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rlluo1
Published
Class_materials_11.454
Edited
Mar 2, 2019
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z = require('https://bundle.run/zebras@0.0.11')
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rainAmounts = [
18.3,
19,
14.1
]
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rainDates = Array.from({length: 3}, (_, i) => Date.now() - 1000 * 60 * 60 * 24 * i)
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newRainfall = {
let rainArray = []
for (var i=0; i<rainAmounts.length; i++) {
let currRow = {rainAmount: rainAmounts[i],
date: rainDates[i]}
rainArray.push(currRow)
}
return rainArray
}
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rainfall = {
let rainDF = []
for (var i=0; i<rainAmounts.length; i++) {
rainDF.push({precipitation: rainAmounts[i], date: rainDates[i]})
}
return rainDF
}
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daySeries = ["Monday", "Tuesday", "Wednesday"]
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myNewDF = z.addCol("Day of the Week", daySeries, newRainfall)
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newRainfall
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z.addCol("day", daySeries, rainfall)
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columns = Object.keys(rainfall[0])
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shape = ({rows: rainfall.length, columns:Object.keys(rainfall[0]).length})
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Object.keys(newRainfall[0]).length
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migrationUrl = "https://gist.githubusercontent.com/cesandoval/b834ac93c07e03ec5205843b97f68017/raw/90951b60444376eebfcbbee9beca00c083f489f6/API_SM.POP.NETM_DS2_en_csv_v2_10473747.csv"
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migration = d3.csv(migrationUrl)
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z.head(5, migration)
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migration.length
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({rows: migration.length, columns:Object.keys(migration[0]).length})
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migrationColumns = Object.keys(migration[0])
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firstRow = Object.values(migration[0])
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lastRow = Object.values(migration[migration.length-1])
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randomRow = Object.values(migration[Math.floor(Math.random() * migration.length)])
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numericalColumns = migrationColumns.slice(0, migrationColumns.length-4)
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migrationParsed = z.parseNums(numericalColumns, migration)
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z.head(5, migrationParsed)
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countryName = z.getCol('Country Name', migrationParsed)
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z.describe(z.getCol('1962', migrationParsed))
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z.valueCounts(z.getCol('Indicator Code', migrationParsed))
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z.unique(z.getCol('Indicator Code', migrationParsed))
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z.filter(r => (r['1967'] > 500 && r['1962'] >5000), migrationParsed)
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z.filter(r => r['1962'] > 0, migrationParsed)
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z.filter(r => r['Country Name'] == "China", migrationParsed)
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z.filter(r => r['1962'] > 0 && r['2012'] < 0, migrationParsed)
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//We can use argument 'asc' to reverse the sorting
migrationSorted = z.sortByCol('2017', 'des', migrationParsed)
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z.head(5, z.pickCols(['Country Name', '2017'], migrationSorted.slice(1)))
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z.filter(r => r['Country Name'] == "Cayman Islands", migrationParsed)
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isNaN(z.getCol('1960', migrationParsed)[1])
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z.filter(r => Number.isNaN(r['1962']) == false, migrationParsed)
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z.getCol('1960', migrationParsed)
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z.dropCol(["1960"], migrationParsed)
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migrationMetaUrl = "https://gist.githubusercontent.com/cesandoval/b834ac93c07e03ec5205843b97f68017/raw/90951b60444376eebfcbbee9beca00c083f489f6/Metadata_Country_API_SM.POP.NETM_DS2_en_csv_v2_10473747.csv"
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migrationMeta = d3.csv(migrationMetaUrl)
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migrationMeta.length
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migrationMetaColumns = Object.keys(migrationMeta[0])
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z.head(5, migrationMeta)
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migration_join = z.merge(migrationParsed, migrationMeta, "Country Code", "Country Code", "_df1", "_df2")
// migration_join = migrationParsed.map(x => Object.assign(x, migrationMeta.find(y => y['Country Code'] == x['Country Code'])));
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migration_2017 = z.getCol('2017', migration_join)
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z.print([
{
minMigration: z.min(migration_2017).toFixed(5),
maxMigration: z.max(migration_2017).toFixed(5),
meanMigration: z.mean(migration_2017).toFixed(5),
medianMigration: z.median(migration_2017).toFixed(5),
stdMigration: z.std(migration_2017).toFixed(5)
}
])
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migration_2017_Piped = z.pipe(
[
z.parseNums(['1992','1997','2002','2007', '2012', '2017']),
z.getCol('2017'),
z.mean()
]
)(migration_join)
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migrationCountRegion = z.gbCount("Region", migrationByRegion)
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d3 = require("d3")
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