotation_infotypes for the English infotype lookup table.

Other tidybrreg data management functions: brreg_annotation_summary(), brreg_historical_names(), brreg_status(), brreg_sync(), brreg_sync_status(), diff_roller_state(), read_changelog()

Examples

brreg_sync()
brreg_annotations()
brreg_annotations(infotype = "FADR")
brreg_annotations(translate = TRUE)

---

Build a director interlock network

Description

Construct a bipartite graph of entities and persons linked by board/officer roles. Returns a tbl_graph (tidygraph) object suitable for centrality analysis and ggraph visualization.

Usage

brreg_board_network(org_nrs = NULL, roles_data = NULL)

Arguments

org_nrs	Character vector of organization numbers to include. Roles are fetched via tidybrreg::brreg_roles() for each entity.
roles_data	Optional pre-fetched roles tibble (from tidybrreg::brreg_roles()). If provided, org_nrs is ignored.

Details

For a full ego network including sub-units, child entities, and legal roles in addition to board roles, use brreg_network() instead.

Value

A tbl_graph with two node types: "entity" (identified by org_nr) and "person" (identified by person_id). Edge attributes include role_code, role_group_code, and org_nr.

See Also

brreg_network() for full entity network graphs, tidybrreg::brreg_roles() to fetch role data, brreg_board_summary() for board-level covariates.

Other tidybrreg governance functions: brreg_board_summary(), brreg_network(), brreg_survival_data()

Examples

net <- brreg_board_network(c("923609016", "984851006"))
net

---

Derive board-level summary covariates from role data

Description

Compute firm-level variables commonly used in corporate governance research: board size, composition counts, and officer indicators.

Usage

brreg_board_summary(roles)

Arguments

roles

A tibble returned by tidybrreg::brreg_roles().

Value

A 1-row tibble with columns: org_nr, board_size, n_chair, n_deputy_chair, n_members, n_alternates, n_observers, n_employee_elected, has_ceo, has_auditor, auditor_org_nr. Counts exclude resigned and deregistered roles.

See Also

tidybrreg::brreg_roles() for the underlying role data.

Other tidybrreg governance functions: brreg_board_network(), brreg_network(), brreg_survival_data()

Examples

brreg_roles("923609016") |> brreg_board_summary()

---

Summarize changes by field and type

Description

Produces a count table of how many changes occurred per field and change type, useful for understanding the volume and distribution of registry mutations.

Usage

brreg_change_summary(from = NULL, to = NULL, registry = NULL)

Arguments

from, to	Date range (inclusive).
registry	Character vector of streams to include. Default includes all four.

Value

A tibble with registry, change_type, field, n.

See Also

brreg_changes() for raw changelog rows, brreg_sync() to populate the changelog.

Other tidybrreg panel functions: as_brreg_tsibble(), brreg_changes(), brreg_events(), brreg_flows(), brreg_panel(), brreg_replay(), brreg_series()

Examples

brreg_change_summary(from = Sys.Date() - 7)

---

Query the change log for field-level mutations

Description

Returns a filtered view of all recorded changes across the four sync streams: enheter, underenheter, roller, and påtegninger. Every call to brreg_sync() appends events to the changelog; this function reads and filters them.

Usage

brreg_changes(
  track = NULL,
  registry = NULL,
  change_type = NULL,
  from = NULL,
  to = NULL,
  org_nr = NULL
)

Arguments

track	Character vector of fields to include (e.g. c("nace_1", "municipality_code", "employees")). NULL returns all fields.
registry	Character vector of streams to include. Default includes all four.
change_type	Character vector of event types to include. Options: "entry", "exit", "change", "annotation_added", "annotation_cleared".
from, to	Date range (inclusive).
org_nr	Optional character vector of organisation numbers.

Value

A tibble with columns: timestamp, org_nr, registry, change_type, field, value_from, value_to, update_id.

See Also

brreg_sync() to populate the changelog, brreg_flows() for aggregated entry/exit counts.

Other tidybrreg panel functions: as_brreg_tsibble(), brreg_change_summary(), brreg_events(), brreg_flows(), brreg_panel(), brreg_replay(), brreg_series()

Examples

brreg_sync()

# All changes this month
brreg_changes(from = Sys.Date() - 30)

# NACE reclassifications only
brreg_changes(track = "nace_1", change_type = "change")

# Entries and exits for a specific entity
brreg_changes(org_nr = "923609016", change_type = c("entry", "exit"))

# All annotation events
brreg_changes(registry = "paategninger")

---

Remove old snapshots from the local store

Description

Delete snapshot partitions by count or age. At least one of keep_n or max_age_days must be provided.

Usage

brreg_cleanup(
  keep_n = NULL,
  max_age_days = NULL,
  type = c("enheter", "underenheter", "roller")
)

Arguments

keep_n	Integer. Keep the keep_n most recent snapshots and delete the rest. NULL to skip this criterion.
max_age_days	Integer. Delete snapshots older than this many days. NULL to skip this criterion.
type	One of "enheter" or "underenheter".

Value

A tibble of deleted snapshots (invisibly).

See Also

brreg_snapshots() to list available snapshots, brreg_data_dir() for storage location.

Other tidybrreg snapshot functions: brreg_data_dir(), brreg_import(), brreg_manifest(), brreg_open(), brreg_snapshot(), brreg_snapshots()

Examples

brreg_cleanup(keep_n = 12)
brreg_cleanup(max_age_days = 365)

---

Path to the tidybrreg snapshot store

Description

Returns (and creates if needed) the directory where tidybrreg stores Parquet snapshots. Location follows R's standard user data directory convention via tools::R_user_dir("tidybrreg", "data"). Override with options(brreg.data_dir = "/custom/path").

Usage

brreg_data_dir()

Value

Character path.

See Also

brreg_snapshot() to save snapshots, brreg_open() to read them.

Other tidybrreg snapshot functions: brreg_cleanup(), brreg_import(), brreg_manifest(), brreg_open(), brreg_snapshot(), brreg_snapshots()

Examples

brreg_data_dir()

---

Detect changes between two snapshots

Description

Compare two dated snapshots and return a tibble of entity-level events: entries (new entities), exits (deleted entities), and field-level changes. Unlike the CDC stream, snapshot diffs provide both old and new values for every changed field.

Usage

brreg_events(
  date_from,
  date_to,
  cols = NULL,
  type = c("enheter", "underenheter")
)

Arguments

date_from, date_to	Dates identifying the two snapshots to compare. Both must exist in the snapshot store. date_from is the "before" state, date_to is the "after" state.
cols	Character vector of columns to track for changes. NULL (default) tracks all columns in tidybrreg::field_dict.
type	One of "enheter" or "underenheter".

Value

A tibble with columns: org_nr, event_type ("entry", "exit", "change"), event_date (the date_to snapshot date), field (column name, NA for entry/exit), value_from (character, previous value), value_to (character, new value).

See Also

tidybrreg::brreg_updates() for the CDC stream (API-level changes), brreg_panel() for full panel construction.

Other tidybrreg panel functions: as_brreg_tsibble(), brreg_change_summary(), brreg_changes(), brreg_flows(), brreg_panel(), brreg_replay(), brreg_series()

Examples

snaps <- brreg_snapshots()
if (nrow(snaps) >= 2) {
  events <- brreg_events(snaps$snapshot_date[1], snaps$snapshot_date[2])
  events
}

---

Compute daily entry and exit flows

Description

Calculate daily counts of entity registrations (entries) and deletions (exits) classified by industry (NACE code) and geography (municipality code). Three data paths, selected automatically:

Usage

brreg_flows(
  data = NULL,
  updates = NULL,
  by = c("nace_1", "municipality_code"),
  from = NULL,
  to = NULL,
  legal_form = NULL
)

Arguments

data	Optional. A tibble from tidybrreg::brreg_download() or a snapshot. Required when no changelog exists. Must contain org_nr, registration_date, nace_1, and municipality_code.
updates	Optional. A tibble from tidybrreg::brreg_updates() with CDC events.
by	Character vector of grouping columns. Default c("nace_1", "municipality_code"). Use NULL for national totals, or any column present in data.
from, to	Date range for the output. NULL defaults to the range of observed events.
legal_form	Optional character vector of legal form codes to include (e.g. c("AS", "ENK")). NULL includes all.

Details

1. Changelog path (preferred) — when brreg_sync() has been run, reads directly from the persistent changelog. Provides timestamped entries, exits, and field-level transitions. No arguments needed.

2. Bulk + CDC path — pass data (from tidybrreg::brreg_download()) and optionally updates (from tidybrreg::brreg_updates()). Registration dates provide historical entries; CDC provides recent entries + exits.

3. Bulk-only path — pass data alone. Only entries are computed (no exit data available).

Value

A tibble with columns: date (Date), grouping columns from by, entries (integer), exits (integer), net (integer: entries - exits). An attribute flow_source records which data sources contributed.

Entry vs. founding date

This function uses registration_date (registreringsdatoEnhetsregisteret), NOT founding_date (stiftelsesdato). Registration date is when the entity entered the registry. Founding date can precede registration by months (AS companies) or years (associations).

See Also

tidybrreg::brreg_download() to get bulk data, tidybrreg::brreg_updates() to get CDC events, brreg_series() for snapshot-based time series, as_brreg_tsibble() for tsibble conversion.

Other tidybrreg panel functions: as_brreg_tsibble(), brreg_change_summary(), brreg_changes(), brreg_events(), brreg_panel(), brreg_replay(), brreg_series()

Examples

entities <- brreg_download()
flows <- brreg_flows(entities)

# With CDC exits
cdc <- brreg_updates(since = "2026-01-01", size = 10000)
flows <- brreg_flows(entities, updates = cdc)

# Monthly by NACE section
flows |>
  dplyr::mutate(month = format(date, "%Y-%m"),
                nace_section = substr(nace_1, 1, 2)) |>
  dplyr::summarise(entries = sum(entries), exits = sum(exits),
                   .by = c(month, nace_section))

---

Harmonize municipality codes across boundary reforms

Description

Remap municipality_code (kommunenummer) to the classification valid at a target date, using correspondence tables from SSB's KLASS system (classification 131). Handles the 2020 municipal reform (428 → 356 municipalities) and 2024 county reversals.

Usage

brreg_harmonize_kommune(
  data,
  target_date = Sys.Date(),
  col = "municipality_code"
)

Arguments

data	A tibble with a municipality code column.
target_date	Date. Remap all codes to the classification valid at this date. Default: today.
col	Column name containing municipality codes. Default "municipality_code".

Value

The input tibble with two added columns: ⁠{col}_harmonized⁠ (the remapped code) and ⁠{col}_target_name⁠ (municipality name at the target date). Unmatched codes pass through unchanged.

See Also

brreg_harmonize_nace() for NACE code harmonization.

Other tidybrreg harmonization functions: brreg_harmonize_nace()

Examples

# Remap old codes to current boundaries
df <- tibble::tibble(municipality_code = c("0301", "1201", "0602"))
brreg_harmonize_kommune(df)

---

Harmonize NACE industry codes across classification revisions

Description

Remap NACE codes between SN2007 (NACE Rev. 2) and SN2025 (NACE Rev. 2.1) using SSB KLASS correspondence tables.

Usage

brreg_harmonize_nace(data, from = "SN2007", to = "SN2025", col = "nace_1")

Arguments

data	A tibble with a NACE code column.
from	Source classification: "SN2007" (default) or "SN2025".
to	Target classification: "SN2025" (default) or "SN2007".
col	Column name containing NACE codes. Default "nace_1".

Value

The input tibble with ⁠{col}_harmonized⁠ (remapped code) and ⁠{col}_ambiguous⁠ (logical, TRUE when the mapping is one-to-many and the first match was used). Unmatched codes pass through unchanged.

See Also

brreg_harmonize_kommune() for municipality harmonization.

Other tidybrreg harmonization functions: brreg_harmonize_kommune()

Examples

df <- tibble::tibble(nace_1 = c("06.100", "64.190", "62.010"))
brreg_harmonize_nace(df, from = "SN2007", to = "SN2025")

---

Retrieve historical names (historiskeNavn) for entities

Description

Norwegian entities keep their organization number across renames; the register exposes prior names with the interval each was in force. BRREG added the historiskeNavn field to enheter and underenheter on 2026-06-16. Name history accrues through brreg_sync(): every rename in the CDC feed appends the superseded name here. History predating your first sync comes from the JSON bulk download and is backfilled when brreg_sync() bootstraps.

Usage

brreg_historical_names(org_nr = NULL)

Arguments

org_nr

Optional character vector of organisation numbers. NULL returns all name history.

Details

Requires brreg_sync() to have been run at least once to populate the local name-history state.

Value

A tibble with columns: org_nr, position (array index), name (the superseded name), from_date, to_date (register timestamps as observed, format "YYYY-MM-DD HH:MM:SS", kept as character).

See Also

brreg_sync() to populate name-history data, brreg_changes() with registry = "historiske_navn" to track rename events over time.

Other tidybrreg data management functions: brreg_annotation_summary(), brreg_annotations(), brreg_status(), brreg_sync(), brreg_sync_status(), diff_roller_state(), read_changelog()

Examples

brreg_sync()
brreg_historical_names()
brreg_historical_names("923609016")

---

Import a historical bulk JSON file as a snapshot partition

Description

Read a brreg bulk JSON file (as downloaded by tidybrreg::brreg_download() with type_output = "path", or from the brreg website), normalize column names via tidybrreg::field_dict, and save as a dated Parquet partition in the snapshot store.

Usage

brreg_import(
  path,
  snapshot_date,
  type = c("enheter", "underenheter", "roller"),
  force = FALSE
)

Arguments

path	Path to a brreg bulk JSON file (gzipped or plain): the entity array of ⁠/enheter/lastned⁠ or ⁠/underenheter/lastned⁠, or the ⁠/roller/totalbestand⁠ array.
snapshot_date	The date this file represents. Required — the file itself contains no date metadata.
type	One of "enheter", "underenheter" or "roller".
force	Logical. Overwrite existing partition.

Value

The file path to the written Parquet partition (invisibly).

See Also

brreg_snapshot() to download and save today's register.

Other tidybrreg snapshot functions: brreg_cleanup(), brreg_data_dir(), brreg_manifest(), brreg_open(), brreg_snapshot(), brreg_snapshots()

Examples

# Import a historical download
brreg_import("enheter_bulk_2024-12-31.json.gz", snapshot_date = "2024-12-31")

---

Read the snapshot manifest

Description

Returns the provenance catalog recording every download: endpoint, timestamps, HTTP headers, file hashes, and CDC bridge metadata. The manifest lives at brreg_data_dir()/manifest.json.

Usage

brreg_manifest()

Value

A tibble with one row per snapshot. Returns an empty tibble if no manifest exists.

See Also

brreg_snapshot() to create snapshots, brreg_snapshots() to list them.

Other tidybrreg snapshot functions: brreg_cleanup(), brreg_data_dir(), brreg_import(), brreg_open(), brreg_snapshot(), brreg_snapshots()

Examples

brreg_manifest()

---

Build an entity network graph

Description

Construct a tbl_graph (tidygraph) representing the relationships around one or more seed entities. At depth 1, the graph includes sub-units, child entities, role holders, and legal role targets — all reachable via direct API calls. At depth 2, the graph expands through person nodes to discover board interlocks, requiring local bulk data (see Details).

Usage

brreg_network(
  org_nr,
  depth = 1L,
  include = c("underenheter", "children", "roles", "legal_roles"),
  download = FALSE
)

Arguments

org_nr	Character vector of seed organization numbers.
depth	Integer. 0 = seed only, 1 = ego network (default), 2 = expand through persons (requires bulk data).
include	Character vector of relationship types to include. Default includes all available types. Current types: "underenheter", "children", "roles", "legal_roles".
download	Logical. If TRUE and depth > 1, offer to download missing bulk data interactively. Default FALSE.

Value

A tbl_graph with node attributes node_id, node_type, name, org_nr, person_id, and edge attributes from, to, edge_type, role_code, role.

Depth and data requirements

• Depth 0: Seed entity only. 1 API call per seed.

• Depth 1: Full ego network. 5-7 API calls per seed.

• Depth 2: Board interlocks via person-to-entity reverse lookup. Requires local bulk data for enheter, underenheter, and roller. Run brreg_snapshot() for each type first, or call with download = TRUE to trigger downloads interactively.

Extensibility

The include parameter controls which relationship types are traversed. Each type maps to an internal collector function. Future versions may add types such as "addresses", "prior_owners", or "accounting".

See Also

tidybrreg::brreg_entity() for single lookups, brreg_board_network() for the roles-only subgraph, brreg_status() to check bulk data availability.

Other tidybrreg governance functions: brreg_board_network(), brreg_board_summary(), brreg_survival_data()

Examples

net <- brreg_network("923609016")
net

tidygraph::as_tibble(net, "nodes")
tidygraph::as_tibble(net, "edges")

---

Open the snapshot store as a lazy Arrow Dataset

Description

Returns an Arrow Dataset with Hive-style partitioning on snapshot_date. No data is loaded until dplyr::collect(). Requires the arrow package.

Usage

brreg_open(type = c("enheter", "underenheter", "roller"))

Arguments

type	One of "enheter" or "underenheter".

Value

An arrow::Dataset object.

See Also

brreg_panel() for the higher-level panel constructor.

Other tidybrreg snapshot functions: brreg_cleanup(), brreg_data_dir(), brreg_import(), brreg_manifest(), brreg_snapshot(), brreg_snapshots()

Examples

ds <- brreg_open()
ds

---

Construct a firm-period panel from accumulated snapshots

Description

Build an unbalanced firm × period panel from the Hive-partitioned snapshot store. For each target period, selects the most recent prior snapshot (LOCF). Requires at least two snapshots saved via brreg_snapshot() or brreg_import().

Usage

brreg_panel(
  frequency = c("year", "quarter", "month", "custom"),
  cols = NULL,
  from = NULL,
  to = NULL,
  dates = NULL,
  max_gap = NULL,
  type = c("enheter", "underenheter", "roller"),
  label = FALSE
)

Arguments

frequency	One of "year" (default), "quarter", "month", or "custom". For "custom", supply target dates via dates.
cols	Character vector of column names to include. NULL (default) returns all columns mapped by tidybrreg::field_dict.
from, to	Start and end dates for the panel. NULL defaults to the range of available snapshots.
dates	Date vector for frequency = "custom".
max_gap	Integer. Maximum number of periods a snapshot may carry forward via LOCF. NULL (default) carries forward indefinitely. Set max_gap = 2 to prevent a quarterly snapshot from representing a firm as active 3+ quarters after its last observation.
type	One of "enheter" or "underenheter".
label	Logical. If TRUE, apply tidybrreg::brreg_label() to the result.

Value

A tibble with columns: org_nr, period (character label for the period), snapshot_date (the actual snapshot used), plus requested cols. Attribute date_mapping records which snapshot was used for each period.

See Also

brreg_snapshot() to accumulate snapshots, brreg_events() for change detection between snapshots.

Other tidybrreg panel functions: as_brreg_tsibble(), brreg_change_summary(), brreg_changes(), brreg_events(), brreg_flows(), brreg_replay(), brreg_series()

Examples

# Annual panel of all entities
panel <- brreg_panel()

# Monthly panel for specific columns
panel <- brreg_panel("month", cols = c("employees", "nace_1", "legal_form"))

---

Reconstruct register state by replaying CDC updates

Description

Given a base snapshot and a stream of CDC updates from tidybrreg::brreg_updates(), reconstruct the state of the register at any arbitrary date. Uses dplyr::rows_upsert() for Ny/Endring events and dplyr::rows_delete() for Sletting events, applied chronologically.

Usage

brreg_replay(base, updates, target_date = Sys.Date(), cols = NULL)

Arguments

base	A tibble from tidybrreg::brreg_download(), brreg_import(), or a snapshot read via brreg_open(). Must contain org_nr.
updates	A tibble from tidybrreg::brreg_updates() with include_changes = TRUE. Must contain org_nr, change_type, timestamp, and changes (list-column of patch tibbles).
target_date	Date. Reconstruct state as of this date. Only updates with timestamp <= target_date are applied.
cols	Character vector of columns to track. If NULL, all columns present in base are used.

Value

A tibble with the same columns as base, reflecting all applied changes up to target_date. Attribute replay_info records the number of inserts, updates, and deletes applied.

Limitations

The brreg CDC stream provides only new values (not old values) in RFC 6902 JSON Patch format, and field-level changes are only available from September 2025. Before that date, only the change type (Ny/Endring/Sletting) is recorded — field-level replay is not possible for those periods. Use brreg_events() (snapshot diff) for pre-September 2025 field-level changes.

See Also

brreg_panel() for multi-snapshot panels, tidybrreg::brreg_updates() to fetch the CDC stream.

Other tidybrreg panel functions: as_brreg_tsibble(), brreg_change_summary(), brreg_changes(), brreg_events(), brreg_flows(), brreg_panel(), brreg_series()

Examples

base <- brreg_download(type_output = "tibble")
updates <- brreg_updates(since = Sys.Date() - 30,
                          size = 10000, include_changes = TRUE)
state <- brreg_replay(base, updates, target_date = Sys.Date())

---

Compute aggregate time series from snapshots

Description

Produce period-level summary statistics from the snapshot store for any combination of variables and summary functions. Returns a tibble suitable for ggplot2 or as_brreg_tsibble() conversion.

Usage

brreg_series(
  .vars = NULL,
  .fns = list(total = function(x) sum(x, na.rm = TRUE)),
  by = NULL,
  frequency = c("year", "quarter", "month"),
  from = NULL,
  to = NULL,
  type = c("enheter", "underenheter", "roller"),
  label = FALSE
)

Arguments

.vars	Character vector of column names to aggregate. NULL (default) counts entities per period.
.fns	Named list of summary functions applied to each column in .vars. Default: ⁠list(total = \(x) sum(x, na.rm = TRUE))⁠. Use list(avg = mean, sd = sd) for multiple summaries. Output columns are named ⁠{variable}_{function}⁠.
by	Character vector of grouping column names (e.g. "nace_1", c("legal_form", "municipality_code")). NULL for national totals.
frequency	One of "year", "quarter", "month".
from, to	Start and end dates for the panel. NULL defaults to the range of available snapshots.
type	One of "enheter", "underenheter", "roller".
label	Logical. Translate group codes to English labels.

Value

A tibble with period (character), optional grouping columns, and one column per variable-function combination. Attribute brreg_panel_meta records metadata for as_brreg_tsibble() conversion.

See Also

brreg_panel() for entity-level panels, as_brreg_tsibble() for tsibble conversion.

Other tidybrreg panel functions: as_brreg_tsibble(), brreg_change_summary(), brreg_changes(), brreg_events(), brreg_flows(), brreg_panel(), brreg_replay()

Examples

brreg_series(.vars = "employees", by = "legal_form")

brreg_series(.vars = "employees",
             .fns = list(avg = mean, total = sum),
             by = "nace_1")

---

Save a dated snapshot of the full register

Description

Download today's complete register and save as a Parquet partition in the local snapshot store. Each call adds one partition to a Hive-partitioned dataset at ⁠tools::R_user_dir("tidybrreg", "data")/{type}/snapshot_date={date}/⁠. Subsequent calls to brreg_panel() and brreg_events() query this partitioned dataset lazily via arrow::open_dataset().

Usage

brreg_snapshot(
  type = c("enheter", "underenheter", "roller"),
  date = Sys.Date(),
  force = FALSE,
  ask = interactive()
)

Arguments

type	One of "enheter" (main entities, default), "underenheter" (sub-entities / establishments), or "roller" (all roles for all entities, via ⁠/roller/totalbestand⁠). Roller snapshots parse the nested JSON into a flat tibble matching tidybrreg::brreg_roles() output.
date	Date for this snapshot (default: today). Used as the partition key, not as an API parameter — the brreg bulk endpoint always returns the current-day state.
force	Logical. If TRUE, overwrite an existing partition for this date. Default FALSE skips if partition exists.
ask	Logical. If TRUE (the default in interactive sessions), prompt before downloading ~145 MB.

Value

The file path to the written Parquet partition (invisibly).

See Also

brreg_import() to add historical snapshots from bulk JSON files, brreg_snapshots() to list available snapshots, brreg_panel() to construct panels from accumulated snapshots.

Other tidybrreg snapshot functions: brreg_cleanup(), brreg_data_dir(), brreg_import(), brreg_manifest(), brreg_open(), brreg_snapshots()

Examples

brreg_snapshot()
brreg_snapshots()

---

List available snapshots

Description

Scan the local snapshot store and return metadata for each partition.

Usage

brreg_snapshots(type = c("enheter", "underenheter", "roller"))

Arguments

type	One of "enheter" or "underenheter".

Value

A tibble with columns: snapshot_date (Date), file_size (numeric, bytes), path (character).

See Also

Other tidybrreg snapshot functions: brreg_cleanup(), brreg_data_dir(), brreg_import(), brreg_manifest(), brreg_open(), brreg_snapshot()

Examples

brreg_snapshots()

---

Check availability of local bulk datasets

Description

Inspects the snapshot store and download cache for each requested dataset type. Used internally by brreg_network() to gate depth > 1 operations that require local data for person-to-entity reverse lookups.

Usage

brreg_status(datasets = c("enheter", "underenheter", "roller"), quiet = FALSE)

Arguments

datasets	Character vector of dataset types to check.
quiet	Logical. If TRUE, suppress informational messages.

Value

A list with components: available (character vector of datasets found locally), missing (character vector of datasets not found), all_ready (logical).

See Also

brreg_snapshot() to download and cache bulk data, tidybrreg::brreg_download() for one-off downloads.

Other tidybrreg data management functions: brreg_annotation_summary(), brreg_annotations(), brreg_historical_names(), brreg_sync(), brreg_sync_status(), diff_roller_state(), read_changelog()

Examples

brreg_status()

---

Prepare firm survival data

Description

Compute time-to-event and censoring indicators from entity registration data, ready for use with survival::Surv() and flexsurv.

Usage

brreg_survival_data(
  data,
  entry_var = "founding_date",
  censoring_date = Sys.Date()
)

Arguments

data	A tibble from tidybrreg::brreg_download(), tidybrreg::brreg_search(), or brreg_panel(). Must contain at least org_nr and a date column for entry.
entry_var	Column name for the entry date. Default "founding_date" (stiftelsesdato).
censoring_date	Date at which surviving firms are right-censored. Default: today.

Value

A tibble with added columns: entry_date, exit_date (Date or NA), duration_years (numeric), event (integer: 1 = exit observed, 0 = right-censored). Compatible with survival::Surv(duration_years, event).

See Also

tidybrreg::brreg_download() for full register data.

Other tidybrreg governance functions: brreg_board_network(), brreg_board_summary(), brreg_network()

Examples

firms <- brreg_search(legal_form = "AS", municipality_code = "0301",
                       max_results = 100)
surv <- brreg_survival_data(firms)
surv[, c("org_nr", "entry_date", "exit_date", "duration_years", "event")]

---

Synchronize local state with the brreg CDC stream

Description

Maintains a local mirror of the Enhetsregisteret by applying incremental CDC (change data capture) events to a persistent state table. On first run, bootstraps from a bulk download. Subsequent runs poll the CDC endpoints from the last cursor position and apply mutations.

Usage

brreg_sync(
  types = c("enheter", "underenheter", "roller"),
  size = 10000L,
  roller_method = c("bulk", "cdc"),
  verbose = TRUE
)

Arguments

types	Character vector of streams to sync. Default syncs all four.
size	Integer. CDC page size per API call (max 10000).
roller_method	One of "bulk" (default) or "cdc". "bulk" downloads the full totalbestand (~131 MB) and computes a field-level diff against previous state — fast and produces granular changelogs. "cdc" fetches current roles per-org via the API for each CDC event — slower but provides sub-daily attribution when syncing multiple times per day.
verbose	Logical. Print progress messages.

Details

Four state files are maintained:

• enheter.parquet — main entities (~1M rows)

• underenheter.parquet — sub-entities (~500K rows)

• roller.parquet — all roles (~3.4M rows)

• paategninger.parquet — registry annotations

Every mutation is logged to a Hive-partitioned changelog under ⁠state/changelog/sync_date={date}/⁠. The changelog drives brreg_changes() and brreg_flows().

Value

A list with sync summary: events processed per type, changelog rows written, elapsed time.

Write ordering

Changelog is written first (WAL), then state files, then cursor. If a crash occurs between state and cursor, the next sync replays from the old cursor. Mutations are idempotent (upsert by org_nr), so replay is safe.

See Also

brreg_sync_status() to check current state, brreg_changes() to query the changelog, brreg_flows() for entry/exit counts.

Other tidybrreg data management functions: brreg_annotation_summary(), brreg_annotations(), brreg_historical_names(), brreg_status(), brreg_sync_status(), diff_roller_state(), read_changelog()

Examples

brreg_sync()
brreg_sync_status()

---

Display sync status

Description

Shows the current state of the sync engine: which state files exist, when the last sync occurred, cursor positions, and changelog size.

Usage

brreg_sync_status()

Value

A list with status components (invisibly).

See Also

brreg_sync() to run a sync, brreg_changes() to query the changelog.

Other tidybrreg data management functions: brreg_annotation_summary(), brreg_annotations(), brreg_historical_names(), brreg_status(), brreg_sync(), diff_roller_state(), read_changelog()

Examples

brreg_sync_status()

---

Compute field-level diffs between two roller state tibbles

Description

Pure function: takes two flattened roller tibbles (as produced by tidybrreg:::flatten_roles() or tidybrreg:::parse_roles_bulk()) and returns a long-format changelog recording every field-level mutation. Detects three categories of change: role additions, role removals, and field-level modifications on continuing roles.

Usage

diff_roller_state(
  old_state,
  new_state,
  timestamp = format(Sys.time(), "%Y-%m-%dT%H:%M:%S"),
  update_id = NA_integer_
)

Arguments

old_state	Tibble. Previous roller state from read_state() or an earlier tidybrreg:::flatten_roles() call. NULL or zero-row tibble treats all current roles as additions.
new_state	Tibble. Current roller state from tidybrreg::brreg_download() or tidybrreg:::flatten_roles().
timestamp	Character or POSIXct. Timestamp for changelog entries (typically the CDC event time or sync time).
update_id	Integer or character. Identifier for the sync batch, used as update_id in the changelog.

Value

A tibble matching the changelog schema: timestamp, org_nr, registry (always "roller"), change_type ("entry", "exit", "change"), field, value_from, value_to, update_id.

Composite key

Each role assignment is identified by ⁠(org_nr, role_group_code, role_code, holder_id)⁠ where holder_id is derived from person_id for person-held roles and entity:{entity_org_nr} for entity-held roles (auditors, accountants). Roles with neither are keyed as unknown:{row_index} within their respective state — these are rare and produce conservative add/remove pairs rather than false modifications.

NA handling

For additions, fields that are NA in the new state are excluded from the changelog (no value to report). For removals, fields that are NA in the old state are excluded. For modifications, a change from NA to a non-NA value (or vice versa) is recorded.

See Also

brreg_sync() for automated sync with changelog persistence, tidybrreg::brreg_roles() for fetching current roles, brreg_changes() for querying stored changelogs.

Other tidybrreg data management functions: brreg_annotation_summary(), brreg_annotations(), brreg_historical_names(), brreg_status(), brreg_sync(), brreg_sync_status(), read_changelog()

Examples

# Detect board changes for a single company
old <- brreg_roles("923609016")
Sys.sleep(1)
new <- brreg_roles("923609016")
diff_roller_state(old, new)

# Bootstrap: NULL old state treats all roles as entries
roles <- brreg_roles("923609016")
diff_roller_state(NULL, roles)

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