Analyze data with diverse structures using crossfiltering technology

Question

Analyze data with diverse structures using crossfiltering technology

Exploring the world of crossfilter is a new adventure for me as I am just starting out and have limited experience with handling data.

The data I am working with has a varying structure, as shown below:

const data = [
  {_id: 001, a: 10, b: 11, c:  12},
  {_id: 002, a: 11,        c:  13},
  {_id: 003,        b: 12, c:  14},
  {_id: 004,        f: 102       },  
  {_id: 005, e:100, f:101, g: 102}
];

Not every object in the dataset shares the same set of keys, which is leading to incorrect values being returned when using

dimension.top(), dimension.bottom()

For instance:

const by_a = cf.dimension(function(d){return d.a};
const max_a = by_a.top(1)[0];
// The expected result is max_a =  { _id: 002, a: 11, c:  13}
// However, a different object is returned.

const by_f = cf.dimension(function(d){return d.f};
const min_f = by_f.bottom(1)[0];
// The expected result should be min_f = { _id: 004, e:100, f:101, g: 102}
// Yet, a different object is returned once again.

Despite consulting the Crossfilter Gotchas to tackle this issue, I am still unable to decipher if any of those scenarios apply to my case or if a solution exists. I have not found a similar question elsewhere. Seeking assistance to successfully execute basic crossfilter queries. Your help is greatly appreciated. Thank you.

javascript crossfilter

Answer 1

Answer №1

Indeed, this is a common pitfall. When attempting to access a field in JavaScript that does not exist, the default value returned is undefined.

Subsequently, if this undefined value is then compared to a number, it will be coerced into NaN.

As NaN always evaluates as false, sorting algorithms can be negatively affected by this behavior.

To ensure the desired (or at least predictable) outcome, define your key function as follows:

const by_a = cf.dimension(function(d){return d.a || 0; };

Alternatively, if you wish to consistently place such values at the bottom, even when dealing with negative numbers:

const by_a = cf.dimension(function(d){return d.a || -Infinity; };

I have included an example illustrating the issue with "natural ordering".

Answer 2

Indeed, this is a common pitfall. When attempting to access a field in JavaScript that does not exist, the default value returned is undefined.

Subsequently, if this undefined value is then compared to a number, it will be coerced into NaN.

As NaN always evaluates as false, sorting algorithms can be negatively affected by this behavior.

To ensure the desired (or at least predictable) outcome, define your key function as follows:

const by_a = cf.dimension(function(d){return d.a || 0; };

Alternatively, if you wish to consistently place such values at the bottom, even when dealing with negative numbers:

const by_a = cf.dimension(function(d){return d.a || -Infinity; };

I have included an example illustrating the issue with "natural ordering".

Answer 3

Answer №2

After following Gordon's advice, I have analyzed the results. Here is the dataset that was considered:

const data = [
  {_id: "001", a: 10, b: 11, c:  12},
  {_id: "002", a: 11,        c:  13},
  {_id: "003", a:-11,        c:  13},
  {_id: "004", a:  0,        c:  13},
  {_id: "005",        b: 12, c:  14},
  {_id: "006",        f: 102       },  
  {_id: "007", e:100, f: 101,g: 102}
];

By using a callback that defaults to zero if a field is missing, the following results were obtained:

// Where dim is the field I'm dimensioning the data by
const by_dim = cf.dimension( d => d.dim || 0 ); 

// Logging the results of by_a.top(7)
0:{_id: "002", a: 11, c: 13} // Correct
1:{_id: "001", a: 10, b: 11, c: 12} //Correct
2:{_id: "007", e: 100, f: 101, g: 102} //Incorrect
3:{_id: "006", f: 102}
4:{_id: "005", b: 12, c: 14}
5:{_id: "004", a: 0, c: 13} //Should have been at index 2
6:{_id: "003", a: -11, c: 13} // Should have been at index 3

// Logging the results of  by_a.bottom(7)
0:{_id: "003", a: -11, c: 13} // Correct
1:{_id: "004", a: 0, c: 13} // Correct
2:{_id: "005", b: 12, c: 14} // Incorrect
3:{_id: "006", f: 102} 
4:{_id: "007", e: 100, f: 101, g: 102}
5:{_id: "001", a: 10, b: 11, c: 12} //Should have been at index 2
6:{_id: "002", a: 11, c: 13} //Should have been at index 3 ..

The dimensioning callback returning 0 when a field is missing seems to place those objects between objects containing that field with values >0 and =< 0. However, this approach is effective for filtering out objects that do not contain the dimension in question using libraries like Lodash.

Thank you.

Answer 4

After following Gordon's advice, I have analyzed the results. Here is the dataset that was considered:

const data = [
  {_id: "001", a: 10, b: 11, c:  12},
  {_id: "002", a: 11,        c:  13},
  {_id: "003", a:-11,        c:  13},
  {_id: "004", a:  0,        c:  13},
  {_id: "005",        b: 12, c:  14},
  {_id: "006",        f: 102       },  
  {_id: "007", e:100, f: 101,g: 102}
];

By using a callback that defaults to zero if a field is missing, the following results were obtained:

// Where dim is the field I'm dimensioning the data by
const by_dim = cf.dimension( d => d.dim || 0 ); 

// Logging the results of by_a.top(7)
0:{_id: "002", a: 11, c: 13} // Correct
1:{_id: "001", a: 10, b: 11, c: 12} //Correct
2:{_id: "007", e: 100, f: 101, g: 102} //Incorrect
3:{_id: "006", f: 102}
4:{_id: "005", b: 12, c: 14}
5:{_id: "004", a: 0, c: 13} //Should have been at index 2
6:{_id: "003", a: -11, c: 13} // Should have been at index 3

// Logging the results of  by_a.bottom(7)
0:{_id: "003", a: -11, c: 13} // Correct
1:{_id: "004", a: 0, c: 13} // Correct
2:{_id: "005", b: 12, c: 14} // Incorrect
3:{_id: "006", f: 102} 
4:{_id: "007", e: 100, f: 101, g: 102}
5:{_id: "001", a: 10, b: 11, c: 12} //Should have been at index 2
6:{_id: "002", a: 11, c: 13} //Should have been at index 3 ..

The dimensioning callback returning 0 when a field is missing seems to place those objects between objects containing that field with values >0 and =< 0. However, this approach is effective for filtering out objects that do not contain the dimension in question using libraries like Lodash.

Thank you.

Analyze data with diverse structures using crossfiltering technology

Answer №1

Answer №2

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