quick-pivot
v2.7.0
Published
a utility for quickly pivoting data
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Readme
What it does
Say you have this example data set:
With this tool you can pivot the data given a particular row and column category:
Or given multiple rows and a column category:
Or multiple columns and a row category:
Or any combination of rows and/or columns
Example use
Install with npm:
npm install --save quick-pivot
import Pivot from 'quick-pivot';
const dataArray = [
['name', 'gender', 'house', 'age'],
['Jon', 'm', 'Stark', 14],
['Arya', 'f', 'Stark', 10],
['Cersei', 'f', 'Baratheon', 38],
['Tywin', 'm', 'Lannister', 67],
['Tyrion', 'm', 'Lannister', 34],
['Joffrey', 'm', 'Baratheon', 18],
['Bran', 'm', 'Stark', 8],
['Jaime', 'm', 'Lannister', 32],
['Sansa', 'f', 'Stark', 12]
];
const rowsToPivot = ['name'];
const colsToPivot = ['house', 'gender'];
const aggregationDimension = 'age';
const aggregator = 'sum';
const pivot = new Pivot(dataArray, rowsToPivot, colsToPivot, aggregationDimension, aggregator);
console.log('pivot.data', pivot.data, 'pivot.data.table', pivot.data.table);
console logs:
pivot.data
{ table:
[ { value: [Object], depth: 0, type: 'colHeader', row: 0 },
{ value: [Object], depth: 1, type: 'colHeader', row: 1 },
{ value: [Object], type: 'data', depth: 0, row: 2 },
{ value: [Object], type: 'data', depth: 0, row: 3 },
{ value: [Object], type: 'data', depth: 0, row: 4 },
{ value: [Object], type: 'data', depth: 0, row: 5 },
{ value: [Object], type: 'data', depth: 0, row: 6 },
{ value: [Object], type: 'data', depth: 0, row: 7 },
{ value: [Object], type: 'data', depth: 0, row: 8 },
{ value: [Object], type: 'data', depth: 0, row: 9 },
{ value: [Object], type: 'data', depth: 0, row: 10 },
{ value: [Object], type: 'aggregated' } ],
rawData:
[ { value: [Object], depth: 0, type: 'colHeader', row: 0 },
{ value: [Object], depth: 1, type: 'colHeader', row: 1 },
{ value: [Object], type: 'data', depth: 0 },
{ value: [Object], type: 'data', depth: 0 },
{ value: [Object], type: 'data', depth: 0 },
{ value: [Object], type: 'data', depth: 0 },
{ value: [Object], type: 'data', depth: 0 },
{ value: [Object], type: 'data', depth: 0 },
{ value: [Object], type: 'data', depth: 0 },
{ value: [Object], type: 'data', depth: 0 },
{ value: [Object], type: 'data', depth: 0 } ] }
pivot.data.table
[ { value:
[ 'sum age',
'Stark',
'Stark',
'Baratheon',
'Baratheon',
'Lannister',
'Totals' ],
depth: 0,
type: 'colHeader',
row: 0 },
{ value: [ 'sum age', 'f', 'm', 'f', 'm', 'm', '' ],
depth: 1,
type: 'colHeader',
row: 1 },
{ value: [ 'Arya', 10, '', '', '', '', 10 ],
type: 'data',
depth: 0,
row: 2 },
{ value: [ 'Bran', '', 8, '', '', '', 8 ],
type: 'data',
depth: 0,
row: 3 },
{ value: [ 'Cersei', '', '', 38, '', '', 38 ],
type: 'data',
depth: 0,
row: 4 },
{ value: [ 'Jaime', '', '', '', '', 32, 32 ],
type: 'data',
depth: 0,
row: 5 },
{ value: [ 'Joffrey', '', '', '', 18, '', 18 ],
type: 'data',
depth: 0,
row: 6 },
{ value: [ 'Jon', '', 14, '', '', '', 14 ],
type: 'data',
depth: 0,
row: 7 },
{ value: [ 'Sansa', 12, '', '', '', '', 12 ],
type: 'data',
depth: 0,
row: 8 },
{ value: [ 'Tyrion', '', '', '', '', 34, 34 ],
type: 'data',
depth: 0,
row: 9 },
{ value: [ 'Tywin', '', '', '', '', 67, 67 ],
type: 'data',
depth: 0,
row: 10 },
{ value: [ 'Totals', 22, 22, 38, 18, 133, '' ], type: 'aggregated' } ]
API
Pivot data
value
The data
value returns an object with keys table
and rawData
. table
is an array of objects with each object containing four keys (except for the last object which is an aggregated row of all the previous data rows based on the selected aggregation function):
value
- Array which contains the result of the pivot to be renderedtype
- Enumerated string describing what this data row contains, [data
,rowHeader
, orcolHeader
]depth
- Number describing how deeply nested the row is within a parent rowrow
- Number describing the original row index within the table
rawData
is an array of objects with three keys:
value
- Array which contains the data that makes up that particular rowtype
- Enumerated string describing what this data row contains, [data
,rowHeader
, orcolHeader
]depth
- Number describing how deeply nested the row is within a parent row
Syntax
Note: If modules are not supported in your environment, you can also require var Pivot = require('quick-pivot');
import Pivot from 'quick-pivot';
const pivot = new Pivot(dataArray, rows, columns, [aggregationDimension or CBfunction], [aggregator or initialValue], rowHeader, sortFunction, columnSortFunction);
First way to use it:
dataArray
required is one of the following:- array of arrays ( the array in first index is assumed to be your headers, see the example above)
- array of objects (the keys of each object are the headers)
- a single array (a single column of data where the first element is the header)
rows
is an array of strings (the rows you want to pivot on) or an empty array requiredcolumns
is an array of strings (the columns you want to pivot on) or an empty array requiredaggregationDimension
is a string (the category you want to accumulate values for) requiredaggregator
is an enumerated string - either'sum'
,'count'
,'min'
,'max'
, or'average'
(the type of accumulation you want to perform). If no type is selected,'count'
is chosen by defaultrowHeader
is a string (this value will appear above the rows)sortFunction
is a custom sorting function for rows. Default sorting used if null- Sort Function should be in the form
(row) => (a,b) => Number
. This Function will be called for each row pivoted on (right to left) and must return a traditional Array.sort function as a result. A Function equaling() => {}
will direct the Pivot to skip the sorting phase.
- Sort Function should be in the form
columnSortFunction
is a custom sorting function for columns. No sorting used if undefined- Column Sort Function should be in the form
(data, columns, columnIndex) => (a,b) => Number
. This Function will be called for each column pivoted on and must return a traditional Array.sort function as a result.
- Column Sort Function should be in the form
Second way to use it:
Parameters are the same as the first except for two, aggregationDimension
and aggregator
. Instead of aggregationDimension
and aggregator
, you can use the following:
CBfunction
is a callback function that receives four parametersCBfunction(acc, curr, index, arr)
whereacc
is an accumulation value,curr
is the current element being processed,index
is the index of the current element being processed andarr
is the array that is being acted on. This function must return the accumulation value (this is very similar to javascript's.reduce
) requiredinitialValue
is the starting value for the callback function. If no starting value is selected,0
is used by default.
Methods/Instance Variables
.data
Instance variable that returns the data array shown above
.update(dataArray, rows, columns, [aggregationDimension or CBfunction], [aggregator or initialValue], rowHeader)
Updates the .data
instance variable. The update
method is chainable.
.collapse(rowNum)
Collapses data into the specified row header provided. rowNum
is the row header's current index within the table (Not the original row index that is provided in the object). The collapse
method is chainable
.expand(rowNum)
Expands collapsed data that has previously been collapsed. The expand
method is chainable.
.collapseAll()
Collapses all data. The collapseAll
method is chainable.
.expandAll()
Expands all data. The expandAll
method is chainable.
.toggle(rowNum)
Toggles data from collapsed to expanded or vice-versa. The toggle
method is chainable.
.getData(rowNum)
Returns the data that comprises a collapsed row
.getUniqueValues(fieldName)
Returns all the unique values for a particular field as an array
.filter([fieldName or CBfunction], filterValues, [filterType])
Filters out values based on either:
- string
fieldName
field to filter on, arrayfilterValues
values to filter, stringfilterType
optional enumerated string either'include'
or'exclude'
(defaults to exclude if not provided) - function
CBfunction(element, index, array)
which iterates over each element in array (similar to Javascript array.filter
method)
Example with callback function
Check out the test spec for more examples.
import Pivot from 'quick-pivot';
function cbFunc(acc, curr, index, arr){
acc += curr.age;
if(index === arr.length - 1) return acc / arr.length;
return acc;
}
const pivot = new Pivot(dataArray, ['gender'], ['house'], cbFunc, 0, 'average age');
console.log(pivot.data.table);
/*
[ { value: [ 'average age', 'Stark', 'Baratheon', 'Lannister', 'Totals' ],
depth: 0,
type: 'colHeader',
row: 0 },
{ value: [ 'f', 11, 38, '', 20 ], type: 'data', depth: 0, row: 1 },
{ value: [ 'm', 11, 18, 44.333333333333336, 28.833333333333332 ],
type: 'data',
depth: 0,
row: 2 },
{ value: [ 'Totals', 11, 28, 44.333333333333336, '' ],
type: 'aggregated' } ]
*/
pivot.update(dataArray, ['gender', 'name'], ['house'], cbFunc, 0, 'average age')
console.log(pivot.data.table);
/*
[ { value: [ 'average age', 'Stark', 'Baratheon', 'Lannister', 'Totals' ],
depth: 0,
type: 'colHeader',
row: 0 },
{ value: [ 'f', 11, 38, '', '' ],
depth: 0,
type: 'rowHeader',
row: 1 },
{ value: [ 'Arya', 10, '', '', 10 ],
type: 'data',
depth: 1,
row: 2 },
{ value: [ 'Cersei', '', 38, '', 38 ],
type: 'data',
depth: 1,
row: 3 },
{ value: [ 'Sansa', 12, '', '', 12 ],
type: 'data',
depth: 1,
row: 4 },
{ value: [ 'm', 11, 18, 44.333333333333336, '' ],
depth: 0,
type: 'rowHeader',
row: 5 },
{ value: [ 'Bran', 8, '', '', 8 ],
type: 'data',
depth: 1,
row: 6 },
{ value: [ 'Jaime', '', '', 32, 32 ],
type: 'data',
depth: 1,
row: 7 },
{ value: [ 'Joffrey', '', 18, '', 18 ],
type: 'data',
depth: 1,
row: 8 },
{ value: [ 'Jon', 14, '', '', 14 ],
type: 'data',
depth: 1,
row: 9 },
{ value: [ 'Tyrion', '', '', 34, 34 ],
type: 'data',
depth: 1,
row: 10 },
{ value: [ 'Tywin', '', '', 67, 67 ],
type: 'data',
depth: 1,
row: 11 },
{ value: [ 'Totals', 11, 28, 44.333333333333336, '' ],
type: 'aggregated' } ]
*/
pivot.collapse(1);
console.log(pivot.data.table);
/*
[ { value: [ 'average age', 'Stark', 'Baratheon', 'Lannister', 'Totals' ],
depth: 0,
type: 'colHeader',
row: 0 },
{ value: [ 'f', 11, 38, '', '' ],
depth: 0,
type: 'rowHeader',
row: 1 },
{ value: [ 'm', 11, 18, 44.333333333333336, '' ],
depth: 0,
type: 'rowHeader',
row: 5 },
{ value: [ 'Bran', 8, '', '', 8 ],
type: 'data',
depth: 1,
row: 6 },
{ value: [ 'Jaime', '', '', 32, 32 ],
type: 'data',
depth: 1,
row: 7 },
{ value: [ 'Joffrey', '', 18, '', 18 ],
type: 'data',
depth: 1,
row: 8 },
{ value: [ 'Jon', 14, '', '', 14 ],
type: 'data',
depth: 1,
row: 9 },
{ value: [ 'Tyrion', '', '', 34, 34 ],
type: 'data',
depth: 1,
row: 10 },
{ value: [ 'Tywin', '', '', 67, 67 ],
type: 'data',
depth: 1,
row: 11 },
{ value: [ 'Totals', 11, 28, 44.333333333333336, '' ],
type: 'aggregated' } ]
*/
console.log(pivot.getData(1));
/*
[ { value: [ 'Arya', [Array], '', '' ], type: 'data', depth: 1 },
{ value: [ 'Cersei', '', [Array], '' ], type: 'data', depth: 1 },
{ value: [ 'Sansa', [Array], '', '' ], type: 'data', depth: 1 } ]
*/
console.log(pivot.getData(1)[0].value)
/*
[ 'Arya',
[ { name: 'Arya', gender: 'f', house: 'Stark', age: 10 } ],
'',
'' ]
*/
pivot.collapse(2);
console.log(pivot.data.table);
/*
[ { value: [ 'average age', 'Stark', 'Baratheon', 'Lannister', 'Totals' ],
depth: 0,
type: 'colHeader',
row: 0 },
{ value: [ 'f', 11, 38, '', '' ],
depth: 0,
type: 'rowHeader',
row: 1 },
{ value: [ 'm', 11, 18, 44.333333333333336, '' ],
depth: 0,
type: 'rowHeader',
row: 5 },
{ value: [ 'Totals', 11, 28, 44.333333333333336, '' ],
type: 'aggregated' } ]
*/
pivot.expand(1);
console.log(pivot.data.table);
/*
[ { value: [ 'average age', 'Stark', 'Baratheon', 'Lannister', 'Totals' ],
depth: 0,
type: 'colHeader',
row: 0 },
{ value: [ 'f', 11, 38, '', '' ],
depth: 0,
type: 'rowHeader',
row: 1 },
{ value: [ 'Arya', 10, '', '', 10 ],
type: 'data',
depth: 1,
row: 2 },
{ value: [ 'Cersei', '', 38, '', 38 ],
type: 'data',
depth: 1,
row: 3 },
{ value: [ 'Sansa', 12, '', '', 12 ],
type: 'data',
depth: 1,
row: 4 },
{ value: [ 'm', 11, 18, 44.333333333333336, '' ],
depth: 0,
type: 'rowHeader',
row: 5 },
{ value: [ 'Totals', 11, 28, 44.333333333333336, '' ],
type: 'aggregated' } ]
Changes
Check out the change log