The avatar for @udg-master-data-science
UdG | Master's in Data Science
Public
Edited
Jan 19, 2024
Fork of Final exercise (pol)
10
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data = emissions
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data
Type Table, then Shift-Enter. Ctrl-space for more options.

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viewof country = Inputs.select(sortedEmissionsByCountry, {label: "Country:"})
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Plot.plot({
subtitle: "Plot auxiliar",
width,
y: {
grid: true
},
marks: [
Plot.barY(country.filter(d => d.year > 1970), {y: (d) => d.total_ghg == '' ? NaN : Number(d.total_ghg), x: "year", tip: true}),
]
})

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entriesArray = Array.from(sortedEmissionsByCountry.entries())
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f1 = entriesArray
.flatMap(([key, values]) => values.filter(entry => entry.year === Years))
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transformedData = f1.map(entry => {
return {
Country: entry.country,
ghg: entry.total_ghg
};
});
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function transformData(sortedEmissionsByCountry) {

const entriesArray = Array.from(sortedEmissionsByCountry.entries());
const filteredData = entriesArray.flatMap(([key, values]) => values.filter(entry => entry.year === Years));

const transformedData = filteredData.map(entry => {
return {
Country: entry.country,
ghg: entry.total_ghg
};
});
return transformedData;
}
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chart1 = embed({
"$schema": "https://vega.github.io/schema/vega/v4.json",
"width": 1500,
"height": 100,
"data": [
{
"name": "places",
"values": toyData
}
],
"scales": [
{
"name": "xscale",
"type": "point",
"domain": {"data": "places", "field": "Country"},
"range": "width"
},
{
"name": "sizescale",
"type": "linear",
"domain": {"data": "places", "field": "ghg"},
"range": [0, 3000]
}
],

"axes": [
{ "orient": "bottom", "scale": "xscale" }
],

"marks": [
{
"type": "symbol",
"from": {"data": "places"},
"encode": {
"enter": {
"fill": {"value": "seagreen"},
"size": {"scale": "sizescale", "field": "ghg"},
"x": {"scale": "xscale", "field": "Country"},
"y": {"value": 50}
},
"update": {
"tooltip": {
"signal": "{Country: datum.Country, GHG: datum.ghg}"
}
}
}
}
]
})

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embed = require('vega-embed@3')
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anos= entriesArray.flatMap(([key, values]) => values.filter(entry => entry.year === 1990 || (entry.year >= 2008 && entry.year <= 2012)))
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anos1= entriesArray.flatMap(([key, values]) => values.filter(entry => entry.year === 1990 || (entry.year >= 2008 && entry.year <= 2020)))
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transformedData_all = anos.map(entry => {
return {
Country: entry.country,
year: entry.year,
ghg: entry.total_ghg,
}
})
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transformedData_all1 = anos1.map(entry => {
return {
Country: entry.country,
year: entry.year,
ghg: entry.total_ghg,
}
})
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function calcularPromedioGHG(data) {
const promedioPorPais = {};

data.forEach(entry => {
if (!promedioPorPais[entry.Country]) {
promedioPorPais[entry.Country] = {
total: 0,
count: 0,
};
}
if (entry.year >= 2008 && entry.year <= 2012) {
promedioPorPais[entry.Country].total += entry.ghg;
promedioPorPais[entry.Country].count += 1;
}
});
const resultados = Object.keys(promedioPorPais).map(pais => {
const { total, count } = promedioPorPais[pais];
const ghgPromedio = count > 0 ? total / count : 0;
return {
Country: pais,
ghg1990: data.find(entry => entry.Country === pais && entry.year === 1990)?.ghg || 0,
ghgPromedio: ghgPromedio.toFixed(2),
};
});
return resultados;
}
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function calcularPromedioGHG1(data) {
const promedioPorPais = {};

data.forEach(entry => {
if (!promedioPorPais[entry.Country]) {
promedioPorPais[entry.Country] = {
total_08_12: 0,
count_08_12: 0,
total_13_20: 0,
count_13_20: 0,
ghg1990: 0,
};
}
if (entry.year === 1990) {
promedioPorPais[entry.Country].ghg1990 = entry.ghg;
} else if (entry.year >= 2008 && entry.year <= 2012) {
promedioPorPais[entry.Country].total_08_12 += entry.ghg;
promedioPorPais[entry.Country].count_08_12 += 1;
} else if (entry.year >= 2013 && entry.year <= 2020) {
promedioPorPais[entry.Country].total_13_20 += entry.ghg;
promedioPorPais[entry.Country].count_13_20 += 1;
}
});

const resultados = Object.keys(promedioPorPais).map(pais => {
const { total_08_12, count_08_12, total_13_20, count_13_20, ghg1990 } = promedioPorPais[pais];
const ghgPromedio_08_12 = count_08_12 > 0 ? total_08_12 / count_08_12 : 0;
const ghgPromedio_13_20 = count_13_20 > 0 ? total_13_20 / count_13_20 : 0;

return {
Country: pais,
ghg1990: ghg1990,
ghgPromedio_08_12: ghgPromedio_08_12.toFixed(2),
ghgPromedio_13_20: ghgPromedio_13_20.toFixed(2),
};
});
return resultados;
}

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Country_90_promedio = calcularPromedioGHG(transformedData_all)
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Country_90_promedio1 = calcularPromedioGHG1(transformedData_all1)
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Informacion_plot= Country_90_promedio.map(entry => {
const { Country, ghg1990, ghgPromedio } = entry;
const porcentajeReduccion = ghg1990 !== 0
? ((ghgPromedio - ghg1990) / ghg1990) * 100
: 0;
const matchingEntry = filteredResults_A.find(filteredEntry => filteredEntry.country === Country);

const burdenSharingPercentage = matchingEntry ? matchingEntry['Burden_Sharing(%)'] : null;

return {
Country: Country,
ghg_1990: ghg1990,
ghgAverage_2008_2012: ghgPromedio,
percentage: porcentajeReduccion.toFixed(2),
'Burden_Sharing(%)': burdenSharingPercentage,
};
});
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Plot.plot({
height: 1000,
width: 500,
marginLeft: 100,
grid: true,
y: {
label: null,
},
marks: [
Plot.dot(Informacion_plot1,{
y: "Country",
r: d => d.ghg_1990,
fill: "currentColor",
color: "black",
}
)
]
})
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Informacion_plot1= Country_90_promedio1.map(entry => {
const { Country, ghg1990, ghgPromedio_08_12, ghgPromedio_13_20 } = entry;
const porcentajeReduccion_08_12 = ghg1990 !== 0
? ((ghgPromedio_08_12 - ghg1990) / ghg1990) * 100
: 0;
const porcentajeReduccion_13_20 = ghg1990 !== 0
? ((ghgPromedio_13_20 - ghg1990) / ghg1990) * 100
: 0;
const matchingEntry = filteredResults_A.find(filteredEntry => filteredEntry.country === Country);

const burdenSharingPercentage = matchingEntry ? matchingEntry['Burden_Sharing(%)'] : null;
const burdenSharingPercentage_1 = matchingEntry ? matchingEntry['Goal_2020(%)'] : null;

return {
Country: Country,
ghg_1990: ghg1990,
ghgAverage_2008_2012: ghgPromedio_08_12,
percentage_08_12: porcentajeReduccion_08_12.toFixed(2),
ghgAverage_2013_2020: ghgPromedio_13_20,
percentage_13_20: porcentajeReduccion_13_20.toFixed(2),
'Burden_Sharing(%)': burdenSharingPercentage,
'Goal_2020(%)': burdenSharingPercentage_1,
};
});
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Plot.plot({
height: 500,
width: 1500,
y: {
grid: true,
type: "linear",
},
marks: [
Plot.barY(Informacion_plot, {
x: "Country",
y: "percentage",
sort: { x: "-y" },
fill: "#4CAF50",
}),
Plot.ruleY([0]),
Plot.dot(Informacion_plot, {
x: "Country",
y: "Burden_Sharing(%)",
fill: "currentColor",
color: "black",
size: 6,
}),
],
});
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Plot.plot({
height: 780,
width: 1000,
marginLeft: 100,
grid: true,
title: `Performance of EU countries against the GHG reduction of the Kyoto Protocol (Period ${option})`,
subtitle: "In blue, the countries that achieved the reduction goal; in red, that did not meet it.",
x: {
axis: "top",
round: true,
label: `Avg GHG dec (%) ${option}`,
labelAnchor: "center",
type: "linear",
},
y: {
label: null,
},
marks: [
Plot.barX(Informacion_plot1, {
y: "Country",
x: x_b,
sort: { y: "-x" },
fill: d => {
if (d.Country === "European Union (27)")
{return "#a2a2a2"}
else if (d[x_b] >= 0) {
return (d[x_b]) >= (d[x_d]) ? "#e15759" : "#4e79a7";
} else {
return Math.abs(d[x_b]) < Math.abs(d[x_d]) ? "#e15759" : "#4e79a7";
}
},
channels: {'GHG 1990': "ghg_1990", 'GHG Avg over time period': Selec, 'Goal (%)': x_d},
tip: {
format: {
y: true,
'Goal (%)': true,
x: true,
'GHG 1990': true,
'GHG Avg over time period': true,
fill: false,
},
}
}),
Plot.dot(Informacion_plot1, {
y: "Country",
x: x_d,
fill: "currentColor",
color: "black",
size: 6,
}),
Plot.ruleX([0]),
],
});
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//viewof summary_raw_data = SummaryTable(emissions_raw, {label: "Emissions Data"})
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d3.max(heatMapValuesStruct[1], (d) => d.ghg);
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d3.min(heatMapValuesStruct, (d, i) => d3.min(heatMapValuesStruct[i], (d) => d.ghg));
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d3.max(heatMapValuesStruct[0], (d) => d[heatmapChosenData])
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function accessHeatMapValue(index) {
debugger
const countryIdx = parseInt(index.slice(0, 2));
const yearIdx = parseInt(index.slice(-2));

return heatMapValuesStruct[countryIdx][yearIdx][heatmapChosenData];
}
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accessHeatMapValue("0501")
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//filteredResults_A = data1
// .filter(entry => entry.UE === 'Yes' && entry.country !== 'Romania')

filteredResults_A = data1
.filter(entry => entry.UE === 'Yes')
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data1
Type Table, then Shift-Enter. Ctrl-space for more options.

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data_UE_World = {
const countriesInData4 = data2.map(item => item.country);

const filteredData4 = emissionsDataset .filter(item => countriesInData4.includes(item.country));

return filteredData4;
}

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data_UE_World
select country,year,sum(co2) from data_UE_World
group by country,year
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data_UE = {
const countriesInData2 = data2.map(item => item.country);

const filteredData = emissions.filter(item => countriesInData2.includes(item.country));
console.log(filteredData);

return filteredData;
}
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data_UE
SELECT
YEAR,
COUNTRY,
CO2,
CO2_1990::DOUBLE AS CO2_1990,
CO2_1990*0.95 AS REDUCTION_CO2_1990
FROM
(SELECT
year,
country,
co2::DOUBLE AS CO2,
(SELECT co2::double FROM data_UE t2 WHERE t2.country = t1.country AND t2.year = 1990) AS co2_1990
FROM
data_UE t1
where co2 <> '')

--WHERE country = 'Austria'
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kioto_UE
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CO2_1990 = data_UE.filter(d => d.year === "1990")
.map((d) => {
return {
country: d.country,
co2_1990: d.co2,
reduction_co2_1990: d.co2 * 0.95
}
});

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kioto_UE_2 = {
const dataZ = [];
kioto_UE
.forEach((yearData) => {
const year = parseInt(yearData.year);
const quinquennium = year + (5 - year % 5);
dataZ.push({
country: yearData.country,
year: year,
quinquennium: quinquennium,
co2: yearData.CO2,
co2_1990: yearData.CO2_1990,
red_co2_1990: yearData.REDUCTION_CO2_1990
});
});
return dataZ;
}
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countries_data = d3.group(kioto_UE_2, d => d.country)
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import {select, slider} from "@jashkenas/inputs"
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