Package: chopin 0.9.3

Insang Song

chopin: Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing

Geospatial data computation is parallelized by grid, hierarchy, or raster files. Based on 'future' (Bengtsson, 2024 <doi:10.32614/CRAN.package.future>) and 'mirai' (Gao et al., 2025 <doi:10.32614/CRAN.package.mirai>) parallel back-ends, 'terra' (Hijmans et al., 2025 <doi:10.32614/CRAN.package.terra>) and 'sf' (Pebesma et al., 2024 <doi:10.32614/CRAN.package.sf>) functions as well as convenience functions in the package can be distributed over multiple threads. The simplest way of parallelizing generic geospatial computation is to start from 'par_pad_*()' functions to 'par_grid()', 'par_hierarchy()', or 'par_multirasters()' functions. Virtually any functions accepting classes in 'terra' or 'sf' packages can be used in the three parallelization functions. A common raster-vector overlay operation is provided as a function 'extract_at()', which uses 'exactextractr' (Baston, 2023 <doi:10.32614/CRAN.package.exactextractr>), with options for kernel weights for summarizing raster values at vector geometries. Other convenience functions for vector-vector operations including simple areal interpolation ('summarize_aw()') and summation of exponentially decaying weights ('summarize_sedc()') are also provided.

Authors:Insang Song [aut, cre], Kyle Messier [aut, ctb], Alec L. Robitaille [rev], Eric R. Scott [rev]

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NEWS

# Install 'chopin' in R:
install.packages('chopin', repos = c('https://packages.ropensci.org', 'https://cloud.r-project.org'))

Reviews:rOpenSci Software Review #638

Bug tracker:https://github.com/ropensci/chopin/issues

Pkgdown site:https://docs.ropensci.org

Datasets:
  • ncpoints - Mildly clustered points in North Carolina, United States
  • prediction_grid - Regular grid points in the mainland United States at 1km spatial resolution

On CRAN:

Conda:

5.96 score 16 stars 23 scripts 15 exports 50 dependencies

Last updated 2 days ago from:722392f21d (on main). Checks:11 OK. Indexed: yes.

TargetResultTotal timeArtifact
linux-devel-x86_64OK291
pkgdown docsOK443
source / vignettesOK351
linux-release-x86_64OK286
macos-release-arm64OK145
macos-oldrel-arm64OK168
windows-develOK259
windows-releaseOK262
windows-oldrelOK233
wasm-releaseOK185
wasm-oldrelOK231

Exports:extract_atkernelfunctionpar_convert_fpar_gridpar_grid_miraipar_hierarchypar_hierarchy_miraipar_merge_gridpar_multirasterspar_multirasters_miraipar_pad_balancedpar_pad_gridpar_split_listsummarize_awsummarize_sedc

Dependencies:abindanticlustclassclassIntclicodetoolscollapsecpp11DBIdigestdplyre1071exactextractrfuturefuture.applygenericsglobalsglueigraphKernSmoothlatticelifecyclelistenvlpSolvemagrittrMASSMatrixmirainanonextparallellypillarpkgconfigproxyR6RANNrasterRcpprlangs2sfspstarsterratibbletidyselectunitsutf8vctrswithrwk

Extracting Weather/Climate Geospatial Data with chopin

Rendered fromv04_climate_examples.Rmdusingknitr::rmarkdownon Jul 09 2025.

Last update: 2025-02-13
Started: 2024-08-09

Generate computational grids

Rendered fromv03_par_pad_grid.Rmdusingknitr::rmarkdownon Jul 09 2025.

Last update: 2025-02-13
Started: 2024-08-09

Getting started with chopin

Rendered fromv01_start.Rmdusingknitr::rmarkdownon Jul 09 2025.

Last update: 2025-07-09
Started: 2024-08-09

Good practice of using chopin

Rendered fromv02_good_practice.Rmdusingknitr::rmarkdownon Jul 09 2025.

Last update: 2025-02-13
Started: 2024-08-09

targets and grid objects

Rendered fromv05_targets.Rmdusingknitr::rmarkdownon Jul 09 2025.

Last update: 2024-08-09
Started: 2024-08-09