The avatar for @cesandoval
Carlos Sandoval Olascoaga
Big walls, cities, computing, and design.
Published
Edited
Feb 21, 2019
Fork of Introduction to zebras - a data analysis library for JavaScript
•
4 forks
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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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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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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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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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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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migrationColumns = Object.keys(migration[0])
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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.print([
{
minReturn: z.min(dailyReturns).toFixed(5),
maxReturn: z.max(dailyReturns).toFixed(5),
meanDailyReturn: z.mean(dailyReturns).toFixed(5),
medianDailyReturn: z.median(dailyReturns).toFixed(5),
dailyVolatility: z.std(dailyReturns).toFixed(5)
}
])
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vegalite({
data: {values: dataRolling},
width: 700,
height: 350,
mark: {type: "line", strokeWidth: .9},
encoding: {
x: {field: "Date", type: "temporal"},
y: {field: "rollingVolatility", type: "quantitative", scale: {type:'linear'}}
}
})
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dates = z.deriveCol(r=>Date.parse(r.Date), dataWithReturns)
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dataDates = z.addCol('DateStamp', dates, dataWithReturns)
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days = ['Sunday','Monday','Tuesday','Wednesday','Thursday','Friday','Saturday']
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daysOfWeek = z.deriveCol(x=>days[(new Date(x.DateStamp)).getDay()], dataDates)
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dataDays = z.addCol('Day', daysOfWeek, dataDates)
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dataGrouped = z.groupBy(x=>x.Day, dataDays)
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dataPiped = z.pipe(
[
z.parseNums(['Open','High','Low','Close', 'Adj Close', 'Volume']),
z.getCol('Close'),
z.pctChange(),
z.mean()
]
)(data)
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data = d3.csv("https://gist.githubusercontent.com/nickslevine/b518a5fc788099d8955e77b4a4dc0628/raw/60edf99d70141a44c05fc73dff758c219f1684b4/sandp.csv")
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dataParsed = z.parseNums(['Open','High','Low','Close', 'Adj Close', 'Volume'], data)
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vegalite = require("@observablehq/vega-lite")
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prices = z.getCol('Close', dataParsed)
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dailyReturns = z.pctChange(prices)
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dataWithReturns = z.addCol('DailyReturns', dailyReturns, dataParsed)

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dataSorted = z.sortByCol('DailyReturns', 'asc', dataWithReturns)
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rollingVolatility = z.rolling(z.std, 252, dailyReturns)
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rollingMean = z.rolling(z.mean, 252, dailyReturns)
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dataRolling = z.pipe([
z.addCol('rollingVolatility', rollingVolatility),
z.addCol('rollingMean', rollingMean),
])(dataWithReturns)
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annualisedVolatility = z.deriveCol((x=>x.rollingVolatility * Math.sqrt(252)), dataRolling)
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