I love to build dataviz for insight discovery. I also love to put technology to the service of humanity
Inputs.table(selected.selectedIds.map(d => map.get(d)))
map = new Map(datadf.objects().map(d => [d.Institution, d]))
datadf.objects().filter(d => d.Rank)
datadf.objects().filter(d => d[y] )
data = FileAttachment("nsf24308-tab007_cleaned.csv").csv({typed: true}).then(res => {
const keys = Object.keys(res[0]).slice(2);
for (let r of res) {
for (let k of keys) {
try {
r[k] = r[k].replace(/\,/g, "");
} catch {
console.log("failed", r, k);
}
}
}
return res;
})
viewof datadf = aq
.from(data)
.orderby(aq.desc("Total_2019")).derive({ rank_total_2019: op.avg_rank() })
.orderby(aq.desc("Total_2020")).derive({ rank_total_2020: op.avg_rank() })
.orderby(aq.desc("Total_2021")).derive({ rank_total_2021: op.avg_rank() })
.orderby(aq.desc("Total_2022")).derive({ rank_total_2022: op.avg_rank() })
.fold([
"Total_2019",
"Total_2020",
"Total_2021",
"Total_2022",
"Federal_2019",
"Federal_2020",
"Federal_2021",
"Federal_2022",
// "rank_total_2019",
// "rank_total_2020",
// "rank_total_2021",
// "rank_total_2022",
])
.rename({ key: "Year", value: "Expenditure" })
.derive({
Type: (d) => op.split(d.Year, "_")[0],
Year: (d) => +op.split(d.Year, "_")[1],
Expenditure: (d) => +op.replace(d.Expenditure, /\,/g, ""),
Rank: (d) => +d.Rank
})
.groupby(["Institution", "Rank", "Year"])
.pivot("Type", "Expenditure")
.view()
data[0]
import {aq, op} from "@uwdata/arquero"
import {TimeSearcher} from "@john-guerra/timesearcher-example"
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