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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().

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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