Senior Data Producer for CBC/Radio-Canada. Computational journalism. Data analysis and data visualizations.
sda = import("https://esm.sh/jsr/@nshiab/simple-data-analysis@3.13.9/web")
sdb = new sda.SimpleWebDB().start()
tableTemperature = {
// We create a new table.
const tableTemperature = sdb.newTable("temperature");
// We fetch daily temperatures for three cities.
await tableTemperature.fetchData(
"https://raw.githubusercontent.com/nshiab/simple-data-analysis/main/test/data/files/dailyTemperatures.csv"
);
// We compute the decade from each date
// and put the result in the decade column.
await tableTemperature.addColumn(
"decade",
"integer",
"FLOOR(YEAR(time)/10)*10" // This is SQL
);
// We summarize the data by computing
// the average temperature
// per decade and per city.
await tableTemperature.summarize({
values: "t",
categories: ["decade", "id"],
summaries: "mean"
});
// We run linear regressions
// to check for trends.
await tableTemperature.linearRegressions({
x: "decade",
y: "mean",
categories: "id",
decimals: 4
});
// We return the table.
return tableTemperature;
}
Inputs.table(await tableTemperature.getData())
tableWithNames = {
// We create a new table.
const tableCities = sdb.newTable("cities");
// We load another file with
// the cities ids and names.
await tableCities.fetchData(
"https://raw.githubusercontent.com/nshiab/simple-data-analysis/main/test/data/files/cities.csv"
);
// We join the two tables based on the ids.
// By default, join will automatically look
// for a common column, do a left join, and
// put the result in the left table.
await tableCities.join(tableTemperature);
// We select the columns of interest.
await tableCities.selectColumns(["city", "slope", "yIntercept", "r2"]);
// We return the table with the joined and selected data.
return tableCities;
}
Inputs.table(await tableWithNames.getData())
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