Vega-lite: skip invalid values instead of treating as 0 for aggregate sum - vega-lite

Question

When doing an aggregate sum on a column and charting it using Vega-Lite, is it possible to skip invalid values instead of treating them as 0 when doing the addition? When there is missing/invalid data, I want to show it as such, rather than as 0.
For example, this graph is what I expect when aggregating on date to get the sums for x and y.
Whereas in this example, the y value for both rows where date=2022-01-20 are NaN, so I would want there to be no data point for the sum of column y and show it as missing data, instead of as 0.
Is there a way to do that? I’ve looked through the documentation but may have missed something. I've tried using filter like so, but that filters out an entire row, rather than just the invalid value of a particular column for the row when doing the sum.
I’m thinking something like pandas GroupBy.sum(min_coun=1), so that if there isn't at least 1 non-NaN value, then the result will be presented as NaN.

Answer 1

OK, try this which removes NaN and null but leaves zero.
Editor.
Or this which removes a load of useless transforms.
Editor
{
"$schema": "https://vega.github.io/schema/vega-lite/v5.json",
"description": "Google's stock price over time.",
"data": {
"values": [
{"date": "2022-01-20", "g": "apples", "x": "NaN", "y": "NaN"},
{"date": "2022-01-20", "g": "oranges", "x": "10", "y": "20"},
{"date": "2022-01-21", "g": "oranges", "x": "30", "y": "NaN"},
{"date": "2022-01-21", "g": "grapes", "x": "40", "y": "20"},
{"date": "2022-01-22", "g": "apples", "x": "NaN", "y": "NaN"},
{"date": "2022-01-22", "g": "grapes", "x": "10", "y": "NaN"}
]
},
"transform": [
{"calculate": "parseFloat(datum['x'])", "as": "x"},
{"calculate": "parseFloat(datum['y'])", "as": "y"},
{"fold": ["x", "y"]},
**{"filter": {"field": "value", "valid": true}},**
{
"aggregate": [{"op": "sum", "field": "value", "as": "value"}],
"groupby": ["date", "key"]
}
],
"encoding": {"x": {"field": "date", "type": "temporal"}},
"layer": [
{
"encoding": {
"y": {"field": "value", "type": "quantitative"},
"color": {"field": "key", "type": "nominal"}
},
"mark": "line"
},
{
"encoding": {
"y": {"field": "value", "type": "quantitative"},
"color": {"field": "key", "type": "nominal"}
},
"mark": {"type": "point", "tooltip": {"content": "encoding"}}
}
]
}