Combining separate temporal measurement series - data-visualization

Question

I have a data set that combines two temporal measurement series with one row per measurement
time: 1, measurement: a, value: 5
time: 2, measurement: b, value: false
time: 10, measurement: a, value: 2
time: 13, measurement: b, value: true
time: 20, measurement: a, value: 4
time: 24, measurement: b, value: true
time: 30, measurement: a, value: 6
time: 32, measurement: b, value: false
in a visualization using Vega lite, I'd like to combine the measurement series and encode measurement a and b in a single visualization without simply layering their representation on a temporal axis but representing their value in a single encoding spec.
either measurement a values need to be interpolated and added as a new value to rows of measurement b
eg:
time: 2, measurement: b, value: false, interpolatedMeasurementA: 4.6667
or the other way around, which leaves the question of how to interpolate a boolean. maybe closest value by time, or simpler: last value
eg:
time: 30, measurement: a, value: 6, lastValueMeasurementB: true
I suppose this could be done either query side in which case this question would be regarding indexDB Flux query language
or this could be done on the visualization side in which case this would be regarding vega-lite

Answer 1

There's not any true linear interpolation schemes built-in to Vega-Lite (though the loess transform comes close), but you can achieve roughly what you wish with a window transform.
Here is an example (view in editor):
{
"data": {
"values": [
{"time": 1, "measurement": "a", "value": 5},
{"time": 2, "measurement": "b", "value": false},
{"time": 10, "measurement": "a", "value": 2},
{"time": 13, "measurement": "b", "value": true},
{"time": 20, "measurement": "a", "value": 4},
{"time": 24, "measurement": "b", "value": true},
{"time": 30, "measurement": "a", "value": 6},
{"time": 32, "measurement": "b", "value": false}
]
},
"transform": [
{
"calculate": "datum.measurement == 'a' ? datum.value : null",
"as": "measurement_a"
},
{
"window": [
{"op": "mean", "field": "measurement_a", "as": "interpolated"}
],
"sort": [{"field": "time"}],
"frame": [1, 1]
},
{"filter": "datum.measurement == 'b'"}
],
"mark": "line",
"encoding": {
"x": {"field": "time"},
"y": {"field": "interpolated"},
"color": {"field": "value"}
}
}
This first uses a calculate transform to isolate the values to be interpolated, then a window transform that computes the mean over adjacent values (frame: [1, 1]), then a filter transform to isolate interpolated rows.
If you wanted to go the other route, you could do a similar sequence of transforms targeting the boolean value instead.