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https://github.com/accurat/data-juggler

A data wrapper and enricher 🤹‍♂️
https://github.com/accurat/data-juggler

data-structures mobx-state-tree

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A data wrapper and enricher 🤹‍♂️

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README 

        
# data-juggler 🤹‍♀️



_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__.py`iated with [typescript starter](https://github.com/bitjson/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.



## Usage



### Installations



```bash

yarn add data-juggler

```



### Basic usage



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":



```javascript

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).



### Properties



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



```javascript



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.



## The rescaling functions



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:



```javascript

const d = [

  {x: 0},

  {x: 1},

  {x: 2}

]

```



You can simply apply `dataJuggler`:



```javascript

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

```



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



```javascript

const newExtent = [0, 4]

const rescaleV = scalers.x(...newExtent)

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

```



## The configuration object



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



```javascript

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...



### Annoyed by having to pass the types yourself? Look no further!



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](https://github.com/iamkun/dayjs) ⏰ library, the type for you and then pass it as key `types` in the object returned by the function.



### Custom formatter



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



```javascript



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



```



### Custom parser



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?



```javascript

  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()

    }

  })

```



## Using other libary functions



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



```javascript

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.



## A more structured example, without having the data structure



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:



```javascript

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(raw)



```



## The Mobx-state-tree in the room



If, for example, you use [Mobx state tree](https://github.com/mobxjs/mobx-state-tree) you could do the following:



```javascript

import {} from ''



export const YourFancyMSTModel = t

  .model('YourFancyMSTModel', {

    juggleType: t.frozen(),

    juggledData: t.frozen(),

    moments: t.frozen(),

  })

```