Design principles

Scope

Reading .npy and .npz files in R.

Writing is out of scope. When working across multiple languages, one should prefer high-performance interoperable format (parquet, zarr, etc.).

When reading .npy files with reticulate, at some point in time, two or three copies of the data are made in memory. This can be problematic for large files. With grumpy, two copies of the data are made in memory, with plans to make just one copy in the cases where the data type matches R native types.
Reading data with reticulate requires a Python installation and additional python packages, which users in restricted environments may not have access to. grumpy is a pure R package with no external dependencies. This is especially important as we expect grumpy to be used deep in the dependency graph of other packages, and we want to minimize the number of dependencies.
A dedicated R package gives us more flexibility in how edge cases such as 64 bits integers are handled. reticulate automatically and silently converts 64 bits integers to double, which is a sensible default for many use cases. But we may want to have more control over this behavior, and grumpy will allow us to do that in the future. Another good example are structured data types (record arrays), which are returned as data.frames, not arrays, with reticulate.