SPC-slice-methods: Slicing of SoilProfilecollection Objects

In aqp: Algorithms for Quantitative Pedology

Description

Slicing of SoilProfilecollection Objects

Usage

# method for SoilProfileCollection objects
slice(object, fm, top.down=TRUE, just.the.data=FALSE, 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 accordin to ‘integer.vector’ ‘integer.vector ~.’.
top.down	Logical, should slices be defined from the top-down? The default is usually what you want.
just.the.data	Logical, return just the sliced data or a new SoilProfileCollection object.
strict	Logical, should the horizonation be strictly checked for self-consistency?

Value

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

Methods

data = "SoilProfileCollection"

Typical usage, where input is a SoilProfileCollection.

Note

slab() and slice() are much faster and require less memory if input data are either numeric or character.

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

# simulate some data, IDs are 1:20
library(plyr)
d <- ldply(1:20, random_profile)

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

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

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

# generate integer slices from 0 - 25 cm
s <- slice(d, 0:25 ~ name + p1 + p2 + p3)
plot(s)

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

# note that pct missing is computed for each slice,
# if all vars are missing, then NA is returned
d$p1[1:10] <- NA
s <- slice(d, 10 ~ ., just.the.data=TRUE)
print(s)

## 
## 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 <- slice(sp1.sub, 0:max(sp1.sub) ~ prop)
hz.slice.mean <- mean(s$prop, na.rm=TRUE)

# same?
if(!all.equal(hz.slice.mean, hz.wt.mean))
	stop('there is a bug in slice() !!!')

aqp documentation built on May 2, 2019, 4:51 p.m.