Add aggregate support

Nobody has claimed this yet.

mpl-altair currently doesn't support conversion for Altair charts that use aggregations.

**Code for reproduction**

data:
```python
df = pd.DataFrame({
'a': [1, 1, 2, 3],
'b': [1.1, 2.2, 3.3, 4.4]
})
```
Example 1:
```python
chart = alt.Chart(df).mark_point().encode(
alt.X('a'), alt.Y('average(b)')
)
mplaltair.convert(chart)
```
Example 2:
```python
chart = alt.Chart(df).mark_point().encode(
alt.X('a'), alt.Y('count()')
)
mplaltair.convert(chart)
```

**Actual outcome**

A `NotImplementedError` is raised for anything that has aggregated data.

**Expected outcome**

Any plot with [aggregation](https://altair-viz.github.io/user_guide/encoding.html#binning-and-aggregation) should work.

The first chart should look something like this:


The second chart should look something like this:


**Possible solution**

During the initial altair chart parsing, manipulate the data to include the aggregations. See `parse_chart.ChannelMetadata()` (specifically `_aggregate_channel()`).

Start in parse_chart.ChannelMetadata(), particularly _aggregate_channel(), and reproduce the issue with the two Python chart examples using average(b) and count(). Trace how aggregated data is parsed and ensure both mplaltair.convert(chart) calls complete without NotImplementedError and produce the expected aggregate plots.

Written by the indexing model from the issue text.