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Edited
Dec 22, 2023
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death-rate-amphetamine@2.csv
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death-rate-from-opioid-use@1.csv
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death-rates-cocaine@1.csv
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death-rates-from-drug-use-disorders@1.csv
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deaths-drug-overdoses@1.csv
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deaths-due-to-illicit-drug-use-by-age@1.csv
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deaths-from-drug-use-disorders-by-age@1.csv
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deaths-illicit-drugs@1.csv
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drug-disorder-dalys-rate@1.csv
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drug-overdose-death-rates@1.csv
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mental-health-as-risk-for-drug-dependency@1.csv
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prevalence-of-drug-use-disorders-by-age@1.csv
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share-with-drug-use-disorders@1.csv
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viewof year03 = Inputs.range([minYear, maxYear], {label: "Год", step: 1})
import {PieChart} from "@d3/pie-chart"
transformedDeathSums = Object.keys(deathSums).map(key => {
if (key !== 'Year') {
return {
Year: deathSums.Year,
name: nameMap[key],
value: deathSums[key]
};
}
}).filter(Boolean);
data731 = deathRateAmphetamine2.concat(deathRateFromOpioidUse1, deathRatesCocaine1, deathRatesFromDrugUseDisorders1);
minYear = deathByDrug.reduce((min, row) => Math.min(min, row.Year), deathByDrug[0].Year);
deathByDrug1 = data731.map( function (i) {
return {
Entity : i['Entity'],
Year : i['Year'],
Deaths_Amphetamine : i['Deaths - Amphetamine use disorders - Sex: Both - Age: Age-standardized (Rate)'],
Deaths_Opioid : i['Deaths - Opioid use disorders - Sex: Both - Age: Age-standardized (Rate)'],
Deaths_Cocaine : i['Deaths - Cocaine use disorders - Sex: Both - Age: Age-standardized (Rate)'],
DrugRelatedDeaths : i['Deaths - Drug use disorders - Sex: Both - Age: Age-standardized (Rate)'],
}})
deathByDrug3 = deathByDrug1.reduce(function(acc, cur) {
var key = cur.Entity + '-' + cur.Year;
if (!acc[key]) {
acc[key] = {
Entity: cur.Entity,
Year: cur.Year,
Deaths_Amphetamine: 0,
Deaths_Opioid: 0,
Deaths_Cocaine: 0,
DrugRelatedDeaths: 0
};
}
acc[key].Deaths_Amphetamine += isNaN(cur.Deaths_Amphetamine) ? 0 : cur.Deaths_Amphetamine;
acc[key].Deaths_Opioid += isNaN(cur.Deaths_Opioid) ? 0 : cur.Deaths_Opioid;
acc[key].Deaths_Cocaine += isNaN(cur.Deaths_Cocaine) ? 0 : cur.Deaths_Cocaine;
acc[key].DrugRelatedDeaths += isNaN(cur.DrugRelatedDeaths) ? 0 : cur.DrugRelatedDeaths;
return acc;
}, {});
deathByDrug = Object.values(deathByDrug3);
Inputs.table(deathByDrug)
maxYear = deathByDrug.reduce((max, row) => Math.max(max, row.Year), deathByDrug[0].Year);
data2019 = deathByDrug.filter(d => d.Year === year01 && (d.Entity === country01 || country01 === "World"));
deathSums = data2019.map(d => ({
Year: d.Year,
Deaths_Amphetamine: d3.mean(data2019, d => d.Deaths_Amphetamine),
Deaths_Opioid: d3.mean(data2019, d => d.Deaths_Opioid),
Deaths_Cocaine: d3.mean(data2019, d => d.Deaths_Cocaine),
DrugRelatedDeaths: d3.mean(data2019, d => d.DrugRelatedDeaths)
}))[0];
nameMap = ({
"Deaths_Amphetamine": "Смерти от амфетамина",
"Deaths_Opioid": "Смерти от опиоидов",
"Deaths_Cocaine": "Смерти от кокаина",
"DrugRelatedDeaths": "Смерти Связанные с Наркотиками"
});
prevalenceOfDrugUseDisordersByAge = prevalenceOfDrugUseDisordersByAge1.map( function (i) {
return {
Entity : i['Entity'],
Year : i['Year'],
age5_14 : i['Prevalence - Drug use disorders - Sex: Male - Age: 5-14 years (Percent)'],
age10_14 : i['Prevalence - Drug use disorders - Sex: Both - Age: 10 to 14 (Percent)'],
age15_19 : i['Prevalence - Drug use disorders - Sex: Both - Age: 15 to 19 (Percent)'],
age20_24 : i['Prevalence - Drug use disorders - Sex: Both - Age: 20 to 24 (Percent)'],
age25_29 : i['Prevalence - Drug use disorders - Sex: Both - Age: 25 to 29 (Percent)'],
age30_34 : i['Prevalence - Drug use disorders - Sex: Both - Age: 30 to 34 (Percent)'],
age70 : i['Prevalence - Drug use disorders - Sex: Both - Age: 70+ years (Percent)'],
age15_49 : i['Prevalence - Drug use disorders - Sex: Both - Age: 15-49 years (Percent)'],
}})
data2chart = prevalenceOfDrugUseDisordersByAge.filter(d => d.Year === year02 && (d.Entity === country02 || country02 === "World"));
nameMap2 = ({
"age5_14": "5-14",
"age10_14": "10-14",
"age15_19": "15-19",
"age20_24": "20-24",
"age25_29": "25-29",
"age30_34": "30-34",
"age70": "70+",
"age15_49": "15-49"
});
chartData = Object.keys( data2chart[0]).map(key => {
if (key.startsWith("age")) {
return {
name: nameMap2[key],
"Возрастная группа": nameMap2[key],
"%": data2chart[0][key]
};
}
}).filter(Boolean); // удаляем undefined элементы
data3 = prevalenceOfDrugUseDisordersByAge.filter(d => (d.Entity === country03 || country03 === "World"));
transformedData3 = data3.flatMap(item => {
return Object.entries(item).map(([key, value]) => {
if (key.startsWith("age")) {
return {
year: item.Year,
name: nameMap2[key],
"Возрастная группа": nameMap2[key],
"%": value
};
}
}).filter(Boolean); // удаляем undefined элементы
});
data5 = deathByDrug.filter(d => d.Year === year03);
groupedData6 = d3.group(data5, d => d.Entity);
deathSums6 = Array.from(groupedData6, ([Entity, data]) => ({
Entity: Entity,
death: d3.mean(data, d => d.Deaths_Amphetamine + d.Deaths_Opioid + d.Deaths_Cocaine + d.DrugRelatedDeaths)/100
}));
prevalenceOfDrugUseDisordersMalesVsFemales = prevalenceOfDrugUseDisordersMalesVsFemales1.filter(d => d.Year === year7 && (d.Entity === country7 || country7 === "World"));
prevalenceOfDrugUseDisordersMalesVsFemales3 = prevalenceOfDrugUseDisordersMalesVsFemales.flatMap(item => [
{Category: "Male", Value: item["Prevalence - Drug use disorders - Sex: Male - Age: Age-standardized (Percent)"]},
{Category: "Female", Value: item["Prevalence - Drug use disorders - Sex: Female - Age: Age-standardized (Percent)"]}
]);
prevalenceOfDrugUseDisordersMalesVsFemales2 = prevalenceOfDrugUseDisordersMalesVsFemales1.filter(d => d.Year === year8);
prevalenceOfDrugUseDisordersMalesVsFemales4 = prevalenceOfDrugUseDisordersMalesVsFemales2.map(item => ({
"Entity": item["Entity"],
"Male": item["Prevalence - Drug use disorders - Sex: Male - Age: Age-standardized (Percent)"],
"Female": item["Prevalence - Drug use disorders - Sex: Female - Age: Age-standardized (Percent)"]
}));
prevalenceOfDrugUseDisordersMalesVsFemales6 = prevalenceOfDrugUseDisordersMalesVsFemales4.filter(item =>
!isNaN(item["Male"]) && !isNaN(item["Female"])
);
Purpose-built for displays of data
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.
