Note
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Loading a RDS file from a URL#
A simple example showing how to read a dataset in the RDS format from a URL.
# sphinx_gallery_thumbnail_path = '_static/download.png'
If the data to read is accesible at a particular URL, we can open it as a
file using the function urllib.request.urlopen().
Thus, we need to import that function as well as the rdata package.
from urllib.request import urlopen
import rdata
For this example we will use a dataset hosted at Zenodo. This is a small dataset containing information about some fungal pathogens.
dataset_url = (
"https://zenodo.org/records/7425539/files/core_fungal_pathogens.rds"
)
The object resulting from calling
urlopen() can be then
passed to parse_file() as if it were a normal file.
with urlopen(dataset_url) as dataset:
parsed = rdata.parser.parse_file(dataset)
/home/docs/checkouts/readthedocs.org/user_builds/rdata/envs/stable/lib/python3.9/site-packages/rdata/parser/_parser.py:1217: UserWarning: Unknown file type: assumed RDS
warnings.warn("Unknown file type: assumed RDS") # noqa: B028
RDS files do not have a special magic number that identifies them. Thus, when reading a RDS file, rdata has to suppose that the file is a valid RDS file, and warns about that. We can omit this warning by passing manually the extension of the file instead.
with urlopen(dataset_url) as dataset:
parsed = rdata.parser.parse_file(dataset, extension=".rds")
This parsed object contains a lossless representation of the internal data contained in the file. This data mimics the internal format used in R, and is thus not directly usable. However, we can retrieve some information about the file that will be lost after the conversion to a Python object, such as the version of the format employed or the encoding used for the strings.
print(parsed.versions.format)
print(parsed.extra.encoding)
3
UTF-8
In order to convert it to Python objects we need to use the function
rdata.conversion.convert().
converted = rdata.conversion.convert(parsed)
RDS files contain just one R object. In this particular case, it is a R dataframe object, that will be converted to a Pandas dataframe by default.
As usually we just want to parse and convert a given dataset, the convenience
functions rdata.read_rds() and rdata.read_rda() can be used with
that purpose.
with urlopen(dataset_url) as dataset:
data = rdata.read_rds(dataset)
data
Total running time of the script: (0 minutes 7.543 seconds)
