all life is fermentation - Feynman

This library serves little purpose, like all of us and everything we do, but is a bit covered and tested, as you can see.

This libary was __init__.pyiated with typescript starter and uses the devil 👹 tslint.

In the context, the idea is to generalize and abstract the data propagation within an application, that we magically create in all of our projects. This library abstracts that, normalizes, counts, calculates and does many inefficient things that feed into our laziness.

yarn add data-juggler

The usage can be easily seen from the test, assume you are fetching some (csv like for now) data from an API and the columns have the following "type":

data = [
  { height: 190, gender: 'male', timeOfMeasure: 1552397833139 },
  { height: 170, gender: 'female', age: 22, timeOfMeasure: 1552397832139 },
  { height: 164, gender: 'female', age: 20, timeOfMeasure: 15523912333139 },
  { height: 176, gender: 'female', age: 12 }
];

types = {
  height: 'continuous',
  gender: 'categorical',
  age: 'continuous',
  timeOfMeasure: 'date'
};

const { data, moments } = dataJuggler(data, { types });

Launch the dataJuggler function with the sample data and instance types, and enjoy a beutiful dataset full of getters and stuff with everything that you need in it (this is, at least for now, a lie).

You'll get your data back (don't worry) with added properties!

const instance = data[0]

t.deepEqual(instance, {
  height: {
    raw: 190,
    scaled: 1
  },
  gender: {
    raw: 'male',
  },
  timeOfMeasure: {
    dateTime: // the day js instance of the dataset,
    isValid: true,
    iso: '2019-03-12',
    raw: 1552397832139,
    scaled: 1
  }
})
// true

On top of that you also get some getters for each variable that return the whole column, this could be cut eventually in a censorship attempt by my boss. Edit: the censorship did happen, this is not there anymore.

It could be annoying having to rescale all your functions because some time having them fitted from min-max to 0-1 is not what you need! We got your back. Another entry in the magical return object of the juggler is scalers! Imaging the classical situation below:

const d = [
  {x: 0},
  {x: 1},
  {x: 2}
]

You can simply apply dataJuggler:

const { data, scalers } = dataJuggler(d, { types: infer})

Now if you need to rescale the data you can simply do this:

const newExtent = [0, 4]
const rescaleV = scalers.x(...newExtent)
const rescaledX = data.map(datum => rescaleX(datum.v.scaled)) // [0, 0.25, 0.5]

The library accepts as a second parameter a config object of the form

const config = {
  types,
  formatter: [{ 'height': [/*stuff*/]}],
  parser: { 'height': (cm: number) => /*more stuff*/ }
}

const { data, moments, types } = dataJuggler(data, {...config});

As in the example above the types object is rather explanatory! Let's look at the other ones, but first...

If you feel adventurous you can only pass the data without any types in the config object and our advanced (read naive) detecting system will try and determine, mostly leveraging the awesome dayjs ⏰ library, the type for you and then pass it as key types in the object returned by the function.

You can also pass a custom formatter for each column type as follow.

const formatter = {
  height: [
    {
      property: 'feet',
      compute: (datum) => datum * 0.0328084
    },
    {
      property: 'rescaled',
      compute: (datum, min, max) => datum / max
    }
  ],
  timeOfMeasure: [
    {
      property: 'year',
      compute: (day) => day.format('YYYY')
    }
  ]
}

//  if we look for the same instance as before

const dataStore = dataJuggler(data, { types, formatter });

t.deepEqual(istance, {
  height: {
    raw: 190,
    scaled: 1,
    // newly added
    rescale: 1,
    feet: 6.233596,
  },
  timeOfMeasure: {
    dateTime: // the day js instance of the dataset,
    isValid: true,
    raw: 1552397832139,
    scaled: 1,
    // newly added
    year: '2019',
  }
})

// true

What if the data you are passing is not parsed correctly by the library. Once again, no worries! Just pass the parser yourself, the typechecker should prevent you from breaking everything, but hey, what do I know?

  const { data: correctlyParsedData } = dataJuggler(datasetWithDates, {
    parser: {
      d: (day: string) => dayjs(day, 'YYYY-MM-DD').unix()
    }
  })
  const { data: wronglyParsedData } = dataJuggler(datasetWithDates, {
    parser: {
      d: (day: string) => dayjs(day, 'MM-DD-YYYY').unix()
    }
  })

You can of course use other functions from within the library. For example, let's say you want to use the autoinference function and do some operations on it you can simply import it and use it

import { autoInferenceType } from 'data-juggler/build/main/lib/utils/dataInference';

const inferredType = autoInferenceType(yourData, yourTypeObject || {}, yourParser || {})

const { data, momemnts } = dataJuggler(yourData, { types: inferredType })

The types you passed in the config will not be overwritten by the library! The same can be done with other functions like computeMoments and parseDates. Not sure why you would do that but knock yourself out.

So far we have been talking about cases where you know your data at "compile time" and neither does Typescript. This hinders the ability of the library to help you structure stuff correctly but all the core functionalities are intact and shining bright. Let's assume you need to fetch some data, of which you know the structure but you don't "have" it, you need not to worry! The function dataJuggler accepts one generic which is the type of the datum so you can do something like this:

interface ExpectedDatum {
  height: number,
  name: string
}

const raw = ky.get('this.is.a.fancy.end.point.with.many.points.com/get').json()
const juggled = dataJuggler<ExpectedDatum>(raw)

If, for example, you use Mobx state tree you could do the following:

import {} from ''

export const YourFancyMSTModel = t
  .model('YourFancyMSTModel', {
    juggleType: t.frozen<InferObject<YourDataStructure || Record<string, unknown>(),
    juggledData: t.frozen<JuggledData<YourDataStructure || Record<string, unknown>(),
    moments: t.frozen<MomentsObject<YourDataStructure || Record<string, unknown>(),
  })