2d:null 2e:["$","title",null,{"children":"Consuming SDMX using sdmx-rest and jsonstat-suite | JSON-stat.org | Observable"}] 33:[{"id":"c77ee6fed328a58b9ea8c22bab8e0fe669c0c38ea32200606570e07425e998045f34027e27c0bdd0dee9c2febabdc943d708f1318f5d5e0c833f3e33b4534374","href":"https://static.observableusercontent.com/files/c77ee6fed328a58b9ea8c22bab8e0fe669c0c38ea32200606570e07425e998045f34027e27c0bdd0dee9c2febabdc943d708f1318f5d5e0c833f3e33b4534374","name":"modestas-urbonas-vj_9l20fzj0-unsplash.jpg","type":"image/jpeg","lastModified":1661345288409.818,"size":387870}] 31:{"id":"e5719fcfe79d89c7","slug":"sdmx-rest","title":"Consuming SDMX using sdmx-rest and jsonstat-suite","thumbnail":"66f029aec083a07b97556ab159b3305dff43e73d7b28baae05fcd28d4e5c651a","access_level":"public","create_time":"$D2022-08-24T07:39:44.912Z","update_time":"$D2026-01-22T15:17:25.173Z","view_time":null,"first_public_time":"$D2022-08-24T14:38:13.711Z","has_unpublished_edits":null,"fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"0f02e1a34acf82cd","login":"jsonstat","name":"JSON-stat.org","avatar_url":"https://avatars-next.observableusercontent.com/03bef2f0aa512420dbe59396de0314608eb5edfd966b7ec15e286cdcef3ff2ca","tier":"starter_2024"},"version":288,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"8de43c67196234cf","login":"jsonstat","name":"JSON-stat.org","avatar_url":"https://avatars-next.observableusercontent.com/03bef2f0aa512420dbe59396de0314608eb5edfd966b7ec15e286cdcef3ff2ca","tier":null}],"members":[{"type":"individual","id":"8de43c67196234cf","login":"jsonstat","name":"JSON-stat.org","avatar_url":"https://avatars-next.observableusercontent.com/03bef2f0aa512420dbe59396de0314608eb5edfd966b7ec15e286cdcef3ff2ca","tier":null,"role":"editor"},{"type":"team","id":"0f02e1a34acf82cd","login":"jsonstat","name":"JSON-stat.org","avatar_url":"https://avatars-next.observableusercontent.com/03bef2f0aa512420dbe59396de0314608eb5edfd966b7ec15e286cdcef3ff2ca","tier":"starter_2024","role":"viewer"}],"comments":[],"custom_thumbnail":true,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"Consuming SDMX using sdmx-rest and jsonstat-suite","cells":[{"id":175,"value":"# Consuming SDMX using sdmx-rest and jsonstat-suite\n\n### _By_ Xavier Badosa","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":179,"value":"[smdx-rest](https://github.com/sosna/sdmx-rest4js/) is JavaScript library to create and execute SDMX REST queries. This learn by example notebook shows how to get data from the OECD using this library and how to deal with it by converting it into JSON-stat with [jsonstat-suite](https://www.npmjs.com/package/jsonstat-suite).\n\n

\n ${await FileAttachment(\"modestas-urbonas-vj_9l20fzj0-unsplash.jpg\").image()}\n \n

Photo by Modestas Urbonas\n on Unsplash

\n\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":187,"value":"## Requirements\n\nThe easiest way to use sdmx-rest in Observable notebooks is importing it like this:\n\nsdmxrest = import(\"https://cdn.skypack.dev/sdmx-rest@2.20.0?min\")","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":0,"value":"sdmxrest = import(\"https://cdn.skypack.dev/sdmx-rest@2.20.0?min\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":191,"value":"To convert SDMX into JSON-stat we need the fromSDMX function from jsonstat-suite. The datalist function can also be handy to display the data in a dataset.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":42,"value":"import { fromSDMX, datalist } from \"@jsonstat/suite\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":193,"value":"To deal with JSON-stat, the [jsonstat-toolkit](https://observablehq.com/@jsonstat/toolkit) will be useful too.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":83,"value":"import { JSONstat } from \"@jsonstat/toolkit\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":196,"value":"Finally, to visualize the data we will be using vega-lite.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":107,"value":"import { vl } from \"@vega/vega-lite-api\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":198,"value":"## Retrieving the data\n\nThe sdmx-rest module includes the request2 function to retrieve data from an SDMX service. The OECD is one of the supported providers (\"OECD_S\"). In practical terms, this means that in the case of the OECD (and several international organizations), you don’t need to know the API end-points.\n\n~~~\nsdmxrest.request2(query, \"OECD_S\")\n~~~\n\nIn the article, we will retrieve a slice of price statistics from the G20_PRICES dataflow filtered by country=USA and G-20, subject=CPALTT01 (Consumer Price Index), measure=GP (Percentage change from previous period) and frequency=A (annual).","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":201,"value":"({ flow: \"G20_PRICES\", key: \"USA+G-20.CPALTT01.GP.A\" })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":205,"value":"Because SDMX-JSON has several flavors and the response will be converted to JSON-stat using fromSDMX (that mainly supports the SDMX-JSON flat format), it is important that our query requests all dimensions in the obsDimension parameter:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":61,"value":"query = ({\n flow: \"DSD_G20_PRICES@DF_G20_PRICES\",\n key: \"G20+USA.A.N..PA...\",\n obsDimension: \"allDimensions\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":211,"value":"To retrieve the data and store it as JSON in the sdmxjson variable:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":31,"value":"sdmxjson = sdmxrest.request2(query, \"OECD_S\").then((r) => r.json())","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":213,"value":"## Working with the data\nThe SDMX format is not very friendly and there are not many libraries to deal with. That’s why we are going to translate it into JSON-stat with the jsonstat-suite fromSDMX function:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":23,"value":"json = fromSDMX(sdmxjson)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":217,"value":"Displaying the data in a friendly way is now very easy. For example, using the jsonstat-suite datalist function:\n\n~~~\ndatalist(json)\n~~~","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":215,"value":"html`${datalist(json)}`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":221,"value":"To benefit from the methods from the jsonstat-toolkit, we need to convert the JSON-stat object into a jsonstat instance:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":81,"value":"js = JSONstat(json)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":224,"value":"Knowing the number of observations in the dataset is now trivial:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":253,"value":"js.n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":255,"value":" Or how many dimensions the dataset has:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":226,"value":"js.length","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":228,"value":"Or the dimension ids:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":230,"value":"js.id","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":237,"value":"Or the different categories of the dimension MEASURE:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":119,"value":"js.Dimension(\"MEASURE\").id","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":239,"value":"Or the label attached to the category GP of the MEASURE dimension:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":131,"value":"js.Dimension(\"MEASURE\").Category(\"GP\").label","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":257,"value":"It is also trivial to convert the data into popular data structures. For example:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":112,"value":"tbl = js.toTable({\n type: \"arrobj\"\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":259,"value":"Or:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":94,"value":"js.toTable({\n type: \"arrobj\",\n by: \"TIME_PERIOD\",\n drop: [\"FREQUENCY\", \"MEASURE\", \"SUBJECT\"]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":261,"value":"These data structures can directly feed data visualization tools like vega-lite:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":109,"value":"viewof bar = vl\n .markLine()\n .data(tbl.filter((d) => d.LOCATION === \"United States\"))\n .encode(vl.x().fieldO(\"TIME_PERIOD\"), vl.y().fieldQ(\"value\"))\n .title(\"CPI. Annual Increase. United States\")\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W33","stdlib":"1","titling":"auto"}} 32:"$31:body"