NickleDave
commented
7 months ago Hi @vnmabus, just letting you know we have started the search for an editor for this review.
In the meantime, here are the initial editor checks. I am happy to report that rdata passes with flying colors.
Please see a couple of comments below. You are not required to address these for us to start the review, but I do think doing so would benefit your package--just trying to help, you get a free review from the editor in chief too :slightly_smiling_face:
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Editor comments
Two suggestions:
- include data in the package (more on this below)
- it's great that you have examples, but add more concrete examples, e.g. using existing datasets on OSF, FigShare, or Zenodo. This will help your users understand how rdata fits into their workflow
More on adding data: I tried to run the snippet in the README but got this error.
>>> parsed = rdata.parser.parse_file(rdata.TESTDATA_PATH / "test_vector.rda")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/pimienta/Documents/repos/coding/opensci/pyos/test-rdata/.venv/lib/python3.10/site-packages/rdata/parser/_parser.py", line 1002, in parse_file
data = path.read_bytes()
File "/home/linuxbrew/.linuxbrew/opt/python@3.10/lib/python3.10/pathlib.py", line 1126, in read_bytes
with self.open(mode='rb') as f:
File "/home/linuxbrew/.linuxbrew/opt/python@3.10/lib/python3.10/pathlib.py", line 1119, in open
return self._accessor.open(self, mode, buffering, encoding, errors,
FileNotFoundError: [Errno 2] No such file or directory: '/home/pimienta/Documents/repos/coding/opensci/pyos/test-rdata/.venv/lib/python3.10/site-packages/rdata/tests/data/test_vector.rda'
I think what's going on is that the test data is not in the built package?
You may have intended this snippet to be run with just the development version, but I would strongly suggest you add small amounts of data to the package itself. It's also possible you meant to "include" the data in your built package, but the setuptools build is not configured correctly? I have found the way setuptools does this a bit confusing in the past, but I think it's now possible for you to include all the files without using a MANIFEST.in file (which if I understand correctly is now a "legacy" way of including files with setuptools that should no longer be used). Note that other build backends have their own methods for this; for example flit would include everything by default, although you can have more fine grained control.
You can then provide access to this data to users (and yourself, for tests) by using importlib-resources as discussed in this talk. Here's an example of using importlib-resources in a package I develop--it might be a little easier to read than more general scientific Python packages that have a lot of internal infrastructure around their built-in datasets. I have a draft section of the guide on how to do this here--it's very rough still but the core ideas are there and might help you. If you have any feedback on this section, we'd love to hear it.
vnmabus
isabelizimm
has2k1
rich-iannone
lwasser
Submitting Author: Name (@vnmabus) All current maintainers: (@vnmabus) Package Name: rdata One-Line Description of Package: Read R datasets from Python. Repository Link: https://github.com/vnmabus/rdata Version submitted: 0.9.2.dev1 Editor: @isabelizimm
JOSS DOI: TBD
Version accepted: 0.11.0
Date accepted (month/day/year): 2/29/2024
Reviewer 1: @rich-iannone Reviewer 2: @has2k1 Archive:
Code of Conduct & Commitment to Maintain Package
Description
.rdaand.rdsfiles, containing serialized R objects, and convert them to Python. The users can influence this conversion and provide conversion routines for custom classes.Scope
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Domain Specific & Community Partnerships
Community Partnerships
If your package is associated with an existing community please check below:
Its main purpose is to be able to read .rda and .rds files, the files used for storing data in the R programming language, and convert them to Python objects for further processing.
The target audience includes users that want to open in Python datasets created in R. These include scientists working in both Python and R, scientists who want to compare results among the two languages using the same data, or simply Python scientists that want to be able to use the numerous datasets available in CRAN, the R repository of packages.
The package rpy2 can be used to interact with R from Python. This includes the ability to load data in the RData format, and to convert these data to equivalent Python objects. Although this is arguably the best package to achieve interaction between both languages, it has many disadvantages if one wants to use it just to load RData datasets. In the first place, the package requires an R installation, as it relies in launching an R interpreter and communicating with it. Secondly, launching R just to load data is inefficient, both in time and memory. Finally, this package inherits the GPL license from the R language, which is not compatible with most Python packages, typically released under more permissive licenses. The recent package pyreadr also provides functionality to read some R datasets. It relies in the C library librdata in order to perform the parsing of the RData format. This adds an additional dependency from C building tools, and requires that the package is compiled for all the desired operating systems. Moreover, this package is limited by the functionalities available in librdata, which at the moment of writing does not include the parsing of common objects such as R lists and S4 objects. The license can also be a problem, as it is part of the GPL family and does not allow commercial use.
@tagthe editor you contacted:https://github.com/pyOpenSci/software-submission/issues/143
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