Using GERDA in Agent-Run Research Workflows
In gerda: German Election Database (GERDA)

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(gerda)

This vignette is a fail-closed workflow for coding agents that use GERDA in a research project. It separates dataset selection, acquisition, schema checks, joins, and provenance so that an agent cannot silently continue after choosing the wrong dataset or losing observations.

Select from the structured catalog

Do not guess a dataset name from a prose description. Query the catalog and require one result before downloading:

catalog <- gerda_data_list(print_table = FALSE)

candidates <- subset(
  catalog,
  election_type == "federal" &
    geographic_level == "municipality" &
    boundary == "2025"
)

candidates[, c(
  "data_name", "year_start", "year_end", "boundary", "formats"
)]
stopifnot(nrow(candidates) == 1L)
data_name <- candidates$data_name[[1]]

The boundary choice is part of the research design. *_unharm files use the geography in force at each election; *_harm files map observations to a common geography. A suffix such as _21, _23, or _25 identifies the target boundary vintage. An agent should record why that vintage matches the analysis.

Download with errors enabled and save a project snapshot

Use RDS for smaller downloads, make load failures fatal, and opt into caching when repeated runs are expected:

elections <- load_gerda_web(
  data_name,
  file_format = "rds",
  on_error = "stop",
  cache = TRUE
)

stopifnot(is.data.frame(elections), nrow(elections) > 0L)

The cache improves speed but does not pin an immutable upstream version. For a reproducible project, save the data actually analyzed and its checksum inside the project, subject to the project's data-storage policy:

dir.create("data/derived", recursive = TRUE, showWarnings = FALSE)
snapshot_path <- file.path("data/derived", paste0(data_name, ".rds"))
saveRDS(elections, snapshot_path)

provenance <- list(
  package = "gerda",
  package_version = as.character(utils::packageVersion("gerda")),
  dataset = data_name,
  format = "rds",
  retrieved_at_utc = format(Sys.time(), tz = "UTC", usetz = TRUE),
  snapshot_path = snapshot_path,
  snapshot_md5 = unname(tools::md5sum(snapshot_path))
)
saveRDS(provenance, "data/derived/gerda_provenance.rds")

load_gerda_web() reads final data artifacts from the GERDA data repository. Use that repository when an audit requires source files, cleaning scripts, or lineage below the package's final artifacts. Files named *_raw are the most source-near GERDA representation; they are not a substitute for inspecting the original source and build pipeline.

Validate the loaded schema before analysis

Geographic identifiers must remain character vectors. Re-read the original source with explicit character column types if an identifier became numeric; padding a damaged numeric value later can conceal leading-zero loss.

For the selected municipal panel, a minimal structural audit is:

required <- c("ags", "election_year")
stopifnot(all(required %in% names(elections)))

if (!is.character(elections$ags) ||
    any(!is.na(elections$ags) & !grepl("^[0-9]{8}$", elections$ags))) {
  stop("ags must contain eight-digit character identifiers")
}
if (any(!stats::complete.cases(elections[required]))) {
  stop("Missing geographic or time keys require investigation")
}
if (anyDuplicated(elections[required])) {
  stop("ags + election_year is not unique; identify additional row keys")
}

expected_years <- sort(unique(elections$election_year))
years_by_unit <- split(elections$election_year, elections$ags)
units_with_gaps <- names(Filter(
  function(x) !identical(sort(unique(x)), expected_years),
  years_by_unit
))

The correct row key varies by dataset. Candidate, election-round, party-long, and stimme files need additional key columns. Never resolve duplicate keys by calling distinct() unless the duplication has been explained. Inspect missingness in every analysis variable before filtering or estimation; do not silently drop rows with missing outcomes, treatments, identifiers, or years.

Use guarded joins and inspect their diagnostics

The two enrichment helpers enforce a many-to-one left-join contract. They reject numeric or malformed geographic identifiers, duplicate reference keys, columns that would be overwritten, and any join that expands the input row count. Set unmatched = "error" in agent-run pipelines:

enriched <- elections |>
  add_gerda_covariates(unmatched = "error") |>
  add_gerda_census(unmatched = "error")

join_report <- gerda_join_diagnostics(enriched)
print(join_report)

stopifnot(
  all(join_report$input_rows == join_report$output_rows),
  all(join_report$unexpected_unmatched_rows == 0L)
)
saveRDS(join_report, "data/derived/gerda_join_diagnostics.rds")

For INKAR covariates, election years outside 1995–2022 are retained with missing joined values and reported as outside_coverage_rows; they are not treated as unexpected matches. unmatched = "error" still stops on valid-year keys that fail to match and on missing join keys in any year. The other modes are "warn" (the interactive default) and "ignore". Capture diagnostics immediately after the join pipeline because unrelated data-frame transformations may remove custom attributes.

A successful key match does not imply complete covariate values. Individual INKAR and Census variables can still be missing, so inspect payload missingness separately. Census values are a single 2022 snapshot and are time-invariant when attached to elections from other years. Exact identifier matches also do not by themselves establish that the election and covariate boundary vintages are substantively comparable.

Use bundled data and codebooks directly

INKAR covariates and Census 2022 are bundled with the package and require no download. Their codebooks are machine-readable data frames:

covariates <- gerda_covariates()
covariate_codebook <- gerda_covariates_codebook()
census <- gerda_census()
census_codebook <- gerda_census_codebook()

stopifnot(
  !anyDuplicated(covariates[c("county_code", "year")]),
  !anyDuplicated(census["ags"]),
  all(names(covariates) %in% covariate_codebook$variable),
  all(names(census) %in% census_codebook$variable)
)

Before handing the project back, an agent should leave the exact dataset name and boundary rationale, package version, saved snapshot and checksum, complete row-key definition, missing-key and panel-gap checks, join diagnostics, variable missingness summary, and the GERDA citation returned by citation("gerda").