| sector | 2016 | 2017 | 2018 | 2019 |
|---|---|---|---|---|
| Commercial Electricity | 325.48 | 278.88 | 252.67 | 226.23 |
| Domestic Electricity | 230.75 | 204.08 | 183.57 | 164.24 |
| Industry Electricity | 116.00 | 111.22 | 107.68 | 80.62 |
| Public Sector Electricity | 86.86 | 71.24 | 68.77 | 61.36 |
| sector | year | territorial_emissions |
|---|---|---|
| Commercial Electricity | 2016 | 325.48 |
| Domestic Electricity | 2016 | 230.75 |
| Industry Electricity | 2016 | 116.00 |
| Public Sector Electricity | 2016 | 86.86 |
| Commercial Electricity | 2017 | 278.88 |
| Domestic Electricity | 2017 | 204.08 |
| Industry Electricity | 2017 | 111.22 |
| Public Sector Electricity | 2017 | 71.24 |
| Commercial Electricity | 2018 | 252.67 |
| Domestic Electricity | 2018 | 183.57 |
| Industry Electricity | 2018 | 107.68 |
| Public Sector Electricity | 2018 | 68.77 |
| Commercial Electricity | 2019 | 226.23 |
| Domestic Electricity | 2019 | 164.24 |
| Industry Electricity | 2019 | 80.62 |
| Public Sector Electricity | 2019 | 61.36 |
| year | start | end |
|---|---|---|
| 2005 | 2005-01-01 | 2005-12-31 |
| 2006 | 2006-01-01 | 2006-12-31 |
| 2007 | 2007-01-01 | 2007-12-31 |
| label | comment |
|---|---|
| KT-CO2 | Kilotonnes of Carbon Dioxide |
| year | area | sector | territorial_emissions | unit_of_measure |
|---|---|---|---|---|
| 2005 | Hartlepool | Commercial Electricity | 77.19 | KT-CO2 |
| 2005 | Hartlepool | Commercial Gas | 16.69 | KT-CO2 |
| 2005 | Hartlepool | Commercial ‘Other Fuels’ | 0.20 | KT-CO2 |
| 2005 | Hartlepool | Domestic Electricity | 80.64 | KT-CO2 |
| 2005 | Hartlepool | Domestic Gas | 131.38 | KT-CO2 |
| 2005 | Hartlepool | Domestic ‘Other Fuels’ | 4.07 | KT-CO2 |
| label | notation | boundary |
|---|---|---|
| Glasgow City | S12000049 | MULTIPOLYGON (((-… |
| Aberdeen City | S12000033 | POLYGON ((-2.0613… |
| City of London | E09000001 | MULTIPOLYGON (((-… |
| Barking and Dagenham | E09000002 | MULTIPOLYGON (((0… |
| Barnet | E09000003 | POLYGON ((-0.1819… |
| Bexley | E09000004 | POLYGON ((0.2031 … |
| label | sort_priority | parent |
|---|---|---|
| Commercial Electricity | 1 | Commercial |
| Commercial Gas | 2 | Commercial |
| Commercial ‘Other Fuels’ | 3 | Commercial |
| Domestic Electricity | 4 | Domestic |
| Domestic Gas | 5 | Domestic |
| Domestic ‘Other Fuels’ | 6 | Domestic |
| year | year_start | year_end | area | area_notation | area_boundary | sector | sector_notation | sector_sort_priority | territorial_emissions | unit_of_measure |
|---|---|---|---|---|---|---|---|---|---|---|
| 2005 | 2005-01-01 | 2005-12-31 | Hartlepool | E06000001 | POLYGON ((-1.24098 54…. | Commercial Electricity | commercial-electricity | 1 | 77.19 | KT-CO2 |
| 2005 | 2005-01-01 | 2005-12-31 | Hartlepool | E06000001 | POLYGON ((-1.24098 54…. | Commercial Gas | commercial-gas | 2 | 16.69 | KT-CO2 |
| 2005 | 2005-01-01 | 2005-12-31 | Hartlepool | E06000001 | POLYGON ((-1.24098 54…. | Commercial ‘Other Fuels’ | commercial-other-fuels | 3 | 0.20 | KT-CO2 |
| 2005 | 2005-01-01 | 2005-12-31 | Hartlepool | E06000001 | POLYGON ((-1.24098 54…. | Domestic Electricity | domestic-electricity | 4 | 80.64 | KT-CO2 |
| 2005 | 2005-01-01 | 2005-12-31 | Hartlepool | E06000001 | POLYGON ((-1.24098 54…. | Domestic Gas | domestic-gas | 5 | 131.38 | KT-CO2 |
| 2005 | 2005-01-01 | 2005-12-31 | Hartlepool | E06000001 | POLYGON ((-1.24098 54…. | Domestic ‘Other Fuels’ | domestic-other-fuels | 6 | 4.07 | KT-CO2 |
| 2005 | 2005-01-01 | 2005-12-31 | Hartlepool | E06000001 | POLYGON ((-1.24098 54…. | Industry Electricity | industry-electricity | 7 | 101.15 | KT-CO2 |
| 2005 | 2005-01-01 | 2005-12-31 | Hartlepool | E06000001 | POLYGON ((-1.24098 54…. | Industry Gas | industry-gas | 8 | 33.21 | KT-CO2 |
| 2005 | 2005-01-01 | 2005-12-31 | Hartlepool | E06000001 | POLYGON ((-1.24098 54…. | Large Industrial Installations | large-industrial-installations | 9 | 181.10 | KT-CO2 |
| 2005 | 2005-01-01 | 2005-12-31 | Hartlepool | E06000001 | POLYGON ((-1.24098 54…. | Industry ‘Other Fuels’ | industry-other-fuels | 10 | 20.44 | KT-CO2 |
| 2005 | 2005-01-01 | 2005-12-31 | Hartlepool | E06000001 | POLYGON ((-1.24098 54…. | Agriculture | agriculture | 11 | 2.09 | KT-CO2 |
| 2005 | 2005-01-01 | 2005-12-31 | Hartlepool | E06000001 | POLYGON ((-1.24098 54…. | LULUCF Net Emissions: Forest land | lulucf-net-emissions-forest-land | 12 | -1.65 | KT-CO2 |
| 2005 | 2005-01-01 | 2005-12-31 | Hartlepool | E06000001 | POLYGON ((-1.24098 54…. | LULUCF Net Emissions: Cropland | lulucf-net-emissions-cropland | 13 | 2.40 | KT-CO2 |
| 2005 | 2005-01-01 | 2005-12-31 | Hartlepool | E06000001 | POLYGON ((-1.24098 54…. | LULUCF Net Emissions: Grassland | lulucf-net-emissions-grassland | 14 | -2.94 | KT-CO2 |
| 2005 | 2005-01-01 | 2005-12-31 | Hartlepool | E06000001 | POLYGON ((-1.24098 54…. | LULUCF Net Emissions: Wetlands | lulucf-net-emissions-wetlands | 15 | 0.00 | KT-CO2 |
Linked Data Frames
Robin Gower
What is {vctrs}?
What is linked-data?
Encoding graphs as triples
BASE <http://example.org/>
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
PREFIX schema: <http://schema.org/>
PREFIX dcterms: <http://purl.org/dc/terms/>
PREFIX wd: <http://www.wikidata.org/entity/>
<bob#me>
a foaf:Person ;
foaf:knows <alice#me> ;
schema:birthDate "1990-07-04"^^xsd:date ;
foaf:topic_interest wd:Q12418 .
wd:Q12418
dcterms:title "Mona Lisa" ;
dcterms:creator <http://dbpedia.org/resource/Leonardo_da_Vinci> .
<http://data.europeana.eu/item/04802/243FA8618938F4117025F17A8B813C5F9AA4D619>
dcterms:subject wd:Q12418 .Melting tidy data to triples
BASE <http://example.org/>
PREFIX dimension: <http://example.org/dimension/>
PREFIX measure: <http://example.org/measure/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX geosparql: <http://www.opengis.net/ont/geosparql#>
<obs#1>
dimension:year <year#2005> ;
dimension:area <area#hartlepool> ;
dimension:sector <sector#commercial-electricity> ;
measure:emissions 77.19 .
<year#2005>
rdfs:label "2005" .
<area#hartlepool>
rdfs:label "Hartlepool" ;
geosparql:hasGeometry <geometry#hartlepool> .
<geometry#hartlepool>
geosparql:asWKT "POLYGON ((-1.24098 54.72318, -1.24401 54.72157 ... ))" .
<sector#commercial-electricity>
rdfs:label "Commercial Electricity" ;
ui:sortPriority 1 .The linked open-data cloud
Working with LDF Resources
Creating resources
uris <- c("http://example.net/id/apple",
"http://example.net/id/banana",
"http://example.net/id/carrot")
labels <- c("Apple", "Banana", "Carrot")
descriptions <- data.frame(uri=uris, label=labels)
(food <- resource(uris, descriptions))<ldf_resource[3]>
[1] Apple Banana Carrot
Description: uri, labelRepeating resources
(kitchen <- tibble(
dish=c("Fruit Salad", "Fruit Salad",
"Carrot Salad", "Carrot Salad"),
food=resource(c("http://example.net/id/apple",
"http://example.net/id/banana",
"http://example.net/id/apple",
"http://example.net/id/carrot"),
descriptions),
quantity=c(2,2,1,3)))# A tibble: 4 × 3
dish food quantity
<chr> <ldf_rsrc> <dbl>
1 Fruit Salad Apple 2
2 Fruit Salad Banana 2
3 Carrot Salad Apple 1
4 Carrot Salad Carrot 3Accessing descriptions
[1] "http://example.net/id/apple" "http://example.net/id/banana"
[3] "http://example.net/id/carrot" uri label
1 http://example.net/id/apple Apple
2 http://example.net/id/banana Banana
3 http://example.net/id/carrot CarrotLoading data
Tabulating linked-data with SPARQL
# A tibble: 100 × 3
uri label comment
<chr> <chr> <chr>
1 http://dbpedia.org/resource/Art_rock Art rock "Art rock …
2 http://dbpedia.org/resource/Bebop Bebop "Bebop or …
3 http://dbpedia.org/resource/Britpop Britpop "Britpop w…
4 http://dbpedia.org/resource/Bubblegum_pop Bubblegum pop "Bubblegum…
5 http://dbpedia.org/resource/Fighting_game Fighting game "A fightin…
6 http://dbpedia.org/resource/Free_improvisation Free improvisation "Free impr…
7 http://dbpedia.org/resource/Greek_hip_hop Greek hip hop "Greek hip…
8 http://dbpedia.org/resource/Grunge Grunge "Grunge (s…
9 http://dbpedia.org/resource/Historical_fiction Historical fiction "Historica…
10 http://dbpedia.org/resource/Morality_play Morality play "The moral…
# … with 90 more rowsmusic_genres <- resource(music_genre_results$uri, description=music_genre_results)
# find music genres where the comment mentions "dance"
music_genres[grep("dance", property(music_genres, "comment"))]<ldf_resource[12]>
[1] Polka Trance music
[3] Vaudeville Zarzuela
[5] Afro/Cosmic music Benga music
[7] Bubblegum dance Waltz (International Standard)
[9] Logobi Sega (genre)
[11] K-pop Western swing
Description: uri, label, commentldf::get_cube
# A tibble: 129,388 × 5
year area sector territorial_emissions
<ldf_ntrv> <ldf_rsrc> <ldf_rsrc> <dbl>
1 2005 Hartlepool Commercial Electricity 77.2
2 2005 Hartlepool Commercial Gas 16.7
3 2005 Hartlepool Commercial 'Other Fuels' 0.2
4 2005 Hartlepool Domestic Electricity 80.6
5 2005 Hartlepool Domestic Gas 131.
6 2005 Hartlepool Domestic 'Other Fuels' 4.07
7 2005 Hartlepool Industry Electricity 101.
8 2005 Hartlepool Industry Gas 33.2
9 2005 Hartlepool Large Industrial Installations 181.
10 2005 Hartlepool Industry 'Other Fuels' 20.4
# … with 129,378 more rows, and 1 more variable: unit_of_measure <ldf_rsrc>Read more about downloading linked-data in the package documentation.
# English Local Authority areas with the highest emissions from transport
(transport_emissions <- emissions %>%
filter(sector %>% property("parent") %>% label() == "Transport") %>%
group_by(area) %>%
summarise(total_emissions=sum(territorial_emissions)) %>%
arrange(-total_emissions))# A tibble: 379 × 2
area total_emissions
<ldf_rsrc> <dbl>
1 Leeds 25831.
2 Birmingham 22273.
3 Buckinghamshire 21006.
4 Wiltshire 19024.
5 Cheshire East 17929.
6 Cornwall 15703.
7 Cheshire West and Chester 14393.
8 County Durham 14210.
9 South Gloucestershire 14013.
10 Doncaster 13778.
# … with 369 more rowstransport_emissions_map <-
description(transport_emissions$area) %>%
filter(!is.na(boundary)) %>%
st_as_sf(wkt="boundary", crs="WGS84") %>%
left_join(mutate(transport_emissions,
geo=uri(area)),
by=c("uri"="geo"))
ggplot(transport_emissions_map) +
geom_sf(aes(fill=log(total_emissions)),
colour="white", size=0.2) +
scale_fill_viridis_c("KT-CO2") +
labs(title="Transport Emissions",
subtitle="Total 2005-2019")
Linking relational data
library(nycflights13)
flights_ldf <- flights %>% mutate(
carrier=resource(carrier, rename(airlines, uri=carrier)),
origin=resource(origin, rename(airports, uri=faa), fill_missing=T),
dest=resource(dest, rename(airports, uri=faa), fill_missing=T),
tailnum=resource(tailnum, rename(planes, uri=tailnum), fill_missing=T))
Gotchas
Lessons for working with {vctrs}
Dispatching on base type
http://example.net/id/apple http://example.net/id/banana
2 1
http://example.net/id/carrot
1 Use {vctrs} to retain descriptions
r v
1 A 1
2 B 2 r v
1 B 3
2 C 4 r v
1 A 1
2 B 2
3 B 3
4 NA 4Tidyverse is {vctrs}-aware
Try it for yourself
Learn more
- Documentation for the
{vctrs}package - vctrs.r-lib.org
- Documentation for the
{ldf}package - swirrl.github.io/linked-data-frames
- Data Cubes from by the Integrated Data Programme
- beta.gss-data.org.uk
- Linked-data schema used by PublishMyData
- ontologies.publishmydata.com
- Occasional tweets from me
- @robsteranium
Crosstab sector 2016 2017 2018 2019 Commercial Electricity 325.48 278.88 252.67 226.23 Domestic Electricity 230.75 204.08 183.57 164.24 Industry Electricity 116.00 111.22 107.68 80.62 Public Sector Electricity 86.86 71.24 68.77 61.36 Tidy sector year territorial_emissions Commercial Electricity 2016 325.48 Domestic Electricity 2016 230.75 Industry Electricity 2016 116.00 Public Sector Electricity 2016 86.86 Commercial Electricity 2017 278.88 Domestic Electricity 2017 204.08 Industry Electricity 2017 111.22 Public Sector Electricity 2017 71.24 Commercial Electricity 2018 252.67 Domestic Electricity 2018 183.57 Industry Electricity 2018 107.68 Public Sector Electricity 2018 68.77 Commercial Electricity 2019 226.23 Domestic Electricity 2019 164.24 Industry Electricity 2019 80.62 Public Sector Electricity 2019 61.36 The useR! audience will be familiar with tidy data, the way we melt or unpivot cross-tabulations into long tables of observations to better work with the data. Instead of communicating dimensions with the structure and layout of the table, we turn them into data - columns in a data frame that may be addressed and manipulated.