R/abundance_difference.R

In codyn: Community Dynamics Metrics

#' @title Abundance Differences
#'
#' @description Calculates the abundance difference for species between two
#'   samples. Differences are on abundance values provided, if relative data is
#'   used, then differences in relative abundance will be calculated. There are
#'   three ways differences can be calculated. 1) Between treatments within a
#'   block (note: block.var and treatment.var need to be specified). 2) Between
#'   treatments, pooling all replicates into a single species pool (note: pool =
#'   TRUE, treatment.var needs to be specified, and block.var = NULL). 3) All
#'   pairwise combinations between all replicates (note: block.var = NULL, pool
#'   = FALSE and specifying treatment.var is optional. If treatment.var is
#'   specified, the treatment that each replicate belongs to will also be listed
#'   in the output).
#'
#' @inheritParams RAC_difference
#'   
#' @return The abundance_difference function returns a data frame with a subset
#'   of the following columns:
#' \itemize{
#'  \item{species.var: }{A column that has same name and type as the species.var
#'  column.}
#'  \item{difference: }{A numeric column of the abundance differences between
#'  the two samples being compared (replicates or treatments). A numeric column
#'  of the change in abundance between consecutive timepoints. A positive value
#'  occurs when a species has greater abundance in replicate.var2 than in
#'  replicate.var and/or in treatment.var2 than in treatment.var.}
#'  \item{replicate.var: }{A column that has same name and type as the
#'  replicate.var column, represents the first replicate being compared. Note, a
#'  replicate column will be returned only when pool = FALSE or block.var =
#'  NULL.}
#'  \item{replicate.var2: }{A column that has the same type as the replicate.var
#'  column, and is named replicate.var with a 2 appended to it, represents the
#'  second replicate being compared. Note, a replicate.var column will be
#'  returned only when pool = FALSE and block.var = NULL.}
#'  \item{time.var: }{A column that has the same name and type as the time.var
#'  column, if time.var is specified.}
#'  \item{treatment.var: }{A column that has same name and type as the
#'  treatment.var column, represents the first treatment being compared. A
#'  treatment.var column will be returned when pool = TRUE, block.var is
#'  specified, or treatment.var is specified.}
#'  \item{treatment.var2: }{A column that has the same type as the treatment.var
#'  column, and is named treatment.var with a 2 appended to it, represents the
#'  second treatment being compared. A treatment.var column will be returned
#'  when pool = TRUE, block.var is specified, or treatment.var is specified.}
#'  \item{block.var: }{A column that has same name and type as the block.var
#'  column, if block.var is specified.}
#' }
#'
#' @references Avolio et al. Submitted
#' @examples
#' data(pplots)
#' # With block and no time
#' df <- subset(pplots, year == 2002 & block < 3)
#' abundance_difference(df = df,
#'                      species.var = "species",
#'                      abundance.var = "relative_cover",
#'                      treatment.var = "treatment",
#'                      block.var = "block",
#'                      replicate.var = "plot")
#'
#' # With blocks and time
#' df <- subset(pplots, year < 2004 & block < 3)
#' abundance_difference(df = df,
#'                      species.var = "species",
#'                      abundance.var = "relative_cover",
#'                      treatment.var = "treatment",
#'                      block.var = "block",
#'                      replicate.var = "plot",
#'                      time.var = "year")
#'
#' # With blocks, time and reference treatment
#' df <- subset(pplots, year < 2004 & block < 3)
#' abundance_difference(df = df,
#'                      species.var = "species",
#'                      abundance.var = "relative_cover",
#'                      treatment.var = "treatment",
#'                      block.var = "block",
#'                      replicate.var = "plot",
#'                      time.var = "year",
#'                      reference.treatment = "N1P0")
#'
#' # Pooling by treatment with time
#' df <- subset(pplots, year < 2004)
#' abundance_difference(df = df,
#'                      species.var = "species",
#'                      abundance.var = "relative_cover",
#'                      treatment.var = "treatment",
#'                      pool = TRUE,
#'                      replicate.var = "plot",
#'                      time.var = "year")
#'
#' # All pairwise replicates with treatment
#' df <- subset(pplots, year < 2004 & plot %in% c(21, 25, 32))
#' abundance_difference(df = df,
#'                      species.var = "species",
#'                      abundance.var = "relative_cover",
#'                      replicate.var = "plot",
#'                      time.var = "year",
#'                      treatment.var = "treatment")
#'
#' # All pairwise replicates without treatment
#' df <- subset(pplots, year < 2004 & plot %in% c(21, 25, 32))
#' abundance_difference(df = df,
#'                      species.var = "species",
#'                      abundance.var = "relative_cover",
#'                      replicate.var = "plot",
#'                      time.var = "year")
#' @export
abundance_difference <- function(df,
                                 time.var = NULL,
                                 species.var,
                                 abundance.var,
                                 replicate.var,
                                 treatment.var = NULL,
                                 pool = FALSE,
                                 block.var = NULL,
                                 reference.treatment = NULL) {

  # validate function call and purge extraneous columns
  args <- as.list(match.call()[-1])
  df <- do.call(check_args, args, envir = parent.frame())

  if (pool) {
    # pool and rank species in each replicate
    allsp <- pool_replicates(df, time.var, species.var, abundance.var,
      replicate.var, treatment.var)
  } else {
    # add NA for species absent from a replicate
    by <- c(block.var, time.var)
    allsp <- split_apply_combine(df, by, FUN = fill_species,
      species.var, abundance.var)
  }

  # specify which variable to use for comparison/"cross join"
  if (!is.null(block.var)) {
    cross.var <- treatment.var
  } else if (pool) {
    cross.var <- treatment.var
  } else {
    cross.var <- replicate.var
  }

  # order cross.var if unordered factor
  to_ordered = is.factor(allsp[[cross.var]]) &
    !is.ordered(allsp[[cross.var]]) &
    is.null(reference.treatment)
  if (to_ordered) {
    class(allsp[[cross.var]]) <- c('ordered', class(allsp[[cross.var]]))
  }

  # cross join for pairwise comparisons
  split_by <- c(block.var, time.var)
  merge_to <- !(names(allsp) %in% split_by)
  cross.var2 <- paste(cross.var, 2, sep = '')
  if (is.null(reference.treatment)) {
    ranktog <- split_apply_combine(allsp, split_by, FUN = function(x) {
      y <- x[merge_to]
      cross <- merge(x, y, by = species.var, suffixes = c('', '2'))
      idx <- cross[[cross.var]] < cross[[cross.var2]]
      cross[idx, ]
    })
  } else {
    ranktog <- split_apply_combine(allsp, split_by, FUN = function(x) {
      y <- x[x[[treatment.var]] != reference.treatment, merge_to]
      x <- x[x[[treatment.var]] == reference.treatment, ]
      merge(x, y, by = species.var, suffixes = c('', '2'))
    })
  }

  # unorder cross.var if orginally unordered factor
  if (to_ordered) {
    x <- class(ranktog[[cross.var]])
    class(ranktog[[cross.var]]) <- x[x != 'ordered']
    class(ranktog[[cross.var2]]) <- x[x != 'ordered']
  }

  # remove rows with NA for both abundances (preferably only when introduced
  # by fill_species)
  idx <- is.na(ranktog[[abundance.var]])
  abundance.var2 <- paste(abundance.var, 2, sep = '')
  idx2 <- is.na(ranktog[[abundance.var2]])
  ranktog[idx, abundance.var] <- 0
  ranktog[idx2, abundance.var2] <- 0
  idx <- ranktog[[abundance.var]] != 0 | ranktog[[abundance.var2]] != 0
  ranktog <- ranktog[idx, ]

  # take abundance difference
  output <- abund_diff(ranktog, species.var, abundance.var, abundance.var2)

  # order and select output columns
  output_order <- c(
    time.var,
    block.var,
    replicate.var, paste(replicate.var, 2, sep = ''),
    treatment.var, paste(treatment.var, 2, sep = ''),
    species.var,
    'difference')

  return(output[intersect(output_order, names(output))])
}

############################################################################
#
# Private functions: these are internal functions not intended for reuse.
# Future package releases may change these without notice. External callers
# should not use them.
#
############################################################################

# A function to calculate abundance differences for a species between two
# samples
# @param df a dataframe
# @param species.var the name of the species column
# @param abundance.var the name of the abundance column
abund_diff <- function(df, species.var, abundance.var, abundance.var2) {

  df[['difference']] <- df[[abundance.var2]] - df[[abundance.var]]
  df[c(abundance.var, abundance.var2)] <- NULL

  return(df)
}