# Frequently asked questions about spatsoc

## Alec L. Robitaille, Quinn Webber and Eric Vander Wal

spatsoc is an R package for detecting spatial and temporal groups in GPS relocations. It can be used to build proximity-based social networks using gambit-of-the-group format and edge-lists. In addition, the randomization function provides data-stream randomization methods suitable for GPS data.

See the other vignettes for further information:

- Introduction to spatsoc
  - temporal grouping
  - spatiotemporal grouping with `group_pts`, `group_lines`, `group_polys`
  - distance based edge-list generation with `edge_dist`
- Frequently asked questions about spatsoc
  - install
  - function details for `group_times`, `group_pts`, `group_lines`, `group_polys`, `edge_dist`, `edge_nn`, and `randomizations`
  - package design including modify-by-reference, data.table column allocation
  - calculating summary information
- Using spatsoc in social network analysis
  - generating gambit-of-the-group data
  - generating observed networks
  - data stream randomization, randomized networks
  - network metrics
- Using distance based edge-lists generating functions, dyad_id, and fusion_id
  - generate distance based edge-lists with `edge_dist` and `edge_nn`
  - generate dyad identifiers for edge-lists with `dyad_id`
  - identify fusion events with `fusion_id`
- Geometry interface
  - using `get_geometry` to setup a geometry column and use the geometry interface
  - details of underlying distance, direction and centroid spatial measures
  - converting to and from related packages
- Interspecific interactions
  - combine two movement datasets
  - identify interspecific interactions

# Usage

`spatsoc` leverages `data.table` to modify by reference and iteratively work on subsets of the input data. The first input for all functions in `spatsoc` is `DT`, an input `data.table`. If your data is a `data.frame`, you can convert it by reference using `setDT(DF)`.

## Spatial and temporal grouping

`spatsoc` is designed to work in two steps: temporal followed by either spatial grouping or edge-list generating. Considering your specific study species and system, determine a relevant temporal and spatial grouping threshold. This may be 5 minutes and 50 meters or 2 days and 100 meters or any other thresholds - the functions provided by `spatsoc` are flexible to user input. In some cases, the spatial grouping function selected is only relevant with certain temporal grouping thresholds. For example, we wouldn’t expect a threshold of 5 minutes with `group_polys`.

    # Load packages
    library(spatsoc)
    library(data.table)

    # Read data as a data.table
    DT <- fread(system.file("extdata", "DT.csv", package = "spatsoc"))

    # Cast datetime column to POSIXct
    DT[, datetime := as.POSIXct(datetime)]

    # Temporal groups
    group_times(DT, datetime = 'datetime', threshold = '5 minutes')

    ##            ID        X       Y            datetime population minutes timegroup
    ##        <char>    <num>   <num>              <POSc>      <int>   <int>     <int>
    ##     1:      A 715851.4 5505340 2016-11-01 00:00:54          1       0         1
    ##     2:      A 715822.8 5505289 2016-11-01 02:01:22          1       0         2
    ##     3:      A 715872.9 5505252 2016-11-01 04:01:24          1       0         3
    ##     4:      A 715820.5 5505231 2016-11-01 06:01:05          1       0         4
    ##     5:      A 715830.6 5505227 2016-11-01 08:01:11          1       0         5
    ##    ---
    ## 14293:      J 700616.5 5509069 2017-02-28 14:00:54          1       0      1393
    ## 14294:      J 700622.6 5509065 2017-02-28 16:00:11          1       0      1394
    ## 14295:      J 700657.5 5509277 2017-02-28 18:00:55          1       0      1449
    ## 14296:      J 700610.3 5509269 2017-02-28 20:00:48          1       0      1395
    ## 14297:      J 700744.0 5508782 2017-02-28 22:00:39          1       0      1396

    # Spatial groups
    group_pts(
      DT,
      threshold = 50,
      id = 'ID',
      coords = c('X', 'Y'),
      timegroup = 'timegroup'
    )

    ##            ID        X       Y            datetime population minutes timegroup
    ##        <char>    <num>   <num>              <POSc>      <int>   <int>     <int>
    ##     1:      A 715851.4 5505340 2016-11-01 00:00:54          1       0         1
    ##     2:      A 715822.8 5505289 2016-11-01 02:01:22          1       0         2
    ##     3:      A 715872.9 5505252 2016-11-01 04:01:24          1       0         3
    ##     4:      A 715820.5 5505231 2016-11-01 06:01:05          1       0         4
    ##     5:      A 715830.6 5505227 2016-11-01 08:01:11          1       0         5
    ##    ---
    ## 14293:      J 700616.5 5509069 2017-02-28 14:00:54          1       0      1393
    ## 14294:      J 700622.6 5509065 2017-02-28 16:00:11          1       0      1394
    ## 14295:      J 700657.5 5509277 2017-02-28 18:00:55          1       0      1449
    ## 14296:      J 700610.3 5509269 2017-02-28 20:00:48          1       0      1395
    ## 14297:      J 700744.0 5508782 2017-02-28 22:00:39          1       0      1396
    ##        group
    ##        <int>
    ##     1:     1
    ##     2:     2
    ##     3:     3
    ##     4:     4
    ##     5:     5
    ##    ---
    ## 14293:  9909
    ## 14294:  9910
    ## 14295:  9911
    ## 14296:  9912
    ## 14297:  9913

# Installation

## Package dependencies

    adehabitatHR (>= 0.4.21),
    data.table (>= 1.15.0),
    igraph,
    sf,
    lwgeom (>= 0.2.15),
    CircStats,
    stats,
    units (>= 0.8-6),
    rlang

See here for help installing `sf`, see here for help installing `units` and here for help installing `lwgeom`.

# Functions

## group_times

`group_times(DT, datetime, threshold)`

- `DT`: input `data.table`
- `datetime`: date time column name in input data.table
- `threshold`: threshold for grouping

### DT

A `data.table` with a date time formatted column. The input `DT` will be returned with columns appended. The `timegroup` column corresponds to the temporal group assigned to each row. Please note that the actual value of the time group is meaningless. Reordered data will return a different time group. What is meaningful, however, is the contents of each group. Each group will contain all rows nearest to the threshold provided.

### datetime format

The `group_times` function expects either one column (`POSIXct`) or two columns (`IDate` and `ITime`).

Given a character column representing the date time, convert it to `POSIXct` or `IDate` and `ITime`:

    DT[, datetime := as.POSIXct(datetime)]
    DT[, c('idate', 'itime') := IDateTime(datetime)]

These are then provided to the function using the names of the column in the input data.

`group_times(DT, datetime = 'datetime', threshold = '5 minutes')`

or

`group_times(DT, datetime = c('idate', 'itime'), threshold = '5 minutes')`

### threshold recommendations

The `threshold` provided to `group_times` should be related to the fix rate of the input dataset and to the specific study system and species. If relocations are recorded every two hours, a `threshold = '2 hours'` will group all rows to the nearest two hour group (10am, 12pm, 2pm, 4pm, …). This, however, means that the relocations can be up to one hour apart from each other. Picking a smaller threshold, e.g.: `threshold = '15 minutes'` may be more relevant in some cases. The flexibility of `spatsoc`’s threshold argument means the user must carefully consider what threshold is reasonable to their specific system.

### Limitations of threshold

The `threshold` of `group_times` is considered only within the scope of 24 hours and this poses limitations on it:

- `threshold` must evenly divide into 60 minutes or 24 hours
- multi-day blocks are consistent across years and timegroups from these are by year.
- number of minutes cannot exceed 60
- `threshold` cannot be fractional

### Columns returned by group_times

The main column returned by `group_times` is “timegroup”. It represents the temporal group of each row, where those nearest (either above or below) within the threshold are grouped. Its actual value does not have any meaning, but the contents of each group do. That means if the data is reordered, a row may have a different time group, but the other rows in that group should not change.

The extra columns are provided to help the user investigate, troubleshoot and interpret the timegroup.

threshold unit | column(s) added
--- | ---
minute | “minutes” column added identifying the nearest minute group for each row.
hour | “hours” column added identifying the nearest hour group for each row.
day | “block” columns added identifying the multiday block for each row.

### Warnings and messages

- “columns found in input DT and will be overwritten by this function”

This message is returned to the user when a column matching those returned by `group_times` is found in the input DT. This is commonly the case when `group_times` is run multiple times consecutively.

- “no threshold provided, using the time field directly to group”

This message is returned to the user when the `threshold` is NULL. This is the default setting of `threshold` and, at times, may be suitable. In this case, the date times in the `datetime` column will be grouped exactly. Usually, a threshold should be provided.

- “the minimum and maximum days in DT are not evenly divisible by the provided block length”

This warning is returned to the user when the `threshold` with unit days does not divide evenly into the range of days in DT. For example, if DT had data covering 30 days, and a threshold of ‘7 days’ was used, this warning would be returned. Note, this warning is returned for the range of days for the entire data set and not by year.

## group_pts

`group_pts(DT, threshold, id, coords, timegroup, splitBy)`

- `DT`: input `data.table`
- `threshold`: threshold for grouping
- `id`: column name of IDs in `DT`
- `coords`: column names of x and y coordinates in `DT`
- `timegroup`: column name of time group
- `splitBy`: (optional) column names of extra variables to group on

### DT

The input `data.table`. It will returned with a column named group appended, which represents the spatial (and temporal if `timegroup` is provided) group.

### threshold

The threshold must be in the units of the coordinates.

## group_lines

`group_lines(DT, threshold, crs, id, coords, timegroup, sortBy, splitBy, spLines)`

- `DT`: input `data.table`
- `threshold`: threshold for grouping
- `crs`: crs of coordinates in `DT`
- `id`: column name of IDs in `DT`
- `coords`: column names of x and y coordinates in `DT`
- `timegroup`: (optional) column name of time group
- `sortBy`: column name of date time to sort rows for building lines
- `splitBy`: (optional) column names of extra variables to group on
- `sfLines`: alternatively, provide a sf LINESTRING object and id column name

### DT

See 3.2.1.

### threshold

The `threshold` argument represents a buffer area around each line. When `threshold = 0`, the lines are grouped by spatial overlap. If the threshold is greater than 0, the lines buffered, then grouped by spatial overlap.

### crs

The `crs` argument expects a character string or numeric defining the coordinate reference system to be passed to `sf::st_crs`. For example, for UTM zone 36S (EPSG 32736), the crs argument is `crs = "EPSG:32736"` or `crs = 32736`. See https://spatialreference.org for a list of EPSG codes. Please note, R spatial has followed updates to GDAL and PROJ for handling coordinate reference systems, see more at https://r-spatial.org/r/2020/03/17/wkt.html.

### sortBy

The `sortBy` argument expects a date time formatted column name, which is used to order the rows for each individual (and `splitBy`).

## group_polys

`group_polys(DT, area, hrType, hrParams, crs, id, coords, splitBy, spLines)`

- `DT`: input `data.table`
- `area`: logical argument if proportional area should be returned
- `hrType`: type of home range created
- `hrParams`: parameters relevant to the type of home range created
- `crs`: crs of coordinates in `DT`
- `id`: column name of IDs in `DT`
- `coords`: column names of x and y coordinates in `DT`
- `splitBy`: (optional) column names of extra variables to group on
- `sfPolys`: alternatively, provide a simple features POLGON or MULTIPOLYGON object and an id column

### DT and area

If `area = FALSE`, see 3.2.1. If `area = TRUE`, the DT will not be appended with a group column instead a `data.table` with IDs and proportional area overlap will be returned.

The default unit for area overlap is square meters.

### crs

The `crs` argument expects a character string or numeric defining the coordinate reference system to be passed to `sf::st_crs`. For example, for UTM zone 36S (EPSG 32736), the crs argument is `crs = "EPSG:32736"` or `crs = 32736`. See https://spatialreference.org for a list of EPSG codes. Please note, R spatial has followed updates to GDAL and PROJ for handling coordinate reference systems, see more at https://r-spatial.org/r/2020/03/17/wkt.html.

### hrType and hrParams

Currently, `spatsoc` offers two types of home ranges provided by the `adehabitatHR` package: ‘mcp’ (`mcp`) and ‘kernel’ (`kernelUD` and `getverticeshr`). The parameters must match the arguments of those functions.

Internally, we match arguments to the functions allowing the user to provide, for example, both the percent (provided to `getverticeshr`) and grid arguments (provided to `mcp`).

    group_polys(
      DT,
      area = FALSE,
      crs = utm,
      hrType = 'mcp',
      hrParams = list(grid = 60, percent = 95),
      id = 'ID',
      coords = c('X', 'Y')
    )

## edge_dist

`edge_dist(DT = NULL, threshold = NULL, id = NULL, coords = NULL, timegroup = NULL, splitBy = NULL, fillNA = TRUE)`

- `DT`: input `data.table`
- `threshold`: threshold for grouping
- `id`: column name of IDs in `DT`
- `coords`: column names of x and y coordinates in `DT`
- `timegroup`: column name of time group
- `splitBy`: (optional) column names of extra variables to group on
- `fillNA`: logical indicating if NAs should be returned for individuals that were not within the threshold distance of any other. If TRUE, NAs are returned. If FALSE, only edges between individuals within the threshold distance are returned.

This is the non-chain rule implementation similar to `group_pts`. Edges are defined by the distance threshold and NAs are returned for individuals within each timegroup if they are not within the threshold distance of any other individual (if `fillNA` is TRUE).

See the vignette Using edge-list generating functions and dyad_id for details about the `edge_dist` function.

## edge_nn

`edge_nn(DT = NULL, id = NULL, coords = NULL, timegroup = NULL, splitBy = NULL, threshold = NULL)`

- `DT`: input `data.table`
- `id`: column name of IDs in `DT`
- `coords`: column names of x and y coordinates in `DT`
- `timegroup`: column name of time group
- `splitBy`: (optional) column names of extra variables to group on
- `threshold`: (optional) spatial distance threshold to set maximum distance between an individual and their neighbour.

This function can be used to generate edge-lists defined either by nearest neighbour or nearest neighbour with a maximum distance. NAs are returned for nearest neighbour for an individual was alone in a timegroup (and/or splitBy) or if the distance between an individual and it’s nearest neighbour is greater than the threshold.

See the vignette Using edge-list generating functions and dyad_id for details about the `edge_nn` function.

## randomizations

`randomizations(DT, type, id, datetime, splitBy, iterations)`

- `DT`: input `data.table`
- `type`: one of ‘daily’, ‘step’ or ‘trajectory’
- `id`: Character string of ID column name
- `datetime`: field used for providing date time or time group - see details
- `splitBy`: List of fields in DT to split the randomization process by
- `iterations`: The number of iterations to randomize

See the vignette Using spatsoc in social network analysis for details about the `randomizations` function (specifically the section ‘Data stream randomization’)

# Package design

## Don’t I need to reassign to save the output?

Many functions in `spatsoc` use data.table’s modify-by-reference to reduce recopying large datasets and improve performance. Included in this list are:

- `group_times`
- `group_pts`
- `group_lines`
- `group_polys(area = FALSE)`
- `centroid_group`
- `direction_to_leader`
- `direction_to_centroid`
- `direction_step`
- `direction_group`
- `direction_polarization`
- `distance_to_centroid`
- `distance_to_leader`
- `fusion_id`
- `dyad_id`
- `leader_direction_group`

Functions that require their outputs be reassigned include:

- `group_polys(area = TRUE)`
- `edge_dist`
- `edge_nn`
- `edge_delay`
- `edge_direction`
- `edge_alignment`
- `centroid_fusion`
- `centroid_dyad`
- `leader_edge_delay`
- `randomizations`

## Why does a function print the result, but columns aren’t added to my DT?

Check that your `data.table` has columns allocated (with `data.table::truelength`) and if not, use `data.table::setDT` or `data.table::alloc.col`. This can happen if you are reading your data from `RDS` or `RData` files. See here.

    if (truelength(DT) == 0) {
      setDT(DT)
    }
    # then go to spatsoc
    group_times(DT, datetime = 'datetime', threshold = '5 minutes')

or simply:

    DT <- readRDS('path/to/data.Rds')
    alloc.col(DT)

# Summary information

Here are some useful code chunks for understanding the spatial and temporal extent of your data and the outputs of `spatsoc` functions.

## Number of individuals

    # Number of unique individuals
    DT[, uniqueN(ID)]

    # Number of unique individuals by timegroup
    DT[, uniqueN(ID), by = timegroup]

## Temporal range

    # Min, max datetime
    DT[, range(datetime)]

    # Difference between relocations in hours
    DT[order(datetime),
       .(difHours = as.numeric(difftime(datetime, shift(datetime), units = 'hours'))),
       by = ID]

    # Difference between relocations in hours
    DT[order(datetime),
       .(difMins = as.numeric(difftime(datetime, shift(datetime), units = 'mins'))),
       by = ID]

## Spatial extent

Simple spatial extents can be calculated for all individuals or by individual.

    # All individuals
    DT[, .(minX = min(X),
           maxX = max(X),
           minY = min(Y),
           maxY = max(Y),)]

    # By individual
    DT[, .(minX = min(X),
           maxX = max(X),
           minY = min(Y),
           maxY = max(Y),),
       by = ID]

## `spatsoc` outputs

After using the grouping functions, we can determine the number of individuals in a temporal or spatial group.

    # Number of unique individuals by timegroup
    DT[, uniqueN(ID), by = timegroup]

    # Number of unique individuals by group
    DT[, uniqueN(ID), by = group]