R/curve_change.R

In codyn: Community Dynamics Metrics

#' @title Curve Change
#' 
#' @description Calculates the area difference between two rank abundance curves
#'   between two time periods. If replicate is specified, it must be measured in
#'   both time points, otherwise it will be dropped for that time period
#'   comparison.
#'   
#' @inheritParams RAC_change
#'   
#' @return The curve_change function returns a data frame with the following
#'   attributes:
#' \itemize{
#'  \item{time.var: }{A column with the specified time.var and a second column,
#'  with '2' appended to the name. Time is subtracted from time2.}
#'  \item{curve_change: }{A numeric column of the change in curves between time
#'  points.}
#'  \item{replicate.var: }{A column that has same name and type as the
#'  replicate.var column, if specified.}
#' }
#' @references Avolio et al. Submitted
#' @examples 
#' data(pplots)
#' # Without replicates
#' df <- subset(pplots, plot == 25)
#' curve_change(df = df,
#'            species.var = "species",
#'            abundance.var = "relative_cover",
#'            time.var = "year")
#'
#' # With replicates
#' df <- subset(pplots, year < 2004 & plot %in% c(6, 25, 32))
#' curve_change(df = df,
#'            species.var = "species",
#'            abundance.var = "relative_cover",
#'            replicate.var = "plot",
#'            time.var = "year")
#'            
#' # With reference year
#' df <- subset(pplots, year < 2005 & plot %in% c(6, 25, 32))
#' curve_change(df = df,
#'            species.var = "species",
#'            abundance.var = "relative_cover",
#'            replicate.var = "plot",
#'            time.var = "year",
#'            reference.time = 2002)
#' @export
curve_change <- function(df, time.var, 
                         species.var, 
                         abundance.var, 
                         replicate.var = NULL,
                         reference.time = NULL) {

  # validate function call and purge extraneous columns
  args <- as.list(match.call()[-1])
  df <- do.call(check_args, args, envir = parent.frame())
  
  # add rank abundance function within each time step and optionally replicate
  by <- c(replicate.var, time.var)
  rankabunddf <- split_apply_combine(df, by, FUN = add_rank_abundance,
    species.var, abundance.var)

  # merge subsets on time difference of one time step
  cross.var <- time.var
  cross.var2 <- paste(cross.var, 2, sep = '')
  split_by <- c(replicate.var)
  merge_to <- !(names(rankabunddf) %in% split_by)
  if (is.null(reference.time)) {
    output <- split_apply_combine(rankabunddf, split_by, FUN = function(x) {
      y <- x[merge_to]
      cross <- merge(x, y, by = NULL, suffixes = c('', '2'))
      f <- factor(cross[[cross.var]])
      f2 <- factor(cross[[cross.var2]], levels = levels(f))
      idx <- (as.integer(f2) - as.integer(f)) == 1
      cross[idx, ]
    })
  } else {
    output <- split_apply_combine(rankabunddf, split_by, FUN = function(x) {
      y <- x[x[[time.var]] != reference.time, merge_to]
      x <- x[x[[time.var]] == reference.time, ]
      merge(x, y, by = NULL, suffixes = c('', '2'))
    })
  }
  
  # split on treatment pairs (and block if not null)
  output[['curve_change']] <- mapply(curve_dissim,
    output[['rankabund']], output[['rankabund2']])

  output_order <- c(
    time.var, paste(time.var, '2', sep = ''),
    replicate.var,
    'curve_change')
  
  return(output[intersect(output_order, names(output))])
}