mongolite | Fast and Simple MongoDB Client for R | SQL Database library

This is indeed possible. MongoDB only creates a new database if you switch to the context of a non-existing database and then insert data into it. Using mongolite, you can connect to a non-existing database and collection and insert data and that database and collection will be created automatically.

So you just do the following:

It would help if you posted the entire output you saw on the failure of renv::restore(). The only thing that stands out to me is that the version is 1.0, not 1.0.0, so that could be why renv is failing to find the package (assuming you're manually modifying the lockfile).

I believe in Mongolite you can avoid that error using MongoDB's $regex with " instead of \ :

Try the code below. The operator $oid, similar to ObjectId() in mongo, has to be applied to each ID

You don't need to perform $group query to calculate $max / $min scores, you can calculate them during $project stage

The comments above raise some valid points. Still, I do believe there is a way to achieve what you're after (whether or not this is a particularly good idea is less clear) by leveraging three functions from the purrr package in combination:

1. purrr::vec_depth allows us to get the (nesting) depth of a given list,
2. purrr::modify_depth allows us to apply a function to an list at the specified level of depth, and
3. purrr::reduce allows us to iteratively apply a function and have the result of each iteration be passed as the input to the subsequent iteration.

In essence, we want to convert any data.frame found at any level in the list to a tibble. This can easily be achieved using several rounds of purrr::modify_depth where we simply alter the depth depending on the level of the list we wish to target. Crucially, however, we want to do this in a way so that changes to level 1, for example, are retained when we move on to targeting level 2; changes to level 1 and 2 are retained when we move on to level 3; and so on. This is where purrr::reduce comes in: each time we apply purrr::modify_depth to convert a data.frame to a tibble, we'll ensure that the resultant output gets passed as the input to the next iteration. This is illustrated in the MWE below

Start with the basic setup of data structures and libraries