pivot
A minimalist pivot table library for TypeScript/JavaScript. While small (a mere 485 bytes when minified), this library is large in capability, supporting derived and custom dimensions, derived fields for dimensions and calculations, composite dimensions, filtering.
The library also provides a modest set of numerical selectors. Suggestions for additions, or better still contributions, are welcome.
Why create another pivot table library?
There are plenty of pivot table libraries in existence, so why create another one? Well, this is a spin-off from the steelbreeze/landscape project, where instead of aggregating numerical data from the pivot cube, non-numerical data is needed.
It also focuses just on dimension and cube creation, without any layout considerations keeping it small and unopinionated.
n-cubes
The libary allows 1-n dimensions to be passed into the pivot function allowing n-cube (or hypercube) generation.
Installation
NPM
For installation via the node package manager:
npm i @steelbreeze/pivot
Web
For web via a CDN:
import * as pivot from 'https://cdn.skypack.dev/@steelbreeze/pivot';Documentation
The documentation can be found here, and more discussion in the Wiki.
Example
The following is the result of pivoting publicly available information about the Fulham Football Club men's squad at the end of the 2020/21 season, calculating the average age of players by position and country.
import { distinct, criteria, pivot, map, average } from '@steelbreeze/pivot';
import { squad } from './fulham';
// the position dimension we want in a custom order
const positions = ['Goalkeeper', 'Defender', 'Midfielder', 'Forward'];
// the countries dimension we derive from the data and order alphabetically
const countries = squad.map(player => player.country).filter(distinct).sort();
// we then create dimensions which also reference a property in the source data
const x = positions.map(criteria('position'));
const y = countries.map(criteria('country'));
// create the pivot cube from the squad data using position and country for x and y axes
let cube = pivot(squad, y, x);
// find the average age of players by position by country as at 2021-05-23
const result = map(cube, average(age(new Date('2021-05-23'))));The full example can be found here.
The selection is the average age of the players grouped by position and country:
Goalke… Defend… Midfie… Forward
Belgium 32
Camero… 25
Denmark 24
England 25 23.25 23
France 28 27
Gabon 27
Jamaica 28 28
Nether… 25
Nigeria 24 22
Portug… 27
Scotla… 31
Serbia 26
Slovak… 24
Spain 33
USA 28
The full example code can be found here.
Alternatively, as can be seen in the web example, non-numerical content can also be queried, mapping the source data to an arbitrary selection:
const result = pivot.map(cube, pivot.select(player => `${player.givenName} ${player.familyName}`));Resulting in this sort of output:
| Goalkeeper | Defender | Midfielder | Forward | |
|---|---|---|---|---|
| Belgium | Denis Odoi | |||
| Cameroon | Andre-Frank Zambo Anguissa | |||
| Denmark | Joachim Anderson | |||
| England | Tosin Abarabioyo, Joe Bryan | Ruben Loftus-Cheek, Harrison Reed, Josh Onomah, Fabio Carvalho | Ademola Lookman | |
| France | Alphonse Areola | Terence Kongolo | ||
| Gabon | Mario Lemina | |||
| Jamaica | Michael Hector | Bobby De Cordova-Reid | ||
| Netherlands | Kenny Tete | |||
| Nigeria | Ola Aina | Josh Maja | ||
| Portugal | Ivan Cavaleiro | |||
| Scotland | Kevin McDonald, Tom Cairney | |||
| Serbia | Aleksander Mitrovic | |||
| Slovakia | Marek Rodak | |||
| Spain | Fabrico Agosto Ramirez | |||
| USA | Tim Ream, Antonee Robinson |
Data and calculations correct as of: 2021-05-23.