R package

R Package

The gerda R package provides tools to download and work with GERDA datasets directly in R. Current CRAN version: 0.5.0 (CRAN); development version: 0.6.0 (GitHub). As of v0.6 the package exposes 39 datasets covering local, state, federal, mayoral, European Parliament, and county (Kreistag) elections, plus crosswalks and covariates. Federal county-level data goes back to 1953; the other election families extend through 2025.

Python users

A lightweight Python loader is also available: gerda on PyPI (source: hhilbig/gerda-py). It exposes three functions — gerda.load(name), gerda.datasets(), and gerda.party_crosswalk(...) — and returns pandas DataFrames (or polars, optionally). Bundled covariate / Census merge helpers are not yet ported; use the R package for those.

pip install gerda

import gerda
df = gerda.load("federal_cty_harm")

Installation

# Install from CRAN
install.packages("gerda")

# Or install development version from GitHub
devtools::install_github("hhilbig/gerda")

Main Functions

Data Loading

gerda_data_list(print_table = TRUE): Lists all available GERDA datasets with descriptions.
- print_table: If TRUE (default), prints a formatted table and invisibly returns a tibble. If FALSE, returns the tibble directly.
load_gerda_web(file_name, verbose = FALSE, file_format = "rds", on_error = "warn"): Loads a GERDA dataset from the web.
- file_name: Dataset name (see gerda_data_list() for options).
- verbose: Print loading messages (default FALSE).
- file_format: File format to download, "rds" or "csv" (default "rds").
- on_error: How to handle failures (unknown name, download error, corrupt file). "warn" (default) emits a warning and returns NULL; "stop" raises an error, which is the safer default inside scripts and pipelines. The global default can be changed with options(gerda.on_error = "stop").
- Includes fuzzy matching for file names and suggests close matches if an exact match isn’t found.
- Party vote-share columns (cdu, spd, etc.) are fractions of valid votes and do not sum to 1 across named major parties: the remainder sits in smaller-party columns and an other category.

Covariates (INKAR county-level, 1995–2022)

add_gerda_covariates(election_data): Appends 30 INKAR county-level socioeconomic indicators (demographics, economy, labour market, education, income, healthcare, childcare, housing, transport, public finances) to county- or municipality-level election data. On municipality data, all municipalities in the same Kreis receive identical covariate values. Coverage is strongest for 1998–2021; several newer indicators are only available for more recent years. See gerda_covariates_codebook() for per-variable coverage.
gerda_covariates(): Returns the raw covariate data as a standalone tibble for manual merging.
gerda_covariates_codebook(): Returns the codebook with variable descriptions, original INKAR codes, and missing-data rates.

Census 2022 (Zensus, municipality-level)

add_gerda_census(election_data): Appends 14 Zensus 2022 indicators (population and age structure, migration background, household size, housing) to county- or municipality-level election data. The census is a single 2022 snapshot, so values do not vary across election years; analyses relying on within-unit variation in these variables are not supported. For county-level data, municipality values are aggregated up using population-weighted means for shares and sums for counts. Most indicators have above 95% municipality coverage; avg_household_size_census22 is missing for roughly 12.5% of municipalities because Destatis suppresses small-cell values.
gerda_census(): Returns the raw census data as a standalone tibble.
gerda_census_codebook(): Returns the codebook with variable descriptions and coverage notes.

Party Mapping

party_crosswalk(party_gerda, destination): Maps GERDA party names to corresponding values from the ParlGov database.
- party_gerda: Character vector of party names.
- destination: Target column name from ParlGov crosswalk.

Usage Examples

library(gerda)

# List available datasets
gerda_data_list()

# Load harmonized municipal election data
municipal <- load_gerda_web("municipal_harm", verbose = TRUE)

# Load federal county data with socioeconomic covariates
federal_county <- load_gerda_web("federal_cty_harm") %>%
  add_gerda_covariates()

# View covariate definitions
gerda_covariates_codebook()

# Map party names to ParlGov
party_crosswalk(c("cdu_csu", "spd", "gruene"), "party_name_english")

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().

Documentation

Feedback

Feedback is welcome. Please email hhilbig@ucdavis.edu or open an issue on GitHub.