slice: Slicing of SoilProfileCollection Objects

In aqp: Algorithms for Quantitative Pedology

Slicing of SoilProfileCollection Objects

Description

A method for "slicing" of SoilProfileCollection objects into constant depth intervals. Now deprecated, see ⁠[dice()]⁠.

Usage

slice.fast(object, fm, top.down = TRUE, just.the.data = FALSE, strict = TRUE)

## S4 method for signature 'SoilProfileCollection'
slice(object, fm, top.down = TRUE, just.the.data = FALSE, strict = TRUE)

get.slice(h, id, top, bottom, vars, z, include = "top", strict = TRUE)

Arguments

object	a SoilProfileCollection
fm	A formula: either integer.vector ~ var1 + var2 + var3 where named variables are sliced according to integer.vector OR where all variables are sliced according to integer.vector: integer.vector ~ ..
top.down	logical, slices are defined from the top-down: 0:10 implies 0-11 depth units.
just.the.data	Logical, return just the sliced data or a new SoilProfileCollection object.
strict	Check for logic errors? Default: TRUE
h	Horizon data.frame
id	Profile ID
top	Top Depth Column Name
bottom	Bottom Depth Column Name
vars	Variables of Interest
z	Slice Depth (index).
include	Either 'top' or 'bottom'. Boundary to include in slice. Default: 'top'

Value

Either a new SoilProfileCollection with data sliced according to fm, or a data.frame.

Details

By default, slices are defined from the top-down: 0:10 implies 0-11 depth units.

Author(s)

D.E. Beaudette

References

D.E. Beaudette, P. Roudier, A.T. O'Geen, Algorithms for quantitative pedology: A toolkit for soil scientists, Computers & Geosciences, Volume 52, March 2013, Pages 258-268, 10.1016/j.cageo.2012.10.020.

See Also

slab

Examples

library(aqp)

# simulate some data, IDs are 1:20
d <- lapply(1:20, random_profile)
d <- do.call('rbind', d)

# init SoilProfileCollection object
depths(d) <- id ~ top + bottom
head(horizons(d))

# generate single slice at 10 cm
# output is a SoilProfileCollection object
s <- dice(d, fm = 10 ~ name + p1 + p2 + p3)

# generate single slice at 10 cm, output data.frame
s <- dice(d, 10 ~ name + p1 + p2 + p3, SPC = FALSE)

# generate integer slices from 0 - 26 cm
# note that slices are specified by default as "top-down"
# result is a SoilProfileCollection
# e.g. the lower depth will always by top + 1
s <- dice(d, fm = 0:25 ~ name + p1 + p2 + p3)
par(mar=c(0,1,0,1))
plotSPC(s)

# generate slices from 0 - 11 cm, for all variables
s <- dice(d, fm = 0:10 ~ .)
print(s)

# compute percent missing, for each slice,
# if all vars are missing, then NA is returned
d$p1[1:10] <- NA
s <- dice(d, 10 ~ ., SPC = FALSE, pctMissing = TRUE)
head(s)

## Not run: 
##
## check sliced data
##

# test that mean of 1 cm slices property is equal to the
# hz-thickness weighted mean value of that property
data(sp1)
depths(sp1) <- id ~ top + bottom

# get the first profile
sp1.sub <- sp1[which(profile_id(sp1) == 'P009'), ]

# compute hz-thickness wt. mean
hz.wt.mean <- with(
  horizons(sp1.sub),
  sum((bottom - top) * prop) / sum(bottom - top)
)

# hopefully the same value, calculated via slice()
s <- dice(sp1.sub, fm = 0:max(sp1.sub) ~ prop)
hz.slice.mean <- mean(s$prop, na.rm = TRUE)

# they are the same
all.equal(hz.slice.mean, hz.wt.mean)

## End(Not run)

aqp documentation built on Oct. 19, 2024, 5:06 p.m.