Skip to contents


rdb is not on CRAN yet1, but you can install the package directly from the development source code repository’s master branch, which we try to keep in a working state at all times.

To install the latest development version of rdb, run the following in R:

if (!("remotes" %in% rownames(installed.packages()))) {
  install.packages(pkgs = "remotes",
                   repos = "")

remotes::install_gitlab(repo = "zdaarau/rpkgs/rdb")


Get data

You can download RDB referendum data via the two functions rdb::rfrnd() and rdb::rfrnds(). The former one fetches the data of a single referendum only, of which you must already know its uniqe RDB id. The latter function allows to retrieve data for an arbitrary number of referendums, depending on the conditions you specify via the function’s various arguments.

To simply retrieve all referendums in the database (excluding draft entries), run


which should output a tibble like this one:

#> # A tibble: 17,766 × 69
#>    id            id_official id_sudd country_code country_name subnational_entity_n…¹ municipality level date       is_former_country title_en title_de title_fr
#>    <chr>         <chr>       <chr>   <fct>        <fct>        <chr>                  <chr>        <ord> <date>     <lgl>             <chr>    <chr>    <chr>   
#>  1 65096e84481d… NA          pl0120… PL           Poland       NA                     NA           nati… 2023-10-15 NA                Privati… Privati… NA      
#>  2 65096dc1481d… NA          pl0220… PL           Poland       NA                     NA           nati… 2023-10-15 NA                raising… Anhebun… NA      
#>  3 65096535481d… NA          pl0320… PL           Poland       NA                     NA           nati… 2023-10-15 NA                Removal… Abbau d… NA      
#>  4 6509625d481d… NA          pl0420… PL           Poland       NA                     NA           nati… 2023-10-15 NA                Europea… Beschlu… NA      
#>  5 650034e4481d… NA          au0120… AU           Australia    NA                     NA           nati… 2023-10-14 NA                Establi… Stimme … NA      
#>  6 64e46f7a481d… NA          ec0920… EC           Ecuador      NA                     NA           nati… 2023-08-20 NA                No crud… Keine R… NA      
#>  7 64e46c8f481d… NA          fm0820… FM           Micronesia   NA                     NA           nati… 2023-07-04 NA                Indepen… Unabhän… NA      
#>  8 64c8b3ca0b8b… NA          fm0720… FM           Micronesia   NA                     NA           nati… 2023-07-04 NA                Providi… NA       NA      
#>  9 64c8b19c0b8b… NA          fm0620… FM           Micronesia   NA                     NA           nati… 2023-07-04 NA                Creatin… Angleic… NA      
#> 10 64c8a28d0b8b… NA          fm0520… FM           Micronesia   NA                     NA           nati… 2023-07-04 NA                Alterin… Verände… NA      
#> # ℹ 17,756 more rows
#> # ℹ abbreviated name: ¹​subnational_entity_name
#> # ℹ 56 more variables: question <chr>, question_en <chr>, committee_name <chr>, result <fct>, subterritories_yes <dbl>, subterritories_no <dbl>,
#> #   electorate_total <int>, electorate_abroad <int>, votes_yes <int>, votes_no <int>, votes_empty <int>, votes_invalid <int>, votes_per_subterritory <list>,
#> #   lower_house_yes <int>, lower_house_no <int>, lower_house_abstentions <int>, upper_house_yes <int>, upper_house_no <int>, upper_house_abstentions <int>,
#> #   position_government <fct>, topics_tier_1 <list>, topics_tier_2 <list>, topics_tier_3 <list>, remarks <chr>, files <list>, url_sudd <chr>,
#> #   url_swissvotes <chr>, sources <chr>, is_draft <lgl>, date_time_created <dttm>, date_time_last_edited <dttm>, type <fct>, inst_legal_basis_type <ord>, …

The RDB referendum data’s individual variables (columns) are documented in the codebook. It is also available as a dataset via rdb::data_codebook.

Results of rdb::rfrnds() and some other functions in this package are by default cached on disk using pkgpins2. You can define the maximum age of cached results you’re willing to tolerate via the argument max_cache_age (defaults to a week). It accepts anything that can be successfully converted to a lubridate duration – e.g. a string like "3 hours", "2 days" or "1 week", or a number which will simply be interpreted as number of seconds.

To only re-download RDB data once every 4 hours and 48 minutes for example, use

rdb::rfrnds(max_cache_age = "4 hours 48 minutes")

Although we usually advise against it, you can also completely opt out of caching by specifying use_cache = FALSE. However, please make sure to not run such code in excess, as it creates additional (and most likely unnecessary) load on our servers.

Augment data

rdb includes various functions to augment the referendum data by additional information which wouldn’t make sense to be stored in the RDB itself.

For example, you can add the period (week, month, quarter, year, decade or century) in which a referendum took place using rdb::add_period(). By default, the recurring numeric week number of the year is added (i.e. period = "week"):

rdb::rfrnds() |>
  rdb::add_period() |>
  dplyr::select(id, date, week)
#> # A tibble: 17,766 × 3
#>    id                       date        week
#>    <chr>                    <date>     <int>
#>  1 65096e84481d20233932cc70 2023-10-15    41
#>  2 65096dc1481d20233932cc6e 2023-10-15    41
#>  3 65096535481d20233932cc66 2023-10-15    41
#>  4 6509625d481d20233932cc63 2023-10-15    41
#>  5 650034e4481d20233932cc39 2023-10-14    41
#>  6 64e46f7a481d20233932cc0b 2023-08-20    33
#>  7 64e46c8f481d20233932cc09 2023-07-04    27
#>  8 64c8b3ca0b8bae0c78c7ec8a 2023-07-04    27
#>  9 64c8b19c0b8bae0c78c7ec86 2023-07-04    27
#> 10 64c8a28d0b8bae0c78c7ec82 2023-07-04    27
#> # ℹ 17,756 more rows

Another frequently required augmentation is rdb::add_country_code_long() which adds an additional column country_code_long containing the ISO 3166-1 alpha-3 code. These three-letter codes are often required to join RDB referendum data with data from other sources.

See the package reference for all available data augmentation functions.

Transform data

For certain analyses, it might come in handy to transform the referendum data to a different shape beforehand. For a few such transformations, rdb provides ready-made functions.

rdb::as_ballot_dates() for example transforms the default referendum-level observations to ones on the level of ballot date and jurisdiction:

rdb::rfrnds() |> nrow()
#> [1] 17766

rdb::rfrnds() |> rdb::as_ballot_dates() |> nrow()
#> [1] 5884

Noteworthy is also rdb::unnest_var() which provides a convenient and standardized way to unnest a multi-value variable of type list like the topics_tier_* variables to long format.

rdb::rfrnds() |> nrow()
#> [1] 17766

rdb::rfrnds() |> rdb::unnest_var(topics_tier_1) |> nrow()
#> [1] 21513

See the package reference for all available data transformation functions.

Tabulate and visualize data

rdb also includes some ready-made convenience functions to create tables and (interactive) plots.

If you’d like a tabular overview of the top-ten countries by number of ballot dates per political level for example, you could simply run

rdb::rfrnds() |>
  rdb::as_ballot_dates() |>
  rdb::tbl_n_rfrnds(by = c(country_name, level),
                    n_rows = 10L,
                    order = "descending")

and you’d get the following nicely formatted gt table:

Political level local subnational national Total


1 2666 325 2992

United States

0 1252 0 1252


0 0 88 88


0 59 9 68


5 20 33 58

New Zealand

0 2 46 48


0 20 25 45


2 16 27 45


0 33 3 36


0 0 31 31


22 4243 1619 5884

A table of the number of referendums in the UN subregion Polynesia since 2010 per a certain period, say years, can be generated via

rdb::rfrnds() |>
  rdb::add_world_regions() |>
  dplyr::filter(un_subregion == "Polynesia" & date > "2009-12-31") |>
  rdb::tbl_n_rfrnds_per_period(period = "year")
2022 12
2019–2021 0
2018 1
2015–2017 0
2014 1
2013 0
2012 2
2011 0
2010 2
Total 18

Or a stacked area chart visualizing the worldwide share of referendums per year since 1950, grouped by political level:

rdb::rfrnds() |>
  dplyr::filter(date >= "1950-01-01") |>
  rdb::tbl_n_rfrnds_per_period(period = "year",
                               by = "level")

Or, as a final example, the overall (hierarchical) segmentation of the political topics all the referendums in the RDB were about:

rdb::rfrnds() |> rdb::plot_topic_segmentation(method = "per_topic_lineage")

Again, see the package reference for all available data visualization and tabulation functions. More will likely be added in the future.