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This function segments or subdivides horizon data from a SoilProfileCollection or data.frame by depth interval (e.g. c(0, 10), c(0, 50), or 25:100). This results in horizon records being split at the specified depth intervals, which duplicates the original horizon data but also adds new horizon depths. In addition, labels (i.e. "segment_id") are added to each horizon record that correspond with their depth interval (e.g. 025-100). This function is intended to harmonize horizons to a common support (i.e. depth interval) for further aggregation or summary. See the examples.
hz_segment(object, intervals, trim = TRUE, depthcols = c("top", "bottom"))segment(object, intervals, trim = TRUE, hzdepcols = c("top", "bottom"))
Either a SoilProfileCollection or data.frame with the original horizon data segmented by depth intervals. There are usually more records in the resulting object, one for each time a segment interval partially overlaps with a horizon. A new column called segment_id identifying the depth interval is added.
either a SoilProfileCollection or data.frame
a vector of integers over which to slice the horizon data (e.g. c(25, 100) or 25:100)
logical, when TRUE horizons in object are truncated to the min/max specified in intervals. When FALSE, those horizons overlapping an interval are marked as such. Care should be taken when specifying more than one depth interval and trim = FALSE.
a character vector of length 2 specifying the names of the horizon depths (e.g. c("top", "bottom")), only necessary if object is a
deprecated being replaced by depthcols.
Stephen Roecker
hz_segment() performs no aggregation or resampling of the source data, rather, labels are added to horizon records for subsequent aggregation or summary. This makes it possible to process a very large number of records outside of the constraints associated with e.g. slice() or slab().
dice(), glom(), hz_dissolve(), hz_lag(), hz_intersect()
# example data
data(sp1)
# upgrade to SPC
depths(sp1) <- id ~ top + bottom
# segment and trim
z <- hz_segment(sp1, intervals = c(0, 10, 20, 30), trim = TRUE)
# display segment labels
# note that there are new horizon boundaries at segments
par(mar = c(0, 0, 3, 1))
plotSPC(z, color = 'segment_id', width = 0.3)
# highlight new horizon records
par(mar = c(0, 0, 2, 1))
plotSPC(z, color = NA, default.color = NA, width = 0.3, lwd = 1)
plotSPC(sp1, color = NA, default.color = NA,
width = 0.3, lwd = 3, add = TRUE, name = NA, print.id = FALSE)
legend('top', horiz = TRUE,
legend = c('original', 'segmented'),
lwd = c(1, 3), cex = 0.85, bty = 'n')
# \donttest{
# same results as slab()
# 10 random profiles
s <- lapply(1:10, random_profile, n_prop = 1, SPC = TRUE, method = 'random_walk')
s <- combine(s)
a.slab <- slab(s, fm = ~ p1, slab.structure = c(0, 10, 20, 30), slab.fun = mean, na.rm = TRUE)
z <- hz_segment(s, intervals = c(0, 10, 20, 30), trim = TRUE)
z <- horizons(z)
z$thick <- z$bottom - z$top
a.segment <- sapply(split(z, z$segment_id), function(i) {
weighted.mean(i$p1, i$thick)
})
res <- data.frame(
slab = a.slab$value,
segment = a.segment,
diff = a.slab$value - a.segment
)
print(res)
res$diff < 0.001
# }
data(sp5)
# segment by upper 25-cm
test1 <- hz_segment(sp5, intervals = c(0, 100))
print(test1)
nrow(test1)
print(object.size(test1), units = "Mb")
# segment by 1-cm increments
test2 <- hz_segment(sp5, intervals = 0:100)
print(test2)
nrow(test2)
print(object.size(test2), units = "Mb")
# segment and aggregate
test3 <- hz_segment(horizons(sp5),
intervals = c(0, 5, 15, 30, 60, 100, 200),
depthcols = c("top", "bottom")
)
test3$hzthk <- test3$bottom - test3$top
test3_agg <- by(test3, test3$segment_id, function(x) {
data.frame(
hzID = x$hzID[1],
segment_id = x$segment_id[1],
average = weighted.mean(x$clay, w = x$hzthk)
)
})
test3_agg <- do.call("rbind", test3_agg)
head(test3_agg)
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