inst/doc/using-in-sna.R

In spatsoc: Group Animal Relocation Data by Spatial and Temporal Relationship

## ----knitropts, include = FALSE-----------------------------------------------
knitr::opts_chunk$set(message = TRUE, 
                      warning = FALSE,
                      eval = FALSE, 
                      echo = TRUE)

## -----------------------------------------------------------------------------
#  ## Load packages
#  library(spatsoc)
#  library(data.table)
#  library(asnipe)
#  library(igraph)

## ---- echo = FALSE, eval = TRUE-----------------------------------------------
data.table::setDTthreads(1)

## -----------------------------------------------------------------------------
#  ## 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)]
#  
#  ## Calculate the year of the relocation
#  DT[, yr := year(datetime)]

## -----------------------------------------------------------------------------
#  ## Temporal groups
#  group_times(DT, datetime = 'datetime', threshold = '5 minutes')
#  
#  ## Spatial groups
#  group_pts(
#    DT,
#    threshold = 50,
#    id = 'ID',
#    coords = c('X', 'Y'),
#    timegroup = 'timegroup'
#  )
#  

## ---- eval = FALSE------------------------------------------------------------
#  # EPSG code for relocations
#  utm <- 32736
#  
#  ## Group relocations by julian day
#  group_times(DT, datetime = 'datetime', threshold = '1 day')
#  
#  ## Group lines for each individual and julian day
#  group_lines(
#    DT,
#    threshold = 50,
#    projection = utm,
#    id = 'ID',
#    coords = c('X', 'Y'),
#    timegroup = 'timegroup',
#    sortBy = 'datetime'
#  )

## ---- eval = FALSE------------------------------------------------------------
#  # EPSG code for relocations
#  utm <- 32736
#  
#  ## Option 1: area = FALSE and home range intersection 'group' column added to DT
#  group_polys(
#    DT,
#    area = FALSE,
#    hrType = 'mcp',
#    hrParams = list(percent = 95),
#    projection = utm,
#    id = 'ID',
#    coords = c('X', 'Y')
#  )
#  
#  ## Option 2: area = TRUE
#  #  results must be assigned to a new variable
#  #  data.table returned has ID1, ID2 and proportion and area overlap
#  areaDT <- group_polys(
#    DT,
#    area = TRUE,
#    hrType = 'mcp',
#    hrParams = list(percent = 95),
#    projection = utm,
#    id = 'ID',
#    coords = c('X', 'Y')
#  )
#  

## -----------------------------------------------------------------------------
#  ## Subset DT to only year 2016
#  subDT <- DT[yr == 2016]
#  
#  ## Generate group by individual matrix
#  # group column generated by spatsoc::group_pts
#  gbiMtrx <- get_gbi(DT = subDT, group = 'group', id = 'ID')

## -----------------------------------------------------------------------------
#  ## Generate observed network
#  net <- get_network(gbiMtrx,
#                     data_format = "GBI",
#                     association_index = "SRI")

## -----------------------------------------------------------------------------
#  # Calculate year column to ensure randomization only occurs within years since data spans multiple years
#  DT[, yr := year(datetime)]
#  
#  ## Step type randomizations
#  #  providing 'timegroup' (from group_times) as datetime
#  #  splitBy = 'yr' to force randomization only within year
#  randStep <- randomizations(
#     DT,
#     type = 'step',
#     id = 'ID',
#     group = 'group',
#     coords = NULL,
#     datetime = 'timegroup',
#     iterations = 3,
#     splitBy = 'yr'
#  )

## -----------------------------------------------------------------------------
#  # Calculate year column to ensure randomization only occurs within years since data spans multiple years
#  DT[, yr := year(datetime)]
#  
#  ## Daily type randomizations
#  # splitBy = 'yr' to force randomization only within year
#  randDaily <- randomizations(
#     DT,
#     type = 'daily',
#     id = 'ID',
#     group = 'group',
#     coords = NULL,
#     datetime = 'datetime',
#     splitBy = 'yr',
#     iterations = 20
#  )

## -----------------------------------------------------------------------------
#  # Calculate year column to ensure randomization only occurs within years since data spans multiple years
#  DT[, yr := year(datetime)]
#  
#  ## Trajectory type randomization
#  randTraj <- randomizations(
#     DT,
#     type = 'trajectory',
#     id = 'ID',
#     group = NULL,
#     coords = c('X', 'Y'),
#     datetime = 'datetime',
#     splitBy = 'yr',
#     iterations = 20
#  )

## -----------------------------------------------------------------------------
#  ## Create a data.table of unique combinations of iteration and year, excluding observed rows
#  iterYearLs <- unique(randStep[!(observed), .(iteration, yr)])
#  
#  ## Generate group by individual matrix
#  # for each combination of iteration number and year
#  # 'group' generated by spatsoc::group_pts
#  # 'randomID' used instead of observed ID (type = 'step')
#  gbiLs <- mapply(FUN = function(i, y) {
#    get_gbi(randStep[iteration == i & yr == y],
#            'group', 'randomID')
#    },
#    i = iterYearLs$iter,
#    y = iterYearLs$yr,
#    SIMPLIFY = FALSE
#  )
#  
#  ## Generate a list of random networks
#  netLs <- lapply(gbiLs, FUN = get_network,
#                  data_format = "GBI", association_index = "SRI")
#  

## -----------------------------------------------------------------------------
#  ## Generate fake population
#  randDaily[, population := sample(1:2, .N, replace = TRUE)]
#  
#  ## Create a data.table of unique combinations of iteration, year, and population, excluding observed rows
#  iterYearLs <- unique(randStep[!(observed), .(iteration, yr, population)])
#  
#  ## Generate group by individual matrix
#  # for each combination of iteration number and year
#  # 'group' generated by spatsoc::group_pts
#  # 'randomID' used instead of observed ID (type = 'step')
#  gbiLs <- mapply(FUN = function(i, y, p) {
#    get_gbi(randDaily[iteration == i &
#                        yr == y & population == p],
#            'group', 'randomID')
#    },
#    i = iterYearLs$iter,
#    y = iterYearLs$yr,
#    p = iterYearLs$population,
#    SIMPLIFY = FALSE
#  )
#  
#  ## Generate a list of random networks
#  netLs <- lapply(gbiLs, FUN = get_network,
#                  data_format = "GBI", association_index = "SRI")
#  

## -----------------------------------------------------------------------------
#  ## Randomized temporal groups
#  # 'datetime' is the randomdatetime produced by randomizations(type = 'trajectory')
#  group_times(randTraj, datetime = 'randomdatetime', threshold = '5 minutes')
#  
#  ## Randomized spatial groups
#  # 'iteration' used in splitBy to ensure only points within each iteration are grouped
#  group_pts(randTraj, threshold = 50, id = 'ID', coords = c('X', 'Y'),
#            timegroup = 'timegroup', splitBy = 'iteration')
#  
#  ## Create a data.table of unique combinations of iteration and year, excluding observed rows
#  iterYearLs <- unique(randStep[!(observed), .(iteration, yr)])
#  
#  ## Generate group by individual matrix
#  # for each combination of iteration number and year
#  # 'group' generated by spatsoc::group_pts
#  # 'ID' used since datetimes were randomized within individuals
#  gbiLs <- mapply(FUN = function(i, y) {
#    get_gbi(randTraj[iteration == i & yr == y],
#            'group', 'ID')
#    },
#    i = iterYearLs$iter,
#    y = iterYearLs$yr,
#    SIMPLIFY = FALSE
#  )
#  
#  ## Generate a list of random networks
#  netLs <- lapply(gbiLs, FUN = get_network,
#                  data_format = "GBI", association_index = "SRI")
#  

## -----------------------------------------------------------------------------
#  ## Generate graph
#  g <- graph.adjacency(net, 'undirected',
#                       diag = FALSE, weighted = TRUE)
#  
#  ## Metrics for all individuals
#  observed <- data.table(
#    centrality = evcent(g)$vector,
#    strength = graph.strength(g),
#    degree = degree(g),
#    ID = names(degree(g)),
#    yr = subDT[, unique(yr)]
#  )

## -----------------------------------------------------------------------------
#  ## Generate graph and calculate network metrics
#  mets <- lapply(seq_along(netLs), function(n) {
#    g <- graph.adjacency(netLs[[n]], 'undirected',
#                         diag = FALSE, weighted = TRUE)
#  
#    data.table(
#      centrality = evcent(g)$vector,
#      strength = graph.strength(g),
#      degree = degree(g),
#      ID = names(degree(g)),
#      iteration = iterYearLs$iter[[n]],
#      yr = iterYearLs$yr[[n]]
#      )
#  })
#  
#  ## Metrics for all individuals across all iterations and years
#  random <- rbindlist(mets)
#  
#  ## Mean values for each individual and year
#  meanMets <- random[, lapply(.SD, mean), by = .(ID, yr),
#                  .SDcols = c('centrality', 'strength', 'degree')]

## -----------------------------------------------------------------------------
#  ## Create a data.table of unique combinations of iteration and year, including observed and random rows
#  iterYearLs <- unique(randStep[, .(iteration, yr)])
#  
#  ## Generate group by individual matrix
#  # for each combination of iteration and year
#  # 'group' generated by spatsoc::group_pts
#  # 'randomID' used instead of observed ID (type = 'step')
#  gbiLs <- mapply(FUN = function(i, y) {
#    get_gbi(randStep[iteration == i & yr == y],
#            'group', 'randomID')
#    },
#    i = iterYearLs$iter,
#    y = iterYearLs$yr,
#    SIMPLIFY = FALSE
#  )
#  
#  ## Generate a list of random networks
#  netLs <- lapply(gbiLs, FUN = get_network,
#                  data_format = "GBI", association_index = "SRI")
#  
#  ## Generate graph and calculate network metrics
#  mets <- lapply(seq_along(netLs), function(n) {
#    g <- graph.adjacency(netLs[[n]], 'undirected',
#                         diag = FALSE, weighted = TRUE)
#  
#    data.table(
#      centrality = evcent(g)$vector,
#      strength = graph.strength(g),
#      ID = names(degree(g)),
#      iteration = iterYearLs$iter[[n]],
#      yr = iterYearLs$yr[[n]]
#      )
#  })
#  
#  ## Observed and random for all individuals across all iterations and years
#  out <- rbindlist(mets)
#  
#  ## Split observed and random
#  out[, observed := ifelse(iteration == 0, TRUE, FALSE)]
#  
#  ## Mean values for each individual and year, by observed/random
#  meanMets <- out[, lapply(.SD, mean), by = .(ID, yr, observed),
#                  .SDcols = c('centrality', 'strength')]
#