Since you are grouping across date (as per your date dimension
), the reduce()
function would be used to perform aggregations grouped by date, as per the highlighted cells in my Mickey Mouse example below:
With a running total you'd need to perform an entirely different operation, looping down the rows:
You can aggregate the data and then append the running total field as follows. I've also included an example of how to calculate an average value, using the reduce
function:
records = [{ "date": "2014-01-01", "field1": "value1", "field2": "value11", "value_field": -20 },
{ "date": "2014-01-02", "field1": "value2", "field2": "value12", "value_field": 100 },
{ "date": "2014-01-03", "field1": "value1", "field2": "value11", "value_field": -10 },
{ "date": "2014-01-04", "field1": "value2", "field2": "value12", "value_field": 150 }
];
var cf = crossfilter(records);
var dimensionDate = cf.dimension(function (d) {
return d.date;
});
function reduceAdd(p, v) {
p.total += v.value_field;
p.count++;
p.average = p.total / p.count;
return p;
}
function reduceRemove(p, v) {
p.total -= v.value_field;
p.count--;
p.average = p.count ? p.total / p.count : 0;
return p;
}
function reduceInitial() {
return {
total: 0,
count: 0,
average: 0,
};
}
var average = dimensionDate.group().reduce(reduceAdd, reduceRemove, reduceInitial).all();
var averageWithRunningTotal = appendRuningTotal(average);
function appendRuningTotal(average) {
var len = average.length,
runningTotal = 0;
for (var i = 0; i < len; i++) {
runningTotal += average[i].value.total;
average[i].RunningTotal = runningTotal;
}
return average;
}
And this returns the following:
{"key":"2014-01-01","value":{"total":-20,"count":1,"average":-20},"RunningTotal":-20}
{"key":"2014-01-02","value":{"total":100,"count":1,"average":100},"RunningTotal":80}
{"key":"2014-01-03","value":{"total":-10,"count":1,"average":-10},"RunningTotal":70}
{"key":"2014-01-04","value":{"total":150,"count":1,"average":150},"RunningTotal":220}