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UdG | Master's in Data Science
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Mar 1, 2024
Fork of Final exercise: Template
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Type Markdown, then Shift-Enter. Ctrl-space for more options. Arrow ↑/↓ to switch modes.

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global_data = FileAttachment("owid-co2-data.csv").csv()

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co2_amb_hdi.csv
Type Table, then Shift-Enter. Ctrl-space for more options.

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co2_amb_hdi_v2.csv
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co2_amb_hdi_3.csv
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Kyoto = FileAttachment("Kyoto_v6.csv").csv()

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Kyoto
Type Table, then Shift-Enter. Ctrl-space for more options.

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ghgcolor = "#00072D"
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//ghgcolorRange = ["#008DC2", "#00567A" , "#004D71","#004367", "#003A5D", , "#003053" , "#00264A","#001D40" , "#001336", "#00072D"]
ghgcolorRange = ["#008DC2", "#00072D"]
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co2color = "#314052"
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co2colorRange = ["#477AAF","#4574A5" ,"#426F9C" , "#406992" , "#3E638B","#3C5D81", "#3A5778","#38516E", "#354C65", "#33465B", "#314052"]
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hdiColorLow = "#F7E425"
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hdiColorMedium = "#EA7457"
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hdiColorHigh = "#BF3984"
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hdiColorVeryHigh = "#0D0887"
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newBlueRange = "blues"

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mywave = FileAttachment("wave.jpg").image()
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radioCO2GHG
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dataTransformed = meanCo2PerCapitaEmissionsPerHDICategoryAndYear.map(d => ({
...d,
year: d.year.getFullYear() // extrae solo el año del objeto Date
}));
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hdi_countries = co2_amb_hdi_4.reduce((map, item) => {
map[item.country] = item.hdi_category;
return map;
}, {});
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anyMaximPaisSeleccionat = co2_amb_hdi_4.filter(d => d.country === country_selected).reduce((max, d) => d.year > max ? d.year : max, 0);
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anyMinimPaisSeleccionat = co2_amb_hdi_4.filter(d => d.country === country_selected).reduce((min, d) => d.year < min ? d.year : min, Infinity);
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co2_amb_hdi_4
Type SQL, then Shift-Enter. Ctrl-space for more options.

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co2_amb_hdi_4
Type SQL, then Shift-Enter. Ctrl-space for more options.

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co2_amb_hdi_5.csv
Type Table, then Shift-Enter. Ctrl-space for more options.

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objetoMaxCO2 = co2_amb_hdi_4.reduce((max, obj) => obj.co2_per_capita > max.co2_per_capita ? obj : max, co2_amb_hdi_v2[0]);

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wetrg = obtenirDadesFiltrades();
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function obtenirDadesFiltrades()
{
let year1 = interval_years[0];
let year2 = interval_years[1];
let hdiCategory = hdiCategory_filter;
let parameter = parameterToAnalize_filter;
let country = country_selected;
// Filtrar los datos
let filteredData = country == "" ?
co2_amb_hdi_4.filter(d => (d.year === year1 || d.year === year2) && d.hdi_category === hdiCategory) :
//co2_amb_hdi_4.filter(d => (d.year === year1 || d.year === year2) && getHDI(d.iso_code, d.country, d.year, true, true) === hdiCategory) :
co2_amb_hdi_4.filter(d => (d.year === year1 || d.year === year2) && d.country === country);
//getHDI(isocode, countryname, year =1990, categorized = false
let transformedData = {};
filteredData.forEach(d => {
if (!transformedData[d.country]) {
transformedData[d.country] = { value_start: null, value_end: null};
}
if (d.year === year1) {
transformedData[d.country].value_start = d[parameter];
}
else if (d.year === year2) {
transformedData[d.country].value_end = d[parameter];
}
});

if(country != "")
{
// The mean of the hdi_category
let filteredDataHDICategory = co2_amb_hdi_4.filter(d => (d.year === year1 || d.year === year2) && d.hdi_category === hdiCategory);
//let filteredDataHDICategory = co2_amb_hdi_5.filter(d => (d.year === year1 || d.year === year2) && getHDI(d.iso_code, d.country, d.year, true, true) === hdiCategory);
let valuesStart = filteredDataHDICategory.filter(d => d.year === year1).map(d => d[parameter]);
let valuesEnd = filteredDataHDICategory.filter(d => d.year === year2).map(d => d[parameter]);
let medianStart = d3.median(valuesStart);
let medianEnd = d3.median(valuesEnd);
transformedData[hdiCategory] = { value_start: medianStart, value_end: medianEnd };

// The mean of the world
let filteredDataWorld = co2_amb_hdi_4.filter(d => (d.year === year1 || d.year === year2));
//let filteredDataWorld = co2_amb_hdi_5.filter(d => (d.year === year1 || d.year === year2));
let valuesStartWorld = filteredDataWorld.filter(d => d.year === year1).map(d => d[parameter]);
let valuesEndWorld = filteredDataWorld.filter(d => d.year === year2).map(d => d[parameter]);
let medianStartWorld = d3.median(valuesStartWorld);
let medianEndWorld = d3.median(valuesEndWorld);
transformedData["World"] = { value_start: medianStartWorld, value_end: medianEndWorld };
}
let resultArray = Object.keys(transformedData).map(key => ({
country: key,
...transformedData[key]
}));

return resultArray;

}
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/*miniPlots3 = html`
<div>
<!-- Fila superior con los dos primeros gráficos -->
<div style="display: flex;">
<div>${lineDirectionPlotVeryHigh}</div>
<div>${lineDirectionPlot}</div>
</div>
<!-- Fila inferior con los dos últimos gráficos -->
<div style="display: flex;">
<div>${lineDirectionPlotMedium}</div>
<div>${lineDirectionPlotLow}</div>
</div>
</div>`*/

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//emissionsCountry = emissions[country].data
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import {Scrubber} from "@mbostock/scrubber"
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import {autoSelect} from "@jashkenas/inputs"
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graficinteractiu1 = FileAttachment("graficInteractiu1.png").image()
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graficinteractiu2 = FileAttachment("graficInteractiu2.png").image()
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grafics3 = FileAttachment("grafics3.png").image()
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co2_amb_hdi_4.csv
Type Table, then Shift-Enter. Ctrl-space for more options.

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