Public
Edited
Jul 6, 2024
1 fork
Importers
1 star
Look at your list of created articles with the Xtools Page Links APIGenderStatsLook at your list of created articles with the Xtools Page Prose APILook at your list of created articles with the Xtools Page ArticleInfo APIMYCA : Map your created articlesCategories of created articles
WICA: Wikidata's Insights for your Created Articles
Also listed in…
Wikipedia tools
claim_labels = get_labels(
[
"P17",
"P21",
"P27",
"P31",
"P106",
"P101",
"P103",
"P1412",
"P6886",
"P140",
"P91",
"P172",
"P69",
"P166",
"P1387",
"P1142",
"P136",
"P1050",
"P1884"
],
lang
).then((d) => d.objects())
labels_map = new Map(claim_labels.map(({ label, qid }) => [label + " (" + qid + ")", qid]))
data = {
let result = [];
let offset = "";
while (offset !== undefined) {
let data = await get_pagescreated({
username: username,
wikipedia: wikipedia,
offset: offset
});
offset = data.continue;
result = result.concat(aq.from(Object.values(data.pages[0])));
yield result;
}
return result;
}
df = data[0].union(data)
page_title_split = df.array("page_title").reduce((all,one,i) => {
const ch = Math.floor(i/50);
all[ch] = [].concat((all[ch]||[]),one);
return all
}, [])
alldata = await Promise.all(page_title_split.map(d => fetch(wdk.getEntitiesFromSitelinks({titles: d, sites: get_shortwikipedianame(wikipedia)}))
.then(d => d.json())
.then(wdk.parse.wb.entities)
.then(d => Object.values(d).map(q => get_claim(q, claim, get_shortwikipedianame(wikipedia))))
.then(d => aq.from([ { article: "", qvalue: ""}])
.union(d.map(q => aq.from(q)))
.filter(aq.escape(p => p.article != "")))
)
)
df_alldata = aq.from([{article: "", qvalue: ""}])
.union(alldata.map(d => aq.from(d)))
.filter(aq.escape(d => d.article != ""))
qvalues = df_alldata.dedupe("qvalue").array("qvalue")
qvalues_split = qvalues.reduce((all,one,i) => { const ch = Math.floor(i/50);
all[ch] = [].concat((all[ch]||[]),one);
return all
}, [])
qvalues_label = await Promise.all(qvalues_split.map((d) => get_labels(d, lang)))
df_labels = aq.from([{qid: "", label: ""}])
.union(qvalues_label.map(d => aq.from(d)))
.filter(aq.escape(d => d.qid != ""))
.rename({qid: 'qvalue', label: 'qlabel'})
df_result = df_alldata.join_left(df_labels, "qvalue")
import {BubbleChart} from "@d3/bubble-chart"
import { get_pagescreated } from "@pac02/look-at-your-list-of-created-articles-with-the-xtools-page-ap"
import { wikipedias } from "@pac02/user-level-gender-statistics-for-wikipedia"
get_default = () => {
const params = new URL(document.URL).searchParams;
const defaults = {
username: "PAC2",
wikipedia: "en.wikipedia.org",
lang: "en",
claim: "P31"
} ;
if (params.has("username")) {
defaults.username = params.get("username")
}
if (params.has("wikipedia")) {
defaults.wikipedia = params.get("wikipedia")
}
if (params.has("claim")) {
defaults.claim = params.get("claim")
}
if (params.has("lang")) {
defaults.lang = params.get("lang")
}
return defaults
}
One platform to build and deploy the best data apps
Experiment and prototype by building visualizations in live JavaScript notebooks. Collaborate with your team and decide which concepts to build out.
Use Observable Framework to build data apps locally. Use data loaders to build in any language or library, including Python, SQL, and R.
Seamlessly deploy to Observable. Test before you ship, use automatic deploy-on-commit, and ensure your projects are always up-to-date.