Introduction to 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

rdb::rfrnds()

which should output a tibble like this one:

#> # A tibble: 17,892 × 67
#>    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 670cd145c3cd… NA          NA      CH           Switzerland  Zug                    NA           subn… 2024-06-09 NA                Cycling… Velonet… NA      
#>  2 66fff490c3cd… NA          NA      LI           Liechtenste… NA                     NA           nati… 2024-09-22 NA                Referen… Referen… NA      
#>  3 66ffdfe3c3cd… NA          NA      CH           Switzerland  Jura                   NA           subn… 2024-09-22 NA                Act con… NA       Acte re…
#>  4 66ffdc31c3cd… NA          NA      CH           Switzerland  Uri                    NA           subn… 2024-09-22 NA                Energy … Energie… NA      
#>  5 66ffd956c3cd… NA          NA      CH           Switzerland  Uri                    NA           subn… 2024-09-22 NA                Partial… Teilrev… NA      
#>  6 66ffbf93c3cd… NA          NA      CH           Switzerland  Nidwalden              NA           subn… 2024-09-22 NA                Amendme… Änderun… NA      
#>  7 66f686cfc3cd… NA          NA      CH           Switzerland  Bern                   NA           subn… 2024-09-22 NA                Concord… Konkord… NA      
#>  8 66f68317c3cd… NA          NA      CH           Switzerland  Bern                   NA           subn… 2024-09-22 NA                Amendme… Änderun… NA      
#>  9 66d6da9cc3cd… NA          NA      CH           Switzerland  Basel Landschaft       NA           subn… 2024-09-22 NA                Partial… Teilrev… NA      
#> 10 66cde603c3cd… NA          NA      CH           Switzerland  Graubünden             NA           subn… 2024-09-22 NA                New civ… Neubau … NA      
#> # ℹ 17,882 more rows
#> # ℹ abbreviated name: ¹​subnational_entity_name
#> # ℹ 54 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>, sources <chr>, is_draft <lgl>,
#> #   date_time_created <dttm>, date_time_last_edited <dttm>, type <fct>, inst_legal_basis_type <ord>, inst_has_urgent_legal_basis <lgl>, …

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: 17,892 × 3
#>    id                       date        week
#>    <chr>                    <date>     <int>
#>  1 670cd145c3cd67046058015c 2024-06-09    23
#>  2 66fff490c3cd670460580111 2024-09-22    38
#>  3 66ffdfe3c3cd6704605800fc 2024-09-22    38
#>  4 66ffdc31c3cd6704605800ee 2024-09-22    38
#>  5 66ffd956c3cd6704605800ec 2024-09-22    38
#>  6 66ffbf93c3cd6704605800df 2024-09-22    38
#>  7 66f686cfc3cd6704605800a0 2024-09-22    38
#>  8 66f68317c3cd67046058009c 2024-09-22    38
#>  9 66d6da9cc3cd670460580053 2024-09-22    38
#> 10 66cde603c3cd67046057fff5 2024-09-22    38
#> # ℹ 17,882 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] 17892

rdb::rfrnds() |> rdb::as_ballot_dates() |> nrow()
#> [1] 5939

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] 17892

rdb::rfrnds() |> rdb::unnest_var(topics_tier_1) |> nrow()
#> [1] 21672

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:

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")

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.