shapeshiftr

Overview

shapeshiftr calculates the inter-individual distances (IID) in a group or population, and compares population- vs individual-level variation in those distances to identify the spatial signatures of range shape and change. shapeshiftr provides 3 functions to calculate the coefficient of variation (CV) for simple location data:

• cvpop() calculates the population-level CV of IID (CV of all pairwise distances)
• cvind() calculates the individual-level CV of IID ( CV of the average pairwise distance per individual amoung all individuals)
• cvratio() calculates the ratio of the individual-level CV to the population-level CV

And provides 2 functions for dataframes with an sf geometry column:

• iidist() measures the inter-individual distance between every pair of individuals within a given sampling unit (day, season, patch)
• cvmetrics_sf() measures the population- and individual-level CV of IID and their ratio within a given sampling unit (day, season, patch)

For a detailed introduction, please see vignette("shapeshiftr") or shapeshiftr.

Installation

For now, the package can be installed directly from GitHub one of two ways.

Recommended:

install.packages("pak")
pak::pak("nicolalove/shapeshiftr")

Or:

install.packages("devtools")
devtools::install_github("nicolalove/shapeshiftr")

Usage

cvpop(), cvind() and cvratio() produce a numeric value, or if used within dplyr verbs, a column, where each row contains the CV of IID for a given sampling unit (time, season, site). This is useful if you have location data in a vector, matrix or dataframe without a geometry column.

library(shapeshiftr)

 # a single numeric value
cvpop(split_population[, c("x", "y")]

# within a dataframe
split_population %>% group_by(year) %>% summarise(pop = cvpop(across(x,y)), ind = cvind(across(x,y)), ratio = cvratio(across(x,y)))

iidist() returns a dataframe containing a column of inter-individual distances for a given sampling unit (time, season, site). This is useful if you have a sf dataframe with locations stored in a geometry column.

library(sf)
distances <- iid(asym_split_sf, nest_by = c("year"), idcol = "id")
head(distances)

cvmetrics_sf returns a dataframe with the three CV metrics in separate columns. This is useful if you already have inter-individual distances measured, or as downstream analysis after iidist().

cvdf <- cvmetrics_sf(distances, distcol = "iidist" idcol = "id", grp_by = c("year"))
head(cvdf)