// Here the query is constructed as a Javascript object which is easier to view and edit in this notebook. The json version is printed below
filterCountryGermany = ({
exactSearch: [ { column: ["location"]}, "Germany" ]
})
recordsGermany = getDataFiltered(filterCountryGermany)
vl.markCircle()
.data(recordsGermany.results.map(record => record.data))
.encode(
vl.x().field("date").type("temporal"),
vl.y().field("new_cases").type("quantitative"),
vl.color().field("location")
)
.render()
filterDatasetsByName = ({
exactSearch: [
{ systemColumn: ["name"] },
"COVID-19 complete dataset by Our World In Data"
]
})
datasetsNameSearchResult = getDatasetsFiltered(filterDatasetsByName)
datasetsNameSearchResult.results[0]
searchAnyWhereUnited = ({
searchAnywhere: [ "United" ]
})
searchAnywhereUnited = getDataFiltered(searchAnyWhereUnited)
vl.markCircle()
.data(searchAnywhereUnited.results.map(record => record.data))
.encode(
vl.x().field("date").type("temporal"),
vl.y().field("new_cases").type("quantitative"),
vl.color().field("location")
)
.render()
filterExactCountryUnitedStates = ({
exactSearch: [ { column: ["location"]}, "United States" ]
})
recordsExactUnitedStates = getDataFiltered(filterExactCountryUnitedStates)
vl.markCircle()
.data(recordsExactUnitedStates.results.map(record => record.data))
.encode(
vl.x().field("date").type("temporal"),
vl.y().field("new_cases").type("quantitative"),
vl.color().field("location")
)
.render()
filterFuzzyCountryStates = ({
fuzzySearch: [ { column: ["location"]}, "States" ]
})
recordsFuzzyCountryStates = getDataFiltered(filterFuzzyCountryStates)
vl.markCircle()
.data(recordsFuzzyCountryStates.results.map(record => record.data))
.encode(
vl.x().field("date").type("temporal"),
vl.y().field("new_cases").type("quantitative"),
vl.color().field("location")
)
.render()
filterEquals100Cases = ({
eq: [ { column: ["new_cases"]}, 100 ]
})
recordsEquals100Cases = getDataFiltered(filterEquals100Cases)
vl.markCircle()
.data(recordsEquals100Cases.results.map(record => record.data))
.encode(
vl.x().field("date").type("temporal"),
vl.y().field("new_cases"),
vl.color().field("location")
)
.render()
filterGt50000Cases = ({
gt: [ { column: ["new_cases"]}, 50000 ]
})
recordsGt50000Cases = getDataFiltered(filterGt50000Cases)
vl.markCircle()
.data(recordsGt50000Cases.results.map(record => record.data))
.encode(
vl.x().field("date").type("temporal"),
vl.y().field("new_cases").type("quantitative"),
vl.color().field("location")
)
.render()
filterLt50CasesGermany = ({
and: [
{lt: [ { column: ["new_cases"]}, 50]},
{exactSearch: [ { column: ["location"]}, "Germany"]}
]
})
recordsLt50CasesGermany = getDataFiltered(filterLt50CasesGermany)
vl.markCircle()
.data(recordsLt50CasesGermany.results.map(record => record.data))
.encode(
vl.x().field("date").type("temporal"),
vl.y().field("new_cases").type("quantitative"),
vl.color().field("location")
)
.render()
keywordsContainsUsa = ({contains: [ {"column": ["keywords"]}, ["covid-19"] ] })
datasetsWithKeywordUsa = getDatasetsFilteredWithMapping(keywordsContainsUsa, [{name: "keywords", jsonPath: "$.keywords[*]", dataType: "xsd:string"}])
operatorNot = ({not: [ {lt: [{"column": ["new_cases"]}, 50000 ]}] })
operatorNotCases = getDataFiltered(operatorNot)
vl.markCircle()
.data(operatorNotCases.results.map(record => record.data))
.encode(
vl.x().field("date").type("temporal"),
vl.y().field("new_cases").type("quantitative"),
vl.color().field("location")
)
.render()
md`##### And/Or`
andOrOperators = ({
and: [
{gt: [ { column: ["new_cases"]}, 2000]},
{or: [
{ exactSearch: [ { column: ["location"]}, "Germany"]},
{ exactSearch: [ { column: ["location"]}, "Italy"]}
]}
]
})
JSON.stringify(andOrOperators)
operatorAndOrCases = getDataFiltered(andOrOperators)
vl.markCircle()
.data(operatorAndOrCases.results.map(record => record.data))
.encode(
vl.x().field("date").type("temporal"),
vl.y().field("new_cases").type("quantitative"),
vl.color().field("location")
)
.render()
similarityCutoffSearch = ({
gt: [{ tanimotoSimilarity: ["c1ccccc1", { column: ["SMILES"] }] }, 0.4]
})
similaritySearchResponse = getChemicalDataFiltered(similarityCutoffSearch)
substructureSearch = ({
substructureSearch: ["c1ccccc1", { column: ["SMILES"] }]
})
substructureSearchResponse = getChemicalDataFiltered(substructureSearch)
castFilter = ({
gt: [{cast: [{ column: ["new_cases"] }, "xsd:integer"]}, 50000]
})
castedNewCasesData = getDataFiltered(castFilter)
edelweissUrl = "https://api.edelweissdata.com"
datasetUrl = "https://api.edelweissdata.com/datasets/b55b229d-6338-4e41-a507-0cf4d3297b54/versions/latest/data"
chemicalDatasetUrl = "https://api.edelweissdata.com/datasets/2940e662-0ba1-4d8a-927d-03689808e99d/versions/1/data"
getDataFiltered = function(query) {
const encodedQuery = JSON.stringify({condition: query})
let url = new URL(datasetUrl)
url.searchParams.append('query', encodedQuery);
return fetch(url).then(response => response.json())
}
getChemicalDataFiltered = function(query) {
const encodedQuery = JSON.stringify({condition: query})
let url = new URL(chemicalDatasetUrl)
url.searchParams.append('query', encodedQuery);
return fetch(url).then(response => response.json())
}
getDatasetsFiltered = function(query) {
const encodedQuery = JSON.stringify({condition: query})
let url = new URL(`${edelweissUrl}/datasets`)
url.searchParams.append('query', encodedQuery);
return fetch(url).then(response => response.json())
}
getDatasetsFilteredWithMapping = function(query, mapping) {
const encodedQuery = JSON.stringify({condition: query, columns: mapping})
let url = new URL(`${edelweissUrl}/datasets`)
url.searchParams.append('query', encodedQuery);
return fetch(url).then(response => response.json())
}
import { vl } from "@vega/vega-lite-api"
Purpose-built for displays of data
Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
