A R Library developed through an EU-US Transatlantic Open Data Partnership
The United States' Department of Commerce and Bureau of Economic Analysis in partnership with the European Commission's DG CONNECT and Eurostat have established a Transatlantic Open Data Partnership focused on economic data. The eu.us.opendata R library is the direct result of this collaborative effort, enabling easy access to comparable datasets from the Eurostat API and BEA API.
As the library is currently available only via Github repository, installation requires a couple additional lines of code:
#Install packages if needed install.packages(c('devtools', 'httr')); library(devtools); library(httr); httr::set_config( config( ssl_verifypeer = 0L )); devtools::install_github('CommerceDataService/eu.us.opendata') library(eu.us.opendata) #Assign your API Key myKey <- 'Your 36-digit BEA API key here'
Using your BEA API key, set as "myKey", get the data as a relationship table:
getRel('gross domestic product', lucky = T, beaKey = myKey) getRel('gdp', lucky = T, beaKey = myKey)
The library supports a free text search for data series. Note that regular expressions are not supported in this version. For a list of all comparable datasets, enter a wild card search ("*")
searchRel('gross domestic product')
Using a relationship ID, return a description of that relationship as a table:
describeRel('<JOINT#GDP_A_2>', asHtml = TRUE)
List the relationships available using a direct SPARQL query of the (online) metadata store.
listRel(asHtml = FALSE)
Using the retrieved dataset from getRel(), returns either (1) a harmonized shapefile of EU and US geographies with the dataset joined for a selected year, (2) an interactive web-enabled leaflet map.
## Look at state/NUTS2 level data: dataset <- getRel('<JOINT#GDP_A_2>', lucky = F, beaKey = myKey) geoMap(dataset, 2014) ## As leaflet map geoMap(dataset, 2014, asSHP = TRUE) ## As shapefile ## Look at metro level data: dataset = getRel('gdp', lucky = T, beaKey = myKey) geoMap(dataset, 2012) ## As leaflet map geoMap(dataset, "all") ##As a shapefile with all years in the data
Provides user easier access to extracts the latest overlapping year of data or all overlapping years.
timeSync(dataset, 1) #latest timeSync(dataset, 2) #all overlapping years