Published
Edited
Mar 3, 2019
// Loading necessary library
z = require('https://bundle.run/zebras@0.0.11')
// Loading necessary library
d3 = require("d3")
// Target appropriate URL for dataset
migrationURL = 'https://gist.githubusercontent.com/cesandoval/b834ac93c07e03ec5205843b97f68017/raw/6dff0d45ed26352a7a3c7afb4ace0bad1ce8ba20/MIG_18022019163824069.csv'
// Load data from target URL
migration = d3.csv(migrationURL)
// check out the dataset
z.head(20, migration)
// get dimensions of dataset
shape = ({rows: z.getCol('CO2', migration).length, columns:Object.keys(migration[0]).length})
// Specify the target column, "Country of birth/nationality," then get the unique values within it
z.unique(z.getCol('Country of birth/nationality', migration))
//overall process:
// create groupby key based on country_variable
// reduce df to the 3 variables we want to look at and parse values into numbers
// group the reduced df by country_variable and sum over those groups (this aggregates over the years)
// put those somes into a wide table, from the long format
// create groupby key from country and variable
groupLabel = z.deriveCol(r => (r.Variable + r.Country), migration)
// add group by key to migration table
migrationKeyed = z.addCol("key", groupLabel, migration)
// Produce a table where each row is a country listed in the migration table's "Country" column (NOT 'Country of birth/nationality' column)
// 3 additional data columns contain sum of inflows, outflows, or stock for that country, aggregated across years.
countryStat = z.pipe([
//subset df to just the relevant variables
z.filter(r => r['Variable'] == "Inflows of foreign population by nationality" ||
r['Variable'] == "Outflows of foreign population by nationality" ||
r['Variable'] == "Stock of foreign-born population by country of birth"),
// parse values into numbers
z.parseNums(['Value']),
// collapse dataframe by country and variable
//z.groupBy(x => x.key),
])(migrationKeyed)
// group by and sum over country and variable
migrationSums = z.gbSum("Value", z.groupBy(d => d.key, countryStat))
// fill new dataframe with the sums
// drop "TOTAL" CO2 from sums?
df_summed = {
let countryList = z.unique(z.getCol("Country", countryStat))
let varList = z.unique(z.getCol("Variable", countryStat))
let columnNames = ["country", "inflow", "outflow", "stock"]
let sums = []
for (var i = 0; i < countryList.length; i++)
{
let currRow = {country: countryList[i],
inflow: 0,
outflow: 0,
stock: 0}
let tag = ""
for (var j = 0; j < varList.length; j++)
{
tag = varList[j] + countryList[i]
currRow[columnNames[j+1]] = z.getCol("sum", (z.filter(r => r["group"] == tag, migrationSums)))[0]
}
sums.push(currRow)
}
return sums
}
{
let a = {country: "A",
inflow: 10,
outflow: 20
}
let columnNames = ["country", "inflow","outflow","stock"]
return a[columnNames[0]]
}
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//Questions
// What to do with TOT CO2? Drop? sometimes multiple totals per year: see Austria, inflows, TOT
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