README.md
In nacnudus/registr: Use the UK Government Registers API
registr
This is an unofficial client for the GOV.UK
Registers
API.
Registers are authoritative lists of things, built and maintained by the
UK government, for example, the country register is a list of
countries.
It doesn’t really wrap the API. Instead, it downloads the ‘raw’
registers in RSF (Register Serialisation Format – not yet documented
publicly), and parses
that.
Installation
# install.packages("devtools") # if you don't already have devtools installed
devtools::install_github("nacnudus/registr")
To install a very early version of the package for running old scripts:
devtools::install_github("nacnudus/registr",
ref = "08c42c95bc65a0cb8131100416a372660b8a1bd5")
Examples
library(registr)
Download registers
Download a single register.
country <- rr_register("country")
Download all registers.
registers <- rr_registers(quiet = TRUE)
names(registers)
#> [1] "country"
#> [2] "field"
#> [3] "territory"
#> [4] "government-organisation"
#> [5] "government-service"
#> [6] "local-authority-sct"
#> [7] "principal-local-authority"
#> [8] "register"
#> [9] "datatype"
#> [10] "internal-drainage-board"
#> [11] "registration-district"
#> [12] "statistical-geography-unitary-authority-wls"
#> [13] "statistical-geography"
#> [14] "prison-estate"
#> [15] "local-authority-nir"
#> [16] "statistical-geography-county-eng"
#> [17] "statistical-geography-london-borough-eng"
#> [18] "statistical-geography-metropolitan-district-eng"
#> [19] "statistical-geography-registration-district-eng"
#> [20] "statistical-geography-registration-district-wls"
#> [21] "statistical-geography-non-metropolitan-district-eng"
#> [22] "statistical-geography-unitary-authority-eng"
#> [23] "allergen"
#> [24] "statistical-geography-local-government-district-nir"
#> [25] "jobcentre"
#> [26] "jobcentre-district"
#> [27] "jobcentre-group"
#> [28] "local-authority-type"
#> [29] "local-authority-eng"
#> [30] "qualification-assessment-method"
#> [31] "qualification-level"
#> [32] "qualification-sector-subject-area"
#> [33] "qualification-type"
#> [34] "government-domain"
By default, the ‘beta’ (‘ready to use’) versions of registers are
downloaded. If you need alpha (‘open for feedback’) registers, use
phase = "alpha")
Explore register schema and data
The schema and data are in $shema and $data.
country <- registers$country
country$schema
#> $ids
#> # A tibble: 1 x 6
#> `entry-number` type key timestamp hash name
#> <int> <chr> <chr> <dttm> <chr> <chr>
#> 1 1 system name 2017-07-17 10:59:47 d3d8e15fbd410e08b… coun…
#>
#> $names
#> # A tibble: 0 x 5
#> # ... with 5 variables: `entry-number` <int>, type <chr>, key <chr>,
#> # timestamp <dttm>, hash <chr>
#>
#> $custodians
#> # A tibble: 2 x 6
#> `entry-number` type key timestamp hash custodian
#> <int> <chr> <chr> <dttm> <chr> <chr>
#> 1 2 system custodian 2017-07-17 10:59:47 6bdb76b1c… Tony Wor…
#> 2 12 system custodian 2017-11-02 11:18:00 aa98858fc… David de…
#>
#> $fields
#> # A tibble: 8 x 11
#> `entry-number` type key timestamp hash field datatype
#> <int> <chr> <chr> <dttm> <chr> <chr> <chr>
#> 1 3 system field… 2017-01-10 17:16:07 a303d05… coun… string
#> 2 4 system field… 2017-01-10 17:16:07 a7a9f22… name string
#> 3 5 system field… 2017-01-10 17:16:07 5c4728f… offi… string
#> 4 6 system field… 2017-01-10 17:16:07 494f6fa… citi… string
#> 5 7 system field… 2017-01-10 17:16:07 1cff4c6… star… datetime
#> 6 8 system field… 2017-01-10 17:16:07 a557fa9… end-… datetime
#> 7 10 system field… 2017-08-29 11:30:00 f09c439… star… datetime
#> 8 11 system field… 2017-08-29 11:31:00 c5845bf… end-… datetime
#> # ... with 4 more variables: phase <chr>, register <chr>,
#> # cardinality <chr>, text <chr>
country$data
#> # A tibble: 207 x 11
#> `entry-number` type key timestamp hash country name
#> <int> <chr> <chr> <dttm> <chr> <chr> <chr>
#> 1 13 user SU 2016-04-05 13:23:05 e94c4a9ab… SU USSR
#> 2 14 user DE 2016-04-05 13:23:05 e03f97c28… DE West…
#> 3 15 user DD 2016-04-05 13:23:05 e1357671d… DD East…
#> 4 16 user YU 2016-04-05 13:23:05 a074752a7… YU Yugo…
#> 5 17 user CS 2016-04-05 13:23:05 0031f311f… CS Czec…
#> 6 18 user GB 2016-04-05 13:23:05 6b1869387… GB Unit…
#> 7 19 user AF 2016-04-05 13:23:05 6bf7f01f2… AF Afgh…
#> 8 20 user AL 2016-04-05 13:23:05 9d04a7e04… AL Alba…
#> 9 21 user DZ 2016-04-05 13:23:05 3548cdf52… DZ Alge…
#> 10 22 user AD 2016-04-05 13:23:05 14fcb5099… AD Ando…
#> # ... with 197 more rows, and 4 more variables: `official-name` <chr>,
#> # `citizen-names` <chr>, `start-date` <chr>, `end-date` <chr>
You probably want to take a snapshot first. This will take the latest
version of the schema, and the latest version of each record (e.g. the
most recent name of a country)
country$schema$custodian
#> # A tibble: 2 x 6
#> `entry-number` type key timestamp hash custodian
#> <int> <chr> <chr> <dttm> <chr> <chr>
#> 1 2 system custodian 2017-07-17 10:59:47 6bdb76b1c… Tony Wor…
#> 2 12 system custodian 2017-11-02 11:18:00 aa98858fc… David de…
rr_snapshot(country)$schema$custodian
#> # A tibble: 1 x 6
#> `entry-number` type key timestamp hash custodian
#> <int> <chr> <chr> <dttm> <chr> <chr>
#> 1 12 system custodian 2017-11-02 11:18:00 aa98858fc… David de…
Each field of each entry can contain more than one value, if the field
has the property cardinality = 'n'. In this case, the field is a
list-column, where each value is a vector of values.
Linked registers
Registers link in two ways.
- Via a field with the
"register" property set in the schema. This
is like a foreign/primary key relationship in a relational database.
- Via CURIEs of the form
"prefix:reference", where prefix is the
name of a register, and reference is a value in the primary field
of that register (the field with the same name as the register).
rr_links(registers$`statistical-geography`)
#> # A tibble: 5 x 4
#> from to type field
#> <chr> <chr> <chr> <chr>
#> 1 statistical-geography register key register
#> 2 statistical-geography country curie area
#> 3 statistical-geography territory curie area
#> 4 statistical-geography government-organisation curie organisation
#> 5 statistical-geography local-authority-eng curie organisation
rr_key_links(registers$`statistical-geography`)
#> # A tibble: 1 x 2
#> field register
#> <chr> <chr>
#> 1 register register
rr_snapshot(registers$`statistical-geography`)$schema$names
#> # A tibble: 0 x 5
#> # ... with 5 variables: `entry-number` <int>, type <chr>, key <chr>,
#> # timestamp <dttm>, hash <chr>
Resolve links with the rr_resolve_*() family of functions. Because
links refer to whole records, whole records are returned in a
list-column of data frames.
If a matching record has multiple entries, every entry is returned in a
multi-row data frame.
If a linking field is cardinality = 'n', a list of data frames is
returned.
rr_resolve_links(registers$`statistical-geography`, registers)$data
#> # A tibble: 175 x 12
#> `entry-number` type key timestamp hash `statistical-ge…
#> <int> <chr> <chr> <dttm> <chr> <chr>
#> 1 11 user E92 2017-08-04 15:14:55 97d105… E92
#> 2 12 user E00 2017-08-04 15:14:55 e57ac6… E00
#> 3 13 user E01 2017-08-04 15:14:55 e66020… E01
#> 4 14 user E02 2017-08-04 15:14:55 8c1f8d… E02
#> 5 15 user E04 2017-08-04 15:14:55 3f07b0… E04
#> 6 16 user E05 2017-08-04 15:14:55 f2cb36… E05
#> 7 17 user E06 2017-08-04 15:14:55 2c8dbf… E06
#> 8 18 user E07 2017-08-04 15:14:55 6cd1d7… E07
#> 9 19 user E08 2017-08-04 15:14:55 e40dcb… E08
#> 10 20 user E09 2017-08-04 15:14:55 f4ff1a… E09
#> # ... with 165 more rows, and 6 more variables: name <chr>, area <list>,
#> # organisation <list>, register <list>, `start-date` <chr>,
#> # `end-date` <chr>
You can resolve to only the latest entry of each record by creating a
registers object with snapshots.
registers_snapshot <- purrr::map(registers, rr_snapshot)
rr_resolve_links(registers$`statistical-geography`, registers_snapshot)$data
#> # A tibble: 175 x 12
#> `entry-number` type key timestamp hash `statistical-ge…
#> <int> <chr> <chr> <dttm> <chr> <chr>
#> 1 11 user E92 2017-08-04 15:14:55 97d105… E92
#> 2 12 user E00 2017-08-04 15:14:55 e57ac6… E00
#> 3 13 user E01 2017-08-04 15:14:55 e66020… E01
#> 4 14 user E02 2017-08-04 15:14:55 8c1f8d… E02
#> 5 15 user E04 2017-08-04 15:14:55 3f07b0… E04
#> 6 16 user E05 2017-08-04 15:14:55 f2cb36… E05
#> 7 17 user E06 2017-08-04 15:14:55 2c8dbf… E06
#> 8 18 user E07 2017-08-04 15:14:55 6cd1d7… E07
#> 9 19 user E08 2017-08-04 15:14:55 e40dcb… E08
#> 10 20 user E09 2017-08-04 15:14:55 f4ff1a… E09
#> # ... with 165 more rows, and 6 more variables: name <chr>, area <list>,
#> # organisation <list>, register <list>, `start-date` <chr>,
#> # `end-date` <chr>
Plot the links between registers with something like the ggraph
package.
library(tidygraph)
#>
#> Attaching package: 'tidygraph'
#> The following object is masked from 'package:stats':
#>
#> filter
library(ggraph)
#> Loading required package: ggplot2
registers$`statistical-geography` %>%
rr_links() %>%
as_tbl_graph() %>%
ggraph(layout = "nicely") +
geom_edge_fan(aes(alpha = ..index..), show.legend = FALSE) +
geom_edge_loop() +
geom_node_label(aes(label = name)) +
theme_void()
edge_arrow <- arrow(length = unit(4, "mm"), type = "closed")
registers %>%
rr_links() %>%
dplyr::distinct(from, to, type) %>%
as_tbl_graph() %>%
ggraph(layout = "nicely") +
geom_node_point() +
geom_edge_fan(aes(colour = type),
arrow = edge_arrow,
end_cap = circle(2, 'mm')) +
geom_edge_loop(aes(colour = type),
arrow = edge_arrow,
end_cap = circle(2, 'mm')) +
geom_node_label(aes(label = name), repel = TRUE, alpha = .5) +
theme_void()
Index registers
You can index registers by any column, using CURIE-like syntax.
country <- registers$country
rr_index(country, "start-date")
#> # A tibble: 50 x 2
#> .curie .data
#> <chr> <list>
#> 1 country: <tibble [155 × 11]>
#> 2 country:1975-11-11 <tibble [1 × 11]>
#> 3 country:1981-11-01 <tibble [1 × 11]>
#> 4 country:1991-09-21 <tibble [1 × 11]>
#> 5 country:1991-08-30 <tibble [1 × 11]>
#> 6 country:1991-08-25 <tibble [1 × 11]>
#> 7 country:1981-09-21 <tibble [1 × 11]>
#> 8 country:1992-03-03 <tibble [1 × 11]>
#> 9 country:1984-01-01 <tibble [1 × 11]>
#> 10 country:1975-07-05 <tibble [1 × 11]>
#> # ... with 40 more rows
rr_index(country, "end-date")
#> # A tibble: 5 x 2
#> .curie .data
#> <chr> <list>
#> 1 country:1991-12-25 <tibble [1 × 11]>
#> 2 country:1990-10-02 <tibble [2 × 11]>
#> 3 country:1992-04-28 <tibble [1 × 11]>
#> 4 country:1992-12-31 <tibble [1 × 11]>
#> 5 country: <tibble [202 × 11]>
rr_index(country)
#> # A tibble: 199 x 2
#> .curie .data
#> <chr> <list>
#> 1 country:SU <tibble [1 × 11]>
#> 2 country:DE <tibble [2 × 11]>
#> 3 country:DD <tibble [1 × 11]>
#> 4 country:YU <tibble [1 × 11]>
#> 5 country:CS <tibble [1 × 11]>
#> 6 country:GB <tibble [1 × 11]>
#> 7 country:AF <tibble [1 × 11]>
#> 8 country:AL <tibble [1 × 11]>
#> 9 country:DZ <tibble [1 × 11]>
#> 10 country:AD <tibble [1 × 11]>
#> # ... with 189 more rows
rr_index(registers$`local-authority-eng`, "local-authority-type")
#> # A tibble: 7 x 2
#> .curie .data
#> <chr> <list>
#> 1 local-authority-eng:UA <tibble [56 × 11]>
#> 2 local-authority-eng:MD <tibble [37 × 11]>
#> 3 local-authority-eng:CTY <tibble [29 × 11]>
#> 4 local-authority-eng:NMD <tibble [203 × 11]>
#> 5 local-authority-eng:LBO <tibble [32 × 11]>
#> 6 local-authority-eng:CC <tibble [1 × 11]>
#> 7 local-authority-eng:SRA <tibble [1 × 11]>
nacnudus/registr documentation built on May 5, 2019, 12:31 p.m.
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registr
This is an unofficial client for the GOV.UK Registers API.
Registers are authoritative lists of things, built and maintained by the
UK government, for example, the country register is a list of
countries.
It doesn’t really wrap the API. Instead, it downloads the ‘raw’ registers in RSF (Register Serialisation Format – not yet documented publicly), and parses that.
Installation
# install.packages("devtools") # if you don't already have devtools installed
devtools::install_github("nacnudus/registr")
To install a very early version of the package for running old scripts:
devtools::install_github("nacnudus/registr",
ref = "08c42c95bc65a0cb8131100416a372660b8a1bd5")
Examples
library(registr)
Download registers
Download a single register.
country <- rr_register("country")
Download all registers.
registers <- rr_registers(quiet = TRUE)
names(registers)
#> [1] "country"
#> [2] "field"
#> [3] "territory"
#> [4] "government-organisation"
#> [5] "government-service"
#> [6] "local-authority-sct"
#> [7] "principal-local-authority"
#> [8] "register"
#> [9] "datatype"
#> [10] "internal-drainage-board"
#> [11] "registration-district"
#> [12] "statistical-geography-unitary-authority-wls"
#> [13] "statistical-geography"
#> [14] "prison-estate"
#> [15] "local-authority-nir"
#> [16] "statistical-geography-county-eng"
#> [17] "statistical-geography-london-borough-eng"
#> [18] "statistical-geography-metropolitan-district-eng"
#> [19] "statistical-geography-registration-district-eng"
#> [20] "statistical-geography-registration-district-wls"
#> [21] "statistical-geography-non-metropolitan-district-eng"
#> [22] "statistical-geography-unitary-authority-eng"
#> [23] "allergen"
#> [24] "statistical-geography-local-government-district-nir"
#> [25] "jobcentre"
#> [26] "jobcentre-district"
#> [27] "jobcentre-group"
#> [28] "local-authority-type"
#> [29] "local-authority-eng"
#> [30] "qualification-assessment-method"
#> [31] "qualification-level"
#> [32] "qualification-sector-subject-area"
#> [33] "qualification-type"
#> [34] "government-domain"
By default, the ‘beta’ (‘ready to use’) versions of registers are
downloaded. If you need alpha (‘open for feedback’) registers, use
phase = "alpha")
Explore register schema and data
The schema and data are in $shema and $data.
country <- registers$country
country$schema
#> $ids
#> # A tibble: 1 x 6
#> `entry-number` type key timestamp hash name
#> <int> <chr> <chr> <dttm> <chr> <chr>
#> 1 1 system name 2017-07-17 10:59:47 d3d8e15fbd410e08b… coun…
#>
#> $names
#> # A tibble: 0 x 5
#> # ... with 5 variables: `entry-number` <int>, type <chr>, key <chr>,
#> # timestamp <dttm>, hash <chr>
#>
#> $custodians
#> # A tibble: 2 x 6
#> `entry-number` type key timestamp hash custodian
#> <int> <chr> <chr> <dttm> <chr> <chr>
#> 1 2 system custodian 2017-07-17 10:59:47 6bdb76b1c… Tony Wor…
#> 2 12 system custodian 2017-11-02 11:18:00 aa98858fc… David de…
#>
#> $fields
#> # A tibble: 8 x 11
#> `entry-number` type key timestamp hash field datatype
#> <int> <chr> <chr> <dttm> <chr> <chr> <chr>
#> 1 3 system field… 2017-01-10 17:16:07 a303d05… coun… string
#> 2 4 system field… 2017-01-10 17:16:07 a7a9f22… name string
#> 3 5 system field… 2017-01-10 17:16:07 5c4728f… offi… string
#> 4 6 system field… 2017-01-10 17:16:07 494f6fa… citi… string
#> 5 7 system field… 2017-01-10 17:16:07 1cff4c6… star… datetime
#> 6 8 system field… 2017-01-10 17:16:07 a557fa9… end-… datetime
#> 7 10 system field… 2017-08-29 11:30:00 f09c439… star… datetime
#> 8 11 system field… 2017-08-29 11:31:00 c5845bf… end-… datetime
#> # ... with 4 more variables: phase <chr>, register <chr>,
#> # cardinality <chr>, text <chr>
country$data
#> # A tibble: 207 x 11
#> `entry-number` type key timestamp hash country name
#> <int> <chr> <chr> <dttm> <chr> <chr> <chr>
#> 1 13 user SU 2016-04-05 13:23:05 e94c4a9ab… SU USSR
#> 2 14 user DE 2016-04-05 13:23:05 e03f97c28… DE West…
#> 3 15 user DD 2016-04-05 13:23:05 e1357671d… DD East…
#> 4 16 user YU 2016-04-05 13:23:05 a074752a7… YU Yugo…
#> 5 17 user CS 2016-04-05 13:23:05 0031f311f… CS Czec…
#> 6 18 user GB 2016-04-05 13:23:05 6b1869387… GB Unit…
#> 7 19 user AF 2016-04-05 13:23:05 6bf7f01f2… AF Afgh…
#> 8 20 user AL 2016-04-05 13:23:05 9d04a7e04… AL Alba…
#> 9 21 user DZ 2016-04-05 13:23:05 3548cdf52… DZ Alge…
#> 10 22 user AD 2016-04-05 13:23:05 14fcb5099… AD Ando…
#> # ... with 197 more rows, and 4 more variables: `official-name` <chr>,
#> # `citizen-names` <chr>, `start-date` <chr>, `end-date` <chr>
You probably want to take a snapshot first. This will take the latest version of the schema, and the latest version of each record (e.g. the most recent name of a country)
country$schema$custodian
#> # A tibble: 2 x 6
#> `entry-number` type key timestamp hash custodian
#> <int> <chr> <chr> <dttm> <chr> <chr>
#> 1 2 system custodian 2017-07-17 10:59:47 6bdb76b1c… Tony Wor…
#> 2 12 system custodian 2017-11-02 11:18:00 aa98858fc… David de…
rr_snapshot(country)$schema$custodian
#> # A tibble: 1 x 6
#> `entry-number` type key timestamp hash custodian
#> <int> <chr> <chr> <dttm> <chr> <chr>
#> 1 12 system custodian 2017-11-02 11:18:00 aa98858fc… David de…
Each field of each entry can contain more than one value, if the field
has the property cardinality = 'n'. In this case, the field is a
list-column, where each value is a vector of values.
Linked registers
Registers link in two ways.
- Via a field with the
"register"property set in the schema. This is like a foreign/primary key relationship in a relational database. - Via CURIEs of the form
"prefix:reference", whereprefixis the name of a register, andreferenceis a value in the primary field of that register (the field with the same name as the register).
rr_links(registers$`statistical-geography`)
#> # A tibble: 5 x 4
#> from to type field
#> <chr> <chr> <chr> <chr>
#> 1 statistical-geography register key register
#> 2 statistical-geography country curie area
#> 3 statistical-geography territory curie area
#> 4 statistical-geography government-organisation curie organisation
#> 5 statistical-geography local-authority-eng curie organisation
rr_key_links(registers$`statistical-geography`)
#> # A tibble: 1 x 2
#> field register
#> <chr> <chr>
#> 1 register register
rr_snapshot(registers$`statistical-geography`)$schema$names
#> # A tibble: 0 x 5
#> # ... with 5 variables: `entry-number` <int>, type <chr>, key <chr>,
#> # timestamp <dttm>, hash <chr>
Resolve links with the rr_resolve_*() family of functions. Because
links refer to whole records, whole records are returned in a
list-column of data frames.
If a matching record has multiple entries, every entry is returned in a multi-row data frame.
If a linking field is cardinality = 'n', a list of data frames is
returned.
rr_resolve_links(registers$`statistical-geography`, registers)$data
#> # A tibble: 175 x 12
#> `entry-number` type key timestamp hash `statistical-ge…
#> <int> <chr> <chr> <dttm> <chr> <chr>
#> 1 11 user E92 2017-08-04 15:14:55 97d105… E92
#> 2 12 user E00 2017-08-04 15:14:55 e57ac6… E00
#> 3 13 user E01 2017-08-04 15:14:55 e66020… E01
#> 4 14 user E02 2017-08-04 15:14:55 8c1f8d… E02
#> 5 15 user E04 2017-08-04 15:14:55 3f07b0… E04
#> 6 16 user E05 2017-08-04 15:14:55 f2cb36… E05
#> 7 17 user E06 2017-08-04 15:14:55 2c8dbf… E06
#> 8 18 user E07 2017-08-04 15:14:55 6cd1d7… E07
#> 9 19 user E08 2017-08-04 15:14:55 e40dcb… E08
#> 10 20 user E09 2017-08-04 15:14:55 f4ff1a… E09
#> # ... with 165 more rows, and 6 more variables: name <chr>, area <list>,
#> # organisation <list>, register <list>, `start-date` <chr>,
#> # `end-date` <chr>
You can resolve to only the latest entry of each record by creating a
registers object with snapshots.
registers_snapshot <- purrr::map(registers, rr_snapshot)
rr_resolve_links(registers$`statistical-geography`, registers_snapshot)$data
#> # A tibble: 175 x 12
#> `entry-number` type key timestamp hash `statistical-ge…
#> <int> <chr> <chr> <dttm> <chr> <chr>
#> 1 11 user E92 2017-08-04 15:14:55 97d105… E92
#> 2 12 user E00 2017-08-04 15:14:55 e57ac6… E00
#> 3 13 user E01 2017-08-04 15:14:55 e66020… E01
#> 4 14 user E02 2017-08-04 15:14:55 8c1f8d… E02
#> 5 15 user E04 2017-08-04 15:14:55 3f07b0… E04
#> 6 16 user E05 2017-08-04 15:14:55 f2cb36… E05
#> 7 17 user E06 2017-08-04 15:14:55 2c8dbf… E06
#> 8 18 user E07 2017-08-04 15:14:55 6cd1d7… E07
#> 9 19 user E08 2017-08-04 15:14:55 e40dcb… E08
#> 10 20 user E09 2017-08-04 15:14:55 f4ff1a… E09
#> # ... with 165 more rows, and 6 more variables: name <chr>, area <list>,
#> # organisation <list>, register <list>, `start-date` <chr>,
#> # `end-date` <chr>
Plot the links between registers with something like the ggraph
package.
library(tidygraph)
#>
#> Attaching package: 'tidygraph'
#> The following object is masked from 'package:stats':
#>
#> filter
library(ggraph)
#> Loading required package: ggplot2
registers$`statistical-geography` %>%
rr_links() %>%
as_tbl_graph() %>%
ggraph(layout = "nicely") +
geom_edge_fan(aes(alpha = ..index..), show.legend = FALSE) +
geom_edge_loop() +
geom_node_label(aes(label = name)) +
theme_void()
edge_arrow <- arrow(length = unit(4, "mm"), type = "closed")
registers %>%
rr_links() %>%
dplyr::distinct(from, to, type) %>%
as_tbl_graph() %>%
ggraph(layout = "nicely") +
geom_node_point() +
geom_edge_fan(aes(colour = type),
arrow = edge_arrow,
end_cap = circle(2, 'mm')) +
geom_edge_loop(aes(colour = type),
arrow = edge_arrow,
end_cap = circle(2, 'mm')) +
geom_node_label(aes(label = name), repel = TRUE, alpha = .5) +
theme_void()
Index registers
You can index registers by any column, using CURIE-like syntax.
country <- registers$country
rr_index(country, "start-date")
#> # A tibble: 50 x 2
#> .curie .data
#> <chr> <list>
#> 1 country: <tibble [155 × 11]>
#> 2 country:1975-11-11 <tibble [1 × 11]>
#> 3 country:1981-11-01 <tibble [1 × 11]>
#> 4 country:1991-09-21 <tibble [1 × 11]>
#> 5 country:1991-08-30 <tibble [1 × 11]>
#> 6 country:1991-08-25 <tibble [1 × 11]>
#> 7 country:1981-09-21 <tibble [1 × 11]>
#> 8 country:1992-03-03 <tibble [1 × 11]>
#> 9 country:1984-01-01 <tibble [1 × 11]>
#> 10 country:1975-07-05 <tibble [1 × 11]>
#> # ... with 40 more rows
rr_index(country, "end-date")
#> # A tibble: 5 x 2
#> .curie .data
#> <chr> <list>
#> 1 country:1991-12-25 <tibble [1 × 11]>
#> 2 country:1990-10-02 <tibble [2 × 11]>
#> 3 country:1992-04-28 <tibble [1 × 11]>
#> 4 country:1992-12-31 <tibble [1 × 11]>
#> 5 country: <tibble [202 × 11]>
rr_index(country)
#> # A tibble: 199 x 2
#> .curie .data
#> <chr> <list>
#> 1 country:SU <tibble [1 × 11]>
#> 2 country:DE <tibble [2 × 11]>
#> 3 country:DD <tibble [1 × 11]>
#> 4 country:YU <tibble [1 × 11]>
#> 5 country:CS <tibble [1 × 11]>
#> 6 country:GB <tibble [1 × 11]>
#> 7 country:AF <tibble [1 × 11]>
#> 8 country:AL <tibble [1 × 11]>
#> 9 country:DZ <tibble [1 × 11]>
#> 10 country:AD <tibble [1 × 11]>
#> # ... with 189 more rows
rr_index(registers$`local-authority-eng`, "local-authority-type")
#> # A tibble: 7 x 2
#> .curie .data
#> <chr> <list>
#> 1 local-authority-eng:UA <tibble [56 × 11]>
#> 2 local-authority-eng:MD <tibble [37 × 11]>
#> 3 local-authority-eng:CTY <tibble [29 × 11]>
#> 4 local-authority-eng:NMD <tibble [203 × 11]>
#> 5 local-authority-eng:LBO <tibble [32 × 11]>
#> 6 local-authority-eng:CC <tibble [1 × 11]>
#> 7 local-authority-eng:SRA <tibble [1 × 11]>
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