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---
title: "Introduction to spatsoc"
author: "Alec Robitaille, Quinn Webber and Eric Vander Wal"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Introduction to spatsoc}
%\VignetteEngine{knitr::knitr}
%\VignetteEncoding{UTF-8}
---
```{r knitropts, include = FALSE}
knitr::opts_chunk$set(message = TRUE,
warning = FALSE,
eval = FALSE,
echo = FALSE)
```
The `spatsoc` package provides functionality for analyzing animal relocation data in time and space to identify potential interactions among individuals and build gambit-of-the-group data for constructing social networks.
The package contains grouping and edge list generating functions that are used for identifying spatially and temporally explicit groups from input data. In addition, we provide social network analysis functions for randomizing individual identifiers within groups, designed to test whether social networks generated from animal relocation data were based on non-random social proximity among individuals and for generating group by individual matrices.
The functions were developed for application across animal relocation data, for example, proximity based social network analyses and spatial and temporal clustering of points.
## Data preparation
```{r libs, eval = TRUE, include = FALSE}
library(spatsoc); library(data.table)
DT <- fread(system.file("extdata", "DT.csv", package = "spatsoc"))
DT[, datetime := as.POSIXct(datetime, tz = 'UTC')]
```
`spatsoc` expects a `data.table` for all of its functions. If you have a `data.frame`, you can use `data.table::setDT()` to convert it by reference. If your data is a CSV, you can use `data.table::fread()` to import it as a `data.table`.
The data consist of relocations of `r DT[, uniqueN(ID)]` individuals over `r DT[, max(yday(datetime)) - min(yday(datetime))]` days. Using these data, we can compare the various grouping methods available in `spatsoc`. Note: these examples will use a subset of the data, only individuals H, I and J.
```{r, echo = TRUE}
# Load packages
library(spatsoc)
library(data.table)
# Read in spatsoc's example data
DT <- fread(system.file("extdata", "DT.csv", package = "spatsoc"))
# Use subset of individuals
DT <- DT[ID %in% c('H', 'I', 'J')]
# Cast character column 'datetime' as POSIXct
DT[, datetime := as.POSIXct(datetime, tz = 'UTC')]
```
```{r, eval = TRUE}
DT <- DT[ID %chin% c('H', 'I', 'J')]
knitr::kable(DT[, .SD[1:2], ID][order(ID)])
```
## Temporal grouping
The `group_times` function is used to group relocations temporally. It is flexible to a threshold provided in units of minutes, hours or days. Since GPS fixes taken at regular intervals have some level of variability, we will provide a time threshold (`threshold`), to consider all fixes within this threshold taken at the same time. Alternatively, we may want to understand different scales of grouping, perhaps daily movement trajectories or seasonal home range overlap.
```{r groupmins, echo = TRUE}
group_times(DT, datetime = 'datetime', threshold = '5 minutes')
```
```{r tableSetUp, eval = TRUE}
nRows <- 9
```
```{r tabgroupmins, eval = TRUE}
knitr::kable(
group_times(DT, threshold = '5 minutes', datetime = 'datetime')[,
.(ID, X, Y, datetime, minutes, timegroup)][
order(datetime)][
1:nRows])
```
A message is returned when `group_times` is run again on the same `DT`, as the columns already exist in the input `DT` and will be overwritten.
```{r grouphours, echo = TRUE}
group_times(DT, datetime = 'datetime', threshold = '2 hours')
```
```{r tabgrouphours, eval = TRUE}
knitr::kable(
group_times(DT, threshold = '2 hours', datetime = 'datetime')[,
.(ID, X, Y, datetime, hours, timegroup)][
order(datetime)][
1:nRows])
```
```{r groupdays, echo = TRUE}
group_times(DT, datetime = 'datetime', threshold = '5 days')
```
```{r tabgroupdays, eval = TRUE}
knitr::kable(
group_times(DT, threshold = '5 days', datetime = 'datetime')[, .SD[sample(.N, 3)], by = .(timegroup, block)][order(datetime)][
1:nRows, .(ID, X, Y, datetime, block, timegroup)])
```
## Spatial grouping
The `group_pts` function compares the relocations of all individuals in each timegroup and groups individuals based on a distance threshold provided by the user. The `group_pts` function uses the "chain rule" where three or more individuals that are all within the defined threshold distance of at least one other individual are considered in the same group. For point based spatial grouping with a distance threshold that does not use the chain rule, see `edge_dist` below.
```{r grouppts, echo = TRUE}
group_times(DT = DT, datetime = 'datetime', threshold = '15 minutes')
group_pts(DT, threshold = 50, id = 'ID', coords = c('X', 'Y'), timegroup = 'timegroup')
```
```{r fakegrouppts, eval = TRUE}
DT <- group_times(DT = DT, datetime = 'datetime',
threshold = '15 minutes')
DT <- group_pts(
DT = DT,
threshold = 50, id = 'ID', coords = c('X', 'Y'),
timegroup = 'timegroup')
knitr::kable(
DT[
between(group, 771, 774)][order(timegroup)][
1:nRows, .(ID, X, Y, timegroup, group)]
)
```
The `group_lines` function groups individuals whose trajectories intersect in a specified time interval. This represents a coarser grouping method than `group_pts` which can help understand shared space at daily, weekly or other temporal resolutions.
```{r fakegrouplines, echo = TRUE}
utm <- '+proj=utm +zone=21 ellps=WGS84'
group_times(DT = DT, datetime = 'datetime', threshold = '1 day')
group_lines(DT, threshold = 50, projection = utm,
id = 'ID', coords = c('X', 'Y'),
timegroup = 'timegroup', sortBy = 'datetime')
```
```{r grouplines, eval = TRUE}
utm <- '+proj=utm +zone=21 ellps=WGS84'
DT <- group_times(DT = DT, datetime = 'datetime',
threshold = '1 day')
DT <- group_lines(DT,
threshold = 50, projection = utm,
id = 'ID', coords = c('X', 'Y'),
sortBy = 'datetime', timegroup = 'timegroup')
knitr::kable(
unique(DT[, .(ID, timegroup, group)])[, .SD[1:3], ID][order(timegroup)]
)
```
The `group_polys` function groups individuals whose home ranges intersect. This represents the coarsest grouping method, to provide a measure of overlap across seasons, years or all available relocations. It can either return the proportion of home range area overlapping between individuals or simple groups. Home ranges are calculated using `adehabitatHR::kernelUD` or `adehabitatHR::mcp`. Alternatively, a `SpatialPolygonsDataFrame` can be input to the `spPolys` argument.
```{r fakegrouppolys, echo = TRUE}
utm <- '+proj=utm +zone=21 ellps=WGS84'
group_times(DT = DT, datetime = 'datetime', threshold = '8 days')
group_polys(DT = DT, area = TRUE, hrType = 'mcp',
hrParams = list('percent' = 95),
projection = utm,
coords = c('X', 'Y'), id = 'ID')
```
```{r grouppolys, eval = TRUE}
utm <- '+proj=utm +zone=21 ellps=WGS84'
DT <- group_times(DT = DT, datetime = 'datetime',
threshold = '8 days')
knitr::kable(
data.frame(group_polys(
DT,
area = TRUE, hrType = 'mcp',
hrParams = list('percent' = 95),
projection = utm,
coords = c('X', 'Y'), id = 'ID')[
, .(ID1, ID2, area, proportion)])
)
```
## Edge list generation
The `edge_dist` function calculates the geographic distance between between individuals within each timegroup and returns all paired relocations within the spatial threshold. `edge_dist` uses a distance matrix like group_pts, but, in contrast, does not use the chain rule to group relocations.
```{r edgedist, echo = TRUE}
group_times(DT = DT, datetime = 'datetime', threshold = '15 minutes')
edge_dist(DT, threshold = 50, id = 'ID', coords = c('X', 'Y'), timegroup = 'timegroup', fillNA = TRUE)
```
```{r fakeedgedist, eval = TRUE}
DT <- group_times(DT = DT, datetime = 'datetime',
threshold = '15 minutes')
edges <- edge_dist(DT, threshold = 50, id = 'ID', coords = c('X', 'Y'), timegroup = 'timegroup', fillNA = TRUE)
knitr::kable(
edges[between(timegroup, 158, 160)]
)
```
The `edge_nn` function calculates the nearest neighbour to each individual within each time group. If the optional distance threshold is provided, it is used to limit the maximum distance between neighbours. `edge_nn` returns an edge list of each individual and their nearest neighbour.
```{r edgenn, echo = TRUE}
group_times(DT = DT, datetime = 'datetime', threshold = '15 minutes')
edge_nn(DT, id = 'ID', coords = c('X', 'Y'), timegroup = 'timegroup')
```
```{r fakeedgenn, eval = TRUE}
DT <- group_times(DT = DT, datetime = 'datetime',
threshold = '15 minutes')
edges <- edge_nn(DT, id = 'ID', coords = c('X', 'Y'), timegroup = 'timegroup')
knitr::kable(
edges[1:6]
)
```
## Notes
Package dependencies for `spatsoc` are `sp`, `rgeos`, `igraph`, `adehabitatHR` and `data.table`. `data.table` provides efficient methods for manipulating large (or small) datasets. As a result, input `DT` for all `spatsoc` functions must be a `data.table` and if it isn't, you can simply use `data.table::setDT(df)` to convert it by reference.
In addition, since the `rgeos` package is used in most functions (`group_lines` and `group_polys`) the input `DT`'s coordinate system is important. `rgeos` expects planar coordinates and this requirement is carried forward for `spatsoc`. Since `rgeos` is used, system dependencies include `GEOS`.
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