Installation
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 = "https://cloud.r-project.org/")
}
remotes::install_gitlab(repo = "zdaarau/rpkgs/rdb")Usage
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: 17,889 × 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 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 66f686cfc3cd… NA NA CH Switzerland Bern NA subn… 2024-09-22 NA Concord… Konkord… NA
#> 5 66f68317c3cd… NA NA CH Switzerland Bern NA subn… 2024-09-22 NA Amendme… Änderun… NA
#> 6 66d6da9cc3cd… NA NA CH Switzerland Basel Landschaft NA subn… 2024-09-22 NA Partial… Teilrev… NA
#> 7 66cde603c3cd… NA NA CH Switzerland Graubünden NA subn… 2024-09-22 NA New civ… Neubau … NA
#> 8 66c46f86c3cd… NA NA CH Switzerland Schwyz NA subn… 2024-09-22 NA Approva… Ausgabe… NA
#> 9 66c46cf6c3cd… NA NA CH Switzerland Solothurn NA subn… 2024-09-22 NA Appoint… Anstell… NA
#> 10 66c46c3ac3cd… NA NA CH Switzerland Solothurn NA subn… 2024-09-22 NA Amendme… Änderun… NA
#> # ℹ 17,879 more rows
#> # ℹ abbreviated name: ¹subnational_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: 17,889 × 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 66f686cfc3cd6704605800a0 2024-09-22 38
#> 5 66f68317c3cd67046058009c 2024-09-22 38
#> 6 66d6da9cc3cd670460580053 2024-09-22 38
#> 7 66cde603c3cd67046057fff5 2024-09-22 38
#> 8 66c46f86c3cd67046057ff53 2024-09-22 38
#> 9 66c46cf6c3cd67046057ff4f 2024-09-22 38
#> 10 66c46c3ac3cd67046057ff4b 2024-09-22 38
#> # ℹ 17,879 more rowsAnother 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] 17889
rdb::rfrnds() |> rdb::as_ballot_dates() |> nrow()
#> [1] 5938Noteworthy 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] 17889
rdb::rfrnds() |> rdb::unnest_var(topics_tier_1) |> nrow()
#> [1] 21669See 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 |
|---|---|---|---|---|
| Country | ||||
| Switzerland | 1 | 2706 | 327 | 3034 |
| United States | 0 | 1252 | 0 | 1252 |
| Liechtenstein | 0 | 0 | 92 | 92 |
| Germany | 0 | 59 | 9 | 68 |
| Italy | 5 | 20 | 33 | 58 |
| New Zealand | 0 | 2 | 46 | 48 |
| Australia | 0 | 20 | 25 | 45 |
| France | 2 | 16 | 27 | 45 |
| Canada | 0 | 33 | 3 | 36 |
| Ireland | 0 | 0 | 32 | 32 |
| … | … | … | … | … |
| Total | 22 | 4283 | 1633 | 5938 |
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")| n | |
|---|---|
| 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.