Vega author here. We actually disagree with the last part.
For visual analysis tools (not just UI charting library), having the ability to quickly summarize data is very useful for analyses (esp exploratory ones).
We are not alone with this design choice. ggplot2 in R and GUIs like Tableau also includes data aggregation as first class citizen in their tools.
For drawing lines with specific coordinate values, you can just encode position (x, y, x2, y2) of lines or rules with constant values?
If you have specific use case that's really cumbersome in Altair/Vega-Lite, please feel free to file an issue. We're happy to help improve the tool. :)
Note that a dataflow graph is generally a directed graph (which is domain agnostic). Thus it can always be visualized in some ways.
There are also many generally applicable directed graph drawing techniques in academic literature. (You can see some in the related work section in the paper.)
That said, in different domains, people may apply different sets of layout and visual encoding techniques due to the differences in semantics and characteristic of the graphs.
For example, in this TensorFlow Graph Vis project, we chose to build a hierarchical clustered graph to provide high-level overview, bundle edges to facilitate interactive expansion, and detach unimportant nodes from the main layout to declutter the graph.
For visual analysis tools (not just UI charting library), having the ability to quickly summarize data is very useful for analyses (esp exploratory ones).
We are not alone with this design choice. ggplot2 in R and GUIs like Tableau also includes data aggregation as first class citizen in their tools.