Package: spacescans 0.3.4

Xuguang Ai

spacescans: Link Time-Referenced Geolocation Data to the Spatial and Contextual Exposome

R client for the 'spacescans' 'Python' pipeline, which links time-referenced geolocation data (location episodes of study participants) to the spatial and contextual exposome and returns one row per participant or per location episode. The pipeline runs in a separate 'Python' process that the package talks to through JSON request and result files and 'Parquet' tables, so a crash or an interrupt in 'Python' never takes the R session down. 'ss_install()' builds the 'Python' environment with 'uv'.

Authors:Xuguang Ai [aut, cre], Regenstrief Institute [aut]

spacescans_0.3.4.tar.gz
spacescans_0.3.4.zip(r-4.7-any)spacescans_0.3.4.zip(r-4.6-any)spacescans_0.3.4.zip(r-4.5-any)
spacescans_0.3.4.tgz(r-4.6-any)spacescans_0.3.4.tgz(r-4.5-any)
spacescans_0.3.4.tar.gz(r-4.7-any)spacescans_0.3.4.tar.gz(r-4.6-any)
spacescans_0.3.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
spacescans/json (API)

# Install 'spacescans' in R:

install.packages('spacescans', repos = c('https://iu-ultraman.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/iu-ultraman/spacescans/issues

On CRAN:

3.90 score 2 stars 37 exports 5 dependencies

Last updated from:b49626c8c4 (on v0.3.4). Checks:6 OK, 3 ERROR. Indexed: yes.

Target	Result	Time
linux-devel	OK	229
source / vignettes	OK	193
linux-release	OK	228
macos-release	OK	257
macos-oldrel	OK	359
windows-devel	ERROR	80
windows-release	ERROR	86
windows-oldrel	ERROR	61
wasm-release	OK	96

Exports:ss_cache_clear ss_cache_info ss_catalog ss_config_schema ss_coverage ss_custom_polygon ss_custom_raster ss_data_dir ss_data_import ss_data_status ss_data_terms ss_data_verify ss_dataset ss_datasets ss_doctor ss_features ss_fetch ss_install ss_job_attach ss_job_cancel ss_job_result ss_job_status ss_job_wait ss_link ss_python ss_quickstart ss_read_config ss_read_result ss_register_dataset ss_remove_dataset ss_run ss_templates ss_update_dataset ss_use_python ss_validate ss_version ss_write_config

Dependencies:jsonlite nanoparquet processx ps R6

Getting started with spacescans

Rendered fromgetting-started.Rmdusingknitr::knitr

Last update: 2026-10-16
Started: 2026-10-13

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Conditions raised by spacescans	spacescans-conditions
The C3 weight cache	ss_cache_clear ss_cache_info
The exposure catalog	ss_catalog
The JSON schema of a pipeline config	ss_config_schema
Estimate coverage before linking	ss_coverage
Bring your own exposure data	ss_custom_polygon ss_custom_raster
The data directory	ss_data_dir
What is in the data directory	ss_data_status
Verify, import and read terms of datasets	ss_data_import ss_data_terms ss_data_verify
Registered datasets: your own data, prepared once and reused	ss_dataset ss_datasets ss_register_dataset ss_remove_dataset ss_update_dataset
Check the spacescans setup	ss_doctor
The feature dictionary	ss_features
Download the data variables need	ss_fetch
Install the Python side of spacescans	ss_install
Background jobs	ss_job ss_job_attach ss_job_cancel ss_job_result ss_job_status ss_job_wait
Link a cohort to catalog variables	ss_link
Run the bundled quickstart demo	ss_quickstart
Read and write pipeline configs as YAML	ss_read_config ss_write_config
Read a pipeline output table	ss_read_result
Run one pipeline config	ss_run
Config templates shipped with the Python package	ss_templates
Choose the Python interpreter spacescans uses	ss_python ss_use_python
Validate a pipeline config without running it	ss_validate
Versions of the R package, the Python package and the bridge protocol	ss_version