R Observatory: Metrics for paisaje version 0.3.0

Balamuta, James Joseph

doi:10.5281/zenodo.21843040

paisaje

0.3.0Package record

Spatial and Environmental Data Tools for Landscape Ecology

0packages dependsuggested by 1

24.8Kdownloads / year

–test coverage

13/13flavors OK

Overview

About

Maintained by Manuel SpínolaCRAN page ↗GitHub ↗Website ↗Issues ↗

Provides functions for landscape analysis and data retrieval. The package allows users to download environmental variables from global datasets (e.g., WorldClim, CHELSA, ESA WorldCover, Nighttime Lights), and to compute spatial and landscape metrics using a hexagonal grid system based on the H3 spatial index. It is useful for ecological modeling, biodiversity studies, and spatial data processing in landscape ecology. Fick and Hijmans (2017) doi:10.1002/joc.5086. Zanaga et al. (2022) doi:10.5281/zenodo.7254221. Uber Technologies Inc. (2022) "H3: Hexagonal hierarchical spatial index". Román et al. (2018) doi:10.1016/j.rse.2018.03.017. Karger et al. (2017) doi:10.1038/sdata.2017.122. Brun et al. (2022) doi:10.5194/essd-14-5573-2022.

Changes in 0.3.0

New functions

get_chelsa_historic() -- Downloads CHELSA v2.1 bioclimatic variables (bio1-bio19) for the 1981-2010 reference period. Uses Cloud Optimized GeoTIFF (COG) streaming via /vsicurl/, so only the spatial subset covering the AOI is retrieved -- no full global download required. Accepts var = "all" to request all 19 variables in a single call. Resolution is fixed at 30 arc-seconds (~1 km); no res parameter.
get_chelsa_future() -- Downloads CHELSA v2.1 future bioclimatic variables under CMIP6 scenarios (SSP126, SSP370, SSP585) for three periods (2011-2040, 2041-2070, 2071-2100) and five ISIMIP3b GCMs (GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL). Also uses COG streaming. Note: SSP245 is not available in CHELSA v2.1. Resolution is fixed at 30 arc-seconds (~1 km).

Show all changes in 0.3.0Hide the rest of the changes in 0.3.0

get_cr_outline() -- Downloads the Costa Rica boundary from GADM 4.1 via geodata::gadm() and returns it as an sf object. The continental argument (default TRUE) controls whether to return only the continental landmass (excluding Isla del Coco and minor islands) or the full national territory. The downloaded file is cached locally.

New datasets

cr_outline -- Costa Rica full outline (continental landmass + Isla del Coco and all minor oceanic islands), derived from GADM 4.1.

Breaking changes

get_nightlight_data() -- Complete rewrite. The function now wraps blackmarbler::bm_raster() (World Bank) instead of scraping the EOG (Earth Observation Group) website. **Interface change**: a new required aoi_sf argument (an sf object) replaces year/month as the first argument, and a bearer argument (NASA LAADS DAAC token) is now required. The function returns a SpatRaster cropped and masked to the AOI instead of a file path. See ?get_nightlight_data for details on obtaining a bearer token.

Improvements

get_nightlight_data() now supports daily (VNP46A1, VNP46A2), monthly (VNP46A3), and annual (VNP46A4) NASA Black Marble products at 500 m resolution, with built-in quality filtering via quality_flag_rm.
cr_outline_c dataset regenerated from GADM 4.1 with a fully reproducible script in data-raw/cr_outline.R.
Migrated from magrittr pipe (%>%) to the native R pipe (|>) throughout the package (extract_cat_raster(), get_records_by_hexagon()). Requires R >= 4.1.0 (already declared in Depends).

Dependency changes

**Added**: blackmarbler (replaces EOG scraping for nightlight data).
**Added**: geodata (used by get_cr_outline()).
**Removed**: rvest (no longer needed after EOG scraper was dropped).
**Removed**: magrittr (replaced by native pipe |>).

From NEWS.md in the release

Install

R (CRAN)pak (dev)Ubuntu (r2u)Fedora (COPR)openSUSE (OBS)

install.packages("paisaje", repos = "https://cloud.r-project.org/")

Installs the current CRAN release. Prebuilt binaries on Windows and macOS.

if (!requireNamespace("pak", quietly = TRUE))
  install.packages("pak", repos = "https://cloud.r-project.org/")
pak::pak("ManuelSpinola/paisaje")

Development version from ManuelSpinola/paisaje. Installs pak first if it is not already available.

Release

# enable cranapt (r2u) if it is not already set up, then install
grep -rqs r2u.stat.illinois.edu /etc/apt/sources.list /etc/apt/sources.list.d/ \
  || curl -fsSL https://raw.githubusercontent.com/eddelbuettel/r2u/master/inst/scripts/add_cranapt_noble.sh | sudo bash
sudo apt install -y r-cran-paisaje

Fast Ubuntu binaries via r2u (no compilation). The cranapt setup runs once and is skipped when it is already enabled. We list this channel from recorded r2u download activity rather than from a live index of the repository, so treat it as last known rather than guaranteed.

bspm (used by r2u) also needs make: sudo apt install make

# enable the iucar/cran COPR if it is not already present, then install
ls /etc/yum.repos.d/*iucar*cran*.repo >/dev/null 2>&1 \
  || sudo dnf copr enable -y iucar/cran
sudo dnf install -y R-CRAN-paisaje

RPMs from the iucar/cran COPR, rebuilt from CRAN.

For install.packages() integration in R, also: sudo dnf install R-CoprManager

# add the autoCRAN OBS repo (Tumbleweed) if it is missing, then install
zypper -q lr | grep -qi autoCRAN \
  || sudo zypper addrepo https://download.opensuse.org/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Tumbleweed/devel:languages:R:autoCRAN.repo
sudo zypper install -y R-paisaje

RPMs auto-built from CRAN on openSUSE OBS (Tumbleweed). We list this channel from recorded autoCRAN download activity rather than from a live index of the repository, so treat it as last known rather than guaranteed.

Health

CRAN checks

13OK

CRAN check page ↗

Slowest check: 7.3 min · r-oldrel-windows-x86_64

Status changes

OK13 OK2026-08-05
NOTE12 OK, 1 NOTE2026-08-01
OK13 OK2026-06-09
ERROR12 OK, 1 ERROR2026-06-08
OK14 OK2026-03-10

Advisories

none known

How this is measured·How advisories are found

Documentation

In release 0.3.0

READMEYes · 183 wordsVignettesYes · 1 · dynamicNEWSYes · 100% structured · mentions 0.3.0Citation fileYes · software entry onlyWebsiteYes · pkgdown

Help pages: 18 (1 internal)includes a package overview page
Examples that run: 12%2 run in a plain check · 6 only when donttest examples are enabled (R CMD check --as-cran) · 5 never run · 4 hold no code
Exported functions with examples: 100%
Documented parameters: 100%
Return-value docs: 100%
Help pages citing references: 44%

Vignettes (1)

paisaje: landscape anlysis HTML

How these are measured

Downloads

24.8K

CRAN downloads in the past year

Rank #15,194 · ~68/day · ~2.1K/mo

Releases R versions

Daily download trend is not available in this view yet.

27330 days

80690 days

24.8K1 year

Compare downloads with other packages →

Also on90 r2u2 autocran

How downloads are counted

Repository

manuelspinola/paisaje ↗Account: ManuelSpinola

6Stars

0Forks

0Open issues

0Open PRs

0Releases

118Commits

2Commit identities

118 commits · Last activity 2026-06-22 · 0% stars, 30d

Responsiveness

not measured

Stars over time

2025-10-12 · 42026-07-07 · 6

How these are measured

PracticesBeta

10 development-tooling and community-health practices detected across 5 families in the upstream repository

Checks run against github.com/manuelspinola/paisaje on 2026-10-21.

Continuous integration (2)

GitHub ActionsR CMD check workflow

The R CMD check workflow runs on Linux, macOS and Windows, and on R-devel

Docs source (4)

README.Rmdpkgdown

GitHub Pages site last deployed 2026-06-22 · The repository has a site deploy workflow

pkgdown site last built 2026-06-22

Reproducibility and dev environment (1)

data-raw/

CRAN release process (2)

cran-comments.mdCRAN-SUBMISSION

Show all practicesHide 1 practice

Lint, format, editor (1)

RStudio project

How this is detected·Detection ruleset v4 (2026-10-20)

Dependencies

Declared dependencies

15 external dependencies (excludes base and recommended)

Installing paisaje adds 147 packages (75 with compiled code, 113 MB of source tarballs). Heaviest: stringi 11 MB, sf 9.3 MB, ggplot2 6.0 MB.

Depends (1)

R >= 4.1.0

Imports (12)

dplyr tidyr sf terra h3jsr httr landscapemetrics spocc exactextractr progress blackmarbler geodata

LinkingTo (0)

none

Suggests (3)

ggplot2 rmarkdown knitr

Enhances (0)

none

Dependency license breakdown →

Reverse dependencies

0direct

0indirect

Suggested by 1

Suggests, most downloaded first

h3sdm

How these are counted

Code & Tests

Datasets

People & History

People (1)

Manuel Spínola

Author, Maintainer since 0.1.1 (2025)

Releases (3)

Pick two releases to compare their code metrics.

0.3.0Latest
record
2026-06-12 · current release · diff ↗ ·
removed 1 export
%>%

added 3 exports
get_chelsa_future, get_chelsa_historic, get_cr_outline
R
R 4.6.0 released · 2026-04-24
0.2.0
record
2026-01-08 · diff ↗
0.1.1
record
2025-10-21
R
R 4.5.0 released · 2025-04-11

Package metadata

First published: 2025-10-21
Total releases: 3 / 1 yrs
License: MIT + file LICENSE OSI
Minimum R: ≥ 4.1.0
Bundled data: 815 KB / 2 files
Download size: 1.6 MB

Cite

Cite this package

Run in R for the authors' entry:

citation("paisaje")

The package provides a citation file, so citation() returns the authors' own entry.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

Format

APA

Balamuta, J. J. (2026). R Observatory: Metrics for paisaje version 0.3.0 [Data set]. HJJB, LLC. Data release v2026-10-25. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-25, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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