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pathroutr

r-universe DOI Lifecycle: stable R build status R-CMD-check

The goal of {pathroutr} is to provide functions for re-routing paths that cross land around barrier polygons. The use case in mind is movement paths derived from location estimates of marine animals. Due to error associated with these locations it is not uncommon for these tracks to cross land. The {pathroutr} package aims to provide a pragmatic and fast solution for re-routing these paths around land and along an efficient path. It is important to note any re-routed paths are not statistical models and may have unintended consequences and biases associated with them that depends on the study animal, study area, and error nature of the original data. Re-routed paths may also exceed the speed or physiological capabilities of the study species.

Credit Where Credit is Due

We believe this is the first implementation of visibility graphs within R for spatial routing. However, three sources were of key importance to developing the approach

  1. Seaway distances with PostgreSQL by Angus Cameron (24 Apr, 2016)
  2. Pyvisgraph Python package by Christian Reksten-Monsen
  3. Jan, Gene Eu, et al. “An $ $ Shortest Path Algorithm Based on Delaunay Triangulation.” IEEE/ASME Transactions On Mechatronics 19.2 (2013): 660-666. DOI: 10.1109/TMECH.2013.2252076

In addition, both the {sf} and {sfnetworks} R packages are of key importance to the functionality within {pathroutr}. A special acknowledgement for the {stplanr} package is also warranted as early development versions relied on {stplanr} functions. And, of course, all of this would not be possible without the {igraph} package

  1. Lucas van der Meer, Lorena Abad, Andrea Gilardi and Robin Lovelace (2021). sfnetworks: Tidy Geospatial Networks. R package version 0.5.2. https://CRAN.R-project.org/package=sfnetworks
  2. Pebesma, E., 2018. Simple Features for R: Standardized Support for Spatial Vector Data. The R Journal 10 (1), 439-446, https://doi.org/10.32614/RJ-2018-009
  3. Lovelace, R. and Ellison, R., 2017. stplanr: A Package for Transport Planning. The R Journal. https://doi.org/10.32614/RJ-2018-053
  4. Csardi G, Nepusz T: The igraph software package for complex network research, InterJournal, Complex Systems 1695. 2006. https://igraph.org

Installation

{pathroutr} is currently not available on CRAN and also requires R >= 4.0. Please upgrade your version of R, if needed, before proceeding. Future versions of {pathroutr} may support pre-4.0 versions of R. But, for now, only 4.0+ is supported.

Install via R-Universe

Starting with v0.2.1, {pathroutr} is available via R-Universe.

# Install new pathroutr version from my R-Universe repository
install.packages("pathroutr", repos = "https://jmlondon.r-universe.dev")

You can also add my repository to your local list of repositories in your .Rprofile and this will ensure update.packages() pulls any new releases of {pathroutr}

#install.packages("usethis")
usethis::edit_r_profile()

# add the following text or replace existing repos option

options(repos = c(jmlondon = 'https://jmlondon.r-universe.dev',
                  CRAN = 'https://cloud.r-project.org'))

Install via Github

The development version of {pathroutr} is available from GitHub with:

# install.packages("remotes")
remotes::install_github("jmlondon/pathroutr")

Package Dependencies

{pathroutr} is highly dependent upon the {sfnetworks} and {sf} packages.

Both are available on CRAN.

Please note that the minimum supported version of {sf} is 0.9. So, this might also be a good time to check and update your {sf} package versions.

Core packages from the tidyverse ({dplyr}, {purrr}, and {tibble}) are also required.

Questions, Contributions, & Code of Conduct

Please note that the pathroutr project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

We strongly encourage you to file Issues and submit Pull Requests so we can ensure the package meets the community needs and functions reliably. Please, please, please do your best to create a reproducible example and use the {reprex} package to share code within your Issue.

Also, we have enabled the Discussion option within the GitHub repository for questions, thoughts, and ideas that may not qualify as an Issue or Pull Request.