R/generalize.hz.R

In aqp: Algorithms for Quantitative Pedology

## TODO: consider always returning an ordered factor, why would this ever not be the intended output?

#' @title Generalize Horizon Names
#'
#' @description Generalize a vector of horizon names, based on new classes, and REGEX
#' patterns. Or create a new column `ghl` in a `SoilProfileCollection` (requires a horizon designation name to be defined for the collection, see details)
#'
#' @param x character vector of horizon names or a `SoilProfileCollection` object
#' @param new character vector of generalized horizon labels (GHL)
#' @param pattern character vector of REGEX patterns, same length as `new`
#' @param non.matching.code character, label used for any horizon not matched by `pattern`
#' @param hzdepm numeric vector of horizon mid-points; `NA` values in `hzdepm` will result in `non.matching.code` (or `NA` if not defined) in result
#' @param ordered logical, `TRUE` when `hzdepm` argument is specified
#' @param ... additional arguments passed to `grep()` such as `perl = TRUE` for advanced REGEX
#' @return factor (possibly an ordered factor) of the same length as `x` (if character) or as number of horizons in `x` (if `SoilProfileCollection`)
#' 
#' @details When `x` is a `SoilProfileCollection` the `ghl` column will be updated with the factor results. This requires that the "horizon designation name" metadata be defined for the collection to set the column for input designations.
#' 
#' @seealso [hzdesgnname()]
#' @author D.E. Beaudette
#' @keywords manip
#' 
#' @references 
#' Beaudette, D.E., Roudier, P., Skovlin, J. (2016). Probabilistic Representation of Genetic Soil Horizons. In: Hartemink, A., Minasny, B. (eds) Digital Soil Morphometrics. Progress in Soil Science. Springer, Cham. https://doi.org/10.1007/978-3-319-28295-4_18
#' 
#' @export
#' @examples
#' 
#' data(sp1)
#' 
#' # check original distribution of hz designations
#' table(sp1$name)
#' 
#' # generalized horizon labels
#' # character vector input
#' sp1$genhz <- generalizeHz(
#'   sp1$name,
#'   new = c('O','A','B','C','R'),
#'   pattern = c('O', '^A','^B','C','R'),
#'   ordered = TRUE
#' )
#' 
#' # see how we did / what we missed
#' table(sp1$genhz, sp1$name)
#' 
#' 
#' ## a more advanced example, requries `perl = TRUE`
#' # example data
#' x <- c('A', 'AC', 'Bt1', '^AC', 'C', 'BC', 'CB')
#' 
#' # new labels
#' n <- c('A', '^AC', 'C')
#' 
#' # patterns:
#' # "A anywhere in the name"
#' # "literal '^A' anywhere in the name"
#' # "C anywhere in name, but without preceding A"
#' p <- c('A', '^A', '(?<!A)C')
#' 
#' # note additional argument
#' res <- generalizeHz(
#'   x, 
#'   new = n, 
#'   pattern = p, 
#'   perl = TRUE
#' )
#' 
#' # double-check: OK
#' table(res, x)
#' 
#' ## apply to a SoilProfileCollection
#' data(sp1)
#' depths(sp1) <- id ~ top + bottom
#' 
#' # must set horizon designation metadata
#' hzdesgnname(sp1) <- 'name'
#' 
#' # result is a SoilProfileCollection
#' x <- generalizeHz(
#'   sp1,
#'   new = c('O','A','B','C','R'),
#'   pattern = c('O', '^A','^B','C','R'),
#'   ordered = TRUE
#' )
#' 
#' # GHL stored in 'genhz' column
#' x$genhz
#' 
#' # GHL metadata is set
#' GHL(x)
#'
generalize.hz <- function(x, new, pattern, non.matching.code = 'not-used', hzdepm = NULL, ordered = !missing(hzdepm), ...) {

	# init vector of 'other', same length as original horizon name vector
	g <- rep(non.matching.code, times = length(x))

	# iterate over new new names, match with patterns, assign new hz names
	for (i in seq_along(new)) {
		g[grep(pattern[i], x, ...)] <- new[i]
	}

	# convert to (ordered) factor with levels set by median depth
	if (!missing(hzdepm) && !is.null(hzdepm) && !all(is.na(hzdepm))) {
	  # more stringent NA handling:
	  # stopifnot(`Number of non-NA \`hzdepm\` does match number of new levels` =
	  #           { sum(!is.na(hzdepm)) == length(new) })
	  
	  # less stringent:
	  # any NA hzdepm will return NA factor level, even when pattern is matched
	  new_sort <- names(sort(tapply(hzdepm, g, median)))
	  new_sort <- new_sort[new_sort != non.matching.code]
	  
	  # use an ordered factor (may be overridden w/ ordered = FALSE)
	  g <- factor(g, levels = c(new_sort, non.matching.code), ordered = ordered)
	  
	  # if any are not matched (i.e. hzdepm is NA), replace with non.matching.code (if defined)
	  if (!is.null(non.matching.code)) {
	    g[is.na(g)] <- non.matching.code
	  }
	  g
	} else {
  	# no adjustment of factor levels based on hz mid-points
  	# no order implied (by default; override with ordered=TRUE)
  	factor(g, levels = c(new, non.matching.code), ordered = ordered)
	}
}

setGeneric("generalizeHz",  function(x, new, pattern, non.matching.code = 'not-used', hzdepm = NULL, ordered = !missing(hzdepm), ...) standardGeneric("generalizeHz"))

#' @export
#' @aliases generalizeHz
#' @rdname generalize.hz
setMethod("generalizeHz", signature(x = "character"), function(x, new, pattern, non.matching.code = 'not-used', hzdepm = NULL, ordered = !missing(hzdepm), ...) {
 generalize.hz(
    x,
    new = new,
    pattern = pattern,
    non.matching.code = 'not-used',
    hzdepm = hzdepm,
    ordered = ordered, 
    ...
  )
})

# SoilProfileCollection method
#' @param ghl Generalized Horizon Designation column name (to be created/updated when `x` is a `SoilProfileCollection`)
#' @export
#' @rdname generalize.hz
setMethod("generalizeHz", signature(x = "SoilProfileCollection"), function(x, new, pattern, non.matching.code = 'not-used', hzdepm = NULL, ordered = !missing(hzdepm), ghl = "genhz", ...) {
  hzdesgn <- hzdesgnname(x, required = TRUE)
  
  x[[ghl]] <- generalize.hz(
    x[[hzdesgn]],
    new = new,
    pattern = pattern,
    non.matching.code = 'not-used',
    hzdepm = hzdepm,
    ordered = ordered, 
    ...
  )
  
  # set label in metadata
  GHL(x) <- ghl
  
  x
})

#' @title Convert cross-tabulation to adjacency matrix.
#' @description Convert a cross-tabulation of e.g. original horizon designations vs. generalized horizon labels to adjacency matrix form.
#' 
#' @param tab table, cross-tabulation of original and generalized horizon labels e.g. `table(original, genhz)`
#' @return matrix of numeric values
#' @author D.E. Beaudette
#' @export
genhzTableToAdjMat <- function(tab) {
  tab <- as.matrix(tab)
  # extract unique set of names
  nm <- sort(unique(unlist(dimnames(tab))))
  # generate full matrix with named dimensions
  m <- matrix(nrow = length(nm), ncol = length(nm), data = 0)
  dimnames(m) <- list(nm, nm)
  # set adjacency information via sub-matrix containing the original cross-tab
  m[rownames(tab), colnames(tab)] <- tab
  return(m)
}