Add collapseHz()

NAMESPACE

@@ -24,6 +24,7 @@ export(buntley.westin.index)
 export(checkHzDepthLogic)
 export(checkSPC)
 export(col2Munsell)
+export(collapseHz)
 export(colorChart)
 export(colorContrast)
 export(colorContrastPlot)

R/collapseHz.R

@@ -0,0 +1,279 @@
+#' Collapse Horizons within Profiles Based on Pattern Matching
+#'
+#' Combines layers and aggregates data by grouping adjacent horizons which match `pattern` in
+#' `hzdesgn` or, alternately, share a common value in `by` argument. Numeric properties are combined
+#' using the weighted average, and other properties are derived from the dominant condition based on
+#' thickness of layers and values in each group.
+#'
+#' @param x A _SoilProfileCollection_
+#' @param pattern _character_. A regular expression pattern to match in `hzdesgn` column. Default:
+#'   `NULL`.
+#' @param by _character_. A column name specifying horizons that should be combined. Aggregation
+#'   will be applied to adjacent groups of layers within profiles that have the same value in `by`.
+#'   Used in lieu of `pattern` and `hzdesgn`. Default: `NULL`.
+#' @param hzdesgn _character_. Any character column containing horizon-level identifiers. Default:
+#'   `hzdesgnname(x, required = TRUE)`.
+#' @param FUN _function_. A function that returns a _logical_ vector equal in length to the number
+#'   of horizons in `x`. Used only when `pattern` is specified. See details.
+#' @param ... Additional arguments passed to the matching function `FUN`.
+#' @param AGGFUN _list_. A _named_ list containing custom aggregation functions. List element names
+#'   should match the column name that they transform. The functions defined should take three
+#'   arguments: `x` (a vector of horizon property values), `top` (a vector of top depths), and
+#'   `bottom` (a vector of bottom depths). Default: `NULL` applies `weighted.mean()` to all numeric
+#'   columns not listed in `ignore_numerics` and takes the dominant condition (value with greatest
+#'   aggregate thickness sum) for all other columns. See details.
+#' @param ignore_numerics _character_. Vector of column names that contain numeric values which
+#'   should _not_ be aggregated using `weighted.mean()`. For example, soil color "value" and
+#'   "chroma".
+#' @param na.rm _logical_. If `TRUE` `NA` values are ignored when calculating min/max boundaries for
+#'   each group and in weighted averages. If `FALSE` `NA` values are propagated to the result.
+#'   Default: `FALSE`.
+#'
+#' @details
+#'
+#' If a custom matching function (`FUN`) is used, it should accept arbitrary additional arguments
+#' via an ellipsis (`...`). It is not necessary to do anything with arguments, but the result should
+#' match the number of horizons found in the input SoilProfileCollection `x`.
+#'
+#' Custom aggregation functions defined in the `AGGFUN` argument should either return a single
+#' vector value for each group*column combination, or should return a _data.frame_ object with named
+#' columns. If the input column name is used as a column name in the result _data.frame_, then the
+#' values of that column name in the result _SoilProfileCollection_ will be replaced by the output
+#' of the aggregation function. See examples.
+#' 
+#' @return A _SoilProfileCollection_
+#' 
+#' @author Andrew G. Brown
+#' 
+#' @seealso `hz_dissolve()`
+#' 
+#' @export
+#'
+#' @examples
+#' data(jacobs2000)
+#' 
+#' # calculate a new SPC with genhz column based on patterns
+#' new_labels <- c("A", "E", "Bt", "Bh", "C")
+#' patterns <- c("A", "E", "B.*t", "B.*h", "C")
+#' jacobs2000_gen <- generalizeHz(jacobs2000, new = new_labels, pattern = patterns)
+#' 
+#' # use existing generalized horizon labels
+#' i <- collapseHz(jacobs2000_gen, by = "genhz")
+#' 
+#' profile_id(i) <- paste0(profile_id(i), "_collapse")
+#' 
+#' plot(
+#'   c(i, jacobs2000),
+#'   color = "genhz",
+#'   name = "name",
+#'   name.style = "center-center",
+#'   cex.names = 1
+#' )
+#'  
+#' # custom pattern argument  
+#' j <- collapseHz(jacobs2000,
+#'                 c(
+#'                   `A` = "^A",
+#'                   `E` = "E",
+#'                   `Bt` = "[ABC]+t",
+#'                   `C` = "^C",
+#'                   `foo` = "bar"
+#'                 ))
+#' profile_id(j) <- paste0(profile_id(j), "_collapse")
+#' plot(c(j, jacobs2000), color = "clay")
+#' 
+#' # custom aggregation function for matrix_color_munsell
+#' k <- collapseHz(jacobs2000,
+#'                 pattern = c(
+#'                   `A` = "^A",
+#'                   `E` = "E",
+#'                   `Bt` = "[ABC]+t",
+#'                   `C` = "^C",
+#'                   `foo` = "bar"
+#'                 ),
+#'                 AGGFUN = list(
+#'                   matrix_color_munsell = function(x, top, bottom) {
+#'                     thk <- bottom - top
+#'                     if (length(x) > 1) {
+#'                       xord <- order(thk, decreasing = TRUE)
+#'                       paste0(paste0(x[xord], " (t=", thk[xord], ")"), collapse = ", ")
+#'                     } else
+#'                       x
+#'                   }
+#'                 )
+#'               )
+#' profile_id(k) <- paste0(profile_id(k), "_collapse_custom")
+#' 
+#' unique(k$matrix_color_munsell)
+#' 
+#' # custom aggregation function for matrix_color_munsell (returns data.frame)
+#' m <- collapseHz(jacobs2000,
+#'                 pattern = c(
+#'                   `A` = "^A",
+#'                   `E` = "E",
+#'                   `Bt` = "[ABC]+t",
+#'                   `C` = "^C",
+#'                   `foo` = "bar"
+#'                 ),
+#'                 AGGFUN = list(
+#'                   matrix_color_munsell = function(x, top, bottom) {
+#'                     thk <- bottom - top
+#'                     if (length(x) > 1) {
+#'                       xord <- order(thk, decreasing = TRUE)
+#'                       data.frame(matrix_color_munsell = paste0(x, collapse = ";"),
+#'                                  n_matrix_color = length(x))
+#'                     } else {
+#'                       data.frame(matrix_color_munsell = x, 
+#'                                  n_matrix_color = length(x))
+#'                     }
+#'                   }
+#'                 )
+#'               )
+#' profile_id(m) <- paste0(profile_id(m), "_collapse_custom")
+#' 
+#' m$matrix_color_munsell.n_matrix_color
+collapseHz <- function(x,
+                       pattern = NULL,
+                       by = NULL,
+                       hzdesgn = hzdesgnname(x, required = TRUE),
+                       FUN = function(x, pattern, hzdesgn, ...) grepl(pattern, x[[hzdesgn]], ignore.case = FALSE),
+                       ...,
+                       AGGFUN = NULL,
+                       ignore_numerics = NULL,
+                       na.rm = FALSE) {
+  idn <- idname(x)
+  hzd <- horizonDepths(x)
+
+  # use exact match of existing genhz labels as default in lieu of pattern
+  if (is.null(pattern) & missing(by)) {
+    by <- GHL(x, required = TRUE)
+  }
+
+  if (length(pattern) == 0) {
+    pattern <- NA
+  }
+
+  # if a named vector of patterns is given, use the names as new labels
+  if (!is.null(names(pattern))) {
+    labels <- names(pattern)
+    pattern <- as.character(pattern)
+  } else {
+    # otherwise, the patterns and labels are the same
+    pattern <- as.character(pattern)
+    labels <- pattern
+  }
+
+  # iterate over patterns
+  for (p in seq(pattern)) {
+
+    h <- data.table::data.table(horizons(x))
+
+    # calculate matches
+    if (!is.null(by) && length(pattern) == 1 && is.na(pattern)) {
+      labels <- h[[by]]
+      r <- rle(paste0(h[[idn]], "-", as.character(labels)))
+      l <- rep(TRUE, nrow(h))
+    } else {
+      l <- FUN(x, pattern = pattern[p], hzdesgn = hzdesgn, na.rm = na.rm, ...)
+      r <- rle(l)
+    }
+
+    if (any(r$lengths > 1)) {
+      g <- unlist(lapply(seq_along(r$lengths), function(i) rep(i, r$lengths[i])))
+      hidx <- unlist(lapply(seq_along(r$lengths), function(i) if (r$lengths[i] == 1) TRUE else rep(FALSE, r$lengths[i]))) & l
+      gidx <- g %in% unique(g[l]) & !hidx
+      naf <- names(AGGFUN)
+
+      # iterate over sets of layers needing aggregation within each matching group
+      if (sum(gidx) > 0){
+        res <- h[gidx, c(list(hzdeptnew = suppressWarnings(min(.SD[[hzd[1]]], na.rm = na.rm)), 
+                              hzdepbnew = suppressWarnings(max(.SD[[hzd[2]]], na.rm = na.rm))),
+
+                              # process numeric depth weighted averages w/ dominant condition otherwise                       
+                              sapply(colnames(.SD)[!colnames(.SD) %in% c(hzd, naf)],
+                                     function(n, top, bottom) {
+                                      v <- .SD[[n]]
+                                      if (length(v) > 1) {
+                                        if (!n %in% ignore_numerics && is.numeric(x)) {
+
+                                          # weighted average by thickness (numerics not in exclusion list)
+                                          weighted.mean(v, bottom - top, na.rm = na.rm)
+
+                                        } else {
+                                          # take thickest value
+                                          # v[which.max(bottom - top)[1]]
+
+                                          # take dominant condition (based on sum of thickness)
+                                          cond <- aggregate(bottom - top, by = list(as.character(v)), sum, na.rm = na.rm)
+                                          cond[[1]][which.max(cond[[2]])[1]]
+                                        }
+                                      } else {
+                                        v
+                                      }
+                                    },
+                                    top = .SD[[hzd[1]]],
+                                    bottom = .SD[[hzd[2]]]),
+
+                           # process custom aggregation functions (may return data.frames)
+                           do.call('c', lapply(colnames(.SD)[colnames(.SD) %in% naf], 
+                                               function(n, top, bottom) {
+                                                         out <- AGGFUN[[n]](.SD[[n]], top, bottom)
+                                                         if (!is.data.frame(out)) {
+                                                           out <- data.frame(out)
+                                                           colnames(out) <- n
+                                                         } else {
+                                                           colnames(out) <- paste0(n, ".", colnames(out))
+                                                         }
+                                                         out
+                                                                           },
+                                               top = .SD[[hzd[1]]], 
+                                               bottom = .SD[[hzd[2]]]))), 
+                 by = g[gidx]]
+        # remove grouping ID
+        res$g <- NULL
+      } else {
+        res <- h[0, ]
+      }
+
+      # allow for replacing values as well as adding new values with data.frame AGGFUN
+      test1.idx <- na.omit(match(colnames(res), paste0(naf, ".", naf))) 
+      test2.idx <- na.omit(match(paste0(naf, ".", naf), colnames(res)))
+      colnames(res)[test2.idx] <- naf[test1.idx]
+
+      # determine matches that are only a single layer (no aggregation applied)      
+      res2 <- h[hidx & l, ]
+      res2$hzdeptnew <- res2[[hzd[1]]]
+      res2$hzdepbnew <- res2[[hzd[2]]]
+      res2[[hzd[1]]] <- NULL
+      res2[[hzd[2]]] <- NULL
+
+      # combine matches
+      res3 <- data.table::rbindlist(list(res, res2), fill = TRUE)
+      if (missing(by) && nrow(res3) > 0){
+        res3[[hzdesgn]] <- labels[p]
+      }
+
+      # combine matches with horizons that did not match
+      agg.idx <- which(g %in% unique(g[l]) | hidx)
+      if (length(agg.idx) > 0) {
+        h <- h[-agg.idx, ]
+      }
+      h <- data.table::rbindlist(list(h, res3), fill = TRUE)
+
+      # replace depths
+      hn <- !is.na(h$hzdeptnew) & !is.na(h$hzdepbnew)
+      h[[hzd[1]]][hn] <- h$hzdeptnew[hn]
+      h[[hzd[2]]][hn] <- h$hzdepbnew[hn]
+      h$hzdeptnew <- NULL
+      h$hzdepbnew <- NULL
+
+      # sort horizons by id name and top depth
+      h <- h[order(h[[idn]], h[[hzd[1]]]),]
+
+      # replace horizons in parent SPC
+      replaceHorizons(x) <- h
+    }
+  }
+  x
+}
+