ordinanze = d3.csv("https://raw.githubusercontent.com/opendatasicilia/ordinanze-covid/main/data.csv")
Inputs.Table(ordinanze)
__ordinanze = Data.import({"source":ordinanze,"type":"json"}).sort("nro_ordinanza").select(filter.filter)
__ordinanze_expanded = __expand_data(__ordinanze);
__expand_data = function (data) {
var table = [];
var lastColumn = data.columnNames().length - 1;
var found = 0
var records = data.records;
var len = records.length;
// go through the records and create for every record (Comune/Ordinanza) as many new table rows
// as days are between data_inizio e data_fine with the appropriate date
for ( var r=0; r<records.length; r++ ){
var data_inizio = records[r][3];
var data_fine = records[r][4];
var giorno = 1;
var giorni = (new Date(data_fine).getTime() - new Date(data_inizio).getTime()) / 1000 / 60 / 60 / 24;
while ( data_inizio != data_fine ) {
table.push({pro_com:records[r][0],comune:records[r][1],pro:com_pro_A["0"+records[r][0]],date:data_inizio,ord:(giorno==1)?1:0,giorni:(giorno==1)?giorni:0,pop:pop_21_A["0"+records[r][0]],pop_g:pop_21_A["0"+records[r][0]]});
data_inizio = new Date(new Date(data_inizio).getTime()+1000*60*60*24).toISOString().split("T")[0];
giorno++;
};
}
// create and return a new Data object from the table
return Data.import({"source":table,"type":"json"}).sort("date");
};
Inputs.Table(__ordinanze_expanded.json())
pivot = __ordinanze_expanded.pivot({
lead: "pro_com",
columns: "date",
keep: ["comune","pro"],
value: "pop"
}).sort("pro")
Inputs.Table(pivot.json())
condensed = Data.import({"source":__ordinanze_expanded.json(),"type":"json"}).condense({
lead: "pro_com",
keep: ["comune","pro_com","pro","pop"]
}).sort("pro")
Inputs.Table(condensed.json())
condensed_pro = {
var tab = Data.import({"source":condensed.json(),"type":"json"})
.condense({
lead: "pro",
keep: ["pro"],
calc: "sum"
})
.addColumn({source:"pro",destination:"pop_pro"},(value)=>{
return pop_pro_A[value];
})
.addColumn({destination:"pop_percent"},(row)=>{
return row[6]/row[8]*100;
})
return tab;
}
Inputs.Table(condensed_pro.json())
condensed_pro_max = {
var tab = Data.import({"source":condensed.json(),"type":"json"})
.condense({
lead: "pro",
keep: ["pro"],
calc: "max"
})
.addColumn({source:"pro",destination:"pop_pro"},(value)=>{
return pop_pro_A[value];
})
return tab;
}
Inputs.Table(condensed_pro_max.json())
columnsA = {
var x = pivot.columnNames().slice(3);
x.pop();
return x;
}
xaxisA = {
var x = pivot.columnNames().slice(3);
for ( var i in x ){
x[i] = new Date(x[i]).toLocaleDateString().replace(/\//g,"-");
}
x.pop();
return x
}
pop_21 = d3.csv("https://raw.githubusercontent.com/opendatasicilia/comuni-italiani/main/dati/popolazione_2021.csv");
pop_21_A = Data.import({"source":pop_21,"type":"json"}).lookupArray({key:"pro_com_t",value:"pop_res_21"})
com = d3.csv("https://raw.githubusercontent.com/opendatasicilia/comuni-italiani/main/dati/comuni.csv")
com_pro_A = Data.import({"source":com,"type":"json"}).lookupArray({key:"pro_com_t",value:"den_prov"})
pop_prov = {
var __pop_prov = Data.import({"source":pop_21,"type":"json"})
__pop_prov.addColumn({source:"pro_com_t",destination:"pro"},(value) => {
return (com_pro_A[value]);
})
__pop_prov.condense({lead:"pro"})
return __pop_prov;
}
pop_pro_A = pop_prov.lookupArray({key:"pro",value:"pop_res_21"})
comuni = d3.json("https://s3.eu-central-1.amazonaws.com/maps.ixmaps.com/Istat/comuni_2020/italia_comuni_2020_g_sicilia.geojson")
ixmaps = require('https://gjrichter.github.io/ixmaps/ui/js/htmlgui_api.js')
Inputs = require("@observablehq/inputs@0.7.21/dist/inputs.umd.min.js")
Data = require('https://gjrichter.github.io/data.js/data.js')
$ = require('https://code.jquery.com/jquery-3.3.1.min.js')
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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.
