Package: plantTracker 1.2.1

plantTracker: Extract Demographic and Competition Data from Fine-Scale Maps

Extracts growth, survival, and local neighborhood density information from repeated, fine-scale maps of organism occurrence. Further information about this package can be found in our journal article, "plantTracker: An R package to translate maps of plant occurrence into demographic data" published in 2022 in Methods in Ecology and Evolution (Stears, et al., 2022) <doi:10.1111/2041-210X.13950>.

Authors:Alice Stears [aut, cre], Jared Studyvin [aut], David Atkins [aut]

plantTracker_1.2.1.tar.gz
plantTracker_1.2.1.zip(r-4.7-any)plantTracker_1.2.1.zip(r-4.6-any)plantTracker_1.2.1.zip(r-4.5-any)
plantTracker_1.2.1.tgz(r-4.6-any)plantTracker_1.2.1.tgz(r-4.5-any)
plantTracker_1.2.1.tar.gz(r-4.7-any)plantTracker_1.2.1.tar.gz(r-4.6-any)
plantTracker_1.2.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
plantTracker/json (API)

# Install 'plantTracker' in R:
install.packages('plantTracker', repos = c('https://aestears.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/aestears/planttracker/issues

Datasets:
  • grasslandData - Chart Quadrat Dataset for grassland species in MT and AZ An example dataset that contains data from mapped plant chart quadrats. Each row is an observation of one plant.
  • grasslandInventory - A dataset indicating sampling years for the grasslandData example dataset

On CRAN:

Conda:

demographyspatial

5.68 score 7 stars 23 scripts 293 downloads 9 exports 22 dependencies

Last updated from:0dda513d2b. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK181
source / vignettesOK242
linux-releaseOK184
macos-releaseOK189
macos-oldrelOK219
windows-develOK158
windows-releaseOK120
windows-oldrelOK136
wasm-releaseOK114

Exports:aggregateByGenetcheckDatdrawQuadMapgetBasalAreasgetLambdagetNeighborsgetRecruitsgroupByGenettrackSpp

Dependencies:classclassIntclicpp11DBIe1071glueigraphKernSmoothlatticelifecyclemagrittrMatrixpkgconfigproxyRcpprlangs2sfunitsvctrswk

Suggested plantTracker Workflow
Introduction | 1. Prepare data | 1.1 The dat data frame must . . . | 1.1.1 Get your data into the sf data format | 1.2 The inv list must . . . | 1.3 Check the inv and dat arguments using checkDat() | 2 Track individuals through time using trackSpp() | 2.1 Function arguments | 2.2 Function output | 3 Calculate local neighborhood density using getNeighbors() | 3.1 Function options and arguments | 3.2 Function outputs | 4 Next steps

Last update: 2026-01-09
Started: 2021-07-27

Using the plantTracker trackSpp() function
Introduction | 1 Input data | 3.3 Assign age and recruitment data to first year | 3.4 Compare sequential years of data to track individuals through time | 3.4.1 Is there a gap between year i-1 and year i? | 3.4.2 Get data for year i | 3.4.3 Are there any observations in the "previous" year (inv[i-1])? | 3.4.4 Add a buffer around the "previous" year data | 3.4.5 Are there actually any observations in the "current" year (inv[i])? | 3.4.6 Store observations as "ghosts" to compare to data from the next year (inv[i+1]) during the next iteration of the loop. | 3.4.7 Are there any overlaps between polygons in tempPreviousYear and tempCurrentYear? | 3.4.8 Compare the overlap between tempPreviousYear and tempCurrentYear to assign trackIDs. | 3.4.9 Flag any suspect observations | 3.4.10 Separate the "ghosts" and the new recruits from the "parent" and "child" data frames | 3.4.11 Store the resulting demographic data | 3.4.12 Get ready for the next iteration of the loop | 4 Prepare the trackSpp() results to be returned! | 4.1 Aggregate the results by genet | 4.2 Informative messages | 5 Examples

Last update: 2025-10-28
Started: 2021-10-12