Absolutely no need to apologize, thanks for taking the time to check out our project.
I have written a lot of R Markdown over the years, and I agree wholeheartedly with most of what you're saying. The R ecosystem is phenomenal. Anyone who is excited about our project, might be 10x more excited about learning R and writing a report with R markdown.
A big part of why we are building Evidence is that my co-founder Sean and I felt like we lost a lot on the presentation side when we graduated from notebooks to primarily working with data warehouses, dbt & BI tools.
The thing is, we gained so much from that transition to 'the modern data stack' that we would never go back. So we're setting out to fix the presentation layer in a way that would have worked for us.
Undoubtably, anything that you could accomplish in Evidence is going to be do-able within the R Markdown or jupyter ecosystems, so I won't try to claim any truly unique features. It's maybe more of a vibe: what's easy in Evidence vs. what's tricky in a notebook?
If you're writing an ML paper, R markdown is definitely the move. If you're trying to build a common, internally consistent understanding across hundreds (thousands) of people about how your business is doing, and what they might do about it, Evidence is going to be a better fit.
Here's a comment from awhile ago discussing the comparison with Jupyter: https://news.ycombinator.com/item?id=27363349
It only supports SQL and Markdown:
That constraint is part of the point.
In a large organization, a fair number of people are going to contribute to your reporting apparatus, and you want to keep it in a state where you can re-factor useful abstractions up into your data warehouse. This gets a lot harder if your reporting is a swirl of R scripts and python snippets and whatever else.
Some order of magnitude more people know SQL and markdown than R or Python. Every business I have been involved in has someone there who is cranking out analysis and data pulls using SQL. Very rarely would that person be comfortable working in R markdown.
You can't in-line an ML model into your reports:
Again, we think this constraint is basically a good thing. If you have a model that is profitable to your business, it should be governed and executed in a purpose built environment and, where feasible, you should be storing the relevant outputs for posterity in your data warehouse.
We will add instructions on setting the port! :)