GERDA: German Election Data for R

This R package provides data on German elections since 1953, together with helpers for merging socioeconomic covariates. As of v0.6 it exposes 39 datasets covering:

• federal elections at the municipality and county level (1953–2025)
• state (Landtag) elections, unharmonized and harmonized to 2021, 2023, and 2025 boundaries
• local (municipal) elections, unharmonized and harmonized to 2025 boundaries
• county (Kreistag) elections
• European Parliament elections at the municipal level
• mayoral elections: results, candidates, and person- and municipality-level panels
• AGS and county crosswalks between boundary vintages (1990 through 2025)
• INKAR county covariates (1995–2022) and Zensus 2022 municipality data, both bundled
• a ParlGov party crosswalk

GERDA was compiled by Vincent Heddesheimer, Florian Sichart, Andreas Wiedemann, and Hanno Hilbig. See the GERDA website and the accompanying publication: doi.org/10.1038/s41597-025-04811-5. The package is under active development; comments and bug reports are welcome at hhilbig@ucdavis.edu or via GitHub issues.

Installation

install.packages("gerda")                  # from CRAN
devtools::install_github("hhilbig/gerda")  # development version

To install the vignette along with the development version, pass build_vignettes = TRUE:

devtools::install_github("hhilbig/gerda", build_vignettes = TRUE)

Then read it with vignette("gerda"). The CRAN release ships the vignette by default.

Main functions

Data access:

• gerda_data_list() prints or returns the full list of available datasets with short descriptions.
• load_gerda_web(file_name, file_format = "rds") downloads a dataset from the GERDA GitHub repository. file_format is "rds" (default) or "csv"; both return the same tibble, so the choice only affects download size. file_name supports fuzzy matching and suggests close alternatives on misspellings.

Bundled data (no download required):

• gerda_covariates() and gerda_covariates_codebook() for INKAR county covariates and their codebook.
• gerda_census() and gerda_census_codebook() for Zensus 2022 municipality data and its codebook.

Merging helpers:

• add_gerda_covariates(election_data) merges INKAR county covariates into county- or municipality-level election data. On municipality data, all municipalities in the same Kreis receive identical covariate values.
• add_gerda_census(election_data) merges Zensus 2022 into county- or municipality-level election data. For county-level merges, municipality values are aggregated: population-weighted means for shares, sums for counts.

Party mapping:

• party_crosswalk(party_gerda, destination) maps GERDA party names to a column of ParlGov's view_party table (e.g. "left_right" for ideology scores).

Example

library(gerda)
library(dplyr)

federal <- load_gerda_web("federal_muni_harm_25") |>
  add_gerda_covariates() |>
  add_gerda_census()

County covariates (INKAR, 1995–2022)

add_gerda_covariates() appends 30 county-level indicators to federal, state, or local election data. Variables cover demographics, GDP and sectoral structure, unemployment (overall, youth, long-term), education, income, healthcare, childcare, housing, transport, and municipal public finances. Coverage is strongest for 1998–2021; newer indicators are available only for recent years. Use gerda_covariates_codebook() for per-variable detail including original INKAR codes and missing-data rates.

Zensus 2022 (municipality-level)

add_gerda_census() appends 14 indicators from the German Zensus 2022. Because the census is a single 2022 snapshot, the same values are attached to all election years; analyses that rely on within-unit variation in these variables are not supported.

Indicators cover population and age structure, migration background, household size, and housing (dwellings, vacancy, ownership, rent per square metre, single-family share). Most variables have above 95% municipality coverage. avg_household_size_census22 is missing for about 12.5% of municipalities because Destatis suppresses small-cell values.

Deprecations

As of v0.6, federal_cty_unharm exposes both the upstream columns (ags, year) and the canonical GERDA county-level names (county_code, election_year). The ags and year aliases will be removed in v0.7. New code should use county_code and election_year, which match the rest of the county-level datasets and work directly with add_gerda_covariates().