2d:null 2e:["$","title",null,{"children":"JSON-stat Empty Datasets | JSON-stat.org | Observable"}] 33:T70b,md`Statistical offices usually disseminate data in multidimensional cubes. The name does not matter: they may call them data cubes, tables, datasets... A statistical API needs a schema to expose data as cubes. The [JSON-stat format](https://json-stat.org/format/) was originally created for this purpose and offers the [JSON-stat 2.0 Dataset Schema](https://json-stat.org/format/schema/2.0/dataset.json). But exposing datasets is not enough. How can a client discover which datasets are available? A statistical API should have a single entry point that allows a client to end up discovering any dataset available in the API. Because the number of datasets offered by a statistical office is big, the entry point won’t probably expose the complete list of datasets. More likely, the entry point will just expose a list of topics. Or a list of statistics. And maybe, for each topic or statistic, a list of datasets. Or perhaps the API can be queried to get a list of datasets that match a certain search term. The granularity may vary but the conclusion is the same: in the API’s ontology, datasets won’t suffice. Another entity is required: lists of things, collections. Collections of datasets, collections of collections (topics, statistics...). That’s why JSON-stat offers a generic collection format defined by the [JSON-stat 2.0 Collection Schema](https://json-stat.org/format/schema/2.0/collection.json). Eurostat uses JSON-stat 2.0 to expose [datasets](https://wikis.ec.europa.eu/display/EUROSTATHELP/API+Statistics+-+data+query) but its API does not expose collections nor has a single entry point: an API client cannot easily discover Eurostat’s datasets. In most circumstances, a human being must manually browse Eurostat’s website or download and parse a very large TSV file. `34:Tac1,md`Do cubes and lists suffice? Statistical datasets are usually big. For performance reasons, statistical offices won’t allow to retrieve a full cube if it has many cells. Besides, consumers are usually interested in just a certain aspect (a side) of the cube. Fetching/serving full datasets is generally not efficient for anybody (consumer or provider): a way to filter the data inside the cube is required. But before filtering a dataset, a consumer needs to know how it can be filtered: to this end, the API must be able to expose the dataset *before* exposing its data. In summary, it needs to provide a way to share dataset metadata. Let’s take Statistics Denmark’s API as an example of an statistical API that covers all the stages. Denmark’s query builder (“API Console” as they call it) supports four types of API calls:

${await FileAttachment("denmark-calls.png").image()}

https://api.statbank.dk/console

As you can see in the pull-down menu, Denmark’s API accepts the following requests: 1. (list of) subjects 2. (list of) tables 3. metadata for a table 4. data (for a table) Four types of calls, four response schemas, four formats. JSON-stat (v.1) is used in the last type of call, but Denmark could use JSON-stat v.2 for calls 1 (collections of subjects), 2 (collection of datasets) and 4 (datasets). What about the third type? What’s the JSON-stat schema to expose specifically dataset metadata? The answer is “None”. Because none is needed. Specifically. A very important design principle of JSON-stat is the strict separation between a cube’s data and a cube’s metadata. As a consequence data can be cleanly removed from JSON-stat datasets: these can be used to expose data but also to describe a cube’s metadata. The exact same schema serves both ends. Where Denmark uses four different formats, a fully JSON-stat-based API would only require two (collections and datasets). If a statistical office API (like Eurostat’s) disseminates datasets using JSON-stat, it can disseminate dataset metadata for (nearly) free. If it can build a full dataset, it can build an empty one because: In JSON-stat, an empty dataset is exactly the same as a full dataset minus the property “status” (which is optional and contains metadata at the cell level) and “value” (which contains the actual data). “value” is required but can be empty. The JSON-stat Dataset Schema allows "value": {} or "value": [] (The [jsonstat-toolkit](https://www.npmjs.com/package/jsonstat-toolkit) is even more tolerant and will accept "value": null or even no value property at all, but that’s beyond the current specification.) `35:T5ab,md`If we want to query this dataset, we are forced to retrieve all the cube’s contents. And if the dataset is too big and can’t be retrieved, then we are left in the dark: how can we query a dataset if we don’t know its contents? The goal of empty datasets is to expose metadata. Unfortunately, Eurostat does not support metadata-only datasets. Empty datasets is one small step for Eurostat, but one giant leap for clients. Exposing dataset metadata allows a client to be aware of the detailed contents of a cube and know how to query it to retrieve relevant data. Empty datasets will even let a third party create its own query builder (as we will do in the next sections). It will also help a client better dealing with the following situation. An existing dataset is at some point enriched by Eurostat: for example, more detailed categories are added to the age dimension in the dataset (this is not a hypothetical case, it has happened in the past). As a consequence, the number of cells in the cube grows and a certain query that was allowed in the past stops working: the client cannot face this problem because it doesn’t have a way to know what has changed and how to avoid the cell limitation. Not supporting collections is not good, but not exposing dataset metadata through empty datasets is even worse, taking into account that, once regular JSON-stat datasets are supported, supporting empty ones is for free or nearly free. `36:T472,md`Then the query builder needs to translate the filter into an API call. This translation depends deeply in the requests’ anatomy which vary in each API. For example, some APIs allow you to “drop” a dimension because the dimension has a default category. In some APIs, what is not specifically requested will not be included in the response: these APIs require a special option to request all the categories in a dimension without referring to each and every one of them. These APIs may require additional dataset metadata to be returned as an [extension](https://json-stat.org/format/#extension). Fortunately, Eurostat uses a very simple and clear requests’ anatomy that does not require any extra metadata. A query in Eurostat’s API cannot remove entire dimensions, it can only remove dimension categories: the filtered dataset will have the same dimensions as the full dataset. In a Eurostat query, if you want to keep just some categories of a dimension, you need to list them explicitly. If a dimension does not appear in the query, all its categories will be kept in the filtered dataset. The filter you have defined `37:T50b,md`Time is not like other dimensions. Because time periods keep increasing over time and consumers are usually interested in up-to-date data, filtering time by selecting a list of time periods is not, in most cases, very useful. That’s why statistical APIs usually offer specific ways to filter time. In particular, Eurostat offers _untilTimePeriod_ (that allows to include time periods until the specified value), _sinceTimePeriod_ (that allows to keep time periods since the specified value) and _lastTimePeriod_ (that allows to ask for a number of last time periods). Having these special parameters is a fine addition to the API but not a required one when empty datasets are supported: thanks to metadata-only responses a user can know exactly the available time periods and request only the ones she needs. For example, using the information on the empty dataset, we know that _lastTimePeriod_=1 is equivalent, in dataset *${dataset}*, to *${ meta.role.time[0] }=${meta.Dimension(meta.role.time[0]).id.slice(-1)[0]}*. If, any way, we want to support these special time parameters in our query builder, the task is trivial because they don’t depend on dataset metadata. As an example, let’s add _lastTimePeriod_ (probably the most useful of all three) to our query builder. `38:T5f3,md`Of course, the use of JSON-stat empty datasets goes beyond query builders. In fact, other use cases have already been mentioned in this notebook. Using metadata-only datasets to create a query builder has only been an example of what’s possible. Here are some general contexts where JSON-stat empty datasets can be useful: ### Limitation avoidance JSON-stat empty datasets can be used to avoid the categories/cells limitation helping customizing the response to real data needs. A query builder is an example of a solution addressed to human beings. Automatic recovering from a failed request is an example of a solution addressed to machines: when getting a 406 error code, a machine could try to use a metadata-only response to find an effective data request. ### Performance An efficient data viewer that has to deal with big datasets can use JSON-stat empty datasets to just get metadata at startup to fill the viewer’s interface, and only retrieve data for the side of the cube that is actually shown, and only retrieve new data when it is needed. ### Conditional data requests A JSON-stat empty dataset can be used to regularly check if some information is available. For example, it allows a client to check if the updated time has changed before retrieving new data. Sometimes a consumer is only interested in some specific time periods (for example, only the first quarter of each year): empty datasets allow a client to conditionally request data only when the new time period meets the criteria. `39:T7dc,helper = ({ dimensions: function (ds) { let list = []; ds.id.map((i) => list.push( "

**" + ds.Dimension(i).label + "**: " + ds.Dimension(i).length + (ds.Dimension(i).length > 1 ? " categories" : " category") + (ds.Dimension(i).role ? " (role: " + ds.Dimension(i).role + ")" : "") + "

" ) ); return "

" + list.join("") + ""; }, table: function (ds) { return ds.hasOwnProperty("class") ? Inputs.table(ds.toTable({ type: "arrobj" })) : md``; }, reset: function (ds, str) { return ds.hasOwnProperty("id") ? html`` : html``; }, query: function (filter) { let param = []; Object.keys(filter).forEach((dim) => { filter[dim].forEach((value) => { param.push(`${dim}=${value}`); }); }); return param.join("&"); }, select: function (ds, dname) { if (!ds.hasOwnProperty("class")) { return html``; } let d = ds.Dimension(dname); return Inputs.select( new Map(d.id.map((id) => [d.Category(id).label, id])), { multiple: true, label: d.label + " (" + d.length + ")" } ); }, button: function (ds, dsname, cond, msg) { return Inputs.button(`JSONstat( ${dsname} )`, { value: md`(Click this button to retrieve the ${dsname} dataset.)`, reduce: () => (cond ? JSONstat(ds) : md`${msg}`) }); }, totalCat: function (ds, sel) { let ncells = 1; ds.id.forEach((d) => { ncells *= sel.hasOwnProperty(d) ? sel[d].length : ds.Dimension(d).length; }); return ncells; }, getClass: function (n) { return n < 51 ? "ok" : "ko"; }, addLast: function (filter, n) { if (!n) { return filter; } const obj = JSON.parse(JSON.stringify(filter)); delete obj.time; obj.lastTimePeriod = [n]; return obj; } })3a:[{"id":"29b60d917c9c16b6b65e993449b751904e37eacdcedc7e65fd93b3d00db1a1a6b2becb563d6aa73f7d1daa73db992e12bb26fdb60ec82b84c4cf979b081ef138","href":"https://static.observableusercontent.com/files/29b60d917c9c16b6b65e993449b751904e37eacdcedc7e65fd93b3d00db1a1a6b2becb563d6aa73f7d1daa73db992e12bb26fdb60ec82b84c4cf979b081ef138","name":"denmark-calls.png","type":"image/png","lastModified":1618128685960.5,"size":30648},{"id":"8c68b2df56bb302defae378d63aebd6f5747e2128218f37b2f7cc61da8c5b7d20bc96a4e3d584e19f19d9b0b86f16c7e68b99e3a5d74227326032423c17cdf9b","href":"https://static.observableusercontent.com/files/8c68b2df56bb302defae378d63aebd6f5747e2128218f37b2f7cc61da8c5b7d20bc96a4e3d584e19f19d9b0b86f16c7e68b99e3a5d74227326032423c17cdf9b","name":"eurostat-first.png","type":"image/png","lastModified":1618075728761.426,"size":178734},{"id":"8d8814ef7329a0d024f2c1849e191bda9e70caed7fc75964755c82d21edda3c048041da22d5af2e0a43ca652c350358d3f6190424b2868b26f4dfb7216af44b6","href":"https://static.observableusercontent.com/files/8d8814ef7329a0d024f2c1849e191bda9e70caed7fc75964755c82d21edda3c048041da22d5af2e0a43ca652c350358d3f6190424b2868b26f4dfb7216af44b6","name":"eurostat-db.png","type":"image/png","lastModified":1617636711894.545,"size":202905},{"id":"a2f3336ee25df64e937e68773fd02e3491c4409f65804bbd4366f16a955354ce42e05c75965e10c69f92129e8be33b2956b2a34714bf6c0d6b19180a689bc537","href":"https://static.observableusercontent.com/files/a2f3336ee25df64e937e68773fd02e3491c4409f65804bbd4366f16a955354ce42e05c75965e10c69f92129e8be33b2956b2a34714bf6c0d6b19180a689bc537","name":"eurostat-une.png","type":"image/png","lastModified":1617637078073.509,"size":219009},{"id":"fc78dfcd534498e9c95e46fc3fe031c34de82edd19603b276a7922c97dda4a0a4c87b2fa4151b3de47726549162069a1217b29fc363d0a76ed437de68957b9e9","href":"https://static.observableusercontent.com/files/fc78dfcd534498e9c95e46fc3fe031c34de82edd19603b276a7922c97dda4a0a4c87b2fa4151b3de47726549162069a1217b29fc363d0a76ed437de68957b9e9","name":"ilze-lucero-l_VVd8nV3PE-unsplash.jpg","type":"image/jpeg","lastModified":1618950883633.768,"size":2661161}] 31:{"id":"2012058d8ece3144","slug":"emptydatasets","title":"JSON-stat Empty Datasets","thumbnail":"6784ba9b07cae814afbf84703d40b608a2016194b39290892d8f23872fa7e565","access_level":"public","create_time":"$D2021-03-27T11:52:33.674Z","update_time":"$D2025-07-14T20:24:12.724Z","view_time":null,"first_public_time":"$D2021-04-22T16:08:29.446Z","has_unpublished_edits":null,"fork":false,"stars":5,"starred":false,"owner":{"type":"team","id":"0f02e1a34acf82cd","login":"jsonstat","name":"JSON-stat.org","avatar_url":"https://avatars-next.observableusercontent.com/03bef2f0aa512420dbe59396de0314608eb5edfd966b7ec15e286cdcef3ff2ca","tier":"starter_2024"},"version":1822,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"8de43c67196234cf","login":"jsonstat","name":"JSON-stat.org","avatar_url":"https://avatars-next.observableusercontent.com/03bef2f0aa512420dbe59396de0314608eb5edfd966b7ec15e286cdcef3ff2ca","tier":null}],"members":[{"type":"individual","id":"aea8e641bbbeafc7","login":"badosa","name":"Xavier Badosa","avatar_url":"https://avatars-next.observableusercontent.com/25d67f3e1008eee8a3ae63f1fab06a82814698ec762fb9ebecf1957384c3b0f1","tier":null,"role":"editor"},{"type":"individual","id":"8de43c67196234cf","login":"jsonstat","name":"JSON-stat.org","avatar_url":"https://avatars-next.observableusercontent.com/03bef2f0aa512420dbe59396de0314608eb5edfd966b7ec15e286cdcef3ff2ca","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":1,"collection":null,"type":"notebook","body":{"title":"JSON-stat Empty Datasets","cells":[{"id":238,"value":"md`# JSON-stat Empty Datasets\n\n### _By_ Xavier Badosa`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1453,"value":"md`This notebook is about one of the probably less known features of JSON-stat. Even though JSON-stat was designed with this use case in mind, no statistical agency is, at the time of writing, using this feature.\n\n

\n ${await FileAttachment(\"ilze-lucero-l_VVd8nV3PE-unsplash.jpg\").image()}\n

Photo by Ilze Lucero on Unsplash

\n\n\nThis notebook is about JSON-stat as a format, not only to disseminate data, but also to describe datasets. And it begins by asking what a full statistical API should look like, as a way to show the features that sometimes are missing. Eurostat’s API is taken as an example, not because it’s a bad API but because it’s a good API that lacks some important components.`\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1562,"value":"md`This notebook covers the following topics:\n\n* Cubes and Lists\n* From Full Datasets to Empty Datasets\n* Multiplication vs. Addition\n* Building a Query Builder\n* From Dataset Metadata to Dataset Query\n* Time Flies\n* Beyond Query Builders\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1047,"value":"md`## Cubes and Lists`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":227,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1054,"value":"md`## From Full Datasets to Empty Datasets`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1050,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":364,"value":"md`## Multiplication *vs.* Addition`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1175,"value":"md`Imagine a cube of data for 100 time periods and 1,000 geographical areas. The data size in this cube is determined by the _multiplication_ of these two figures: 100 x 1,000 = 100,000. But the metadata size in this cube is determined by the _addition_ of these two figures: 100 + 1,000 = 1,100. As a consequence, while the order of magnitude of the full dataset is about 100,000 + 1,100 = 101,100, the order of magnitude of the empty dataset is just 1,100. Full datasets grow quickly, empty datasets don’t.\n\nAllowing to fetch some full dataset might be impractical but empty dataset are always fetchable.\n\nHere is an example from Eurostat. Supose we are interested in quarterly unemployment by sex and age. After browsing Eurostat’s website (because this information is not exposed by the API) we discover that the dataset we need is *une_rt_q*.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1737,"value":"

\n ${await FileAttachment(\"eurostat-db.png\").image()}\n\n\nThe URL of the full dataset (in English) is then:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":324,"value":"full = `https://ec.europa.eu/eurostat/api/dissemination/statistics/1.0/data/${dataset}?lang=en`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":670,"value":"md`The full dataset has many cells. In the past, Eurostat did not allow you to get it. Downloading the full dataset can be a slow process in some cases, and usually is an inefficient approach as it is unlikely that we need all the data in it.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":673,"value":"viewof test = helper.button(full, \"full\", true, null)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":678,"value":"test","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1199,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":774,"value":"md`## Building a Query Builder`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":779,"value":"md`Fortunately, the [jsonstat-euro package](https://www.npmjs.com/package/jsonstat-euro) comes to the rescue as it provides a function (*fetchEmptyDataset*) to build an empty dataset from Eurostat’s API.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1708,"value":"import { fetchEmptyDataset, getURL } from \"@jsonstat/euro\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1717,"value":"meta = fetchEmptyDataset(dataset, true)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":886,"value":"meta.hasOwnProperty(\"id\")\n ? md`The empty dataset has been retrieved!\n\n(Using a metadata-only response we have avoided fetching ${meta.n.toLocaleString(\n \"en\"\n )} values.)\n\nThis metadata-only dataset will help us filtering the dataset to meet our needs.\n\nThe dataset has the following **dimensions**: \n\n${helper.dimensions(meta)}\n\n`\n : md``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":952,"value":"The full dataset is a big one: ${meta.n.toLocaleString(\n \"en\"\n )}. This figure is important because requesting a lot of data has an impact on performance. Most statistical offices take this figure into account to decide whether a dataset can be retrieved or not.\n\nOnce the empty dataset has been retrieved it is simple to build a query builder.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":966,"value":"md`${figures ? \"### Query Builder ###\" : \"\"}`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1305,"value":"meta.id\n ? md`Select different categories in the following form. See how the figure changes.`\n : md``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1294,"value":"figures.get(meta, filter)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1752,"value":"viewof freq = helper.select(meta, \"freq\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1780,"value":"{\n const rfreq = helper.reset(meta, \"rfreq\");\n rfreq.onclick = () => {\n viewof freq.value = \"\";\n viewof freq.dispatchEvent(new CustomEvent(\"input\"));\n };\n return rfreq;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":435,"value":"viewof time = helper.select(meta, \"time\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":611,"value":"{\n const rtime = helper.reset(meta, \"rtime\");\n rtime.onclick = () => {\n viewof time.value = \"\";\n viewof time.dispatchEvent(new CustomEvent(\"input\"));\n };\n return rtime;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":378,"value":"viewof geo = helper.select(meta, \"geo\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":607,"value":"{\n const rgeo = helper.reset(meta,\"rgeo\");\n rgeo.onclick = () => {\n viewof geo.value = \"\";\n viewof geo.dispatchEvent(new CustomEvent(\"input\"));\n };\n return rgeo;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":468,"value":"viewof sex = helper.select(meta, \"sex\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":591,"value":"{\n const rsex = helper.reset(meta,\"rsex\");\n rsex.onclick = () => {\n viewof sex.value = \"\";\n viewof sex.dispatchEvent(new CustomEvent(\"input\"));\n };\n return rsex;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":472,"value":"viewof age = helper.select(meta, \"age\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":596,"value":"{\n const rage = helper.reset(meta,\"rage\");\n rage.onclick = () => {\n viewof age.value = \"\";\n viewof age.dispatchEvent(new CustomEvent(\"input\"));\n };\n return rage;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":475,"value":"viewof unit = helper.select(meta, \"unit\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":601,"value":"{\n const runit = helper.reset(meta,\"runit\");\n runit.onclick = () => {\n viewof unit.value = \"\";\n viewof unit.dispatchEvent(new CustomEvent(\"input\"));\n };\n return runit;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":478,"value":"viewof s_adj = helper.select(meta, \"s_adj\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":604,"value":"{\n const rs_adj = helper.reset(meta,\"rs_adj\");\n rs_adj.onclick = () => {\n viewof s_adj.value = \"\";\n viewof s_adj.dispatchEvent(new CustomEvent(\"input\"));\n };\n return rs_adj;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":917,"value":"figures.get(meta, filter)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":733,"value":"meta.id\n ? md`In the rest of this notebook we will be using the dataset that results from the categories selected in this query builder.`\n : md``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":487,"value":"md`## From Dataset Metadata to Dataset Query`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1318,"value":"md`The goal of a query builder is to expose the dataset metadata to the user and allow her to define a filter (the subset of the dataset she is interested in). This is the filter you have defined:`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":490,"value":"filter = ({ freq, geo, time, sex, age, unit, s_adj })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1322,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1335,"value":"filter","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1340,"value":"md`then becomes the following API request:`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1760,"value":"filtered = getURL({ dataset, filter })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":688,"value":"viewof ds = helper.button(filtered, \"filtered\", true, null)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1650,"value":"ds","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":742,"value":"helper.table(ds)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1369,"value":"md`## Time Flies`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1372,"value":"$37","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1074,"value":"viewof lastTimePeriod = Inputs.range([1, 64], {\n value: 4,\n label: \"# of periods\",\n step: 1\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1406,"value":"md`The value selected in the previous form (${lastTimePeriod}) will replace the time dimension filter previously applied (_${\n filter.hasOwnProperty(\"time\") && filter.time.length\n ? JSON.stringify(filter.time)\n : \"no filter was applied to time\"\n}_).\n\nThe new filter is:`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1063,"value":"lastTimePeriodFilter = helper.addLast(filter, lastTimePeriod)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1426,"value":"md`And the new URL:`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1080,"value":"lastTimePeriodFiltered = helper.query(lastTimePeriodFilter)\n ? full + \"&\" + helper.query(lastTimePeriodFilter)\n : null","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1083,"value":"viewof dslast = helper.button(\n lastTimePeriodFiltered,\n \"lastTimePeriodFiltered\",\n true,\n null\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1682,"value":"dslast","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1163,"value":"helper.table(dslast)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1450,"value":"md`## Beyond Query Builders`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1483,"value":"$38","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":230,"value":"md`## Appendix`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1671,"value":"md`Stuff used in this notebook:`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1469,"value":"md`### Libraries`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":0,"value":"import { JSONstat } from \"@jsonstat/toolkit\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1472,"value":"md`### Helper Functions and Variables`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":196,"value":"$39","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":925,"value":"figures = ({\n totalcats: helper.totalCat(meta, filter),\n get: function (ds, filter) {\n const totalcats = helper.totalCat(ds, filter);\n return html`

${totalcats.toLocaleString(\"en\")}

`;\n }\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1476,"value":"md`### Styles`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":254,"value":"html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1800,"value":"### Constant","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1797,"value":"dataset = \"une_rt_q\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1802,"value":"Changing this value will break the query builder as it has been designed specifically for the *une_rt_q* dataset.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"}],"files":"$W3a","stdlib":"1","titling":"auto"}} 32:"$31:body"