In karinorman/biodivTS: What the Package Does (One Line, Title Case)

library(funrar)
library(biodivTS)

Calculate functional distinctiveness for each species based on the species pool (defined by all species observed over the course of the timeseries)

Get data

pins::board_register_github(name = "github", repo = "karinorman/biodivTS_data", branch = "master")

##  Get the gridded data locally
bt_data <- pins::pin_get("bt-traitfiltered", board = "github") %>%
  rename_with(tolower)

trait_ref <- pins::pin_get("trait-ref", board = "github")

bt_abund <- bt_data %>%
  filter(abundance_type %in% c("Count", "Density", "MeanCount"))

bt_pres <- bt_data %>%
  filter(abundance_type == "Presence/Absence")

loadRData <- function(file_name){
  load(file_name)
  get(ls()[ls() != "file_name"])
}

get_ts_distinct <- function(file_path, trait_data){

  rare_comm_save <- loadRData(file_path)

  species_mat <- rare_comm_save %>%
    column_to_rownames("YEAR") %>%
    dplyr::select(-c(rarefyID, cell, type, rarefy_resamp))

  rel_species <- make_relative(as.matrix(species_mat))

  trait_dist <- get_traitMat(colnames(species_mat), trait_data)

  trait_dist <- trait_dist %>%
    mutate_if(is.character, as.factor) %>%
    compute_dist_matrix(., metric = "gower")

  distinctive <- as.data.frame(distinctiveness(pres_matrix = rel_species, dist_matrix = trait_dist)) %>%
    rownames_to_column("year") %>%
    mutate(rarefyid = unique(rare_comm_save$rarefyID), 
           rarefy_resamp = unique(rare_comm_save$rarefy_resamp))

  return(distinctive)
}


path <- here::here("data", "rarefied_samples")
files <- dir(path, "*.rda") %>%
  paste0(path, "/", .)

plan("multiprocess", workers = 120)
yearly_distinct <- furrr::future_map_dfr(files, get_ts_distinct, trait_data = trait_ref) %>%
  pivot_longer(starts_with("ITIS:"), names_to = "species", values_to = "distinctiveness") %>%
  filter(!is.na(distinctiveness))

Get species-pool level classifications of distinctiveness/scarcity

sp_data <- bt_data %>%
  select(species, rarefyid, abundance, abundance_type)

## Break data down into different kinds of observations to aggregate across years
# first presence/absence
presence_sp <- sp_data %>%
  filter(abundance_type == "Presence/Absence") %>%
  select(species, rarefyid) %>%
  distinct() %>%
  mutate(abundance = 1)

# and then things that act like counts
count_sp <- sp_data %>%
  filter(abundance_type != "Presence/Absence") %>%
  group_by(rarefyid, species, abundance_type) %>%
  mutate(abundance = sum(abundance))