aqp (version 1.8-6)

slice-methods: Slicing of SoilProfilecollection Objects

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.

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() !!!')