## ----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, # crs = 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), # crs = 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), # crs = 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')] #