#' @title Rank Abundance Curve Differences
#'
#' @description Calculates differences between two samples for four comparable
#' aspects of rank abundance curves (richness, evenness, rank, species
#' composition). 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 will be 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).
#'
#' @param df A data frame containing a species, abundance, and replicate columns
#' and optional time, treatment, and block columns.
#' @param time.var The name of the optional time column.
#' @param species.var The name of the species column.
#' @param abundance.var The name of the abundance column.
#' @param replicate.var The name of the replicate column. Replicate identifiers
#' must be unique within the dataset and cannot be nested within treatments or
#' blocks.
#' @param treatment.var The name of the optional treatment column.
#' @param block.var The name of the optional block column.
#' @param pool An argument to allow abundance values to be pooled within a
#' treatment. The default value is "FALSE", a value of "TRUE" averages
#' abundance of each species within a treatment at a given time point.
#' @param reference.treatment The name of the optional treatment that all other
#' treatments will be compared to (e.g. only controls will be compared to all
#' other treatments). If not specified all pairwise treatment comparisons will
#' be made.
#'
#' @return The RAC_difference function returns a data frame with the following
#' attributes:
#' \itemize{
#' \item{time.var: }{A column that has the same name and type as the time.var
#' column, if time.var is specified.}
#' \item{block.var: }{A column that has same name and type as the block.var
#' column, if block.var is specified.}
#' \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 is 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 is FALSE and block.var = NULL.}
#' \item{treatment.var: }{A column that has the same name and type as the
#' treatment.var column, represents the first treatment being compared. A
#' treatment.var column will be returned when pool is TRUE or block.var is
#' present, 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 is TRUE or block.var is present, or treatment.var is specified.}
#' \item{richness_diff: }{A numeric column that is the difference between the
#' compared samples (treatments or replicates) in species richness divided by
#' the total number of unique species in both samples. A positive value occurs
#' when there is greater species richness in replicate.var2 than replicate.var
#' or treatment.var2 than treatment.var.}
#' \item{evenness_diff: }{A numeric column of the difference between the
#' compared samples (treatments or replicates) in evenness (measured by Evar).
#' A positive value occurs when there is greater evenness in replicate.var2
#' than replicate.var or treatment.var2 than treatment.var.}
#' \item{rank_diff: }{A numeric column of the absolute value of average
#' difference between the compared samples (treatments or replicates) in
#' species' ranks divided by the total number of unique species in both
#' samples.Species that are not present in both samples are given the S+1 rank
#' in the sample it is absent in, where S is the number of species in that
#' sample.}
#' \item{species_diff: }{A numeric column of the number of species that are
#' different between the compared samples (treatments or replicates) divided by
#' the total number of species in both samples. This is equivalent to the
#' Jaccard Index.}
#' }
#' @references Avolio et al. Submitted
#' @examples
#' data(pplots)
#' # With block and no time
#' df <- subset(pplots, year == 2002 & block < 3)
#' RAC_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)
#' RAC_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)
#' RAC_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)
#' RAC_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))
#' RAC_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))
#' RAC_difference(df = df,
#' species.var = "species",
#' abundance.var = "relative_cover",
#' replicate.var = "plot",
#' time.var = "year")
#' @export
RAC_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
rankdf <- 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)
# rank species in each replicate
by <- c(block.var, time.var, treatment.var, replicate.var)
rankdf <- split_apply_combine(allsp, by, FUN = add_ranks, 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(rankdf[[cross.var]]) &
!is.ordered(rankdf[[cross.var]]) &
is.null(reference.treatment)
if (to_ordered) {
class(rankdf[[cross.var]]) <- c('ordered', class(rankdf[[cross.var]]))
}
# cross join for pairwise comparisons
split_by <- c(block.var, time.var)
merge_to <- !(names(rankdf) %in% split_by)
cross.var2 <- paste(cross.var, 2, sep = '')
if (is.null(reference.treatment)) {
ranktog <- split_apply_combine(rankdf, 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(rankdf, 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, ]
# split on treatment pairs (and block if not null)
split_by <- c(block.var, time.var, cross.var, cross.var2)
output <- split_apply_combine(ranktog, split_by, FUN = SERSp,
species.var, abundance.var, abundance.var2)
if(any(is.na(output$evenness_diff)))
warning(paste0("evenness_diff values contain NAs because there are plots",
" with only one species"))
# 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 = ''),
'richness_diff', 'evenness_diff', 'rank_diff', 'species_diff')
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 RAC difference between two samples
# @param df a dataframe
# @param rank.var the name of the rank column at time 1
# @param rank.var2 the name of the rank column at time 2
# @param abundance.var the name of the abundance column at time 1
# @param abundance.var2 the name of the abundance column at time 2
SERSp <- function(df, species.var, abundance.var, abundance.var2) {
out <- c(species.var, 'rank', 'rank2', abundance.var, abundance.var2)
out <- unique(df[!(names(df) %in% out)])
df <- subset(df, df[[abundance.var]] != 0 | df[[abundance.var2]] != 0)
#ricness and evenness differences
s_r1 <- S(df[[abundance.var]])
e_r1 <- Evar(as.numeric(df[[abundance.var]]))
s_r2 <- S(df[[abundance.var2]])
e_r2 <- Evar(as.numeric(df[[abundance.var2]]))
sdiff <- (s_r2-s_r1)/nrow(df)
ediff <- e_r2-e_r1
#Species diff beta -2 based on Carvalho (2012; 10.1111/j.1466-8238.2011.00694.x)
idx <- df[[abundance.var]] != 0
idx2 <- df[[abundance.var2]] != 0
a <- sum(idx & idx2)
b <- sum(idx & !idx2)
c <- sum(!idx & idx2)
spdiff <- 2*(min(b, c) / (a+b+c))
#Mean Rank Difference
rank_diff <- mean(abs(df[['rank']]-df[['rank2']])) / nrow(df)
measures <- data.frame(richness_diff = sdiff, evenness_diff = ediff,
rank_diff = rank_diff, species_diff = spdiff)
return(cbind(out, measures))
}
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