Scope

Reading .npy and .npz files in R.

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

Dependencies

Because {grumpy} is a intended to be used deep in the dependency graph of other packages, we want to minimize the number of dependencies. There is currently no dependency, and no new dependency should be added unless it provides a significant performance improvement.

Function signatures

Inputs

The first argument of read_npy() and read_npz() is named file and is a connection or the path to the file to read. This is consistent with base R functions such as read.csv(), readRDS(), etc.

Outputs

The output of read_npy() is always an array (i.e., is.array() is TRUE), even for structured datatypes. This is to keep the output consistent and as conceptually close as possible to the original NumPy array.
The output of read_npz() is always a named list of arrays, even if the .npz file contains only one array. A stable output type is important for downstream analysis.

FAQ

Why not use `reticulate`?

When reading .npy files with {reticulate}, at some point in time, two (one in Python and one in R) or three copies of the data are held in memory. This can be problematic for large files. With {grumpy}, one (for types matching R native types; i.e., int32, float64/double) or two copies of the data are held in memory.

f <- tempfile(fileext = ".npy")
np <- reticulate::import("numpy")
x <- np$random$rand(1000L, 1000L)
np$save(f, x)

microbenchmark::microbenchmark(
  reticulate = {
    x <- np$load(f)
  },
  grumpy = {
    x <- grumpy::read_npy(f)
  },
  times = 1e3
)

Unit: milliseconds
       expr      min       lq     mean   median       uq     max neval
 reticulate 1.011419 1.366769 2.785608 1.493583 3.370718 125.638  1000
     grumpy 2.253922 2.531856 6.529516 3.154470 4.504708 131.013  1000

## On platform that support it, lazy reading offers even better performance for large files
if (Sys.info()[["sysname"]] %in% c("Linux", "Darwin")) {
  microbenchmark::microbenchmark(
    reticulate = {
      x <- np$load(f)
    },
    grumpy = {
      x <- grumpy::read_npy(f, lazy = TRUE)
    },
    times = 1e3
  )
}

Unit: microseconds
       expr      min       lq     mean   median       uq      max neval
 reticulate 1032.500 1281.105 2919.499 1445.410 3187.024 124776.2  1000
     grumpy  429.851  608.102 2182.467  810.829 1847.149 124130.6  1000

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-bit integers are handled. {reticulate} automatically and silently converts 64-bit 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}.

Design principles